Communication Flexible production systems for the apparel and metal-working industries: a contrast study on technologies...
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Communication Flexible production systems for the apparel and metal-working industries: a contrast study on technologies and contributions F. Frank Chen
Apparel and metal working industries 11 Received May 1996 Revised July 1996 Accepted August 1997
Department of Mechanical, Industrial & Manufacturing Engineering, The University of Toledo, Toledo, Ohio, USA Introduction The original concept of flexible manufacturing systems (FMS) emerged in the mid- to late 1960s as a logical outgrowth of progress in applying numerical control within processes as well as company-wide operations. The flexible manufacturing concept represents a relatively new strategy to increase flexibility, productivity, and quality. The technology is especially attractive for manufacturers who produce in the middle range of production volumes, neither mass production nor one of a kind. Boosting productivity and responding quickly to an increasing fickle marketplace seem to be mutually exclusive goals. Yet these could overlap in the realm of an FMS – a marriage of transfer line volume and stand-alone machine flexibility. With FMS, setup time and its related costs are eliminated or drastically reduced; it becomes as economical to produce products in small lots as it is to produce in large lots. This situation has made economic order and production lot sizing concepts, as well as conventional methods of economic justification, obsolete (Saloman and Biegel, 1986). Strategic benefits — such as increased flexibility and reduced production lead times — may well be more important factors for successful competing in world markets than the financial savings themselves. A typical definition for FMS that has been frequently cited in studies performed for metal cutting/removal industries is: a computer-controlled configuration of semi-independent work stations and a material handling system designed to efficiently manufacture more than one part type at low to medium volumes (Draper, 1984).
Such a definition leads to three required physical components of an FMS: (1) standard numerically controlled machine tools; The author’s work on this paper has been funded through a grant awarded to the University of Southwestern Louisiana from the Louisiana Board of Regents via the Louisiana Education Quality Support Fund (LEQSF) – Enhancement Program 1993-1994.
International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 11-20. © MCB University Press, 0955-6222
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(2) a conveyance network to move parts and perhaps tools between machines and fixturing stations; and (3) an overall control system that coordinates the machine tools, the partmoving elements, and the workpieces. Such a perceived set of system requirements has been closely followed by the pioneering FMS installations in the USA such as the early 1970s Sundstrand FMS at the Caterpillar Tractor manufacturing facility in East Peoria, Illinois. Interestingly, the AAMA Technical Advisory Committee has a very loosely defined interpretation of flexible manufacturing in 1988: Any departure from traditional mass production systems of apparel toward faster, smaller, more flexible production units that depend on the coordinated efforts of minimally supervised teams of workers (Hill, 1991).
Choosing such a generic definition for apparel manufacturers is certainly well justified owing to the substantial difference in the nature of operations performed in apparel versus metal cutting/removal industries. While the flexible manufacturing concept has already been well accepted in many hard-goods manufacturing industries, the soft-goods apparel industry seems to have struggled in recognizing the potential benefits and limits of the flexible manufacturing technology. The purpose of this paper is to perform a contrast study on the technologies and contributions of FMS in apparel and metal cutting/removal industries. Owing to the much earlier adoption of FMS in metal cutting/removal industries and a much more abundance of published studies on metal-working FMS, this paper is also intended to exploit lessons learned from existing metal-working FMS for use by the apparel professionals who are interested in employment of the flexible manufacturing technology. Two critical published papers are to serve as the prime source for this study. They are, Chen and Adam (1991) who summarized an extensive empirical study on 84 metal-working FMS project cases, and Hill (1991) who visited and performed a survey on the use of flexible work group (FWG) and unit production systems (UPS) in 12 US and five Japanese apparel manufacturing companies. Recommendations for further research are also provided. Comparison of FMS technologies and operations An FMS, as perceived by the metal-working professionals, requires technology intensive mechanism such as automated guided vehicles (AGV), robots, and coordinate measuring machines (CMM) as its core construct. Conversely, FMS in the apparel industry mainly refers to the implementation of FWG or UPS. FWG is basically a management concept involving a team of apparel associates, while UPS may well be considered an FWG with automated material handling mechanism, according to Hill (1991). Cellular manufacturing is the term widely used by traditional metal-working professionals to be the distinct philosophy from the high-volume, fixed automation (transfer-line settings). Similarly, modular manufacturing is used by the apparel manufacturing professionals as an alternative manufacturing concept to the traditional progressive bundle
system (PBS). Manufacturing cells for metal-working processes can be implemented with fully automated components (CNCs, AGVs, automated tool handling system, automated part loading/unloading system, etc.) or simply a group of semi-automated machining centers with no or minimum cell control and intra-cell material handling functions. Simple machining cells usually require several tending operators who are interchangeable among tasks to be performed within the cell. This is much like the flexible work groups (or modular manufacturing systems) in the apparel industry except there may be more operators in a modular work group than in a machining cell owing to the more labor-intensive nature of the apparel manufacturing process. Table I summarizes the basic difference in technology and operational characteristics of FMS in apparel as well as metal cutting/removal industries. It becomes clear that the migration into flexible manufacturing systems for the apparel manufacturers is much less capital intensive owing to the comparably lower level of hardware and software automation requirements as exhibited in FWG and UPS. It is the quality and lead-time (responsiveness) that drive the apparel manufacturers to migrate from the progressive bundle system (PBS) to FWG or UPS, not the automation itself. The results are that only very limited new hard technologies are needed to implement FWG and UPS, but philosophical changes in both the management and hourly workers seem enormous. With the intent of not trying to replace workers by automated machinery but trying to create a more productive work group and team spirit, the apparel manufacturers will certainly face much less resistance in adopting
Apparel (FWG and UPS)
Apparel and metal working industries 13
Metal-working (FMS or FMC)
Machine/equipment Mostly manual or semi-automatic
Materials handling
System control
Labor Management
Mostly standard numerically controlled machining centers, and are linked together via central computer control Manually move garment units Computer-controlled conveyance between stations (FWG), or use network to parts and tools between transporter to move a single unit machines and overhead fixturing between stations (UPS) stations Little or no computer control and Overall computer (cell) control coordination among stations, system that coordinates all production mostly relying on manual machines and equipment, and tracks coordination workpieces and tools throughout the entire manufacturing process Intensive – operators are needed Minimum – operators are usually to operate nearly all production needed only at load/unload and tool stations pre-setting stations Self-directed teamwork with Planned and controlled by a system employee empowerment and manager (foreman) with constraints continued problem solving given by the upstream MRP and the training (especially true for FWG) downstream assembly plans
Table I. FMS technology and operational contrast
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the FWG or UPS concepts. With the apparent more labor-intensive nature, FWG and UPS certainly call for more human coordinations than in metalworking FMS. Self-directed team work, employee empowerment, and employee problem-solving skill are keys to successful FWG and UPS. On the contrary, metal-working FMS are usually designed to minimize human interventions and possibly to operate an “unmanned” third shift operation for justification of the intensive capital requirements. Initial training for metal-working FMS operators may be burdensome owing to the need to operate sophisticated computer-controlled machinery and equipment, but continued training is deemed unnecessary. Comparison of FMS contributions Owing to the significant difference in technologies and operational philosophies found between the apparel FWG/UPS and the metal-working FMS, contributions resulting from flexible manufacturing implementations for hardgoods and soft-goods manufacturing are expected to show some difference. Table II provides a summary of the comparison by looking into various dimensions such as quality, direct labor, productivity, flexibility, work-inprocess inventory, etc. The comparison was mainly based on information provided by Hill (1991) who studied 30 apparel FWG/UPS and by Chen and Adam (1991) who studied 84 metal-working FMS cases. The purpose of this comparison is to identify any missing dimension(s) that should be examined when measuring the effectiveness of apparel FWG/UPS in future studies if deemed necessary. Quality improvement on apparel products which resulted from changing from the progressive bundle system (PBS) to FWG is very significant, as measured both by the number of defects and customer returns. Conversely, a very weak relationship has been found between metal-processing FMS and quality improvement of metal part processing judging from the fact that only four projects specifically reported improvement figures, though 22 FMS reported some quality improvement. The apparel FWG seemed to be a very effective tool for quality improvement as the human factor plays the key role for apparel manufacturing process. According to Hill (1991), the facts that the modular team became responsible for final product quality (rather than inspector/supervisor), the sense of ownership via making a complete product or at least complete components of the product, the peer pressure/support, and very low level of WIP have all explained the superior quality achieved by FWP. The team work synergism and the care for team performance evidently did make the difference. With the material handling mechanization, UPS did not contribute as much to quality improvement as FWG did. Maybe a more “humanized” UPS (i.e. a UPS with similar employee empowerment and team spirits that of FWG) will greatly improve the quality performance. Direct labor savings from implementing FWG is considered trivial (0.3 percent) compared to the 46 percent average direct labor reductions reported among metal-cutting FMS. It is not difficult to comprehend such a difference
Apparel (FWG and UPS) (Hill, 1991)
Metal working (FMS or FMC) (Chen and Adam, 1991)
FWG: defects reduced by 12-97% with an average of 65.3% UPS: improved by an average of 11.1%
Out of 84 projects, 22 mentioned improvement in quality; four installations specifically reported rework-defect reduction from 35-50% with an average of 46%
Direct labor
FWG: reduced by an average of 0.3% UPS: reduced by an average of 9.7%
Out of 84 projects, 24 reported reduced direct labor; 17 projects provided figures ranging from 0-94% with an average of 49%
Productivity
FWG: increased by 13.4% UPS: increased by 18.4%
Out of 84 projects, 11 reported throughput increase; seven projects provided figures showing 1.5-10 times of production throughput
Indirect\direct labor ratio
FWG: reduced by 10% UPS: reduced by 11.8%
Not reported
Throughput time
FWG: reduced by 71.1% UPS: reduced by 60.4%
Out of 84 projects, 41 mentioned significant throughput time reductions; 19 cases reported reductions ranging from 40-96% with an average of 71.6%
Flexibility
All reported easier product style changes and easier operator/ machine regrouping, additions, and replacements
Out of 50 Kearney and Trecker FMSs, 37 had flexibility as one of the key objectives: eight of the remaining FMSs reported increased flexibility in process, volume and design
Morale
Significant improvement evidenced by accelerated work pace, less time spent time in restrooms/breakrooms, and arrival at work earlier
Not reported
Turnover rate
FWG: reduced by 39.7% UPS: reduced by 29.5%
Not reported
Attendance/ absenteeism
FWG: improved by 2.6% UPS: improved by 1.1%
Not reported
Space
FWG: reduced by 36.9% UPS: reduced by 28.7% (square feet per operator)
Out of 84 projects, 15 reported to have utilization reduced floor space; two projects specifically provided reduction figures at 50% and 71%, respectively (production space per system)
WIP inventory
Not specifically reported Some drastic reductions were mentioned
Out of 84 projects, 33 experienced inventory reduction; nine projects indicated reduction numbers ranging from 50-97% with an average of 74.6%
Quality
(Continued)
Apparel and metal working industries 15
Table II. Comparison of flexible manufacturing contributions
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Apparel (FWG and UPS) (Hill, 1991)
Metal working (FMS or FMC) (Chen and Adam, 1991)
Setup time
Not reported
Out of 84 projects, 21 reported reductions in setup time with two projects giving specific percentages at 75 and 85%
Materials handling
Not reported
Out of 84 projects, 17 experienced reduced material handling requirements, but none provided a specific figure
Unit/system cost
Not reported
Out of 84 projects, 14 reported reductions in unit product cost or overall system cost; 11 projects gave figures ranging from 18-80% with an average of 37.7%
16
Table II.
when taking into consideration the level of automation in metal cutting FMS is far more advanced than that of the apparel’s modular work groups. In FWG, while bundle handling and piece work ticket functions are generally eliminated, this reduction is offset by the time which must be allowed for the movement of operators between workstations. Furthermore, the much less capital intensive apparel FWG does not need to use direct labor savings for justification. With the mechanized material handling functions, UPS reported to have achieved some visible direct labor savings (9.7 percent). Productivity increase resulting from FWG and UPS were considered moderate. On the other hand, with the massive automated machinery/equipment in metal-cutting FMS, throughput rates were reported to be 1.5-10 times higher. Yet, all such figures should be used with a great deal of caution. The true productivity measure should be based on a so-called total productivity formula (Adam et al., 1986): Outputs Productivity = Labor + Capital + Material + Energy Hill (1991) compared the number of apparel units produced by a group of individuals in the PBS versus the same number of individuals producing a like product in FWG. This method clearly indicated that a partial productivity (output relative to labor only) measure was made. Many of the metal-cutting FMS project cases collected and analyzed by Chen and Adam (1991) also showed that either partial productivity measures were used or the term productivity was used without specifying output and relative input. Indirect/direct labor ratios decreased by approximately 10 percent for FWG and 11.8 percent for UPS. There was no such report or discussion in metal cutting FMS projects. With the shifting of quality responsibility to modular
workers and empowerment for workers to make many decisions which used to be made by supervisors or corrected by service personnel, it should be easy to comprehend such a trend. On the contrary, with the need for maintaining sophisticated machinery/equipment and extensive system programming/ control/planning personnel, indirect labor required by metal cutting FMS is likely to be increased. This explained the reason why this factor was not mentioned or discussed in all project cases collected by Chen and Adam (1991). Throughput time reduction is certainly the main justification factor for the flexible manufacturing technology currently employed by soft-goods apparel as well as hard-goods metal cutting industries. To respond quickly to smaller but more frequent orders of numerous styles, some apparel manufacturers had successfully used FWG/UPS to cut the work-in-process time by 60-70 percent. Similarly, an average of approximately 70 percent reduction in throughput times was reported in metal-cutting FMS. Flexibility is another key dimension that should be very desirable for implementation of flexible manufacturing technology. With FWG, apparel manufacturers reported to have easier product style changes, easier operator/machine regrouping, additions, and replacement. Similarly, FMS in the metal-working industry have provided flexibilities for engineering design changes and product mix and volume changes, and the ability to weigh alternatives for in-house production or out-sourcing. However, the issue of flexibility has seemingly been poorly understood and managed. There is a great need to develop a common understanding of the term flexibility. Flexibility should be defined and measured by various attributes with respect to the functional capabilities of an FMS. In the Appendix, Chen and Adam (1991) present a modified and augmented discussion for the eight types of flexibility defined by Browne et al. (1984). Morale, turnover rate, and attendance/absenteeism are all key factors in the US apparel industry. However, they are rarely mentioned (with no statistics reported) in metal-working FMS cases. Significant improvement in morale of modular members was evidenced by an accelerated work pace, less time spent in restrooms/breakrooms, and arrival at work earlier. Employee turnover improved significantly in plants with properly functioning FWG and/or UPS. The modular group member’s sense of ownership, team spirit in a noncompetitive environment, and opportunity to learn and grow personally were the factors contributing to reduced turnover rate (Hill, 1991). For the same reasons mentioned above, attendance rates improved after successful implementation of FWG. Peer pressure and peer support were also cited as important factors in improving attendance. Space utilization as measured by square feet per operator has been considerably reduced by employing either FWG or UPS for apparel manufacturing. With the intent to eliminate labor content in manufacturing processes, the metal-cutting FMS also reported some significant reductions in shop floor space needed to install the systems, but not on the basis of square feet per operator. The space saving in FWG was mainly attributed to drastic
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reduction in WIP levels, according to Hill (1991). In addition, machines in FWG tend to be arranged closer together than machines in the PBS to facilitate some direct manual handling of work units between stations. Reductions in WIP inventory were mentioned in Hill (1991) after the FWG/UPS were implemented for apparel manufacturing, but no specific figures were reported. Those metal-working FMS reporting figures have achieved an average of nearly 75 percent reduction in WIP inventory – an amazing result which was also expected to have happened in apparel FWG or UPS. Since the amount of WIP inventory normally provides a good indication of quality performance in addition to the concern for the monetary investment of the in-process units themselves, WIP inventory in apparel manufacturing seems to be a critical dimension that deserves closer attention and precise measurement. The issues on setup time and material handling requirements were not mentioned or discussed in Hill (1991). Reduction in setup time is usually considered as one of the prime justification factors to adopt the flexible manufacturing technology for metal-cutting processes. Many FMS reported to have totally eliminated the setup process with the numerically controlled machinery/equipment and flexible fixturing/palletizing devices (Chen and Adam 1991). Similarly, a good portion of FMS projects experienced reduced material handling requirements though none provided specific numbers. Product unit cost or overall system cost were not discussed in Hill (1991), either. However, among those FMS cases studied by Chen and Adam (1991), 11 metal-working FMS projects produced a cost saving ranging from 18 percent to 80 percent, but only two indicated a payback within a two-year period. Summary and discussions It appears that there are clear distinctions in technological and operational characteristics between apparel FWG/UPS and metal-working FMS (FMC). Instead of seeking automation to replace labor as shown in sophisticated metalworking FMS, the more labor intensive apparel industry seems to have taken evolutionary steps on the journey to flexible manufacturing settings. An FWG, in essence, utilizes the fundamental cellular manufacturing concepts but at a less capital intensive manner. A UPS, with its mechanized material handling system, is a step closer to the metal working FMS. Considering the complex human assisted processes involved in various sewing, assembling, and packaging operations, unmanned apparel manufacturing cells (modules) will still be theoretically impossible in the foreseeable future. The much lower wages in the US apparel industry will also make it very difficult to justify any further significant automation efforts in modular apparel work groups. Since FWG have enabled a dramatic improvement in quality and a drastic reduction in throughput times of apparel products among other benefits summarized in Hill (1991), there is practically little need for further hard automation. On the other hand, soft automation in management systems integration and control is
necessary as the apparel industry is moving on the course of computer integrated manufacturing (CIM). Some leading US apparel manufacturers have taken advantage of the FWG concepts to strengthen their competitiveness worldwide. The Levi Strauss & Co. started to implement so-called “alternative manufacturing systems (AMS)” in 1991 in its 26 plants and four finishing centers in the USA (Ansel, 1991). The AMS employ the prime concepts of FWG team work, group problem solving, and cross-training in several job functions. With AMS, Levi Strauss has reaped most of the typical benefits found in FWG with a highlight of reducing the supply chain lead time from 49 days in 1989 to 28 days in 1992. The company expects to further cut it down to 13 days by 1994. Levi Strauss’ experience in its AMS has provided a solid and vivid example of using flexible manufacturing concepts to achieve competitiveness and to maintain leadership in the apparel manufacturing industry. Future research recommendations It is believed that research projects leading to address the following issues will further enhance the understanding of flexible manufacturing concepts, and thereby provoke a wider adoption of FWG/UPS by the smaller US apparel manufacturers : (1) Further surveys on FWG and UPS cases should be carried out to collect information on WIP inventory, setup time, material handling requirements, and the impact on bottom-line product costs in addition to those dimensions investigated in the study by Hill (1991). (2) A detailed planning and implementation procedure for migrating from the conventional PBS to FWG/UPS should be researched and documented. It will also serve as the complete guideline for apparel manufacturers to assess the appropriateness of FWG/UPS for their respective operations and market domains. (3) The needs of soft automation in management system integration and control along with implementation of FWG/UPS should be carefully enumerated in order to assist US apparel manufacturers in the transition into computer-integrated manufacturing enterprise. References Adam Jr, E., Hershauer, J. and Ruch, W. (1986), Productivity and Quality: Measurement as a Basis for Improvement, 2nd ed., Research Center, College of Business & Public Administration, University of Missouri-Columbia. Ansel, J. (1991), “Alternative manufacturing systems at Levi Strauss & Co.”, Proceedings of the 20th International Apparel Research Conference, Atlanta, GA, December. Browne, J., Dubois, D., Rathmill, K., Sethi, S. and Stecke, K. (1984), “Classification of flexible manufacturing systems”, The FMS Magazine, Vol. 2 No. 2, February, pp. 114-17. Chen, F. F. and Adam Jr, E.E. (1991), “The impact of flexible manufacturing systems on productivity and quality”, IEEE Transactions on Engineering Management, Vol. 38 No. 1, February, pp. 33-45.
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Table AI. Flexibility type and measurement
Draper, C. (1984), Flexible Manufacturing Systems Handbook, Automation and Management Systems Division, The Charles Stark Draper Laboratory, Inc., Noyes Publication. Hill, J.E. (1991), “Flexible manufacturing systems”, Proceedings of the 18th International Apparel Research Conference, November, Atlanta, GA. Saloman, D.P. and Biegel, J.E. (1986), “Assessing economic attractiveness of FMS applications in small batch manufacturing”, Industrial Engineering, Vol. 96 No. 1, January, pp. 88-96. Appendix. Type
Definition
Measurement
Machine flexibility
The ease of making changes required to produce a given set of part types
Time to replace worn-out or broken cutting tools, time to change tools in tool magazine to produce a different subset of part types, time to assemble or movement of new fixtures required
Process flexibility
The ability to produce a given set Number of part types that can of part types possibly using simultaneously be processed different materials in several ways without using batches
Product flexibility
The ability to change over to produce a new set of products very economically and quickly
Time required to switch from one part mix to another, not necessarily of the same part types
Routing flexibility
The ability to handle breakdowns and to continue producing the given set of part types
Robustness of the FMS when breakdowns occur – the production rate does not decrease dramatically and parts continue to be processed
Volume flexibility
The ability to operate an FMS profitability at different production volumes
The smallest volume can be for all part types with the FMS still being run profitably
Expansion flexibility The capability of building a system, and expanding it as needed, easily and modularly
The magnitude the FMS can become
Operation flexibility
The number of alternative operation orders for each part type that the FMS can accommodate
The ability to interchange the ordering of several operations for each part type
Production flexibility The universe of part types that the FMS can produce Source: Chen and Adam, 1991
The level of existing technology
Handling the assembly line balancing problem in the clothing industry using a genetic algorithm Keith C.C. Chan Department of Computing, The Hong Kong Polytechnic University, Hong Kong and
Assembly line balancing in the clothing industry 21 Received June 1997 Revised July 1997 Accepted December 1997
Patrick C.L. Hui, K.W. Yeung and Frency S.F. Ng Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 1. Introduction In clothing production under the progressive bundle system, garment components are assembled through a sub-assembly process until they are gathered into a finished garment. The production process involves a set of workstations in each of which a specific task in a restricted sequence is carried out. In order to avoid the completion time of the task at a workstation exceeding the predetermined cycle time, it is important that tasks be allocated to each workstation as evenly as possible. Since the assembly line consists of different sections involving different operations being performed at different production rates, balance control is necessary to make sure that the right person be assigned the right task. Balance control depends mainly on the supervisor’s interpretation and prediction of the line performance; the skill and experience of supervisors are therefore important for it to be successful. Unfortunately, since the skill and experience of supervisors are difficult to capture, as they vary from one to the other, it is not easy for a model of the supervisory behaviour in line balancing to be formulated. Over the years, several algorithms have been developed to solve this assembly line balancing problem, but they do not seem to have provided a satisfactory solution (Bowman, 1960; Johnson, 1983; Kao and Queyranne, 1982). Owing to competitive market forces, a trend of decreasing contract size, increasing product complexities, and the demand for quick response, existing line balancing techniques have to be improved. In response to this need, this paper introduces a new approach to dealing with line balancing by using genetic algorithms. This technique is able to improve line efficiency as well as minimise the time spent in balance control planning.
International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 21-37. © MCB University Press, 0955-6222
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2. Classic definition of an assembly line balancing problem The classic definition of an assembly line balancing problem (Hoffman, 1990) involves: • a set of n tasks each of which takes ti time to complete; • a set of precedence relationships between the tasks; and • the cycle time C. The problem is to assign the tasks in such a manner so as to minimise the number of work stations, N, on the line without violating the precedence constraints or without having the sum of the task times at any work station exceed the cycle time. The difference between the cycle time and the sum of the task times at any one work station is referred to as the “slack time” or sj. The “total slack time”, S, is the sum of sjs over all work stations. S can also be calculated by multiplying the cycle time by the number of work stations and subtracting the total of the task times, T. The “theoretical” minimum total slack, S*, is simply N*C–T. Tackling line balancing problems, therefore, requires that the issues of precedence, task time and cycle time be dealt with. Based on these three aspects, there are three different approaches to handle the line balancing problem. The first approach was to find the minimum possible cycle time with a given precedence and task time set. The second approach was to search for the minimum number of work stations for a given cycle time and with a restricted precedence. The third approach was to vary the task time by holding the precedence relationships constant and with the cycle time being fixed (Hoffman, 1990) An assembly line is said to be perfectly balanced if the total slack (i.e. the sum of the idle time of all the stations along the line) is zero (Baybars, 1986). In real situations, it is very difficult to achieve perfect balance because the production rate of each work station is not equal. Slack time may occur as a result of line perturbations caused by operator absenteeism, machine breakdowns and repair, variations on material handling, and also operators’ varying performance. 3. The assembly line balancing problem in the clothing industry Conceptually, if each unit being manufactured is processed in a definite order, and no two stations operate on the same unit simultaneously, thus an assembly line can be run smoothly without any balancing problem. However, in practice, the assembly line of garment manufacturing is arranged in a hybrid approach, i.e. the combination of serial and parallel order sequences for operation. Line balancing problems will occur when parallel sub-assembly lines exist that can be integrated into the main line. In order to ensure the flow of work through each station be as smooth as possible, such combination has to be carefully considered by the supervisor. The supervisor will play a role of allocating the resources such as workers, machinery, etc. among the main line and sub-
assembly lines in order to maintain the precedence relationships, and to ensure Assembly line the sum of the task times at any work station does not exceed the cycle time. balancing in the In garment manufacturing, since the layout of a factory and the number of clothing industry work stations are fixed, it is difficult for a supervisor to balance an assembly line by minimising the number of work stations along the assembly line. Therefore, instead of assigning more than one task to a work station, the supervisor 23 generally will attempt to balance the assembly line by minimising the total slack time (i.e. the sum of the idle times of all the stations along the line). The slack time can be minimised by upgrading the skill of operators, or reducing process delay by strengthening the material handling method, such as unit production system. In practice, the supervisor prefers to allocate different skill level of workers to each work station in order to minimise the total slack time for a production. As standard allowable minutes (SAM) is a standard measuring unit to determine the completion time of a particular task, i.e. the sum of task time and slack time, thus, minimising the slack time results in reducing the SAM. Several models of line balancing have been proposed. For example, the procedure for implementing line balancing in a progressive bundle system was studied by Whitaker (1973), but validation of Whitaker’s model is relatively unclear and insufficient. A discrete event simulation model for trousers manufacturing was studied by Rosser et al. (1991), but it is concerned primarily with material flows and the problems resulting from the absence of supervision. The simulation of the bundle system described by Oliver et al. (1994) fail to make reference to supervisory issues. Fozzard et al. (1996) adopted the visual interactive simulation for modelling balancing control, but the approach is not universal as it is limited only to some types of experiment. They concluded that an exploration of a knowledge-based approach to clothing production supervision would be a feasible solution to solve the problem encountered in their study. 4. A genetic algorithm-based solution approach Genetic algorithms (GA) are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. It was first proposed by Holland (1975) and has been used in a diverse number of optimisation applications. GA employs a random directed search for locating the globally optimal solution. They are superior to many “gradient descent” techniques as they possess the ability to locate the globally optimal solution for a multimodal objective function. Thus, GA is suited for applications in nonlinear function optimisation and the nonlinear programming problem (Man et al., 1996; Srinivas and Patnaik, 1996). GA works with a population of individuals representing potential solutions to a problem. Each individual is usually represented by a single string of characters. At every iteration of the algorithm, a fitness value, f(i), is calculated for each of the current individuals. Based on this fitness function, a number of individuals are selected as potential parents. Two new individuals can be obtained from two parents by choosing a random point along the string,
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splitting both strings at that point and then joining the front part of one parent to the back part of the other parent and vice versa. This process is usually called crossover, at an operation rate with a typical value of between 0.6-1.0 (Man et al., 1996). Individuals may also change through random mutation when elements within a string are changed directly at a smaller probability with a typical value of less than 0.1 (Man et al., 1996). The processes of crossover and mutation are collectively referred to as reproduction. The end result is a new population (or the next generation) and the whole process repeats. Over time, this algorithm leads to convergence within a population with fewer and fewer variations between individuals. When a GA works well, the population converges to a good solution of the underlying optimisation problem and the best individual in the population after many generations is likely to be close to the global optimum. In summary, a GA works as follows: • Create the initial generation. • Evaluate the fitness of each individual in the initial generation. • Perform the following steps until the termination condition is true: – create new individuals by mating individuals in the current generation using the genetic operators of select, crossover and mutation; – evaluate the fitness of each newly created individual; – create a new generation by inserting new and deleting old individuals in the current generation. • Return the best individual(s). We discuss a GA for the ALB problem in terms of the five components: representation, initialisation, evaluation, operators and parameters. 5. An order-based genetic algorithm solution to assembly line balancing problems 5.1 Chromosome representations Solutions in genetic algorithms are represented by strings of different types (genomes). The choice of solution representation (structure) in GA is related to the nature of the problem. It is encoded to form a chromosome. In our case, each operation is associated with a fixed position on the string and the code is simply the worker number with arbitrary skill level to which that operation is assigned. A chromosome representation for the ALB problem is shown in Figure 1. In this chromosome representation, we have put the tasks into an order depending on the precedence graph.
Figure 1. Example of chromosome representation
W1 W2
W3
–
–
–
–
–
Wi
Wi = worker i
T1
T3
–
–
–
–
–
Tj
Tj = task j
T2
5.2 Initialisation Assembly line A starting population is initialised by randomly generated bit strings. The balancing in the format of a bit string is the same as the representation of a chromosome clothing industry mentioned in section 5.1. 5.3 Population size The choice of population size can have an impact on the performance of a GA. If a large population size is chosen, the evolution process may be too slow. But if the size is too small, the population may not contain enough alleles for the best chromosomes to evolve. For our GA, we choose a population size of 50.
25
5.4 The fitness function In a GA, the fitness function provides a way of evaluating the status of each chromosome. It is used to help determine which individual survives into the next generation. Since the objective of ALB in the clothing industry is to minimise the total slack time along the assembly line, the fitness function is defined in terms of it. To describe the fitness function we used, an illustrating example for ALB in clothing industry is presented in Figure 2. In this example, let us introduce the following notation: s = SAM, standard allowable minutes to complete a particular task. = skill level of the worker. Sw Ti = time taken to complete task i, where i = 1, 2, …, n = s/Sw, if Sw > 0 = 0 otherwise. TB1 = time taken to finish all tasks of branch 1 3
( ∑ Ti ) i=1
TB2
= time taken to finish all tasks of branch 2 7
( ∑ Ti ) i= 4
B1
T1
T4
T2
T5
T3
T6
T7
B2
Figure 2. An illustrative example for ALB in the clothing industry
IJCST 10,1
26
TB1+B2 = the longest time to complete B1 and B2. To find the best solution, a GA is used to minimize the total slack time among all solutions. In other words, we are to find the total slack time = min[i = 1 ∑7 (Ti – TB1+B2)]. To make a fast convergence towards the local optimum, the previous research works (Anderson and Ferris, 1990; Goldberg, 1989; Zbigniew, 1996) proposed that a linear scaling of the fitness values is implemented. The scaling is performed in such a way that the average fitness remains constant but the maximum fitness is a multiple (usually 1.5) of this average value (Anderson and Ferris, 1990). In other words, the actual chromosome’s fitness is scaled as f(i)′ = a*f(i) + b, where a and b are chosen to enforce the scaled average fitness values (Man et al., 1996). 5.5 Selection of parents The selection technique used by our GA is the roulette wheel selection technique. The reason for such a name is that it can be viewed as allocating pieshaped slices on a roulette wheel to population members, with each slice proportional to the member’s fitness. Selection of a population member to be a parent can be viewed as a spin of the wheel, with the winning population member being the one in whose slice the roulette spinner ends up. Although this selection procedure is random, each parent’s chance of being selected is directly proportional to its fitness. Over a number of generations this algorithm will drive out the least fit members and contribute to the spread of the genetic material in the fittest population members. Even though it is possible for the worst population member to be selected by this algorithm each time it is used, the odds of this happening are negligible (Zbigneiw, 1996). 5.6 Operators: uniform order-based crossover and scramble sublist mutation In our GA, reproduction involves two parents. After two chromosomes are selected from the current population, our GA applied a “uniform order-based crossover” operator by recombining the generic materials in the two parent chromosomes to create two children. A crossover rate used in our case is 0.65. The working principle and an illustrative example of the uniform order-based crossover is presented in Figure 3. Working principle: • Generate a bit string that is the same length as the parent.
Figure 3. Working principle and illustrative example of uniform order-based crossover
An illustrative example: Parent 1 1 2 Parent 2 8 6
3 4
4 2
5 7
6 5
7 3
8 1
Ordered List 0
1
1
0
1
1
0
0
Child 1 Child 2
2 4
3 5
4 2
5 6
6 7
7 3
1 1
8 8
Fill in some positions on Child 1 by copying them from Parent 1 Assembly line wherever the bit string of an ordered list contains a 1. balancing in the • Make a list of elements from Parent 1 associated with a 0 in the bit string clothing industry of an ordered list. • Permute these elements so that they appear in the same order on Parent 2. 27 • Fill these permutated elements in the gaps on Child 1 in the order generated in statement 4. • To make Child 2, carry out a similar process. To ensure that a GA is able to escape from a local optimum, a scramble sublist mutation operator (Anderson and Ferris, 1990; Zbigniew, 1996) is used. This operator selects a sublist of the items on a parent order-based chromosome and permutes them in the child, leaving the rest of the chromosomes as they were in the parent. An illustrative example of a scramble sublist mutation is presented in Figure 4. The rate of mutation used in our ALB problem is 0.008. •
Parent = ( 2 4 7 1 4 8 3 5 9 ) Child = ( 2 4 4 8 1 7 3 5 9 ) Remarks: with the beginning and the end of the selected sub-list marked by .
5.7 Delete members of the population to make room for the new chromosome The deletion technique used in our GA is to delete all members of the old population when reproduction has occurred. For each iteration, each old population is replaced by a new one. Thus, this reproduction is by generational replacement. 6. Experimental results and discussions In our experiment, the 41-tasks precedence relationship of men’s shirt manufacturing with simulated processing times is used as shown in Figure 5 and Table I respectively. The name of each task is tabulated in Table II. To justify the results of our experiment, two test cases were used. Test case 1 Forty-one workers with arbitrary skill level ranging from 0 (no-skilled) to 1.5 (fully-skilled) for each task are generated randomly. Thus, these workers with arbitrary skill level for each task are listed in Tables III and IV. Using our GA, set out in the previous section, the results of six trial runs are shown in Table V. Each trial run was carried out for about 5,000 seconds. Therefore, the best solution that has been obtained in the six trials on our numerical experiments is: • Workers’ assignment from task 1-41 is 6, 30, 39, 31, 2, 40, 32, 35, 17, 28, 22, 20, 27, 5, 26, 9, 1, 19, 10, 33, 37, 34, 29, 12, 24, 7, 15, 23, 38, 21, 16, 8, 25, 14, 13, 41, 11, 18, 4, 3, 36 respectively.
Figure 4. Example of scramble sublist mutation
IJCST 10,1
1
28
34
2
13
25
35
3
14
26
36
4
15
21
27
37
5
16
18
22
28
38
17
19
23
29
39
6
7
8
20
9
24
10
30
11
31
12
32 33
Figure 5. The 41-task precedence relationship of men’s shirt manufacturing
40
41 Task No.
Table I. Processing time for 41 tasks in SAM per 100 pieces
1 2 3 4 5 6 7 8 9 10 11
SAM
Task No.
SAM
Task No.
SAM
Task No.
SAM
41 30 25 48 30 65 28 46 57 56 32
12 13 14 15 16 17 18 19 20 21 22
41 37 19 28 30 29 47 66 54 33 20
23 24 25 26 27 28 29 30 31 32 33
34 55 47 67 44 36 65 58 64 23 32
34 35 36 37 38 39 40 41
28 57 49 50 55 32 43 48
Task No.
Task name
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Spot fuse collar fall Top fuse collar fall Sew collar stay pocket Runstitch collar fall Trim, turn and press collar fall Topstitch collar fall Hem collar band Attach collar band Turn and press collar band Topstitch collar band Sew collar band buttonhole Sew collar band button Set centre front placket Hem right front edge Trim neckline Sew centre front buttonhole Sew right front button Hem pocket mouth Crease pocket Set pocket Sew yoke pleats
•
Task No. 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Task name Set yoke label Set yoke Join shoulder Set sleeve under placket Set sleeve top placket Finish sleeve placket Sew sleeve placket buttonhole Sew sleeve placket button Set sleeve Topstitch armhole Join side seam Hem bottom Hem cuff Runstitch cuff Turn and press cuff Topstitch cuff Sew cuff buttonhole Sew cuff button Set cuff Set and close collar
Final solution is 474.567 minutes required to produce 100 pieces. Thus, the running time required for making up 1,000 pieces is 2.2 working days assuming that there are eight hours in one working day (i.e. 480 minutes).
Test case 2 The same set of workers is also used in this case but the worker’s assignment on each task is made by a greedy algorithm. A greedy algorithm performs optimisation by proceeding through a series of alternatives by making the best decision at each point in the series. In this case, this algorithm is to assign the most skillful worker on each individual task. Using a greedy algorithm, the best solution of worker’s assignment from task 1-41 is 6, 16, 37, 38, 10, 9, 31, 3, 26, 29, 34, 21, 30, 20, 41, 2, 32, 19, 7, 23, 15, 22, 35, 28, 33, 36, 11, 5, 8, 39, 14, 17, 1, 25, 24, 13, 27, 12, 18, 4, 40 respectively. By simulation, the running time required for making up 100 pieces is 2,877 minutes. To complete 1,000 pieces is many times slower than the GAs solution. Thus, the solution of Case 2 is worse than Case 1. As a result, it was proved in our experiment that using a greedy algorithm as currently adopted by a line supervisor cannot effectively reach a more optimal solution than using GA within a reasonable time limit. It demonstrated that a GA is a more powerful than the current practice used in line balancing in apparel manufacturing.
Assembly line balancing in the clothing industry 29
Table II. The 41 tasks required in men’s shirt manufacturing
3
4
5
6
7
8
9
10
11
Worker number 12
13
14
15
16
17
18
19
20
0.712 1.022 0.266 1.150 0.465 1.470 0.283 0.216 1.172 0.919 1.056 0.983 1.156 0.868 1.041 0.422 0.823 0.570 0.037 0.639
0.106 0.889 0.865 1.111 1.356 0.232 0.018 0.006 0.025 1.201 1.171 0.269 0.287 0.557 0.610 0.966 0.905 0.444 1.004 1.055
1.261 1.433 0.747 0.828 0.803 0.059 0.337 0.794 0.549 0.696 0.474 1.441 1.342 0.564 0.733 1.321 1.269 0.412 1.002 1.474
0.090 0.966 0.428 1.085 0.491 0.277 1.349 1.143 0.480 0.253 0.053 0.053 1.217 1.183 0.782 0.380 0.640 0.103 1.269 0.644
0.440 1.498 0.234 0.617 0.664 0.015 0.484 0.856 0.112 0.952 0.907 0.486 0.962 1.372 0.150 0.936 0.032 0.447 0.541 0.052
1.376 0.366 0.915 0.813 1.351 0.568 1.294 0.346 1.176 0.112 0.438 1.342 0.731 1.022 1.407 0.068 1.417 1.263 1.242 1.110
0.552 1.014 1.110 0.894 0.521 0.592 1.145 1.294 0.390 1.443 0.088 0.701 1.172 0.860 0.487 0.711 0.057 0.363 1.207 0.164
1.162 0.444 1.120 1.321 0.531 0.546 1.450 0.093 1.283 1.181 0.699 0.771 1.121 0.791 1.426 1.234 1.099 0.562 1.379 0.892
0.492 0.129 0.121 1.003 0.732 0.123 0.411 0.728 1.377 1.305 0.329 1.316 0.287 1.394 0.411 1.301 1.360 0.877 0.120 0.921
13
14
15
16
17
18
19
20
(Continued)
0.123 0.309 0.402 0.445 0.025 0.658 1.446 0.628 1.099 0.048 0.753 1.087 0.872 0.754 0.178 0.869 1.342 0.155 1.258 1.206
0.243 1.156 1.441 1.354 1.046 0.349 0.443 0.237 1.074 0.656 0.326 0.462 0.996 0.293 0.955 0.627 0.818 0.232 0.137 1.069
8
12
0.478 0.976 0.816 1.038 0.832 0.223 0.401 0.048 1.154 0.298 1.199 0.924 0.121 0.336 0.094 0.886 0.837 0.509 0.527 0.135
7
11
1.007 1.162 0.410 0.985 1.300 1.121 1.163 1.027 1.499 0.359 0.897 0.806 1.034 0.806 0.156 1.232 1.072 0.197 0.870 0.617
6
0.639 0.836 0.236 0.718 1.003 1.071 0.685 0.619 1.492 1.139 0.606 0.612 0.928 1.455 1.206 1.263 1.099 0.747 0.874 0.324
0.409 0.014 0.242 0.472 0.153 0.146 0.280 1.327 0.990 1.494 0.143 0.684 1.350 0.771 0.281 1.341 0.661 1.488 0.350 1.042
5
0.558 1.062 0.262 0.478 0.151 1.320 1.054 0.233 1.341 1.034 0.712 0.360 0.022 0.623 0.409 0.448 0.773 0.627 1.284 1.454
0.304 0.889 1.316 0.472 0.082 0.811 1.353 0.384 0.767 0.728 1.309 1.219 0.058 0.793 0.008 0.686 1.386 0.206 0.639 0.454
4
9
1.292 0.033 0.420 1.256 0.459 1.037 1.128 0.033 1.384 0.748 1.200 1.137 0.372 0.121 1.452 0.826 0.243 0.242 1.001 0.862
10
0.047 0.751 0.943 0.549 1.480 0.945 1.256 1.344 1.203 1.428 0.223 0.954 0.516 0.582 0.124 1.496 1.054 0.419 1.002 0.655
3
0.215 0.140 0.382 0.190 1.494 1.047 0.849 0.890 0.954 0.659 0.645 0.414 0.354 0.619 0.271 1.147 1.401 0.064 0.937
2
2
–
1
Table III. Table of workers (worker Nos. 1-20) with arbitrary skill level (0-1.5) for each task
1
30
Task No.
IJCST 10,1
41
40
39
38
37
36
35
34
33
32
31
30
29
28
27
26
25
24
23
22
21
Task No.
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
0.212 0.432 0.856 0.547 1.036 0.308 0.983 0.048 1.215 0.703 0.976 1.121 1.384 0.549 0.096 0.859 1.109 0.684 0.525 1.368
0.030 1.034 0.341 1.420 0.277 0.686 1.184 0.746 0.969 0.841 0.553 0.265 0.416 0.273 0.698 0.504 0.760 0.715 0.149 0.709
1.241 1.297 1.009 0.345 0.340 1.170 0.495 1.470 0.766 1.225 0.632 0.233 0.601 1.298 0.534 0.906 0.704 1.255 0.290 0.388
1.332 0.884 0.291 0.237 0.672 0.933 1.058 1.145 0.534 0.026 0.376 1.285 0.143 0.413 0.725 0.830 1.182 0.395 0.204 0.235
0.739 1.176 0.090 0.996 0.489 0.546 0.115 0.320 0.925 0.285 0.657 1.233 0.537 0.983 0.627 0.377 0.911 0.933 1.047 0.563
1.465 1.370 0.101 0.750 0.811 0.541 0.439 0.173 0.036 1.238 1.291 0.994 1.483 0.591 0.487 0.768 0.034 1.211 0.055 1.307
1.159 0.215 0.140 1.102 1.392 1.318 0.227 1.432 1.485 0.992 1.327 0.496 1.107 1.231 0.263 0.736 0.039 0.793 0.670 0.108
0.431 0.297 0.212 1.051 0.935 0.837 0.391 1.205 0.152 0.231 0.522 0.216 1.019 1.298 0.724 0.344 0.580 0.360 0.135 0.265
1.312 1.124 1.226 0.416 0.165 1.045 0.822 0.152 1.453 0.319 0.055 0.698 1.371 1.057 1.065 0.624 1.188 0.144 0.887 1.328
0.224 1.161 1.228 1.391 0.881 0.386 0.593 0.088 0.765 1.472 0.378 0.203 0.860 0.577 0.879 0.037 1.479 0.006 0.931 0.488
0.723 1.483 0.512 1.022 1.211 0.912 0.469 0.070 0.944 1.082 1.213 0.725 0.634 1.497 0.542 0.912 0.880 1.088 1.393 0.017
0.419 1.058 0.741 1.258 0.311 1.312 1.064 0.241 0.512 1.289 0.126 1.225 0.530 0.092 1.460 1.212 1.417 0.664 0.211 0.567
1.239 0.011 1.111 1.383 0.605 0.874 0.225 1.488 1.044 1.305 0.615 0.921 0.517 0.649 0.648 0.529 0.305 0.773 0.709 0.109
0.370 0.508 0.655 0.121 1.339 0.559 0.001 0.059 1.120 1.397 1.085 0.470 0.163 0.143 0.639 0.718 1.323 1.069 1.337 0.392
0.699 1.027 0.918 1.010 0.191 0.380 0.634 0.628 0.633 0.711 1.375 0.230 0.890 1.240 0.502 0.354 0.539 0.045 0.255 0.408
0.494 1.430 0.572 0.823 0.740 0.850 0.762 0.782 0.567 0.088 0.707 0.706 0.448 0.310 1.089 1.359 0.144 0.623 0.582 1.307
0.244 0.860 1.209 0.081 1.384 1.226 0.079 1.206 1.034 1.324 0.458 1.463 1.092 1.456 0.429 0.402 0.085 1.146 0.675 0.938
0.460 0.765 1.193 0.172 0.271 1.013 0.370 0.309 0.732 0.866 0.514 0.217 1.312 0.743 0.756 1.051 1.173 0.114 1.167 0.479
1.077 1.323 0.160 0.605 1.140 0.691 0.031 0.996 1.386 0.333 1.284 0.874 0.790 1.469 0.631 1.003 1.401 0.591 1.120 1.159
1.266 0.743 0.090 0.708 1.276 0.757 1.254 0.551 1.341 0.009 0.983 0.622 1.359 0.034 1.283 1.467 0.966 0.396 0.773 0.914
1.047 0.158 0.114 0.169 0.107 0.681 0.594 0.113 0.073 0.645 0.126 1.010 0.567 0.343 1.190 0.332 0.389 0.085 0.397 1.344
1
Worker number
Assembly line balancing in the clothing industry 31
Table III.
Table IV. Table of workers (worker Nos. 21-41) with arbitrary skill level (0-1.5) for each task 37
38
39
40
41
0.253 0.852 0.905 0.826 1.213 0.547 1.132 0.502 1.264 0.966 0.054 0.288 0.364 0.571 0.945 1.049 0.080 1.479 0.292 0.033 0.554
1.115 0.015 0.763 0.502 0.623 1.390 0.245 0.969 0.230 0.524 0.594 1.190 1.427 1.465 0.990 0.268 0.693 1.480 0.994 0.610 0.453
1.145 1.320 0.468 1.014 0.010 0.416 0.666 0.519 1.040 0.552 0.194 0.185 0.359 0.767 0.115 0.510 1.158 1.471 0.999 1.034 1.284
1.038 0.873 0.489 0.836 1.397 0.557 0.242 0.349 0.250 1.397 0.227 1.451 0.657 0.279 1.208 1.181 0.706 0.215 1.455 0.382 0.365
0.244 0.593 1.170 0.551 0.813 1.403 0.266 0.217 1.203 0.664 1.011 0.455 0.156 1.355 0.628 0.787 0.104 0.483 0.158 1.291 0.481
0.535 1.394 0.125 0.488 0.701 0.813 1.409 0.209 0.882 0.664 1.290 0.825 0.153 0.229 0.532 0.700 0.989 0.421 1.456 0.159 1.457
0.443 1.371 1.027 0.594 0.697 0.404 0.099 1.064 0.134 1.146 0.587 1.168 0.125 1.091 1.153 0.624 0.226 1.298 0.107 0.381 0.902
1.386 0.866 1.336 0.145 0.816 0.634 0.173 1.342 0.611 1.096 0.015 1.478 0.772 0.750 0.965 0.749 0.586 0.505 1.250 0.134 0.132
0.623 0.975 0.069 0.436 1.241 0.413 1.338 0.835 0.085 0.519 1.425 0.081 0.035 0.624 0.007 0.787 0.151 0.909 0.192 0.669 1.231
0.328 0.583 0.586 1.190 0.149 1.084 1.221 0.084 1.346 0.870 0.488 0.517 0.830 0.415 1.160 0.812 0.629 0.979 0.284 1.286 0.373
0.363 0.186 1.365 0.804 0.078 1.319 1.338 0.493 1.128 1.397 1.469 0.981 0.696 0.555 1.164 0.861 0.253 0.418 0.850 0.005 1.258
11
12
13
14
15
16
17
18
19
20
(Continued)
1.256 1.337 0.430 1.039 1.126 1.426 1.362 1.072 0.108 1.002 0.789 0.651 1.230 1.375 0.879 0.861 1.106 0.183 0.590 1.390 0.521
9
10
1.279 0.621 0.781 1.079 1.454 0.301 1.464 0.037 0.786 0.818 0.557 1.072 0.653 0.160 0.939 1.147 0.943 0.885 1.240 1.433 0.989
36
0.857 1.330 0.224 1.466 0.649 1.173 1.101 0.163 0.947 0.998 1.480 1.097 0.306 0.111 0.134 1.315 0.060 0.216 0.020 1.283 0.258
35
8
34
7
33
0.588 1.177 1.216 0.991 0.333 0.546 1.223 0.178 0.968 0.351 0.331 1.007 0.044 0.381 0.018 0.422 0.365 1.019 0.967 1.129 0.641
32
0.457 0.649 1.439 0.051 1.062 1.462 1.128 0.491 0.118 0.135 1.344 0.404 1.067 0.134 1.333 0.892 1.124 1.230 0.934 0.062 0.594
31
6
30
Worker number
0.950 0.647 1.477 1.013 1.174 1.114 0.756 0.585 1.433 0.171 0.516 0.777 1.269 1.000 0.560 1.057 0.698 1.498 1.082 0.486 0.601
29
5
28
4
27
0.182 0.627 0.295 0.155 0.770 1.212 0.392 0.715 0.276 0.121 0.215 1.447 0.644 0.690 1.440 0.220 1.497 1.115 1.017 0.979 1.361
26
0.228 0.182 0.391 0.600 0.199 0.592 0.989 1.471 1.304 0.039 1.129 1.431 0.724 0.438 0.275 0.173 1.053 0.293 1.143 0.138 0.810
25
3
24
2
23
0.631 1.037 0.017 0.357 0.653 1.156 0.359 0.773 1.221 1.416 1.470 0.278 0.696 1.173 0.475 0.441 1.194 0.967 0.907 0.564 0.788
22
1
21
32
Task No.
IJCST 10,1
41
40
39
38
37
36
35
34
33
32
31
30
29
28
27
26
25
24
23
22
21
Task No.
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
1.341 0.399 1.066 0.801 0.456 0.305 1.395 0.874 1.006 0.426 1.169 1.207 1.201 0.817 1.192 0.729 1.003 0.368 0.712 1.234 0.577
0.389 1.469 0.423 0.894 0.363 0.668 0.445 0.890 0.081 0.092 1.341 0.957 1.299 0.284 1.133 1.405 0.963 0.102 1.274 0.555 0.225
0.139 1.429 0.001 0.483 0.099 1.094 0.554 1.280 0.286 0.947 0.779 0.544 0.559 0.947 0.977 0.406 0.044 0.247 0.724 0.528 1.279
1.345 0.959 0.158 0.985 0.093 0.613 0.850 0.169 1.395 0.631 0.911 1.246 0.130 1.259 0.167 0.781 0.676 1.349 1.314 1.273 1.106
1.326 0.921 1.416 0.740 0.003 0.363 1.489 0.022 0.748 0.327 1.410 0.769 0.122 0.837 1.022 0.547 1.156 1.274 1.046 0.712 0.984
0.246 0.237 1.168 0.316 1.427 0.361 1.061 1.142 0.123 0.324 0.732 1.335 0.489 0.771 1.070 0.584 0.548 0.225 1.461 1.477 0.835
0.370 0.827 1.281 1.082 0.435 0.497 0.505 0.757 0.065 0.204 1.062 0.144 1.106 0.368 0.297 0.520 1.010 0.648 0.202 0.152 0.210
1.092 0.317 0.587 0.861 1.483 0.412 0.039 0.646 0.068 1.020 0.124 1.305 0.823 0.649 0.029 0.767 0.875 0.226 0.332 1.040 0.239
0.269 1.318 1.430 0.409 0.157 0.996 0.606 0.073 0.110 0.496 0.683 0.159 0.849 0.209 1.309 1.274 0.652 0.625 0.515 0.373 1.293
1.181 0.224 0.367 0.965 0.011 1.012 1.120 0.415 1.026 0.409 0.357 1.271 1.459 1.435 0.953 1.023 1.262 0.993 0.465 1.478 1.108
0.695 1.138 0.130 0.646 1.402 1.027 0.052 0.895 0.577 1.041 0.521 1.307 0.130 1.340 0.948 0.060 0.456 0.050 0.776 1.284 0.978
0.843 0.125 1.217 1.359 0.038 1.161 0.425 0.516 0.657 1.430 0.139 0.901 1.459 0.800 1.432 0.852 0.609 0.610 1.465 1.032 0.753
0.820 0.097 1.364 0.789 1.287 1.147 0.784 1.281 1.310 1.398 0.142 0.713 1.200 0.596 0.535 0.159 1.333 1.077 0.896 1.324 1.156
09.44 0.705 0.195 0.326 0.210 1.137 1.129 0.592 0.383 0.776 1.202 0.413 1.381 0.709 0.974 0.808 1.074 0.662 1.257 0.679 0.498
0.425 1.008 0.423 0.845 0.078 0.285 0.051 1.131 0.304 0.688 0.182 0.795 0.143 0.667 0.020 0.726 0.271 1.024 0.279 0.762 1.293
0.786 0.603 1.305 0.443 1.221 1.134 1.085 0.874 0.808 0.629 0.621 0.929 0.171 0.570 0.039 0.908 0.850 0.194 0.175 0.595 0.630
0.276 0.416 1.020 0.474 1.329 0.717 1.216 0.101 0.660 0.447 0.827 0.713 1.481 1.325 0.879 0.280 1.426 0.300 1.364 0.866 1.011
0.022 1.450 0.136 0.752 1.055 0.301 1.297 1.487 0.249 0.394 0.172 1.233 0.588 0.004 0.572 0.771 0.211 1.225 0.165 1.461 1.317
1.257 0.772 1.198 1.011 0.014 0.099 0.993 0.948 1.251 0.711 0.937 0.759 0.259 1.295 1.238 0.462 0.513 0.756 0.589 0.443 0.396
1.295 1.491 0.622 0.045 0.265 1.275 0.121 0.456 0.715 0.621 0.628 0.185 0.026 0.858 0.977 0.575 0.787 1.447 0.844 0.690 1.409
0.055 0.131 0.721 0.819 0.950 0.805 0.358 1.403 1.353 1.336 0.660 0.625 0.450 1.332 0.956 0.387 0.861 0.189 0.975 0.164 0.561
21
Worker number
Assembly line balancing in the clothing industry 33
Table IV.
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Table V. Results of six trial runs
Run time (seconds)
Minutes required for producing 100 pieces
For 1st trail run 0.0 0.2 0.4 0.5 0.6 0.9 1.1 1.2 1.8 3.7 4.5 5.1 14.8 21.7 128.3 325.2 332.1 2,727.7 3,787.0 4,622.5 4,915.7 5,000.2
1,755.132 1,545.480 1,498.528 1,257.384 1,159.672 924.878 908.817 874.643 816.412 744.159 661.867 620.456 617.957 587.683 568.423 558.130 540.996 537.703 530.217 518.043 515.454 515.454
For 2nd trial run 0.0 0.1 0.1 0.2 0.3 0.3 0.8 0.9 1.2 5.9 17.5 31.1 32.4 1,057.2 1,057.6 1.058.3 1,639.1 2,042.6
23,976.244 3,027.356 2,908.633 1,734.647 1,217.030 857.192 791.374 782.822 732.812 647.108 613.117 609.480 555.051 549.690 544.105 533.953 532.080 522.420
Worker’s assignment from task 1 to task 41
{29, 26, 18, 6, 8, 19, 36, 2, 33, 20, 3, 37, 16, 15, 22, 23, 10, 35, 25, 11, 28, 7, 27, 12, 34, 31, 39, 21, 4, 14, 32, 41, 5, 38, 40, 17, 30, 9, 13, 24, 1}
{10, 40, 18, 6, 28, 3, 17, 11, 32, 19, 31, 23, 26, 39, 38, 25, 1, 41, 9, 36, 12, 22, 34, 20, 4, 21, 2, 35, 15, 16, 8, 7, 30, 14, 5, 33, 37, 27, 13, 29, 24}
(Continued)
Run time (seconds) 2,719.4 2,766.7 5,000.1
Minutes required for producing 100 pieces
Worker’s assignment from task 1 to task 41
517.487 505.078 505.078
Assembly line balancing in the clothing industry 35
For 3rd trial run 0.0 0.1 0.3 0.6 1.4 1.7 1.9 27.5 62.8 215.4 223.3 764.1 4,916.3 5,000.1
2,137.128 1,660.912 1,039.710 910.904 734.345 666.692 651.979 607.998 568.656 557.481 526.235 517.744 504.968 504.968
{39, 19, 25, 14,31, 35, 6, 27, 37, 36, 12, 2, 29, 24, 13, 18, 23, 5, 32, 40, 33, 26, 30, 38, 8, 17, 20, 11, 10, 34, 22, 15, 28, 21, 9, 16, 4, 41, 1, 7, 3}
For 4th trial run 0.0 0.1 0.3 0.3 0.7 1.5 7.9 24.0 24.2 36.1 71.2 71.5 1,249.5 1,544.6 2,798.3 4,529.1 5,000.2
10,234.165 1,172.557 914.375 905.982 794.460 652.810 642.656 629.464 612.067 590.272 538.931 527.517 522.944 518.418 513.117 496.882 496.882
{34, 36, 7, 10, 17, 9, 20, 2, 1, 25, 19, 41, 6, 11, 3, 40, 28, 35, 32, 22, 39, 27, 23, 29, 26, 30, 33, 31, 38, 13, 14, 8, 21, 18, 15, 37, 5, 16, 4, 24, 12}
For 5th trial run 0.0 0.2 0.3 0.4
1,720.063 1,579.982 1,431.533 1,102.631
{19, 16, 4, 29, 7, 24, 14, 38, 3, 31, 36, 5, 10, 27, 15, 25, 9, 12, 22, 23, 33, 13, 20, 35, 26, 6, 2, 17, 21, 34, 11, 30, 28, 39, 37, 32, 40, 41, 1, 8, 18} (Continued)
Table V.
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Table V.
Run time (seconds)
Minutes required for producing 100 pieces
0.6 0.9 0.9 2.0 3.2 7.0 8.6 9.3 26.7 27.4 222.2 304.8 305.0 5,000.1
1,014.611 910.961 855.581 834.057 762.128 731.472 691.279 647.269 636.713 579.979 561.292 503.399 495.709 495.79
For 6th trail run 0.0 0.1 0.2 0.3 0.4 0.5 0.9 1.1 1.4 1.6 1.6 2.1 2.4 2.8 5.2 6.1 9.5 10.9 18.9 21.9 26.7 52.2 107.0 165.5 225.6 226.0 226.2 448.7 514.8 5,000.2
2,333.627 2,054.006 1,458.752 1,186.272 1,111.605 1,006.604 1,003.631 830.645 814.738 791.902 784.169 780.760 746.748 705.693 699.834 685.861 682.148 672.053 653.879 611.126 605.234 595.430 584.864 567.486 565.132 557.635 556.989 552.983 474.567 474.567
Worker’s assignment from task 1 to task 41
{6, 30, 39, 31, 2, 40, 32, 35, 17, 28, 22, 20, 27, 5, 26, 9, 1, 19, 10, 33, 37, 34, 29, 12, 24, 7, 15, 23, 38, 21, 16, 8, 25, 14, 13, 41, 11, 18, 4, 3, 36}
7. Conclusion Assembly line In this paper, we present how a GA can be used for solving the ALB problem in balancing in the the clothing industry. By using GA, the ALB problem can be solved in an clothing industry effective manner to meet the realistic production conditions. The result of our numerical experiment showed that the performance of GA in handling ALB is much better than the performance of a greedy algorithm. We can conclude that 37 GA is an appropriate tool to solve the assembly line balancing problem, particularly for a dynamic and complex production environment like the clothing industry. References Anderson, E.J. and Ferris, M.C. (1990), “A genetic algorithm for the assembly line balancing problem”, Computer Science Technical Report No. 926, University of Wisconsin, March. Baybars, I. (1986), “A survey of exact algorithms for the simple assembly line balancing problem”, Management Science, Vol. 32 No. 8, p. 909. Bowman, E.H. (1960), “Assembly line balancing by linear programming”, Operational Research, Vol. 8 No. 3, p. 385. Fozzard, G., Spragg, J. and Tyler, D. (1996), “ Simulation of flow lines in clothing manufacture – part 1: model construction”, International Journal of Clothing Science and Technology, Vol. 8 No. 4, pp. 17-27. Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, pp. 122- 4. Hoffmann, T.R. (1990), “Assembly line balancing: a set of challenging problems”, International Journal of Production Research, Vol. 28, October, pp. 1807-15. Holland, J.H. (1975), Adaption in Natural and Artificial Systems, MIT Press, Cambridge, MA. Johnson, N.V. (1983), “A branch and bound algorithm for assembly line balancing problems with formulations irregularities”, Management Science, Vol. 29 No. 11, p. 1309. Kao, P.C. and Queyranne, M. (1982), “On dynamic programming methods for assembly line balancing”, Operations Research, Vol. 30 No. 2, p. 375. Man, K.F., Tang, K.S. and Kwong, S. (1996), “Genetic algorithms: concepts and applications”, IEEE Trans. on Industrial Electronics, Vol. 43 No. 5, October, pp. 519-33. Oliver, B.A., Kincade, D.H. and Albrecht, D. (1994), “Comparison of apparel production systems: a simulation”, Clothing and Textiles Research Journal, Vol. 12 No. 4, pp. 45-50. Rosser, P.S., Sommerfeld, J.T. and Tincher, W.C. (1991), “Discrete-event simulation of trousers manufacturing”, International Journal of Clothing Science and Technology, Vol. 3 No. 2, pp. 1831. Srinivas, M. and Patnaik, L.M. (1996), “On modelling genetic algorithms for functions of unitation”, IEEE Trans. on Systems, Mans. and Cybernetics, Vol. 26 No. 6, December, pp. 80921. Whitaker, D. (1973), “A study of a production line in the garment industry”, Clothing Institute Journal, Vol. XXI, pp. 113-20. Zbigniew, M. (1996), Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed., Springer-Verlag Berlin Heidelberg, New York, NY.
IJCST 10,1
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International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 38-49. © MCB University Press, 0955-6222
A new collision detection algorithm for garment animation G. Stylios and T.R. Wan University of Bradford, UK 1. Introduction With the rapid development of computer technology, it is possible to simulate fashion design with dynamic garment draping performance, using real mechanical properties of textile materials. One may choose a given fashion design and be able to see it on himself/herself, performing a “virtual wear trial” (Stylios, 1995) or to automatically view it on a super model as in a virtual fashion show before it is actually manufactured. When these techniques are fully developed, home shopping could become a reality (Stylios, 1995). Modelling of fabric drape has been challenging for many years and tremendous efforts have been made towards achieving this target (Amirbayat and Hearle, 1986; Breen et al., 1994; Carignan et al., 1992; Eberhardt et al., 1996; Lafleur et al., 1991; Lloyd, 1988; Moore and Wilhems, 1988; Stylios et al., 1996; Terzopoulos et al., 1987; Volino et al., 1995; Volino and Thalmann, 1994, 1997). One of the difficulties is due to the complex mechanics of textile materials, which appear non-linear, visco-elastic, history dependent and have large deformations. In the computer animation field, people are more interested in the appearance rather than in the precise simulation of materials, and hence compromises with heuristic methods may be acceptable, but introduce nongeneric solutions to this problem. These difficulties have slowed down the progress of research, especially in textile engineering areas. Review of the work concerning the use of conventional continuum mechanics and finite element approaches for simulating complex fabric draping (Amirbayat and Hearle, 1986; Lloyd, 1988; Werner, 1993) concludes that these methods have to overcome great obstacles, and only limited success has been achieved in some cases, because of the nature of the textile materials undergoing complex and large deformation. Recently, particle-based approaches for the modelling of fabrics (Breen et al., 1994; Eberhardt et al., 1996) appeared to be successful and has shown some potential. A “particle model” is based on a set of particles interacting with each other according to certain physical laws. Although the particle model is successful in simulating fabric drape, it has difficulties in modelling the actual textile materials, in a practical This project is conducted at the Center of Objective Measurement Technologies, which is supported by RETEX II project. The visualization tool has been, kindly, provided by ALIAS Inc. Thanks also go to OCF Ltd. for their support for the hardware facility provided.
way, and has also difficulties in simulating complex cases, for example, the dynamic performance of garments worn by animated virtual humans. Another physical approach is the use of deformation energies with dynamic constraints (Boulic et al., 1990; Carignan et al., 1992; Stylios et al., 1996; Terzopoulos et al., 1987), which appears most suitable in simulating complex fabric drape, but has difficulties in handling various garment collision situations. A recent particle approach (Volino and Thalmanny, 1997) appears effective in simulating deformation of garments by using complex collision and deformation control. Our work is based on the development of a dynamic fabric drape model (Stylios et al., 1996) and on our synthetic human model (Stylios et al., 1996), which aims to provide a complete solution for garment design and animation, and to try to establish the basis for the next generation of 3D CAD systems for fashion design, textile garment manufacture and retailing industries. The major parts of the work are as follows: • Textile material modelling. • Drape model stability and efficiency. • Virtual human modelling. • Collision detection and response. • Virtual fashion show implementation. We report the latest work of the drape model, with particular reference to the collision detection technique, which results in the production of a virtual fashion show. The implementation of the virtual fashion show has verified the stability and efficiency of our drape model. 2. Introduction to the fabric drape model In order to understand the concept of modelling of dynamic drape of fabrics, it is necessary to provide a general description of our model. Our approach is based on a physical analog to deep shell system. The fabric is treated as a continuum shell system initially, and then is discretized by lumping the distributive mass of the fabric and its mechanical properties to a large number of deformable node elements according to the mesh layout employed, where the elements can be equal or unique in size. It should be noted that textile materials display macrostructure (Lloyd, 1988), and the sizes of these basic fabric elements are selected to be large enough in comparison with their macrostructure but still very small compared with the size of the fabric itself. This is because fine fabric elements could provide accurate prediction of the behaviour of fabric. Using the approach described above, the behaviour of fabric is represented by the deformation process of the basic fabric elements. As shown in Figure 1, the deformation of a fabric element can be described by u and v displacements, along the first two surface coordinates α1, α2 within the tangent plane, and w displacement along the third surface coordinate α3 (the normal direction). The material properties of the continuum in all elements can be lumped at these
A new collision detection algorithm 39
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deformable nodes by integrating all the energies within those elements. The governing differential equations of the deformation of fabric are then derived from the discretization of the system energies over all fabric elements of fabric. Considering all energies of fabric elements, using Euler-Lagrange equations (Moiseiwitsch, 1966), a general description of the deformation of fabric can be determined by: 3 ∂L ∂ ∂L ∂ ∂ =∑ – L ( i = 1, 2, … , 3 ∂ψ i k =1 ∂X k ∂ (∂ψ i / ∂uk ) ∂t ∂ψ˙ i
where ψi are general functions –of energy component, representing ui (i = 1, 2, 3). – Using matrix notation ψ and X gives:
[
ψ = uvw and
[
]
T
X = α1 α 2 α 3 t
]
T
where t is the time variable. 3
w 2
v 0
Figure 1. Configuration of a basic fabric element
4
u 1
When considering energy function derivation within one basic fabric element in the model, we assume that the virtual energy density of strain will change continually and smoothly within the basic fabric element. This indicates that the energy density function, which is related to stresses and strains, is continuous. We also assume that they have continuous derivatives everywhere within the basic element and that the resultant effect on the whole shape of fabric, especially at locations of each node, will be identical to the effect of using lumped node-bar element treatment. Our model is also capable of including visco-elastic properties of fabrics. In order to represent precisely the behaviour of fabric, we have also added viscoelastic terms in the general energy equation. The simulation of the whole fabric drape process is divided into small time step sequences. At each time step, the initial locations of each point in the fabric are first identified and all the forces, such as gravity, boundary forces and collision forces between the fabric and the synthetic human are calculated, so that we can find all the energy terms in the equations of the fabric model. Then,
energy minimization is applied to find the new locations at each point in the fabric. 3. Collision detection One of the major problems of modelling the dynamic behavior of fabrics in garments is concerned with the interactions with fabric and the synthetic human body. This is called the collision detection and collision response which influence the movement of the garments when worn by a synthetic human. Generally speaking, when comparing textile material collision with collision in other areas of computer animation, the collision of fabric is more complex and difficult, for the following reasons: a fabric drape model involves a very large number of fabric elements, hence collision detection could be very time consuming. Therefore, since textile materials undergo complex, large deformation which appears non-linear, visco-elastic and history-dependent, the conventional continuum approaches appear unsuitable for the modelling of textile materials. These reasons can induce difficulties such as stability, versatility and precision of the model. A suitable approach should be able to incorporate complex properties of textile materials, have efficient and reliable collision detection. Such an approach should also be able to deal with various deformation situations such as dynamic folding and bulking etc. In the last ten years or so, some techniques dealing with collision have been developed. Lafleur et al. (1991) described a method to solve collision problems by simulating flexible fabrics with rigid bodies which were composed of polygons. Their method is a modification of Moore-Wilhelms methods (Moore and Wilhelms, 1988), which deal with surface collision problems. To avoid collisions when fabrics undergo deformation, they used a method consisting of creating a very thin repulsive force field around the obstacle surface. Volino and Thalmann (1994) presented a method for collision detection for garment animation, which was based on surface shape regularity, using a hierarchical representation of the garment surface. Recently, Volino et al. (1995) improved their techniques for fabric collision. In order to recognize the “right side” of a vertex from a surface polygon when considering a collision situation, they used extra memory to record the pre-collision “history”, so that, in case of collisions occurring, their system was able to correctly orientate the collisions and to correct wrong situations. In order to overcome the problem concerning garment surface orientation, we developed a precise technique which is able to detect the collision efficiently and reliably. The collision detection algorithm presented is based on a hierarchical fabric data base. Since the collisions occurring in garment animation are mainly related to the fabric folding, the following assumptions concerning garment deformations should be made: • The geometrical construction of fabric in garments is smooth and the basic deformation is formed in the ways of simple folding or bulking which indicates that the further folding is evolved naturally only by
A new collision detection algorithm 41
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basic body movements such as leg movements, and the forces such as fabric’s weights and collision forces between fabric and body, at different garment parts. Under this assumption, the deformations due to the consequence of complex actions such as grasping for instance, are not considered. • The initial draping occurs over the body shape of the synthetic human and then the fabric is deformed and driven mainly by basic body movements. Under the assumptions above, the collision cases, in principle, can be refereed as three basic types which are as follows: (1) The collisions between fabrics and body: this is a case of two colliding surfaces which belong to different objects. (2) The collisions between distant garment parts: the cases include the collisions occurring between separate parts of garments, such as a sleeve with the body panel. Cases such as the collisions occurring between one or more vertexes and the vertexes which are distant in geometrical structure are also treated as collisions between two separate parts of garments. Also, more than one vertex may be involved in collision at one time. (3) The local fabric collisions: collisions occur in a relative small region or locally. Different methodologies for dealing with collisions can be described as follows. Collisions occurring between fabrics and body surface The situation of collision between fabric and body is based on the collisions occurring between different objects. The procedure for collision detection and avoidance is as follows: given an existing position of a vertex (fabric element represented by a number of vertexes) at one time step, the system will send rays to its surroundings to check if any potential penetration or collision with body surface could occur. Actually, when real collision occurs, we can find exactly the collision position on the body surface. However, such a calculation is very timeconsuming. Instead of doing that, the system will calculate precisely the distances between the vertex examined and the nearest intersection point on the body surface nearby. If there is a case indicating that the two points are too close, this will be flagged as a collision case, then the collision response will be →c added to the corresponding fabric elements. Precisely, let V be the position of i → the examined vertex i, Vkb be the position of the nearest intersection point on the body surface, the collision between the two colliding→ surfaces can be →b c – V determined by examining if the projection of the vector V along the i k → normal direction Nhb is less than the minimum search distance tm, that is, if: r r r d i ,k = Vic – Vkb . N kb
(
)
then, a collision will occur and the collision response will be added to the fabric elements concerned. The forces, positions and the velocities of the corresponding elements will be adjusted to avoid evolving possible collisions. One of the simplest ways to add response is simply to cancel the components which lie along the normal direction of the collision point on the body surface. If di,k < 0, this indicates there are some penetrations occuring between two surfaces. The new position will be adjusted to adding an extra amount di,k but in the opposite direction.
A new collision detection algorithm 43
Collision between distant garment parts The situation of the collisions between distant garment parts can be treated like the collision between different objects, so that the same techniques can be applied. However, you need to remember or to code the part boundaries dynamically. In principle, the collision system will check the geometrical structure of the fabrics concerned when a collision is detected. If the two colliding points are far separated in view of initial structure, these two colliding parts will be catalogued as different parts of the garments and the techniques for collision between different objects will be applied. Figure 2 shows a typical case of the collision between two garment parts. Figure 2(a) shows the two parts move closer and Figure 2(b) shows a penetration occurred.
Figure 2. An example of the collision between the two garment parts (a)
(b)
Collision in a local region Compared with the collisions between distant garment parts, the situation of collisions occuring in a local region are more complex. Using only the surface information as described in the previous sections is not sufficient. A major problem is to recognize the orientation of surface of the garment parts, since the two colliding surfaces may penetrate each other. In order to deal with surface orientation, extra measures must be taken into consideration. The surface orientation problem can be illustrated by two simplified examples of typical cases, as shown in Figures 3 and 4. As can be seen, in order to find the surface orientation of the garment parts, when dealing with the matter of fabric collision and penetration, one must recognize whether the penetration is of the type of “inside to inside” of the surface, or “outside to outside”. For example, we define that the “outside” side of the surface is indicated by the direction of the
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→
→
normal Np and the “inside” side of the surface is opposite to the normal Np. In the case of the collision type of “inside to inside” as shown in Figure 3, two collision vertexes v1 and v2 move from lying inside with respect to each other’s normal direction to the outside with respect to each other’s normal position. Figure 4 shows an example of “outside to outside” type, which is a similar movement but in the reverse order. It is difficult to identify the two cases only use a current relative position of the two vertexes, since the cases are actually a history dependent. Np tm
Figure 3. An example of a simplified case of collision; inside to inside type (the figure only shows the outline of major collision section)
pm
outside
(a)
v2
v1
(b)
v1
v2 inside
dr
outside v2
pm
outside
inside
dr Figure 4. An example of a simplified case of collision; outside to outside type (the figure only shows the outline of major collision section)
Np tm
outside v1
v1
(a)
v2
(b)
Np
Np tm dr
pm
inside
tm
pm
inside
dr
Using the surface information as described in previous cases is insufficient to deal with the matter of the surface orientation of the fabric. In order to correctly recognize the surface orientation, extra measures concerned with the relative positions of local vertices with each other and their normal direction were taken into consideration. The algorithm is as follows: we first compute the common normal vector Np in the local collision region, as shown in Figures 3 and 4 and the intersection point Pm. The tangential plane of the surface at Pm can then be determined. Let Vi and Vj be the position vectors of the potential collision vertexes vi and vj, and dij = Vi – Pm, and tm is the standardized tangential vector along the curve with maximum curvature on the tangential plane at Pm. If dij. Np > 0, the collision type is properly of the “inside to inside”, as the case in Figure 5. If dij. Np < 0, the collision type is properly of the “outside to outside”, as the case in Figure 4. If dij. tm > 0, the direction of the collision avoiding will be set along the direction of tm and if dij. tm < 0, the direction of the collision avoiding will be set along the direction opposite to tm. In this way, we know exactly the right side of surface when a collision or penetration occurred.
Hierarchical data base: in order to improve the efficiency of the collision detection, our system used a hierarchical data structure. In principle, we define the vertices of a garment surface according to a hierarchical structure as shown in Figure 6. The collision process will start in the highest level nodes and a measure will be added at this stage. If a distance threshold is reached, which indicates potential collisions could occur in lower level nodes, the system will search further in a lower node level. The same principle will be applied to each level except the base level. If the threshold is not reached, the system will skip the search to lower node levels.
A new collision detection algorithm 45
Figure 5. An example of the collision in a local region
node (1,k)
higher level nodes
node (1,k+1)
base level nodes node (b,i)
node (b,i +1)
node (b,i +2)
node (b,i)
node (b,i +1)
node (b,i +2)
4. Modelling of synthetic humans For modelling an animated synthetic human, the following tasks need to be taken into consideration: • Development of an animated skeleton model incorporating various motion data, which can be used to simulate a real cat walk and to be able to animate a skinned human body. • Development of a skinned human model, which can be animated by a skeleton motion. • A method for the relocation of the shape data of the human model when the body is in motion. In order to model a synthetic human, a skeleton model was first developed, which can be regarded as a special linkage system, like an industrial robot, or a mechanical manipulator. There are a number of body modelling research of
Figure 6. A hierarchical structure of garment data
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complex cases reported (Badler, 1986; Boulic et al., 1990; Bruderlin et al., 1994; Sturman, 1986), in which the researchers try to model complex body movements precisely and efficiently. However, our main interest is to first, focus on fabric dynamic behaviour, where a relatively simple animation model may be adequate. At the current stage, our synthetic human model is able to complete a number of basic body movements, like lady’s or man’s walk, running and cat walking. The current skeleton model used consisted of 22 body segments arranged in accordance with the hierarchical structure shown in Figure 6. The model uses a group of rotational joints, each of which has three degrees of freedom. The skeleton movement is calculated by the joint angles and its reference point. Figure 7 shows an example of motion curves which control the skeleton joints over given time steps. These motion curves are generated in a local coordinate system relative to the position and orientation of the joint above in the hierarchical structure.
125
Figure 7. An example of a set of motion curves
0
00
125
250
375
In order to produce a realistic shape of body and its movement, a measure of a real body was used in the current work. This is the basic requirement for modelling the movement of a garment as it is being worn by the virtual human, since this information is required by the fabric drape model as a constraint to the garment, and also, to stop penetrating the skin of the synthetic actor during the motion of the garment. The next task is then to attach the skin in terms of body data to the skeleton. The body movement generation was created by keyframe techniques (Sturman, 1986). Figure 8 shows a skinned synthetic human lady with an animated skeleton. 5. Implementation The garment animation can be divided into two processes. The first stage is the drape of the garment as it follows the 3D body shape of the synthetic human. The resultant shape of the garment is determined by material mechanical properties, body shape and the defined garment design pattern. The second stage is the fabric movement driven by the animated synthetic human, whereby the shape of the fabric will try to follow the movement of the body. An example has been implemented and presented in this paper. Figure 9 shows an instance
A new collision detection algorithm 47
Figure 8. An example of a skinned synthetic lady
Figure 9. A virtual fashion show sequence
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of a cat-walk sequence taken from a virtual fashion. The skirt shown in these sequences was modelled using 1,440 (20 × 72) deformable node elements. The model is implemented in C++ with Alias Openmodel Library and the work was carried out on a SGI Indigo 2. 6. Conclusion and further work We reported on an approach for modelling fabric drape and its dynamic performance. Our model uses deformable node-bar notation which is based on a physical analog to a deep shell system. The major advantages of this model over other models are that its configuration is based on the surface coordinate system and that it uses fabric mechanical parameters. A new collision technique has been presented and addressed. The implementation of this collision system to our fabric model has shown that it is reliable and capable of dealing with complex deformation of garments during walking, using fully skinned virtual humans. Our aim is to provide new generic methodologies for garment design and manufacture as well as entertainment. Consequently, the fabric model in development should be able to model various textile materials. It would be then possible for people to choose the design or garment in accordance with their specific requirements, and to also establish a basis for the next generation of CAD systems for fashion design and garment industries. It may be possible in the near future for home shopping to become a reality. People could see a virtual fashion show in the comfort of their lounge. In this way, people can purchase garments by conducting virtual wear trials using their own body size and shape. And as far as the making up of the chosen garment is concerned, we have already developed an intelligent sewing machine (Stylios and Sotomi, 1994), which can automatically deal with optimum sewing data for the fabrics concerned. References Amirbayat, J. and Hearle, J.W.S. (1986), “The complex buckling of flexible sheet materials: part 1 – theoretical approach”, International Journal of Mechanical Sciences. Badler, N.I. (1986), “Animating human figures: perspectives and directions”, Graphics Interface ’86, Proceedings, pp. 115-20. Boulic, R., Thalmann, N.M. and Thalmann, D. (1990), “A global human walking model with realtime kinematic personification”, The Visual Computer, Vol. 6 No. 6, pp. 344-58. Breen, D.E., House, D.H. and Wozny, M.J. (1994), “A particle-based model for simulating the draping behaviours of woven cloth”, Textile Research Journal, November, pp. 663-85. Bruderlin, A., Teo, C.T. and Calvert, T. (1994), “Procedural movement for articulated figure animation”, Computer Graphics, Vol. 18 No. 4, pp. 453-61. Carignan, M., Yang, Y., Magnenat-Thalmann, N. and Magnenat-Thalmann, D. (1992), “Dressing animated synthetic actors with complex deformable clothes”, Computer Graphics (Proc. SIGGRAPH), Vol. 26 No. 2, pp. 99-104. Eberhardt, B., Weber, A. and Strasser, W. (1996), “A fast, flexible, particle-system model for cloth draping”, Computer Graphics in Textile and Apparel, IEEE Computer Graphics and Applications, pp. 52-9.
Lafleur, B., Magnenat-Thalmann, N. and Thalmann, D. (1991), “Cloth animation with self-collision detection”, Proceedings of the IFIP Conference on Modelling in Computer Graphics, pp. 179-87. Lloyd, D.W. (1988), “The analysis of complex fabric deformation in mechanics of flexible fibre assemblies”, in Hearle, J.W.S., Thwaites, J.J. and Amirbayat, J. (Eds), NATO Advanced Study Institute Series E: Applied Science No. 38, Sijthoff and Noordhoff, pp. 311-42. Moiseiwitsch, B.L. (1966), Variational Principles, John Wiley & Sons Ltd, London. Moore, M. and Wilhems, J. (1988), “Collision detection and response for computer animation”, Computer Graphics, Vol. 22 No. 4, pp. 289-98. Sturman, D. (1986), “Interactive keyframe animation of 3D articulated models”, Graphics Interface ’86, Tutorial on Computer Animation. Stylios, G. (1995), “Living without frontiers: the global retailer”, International Journal of Clothing Science and Technology, Vol. 7 No. 4, pp. 5-8. Stylios, G. and Sotomi, O.J. (1994), “A neuro-fuzzy control system for intelligent sewing machines”, Intelligent Systems Engineering, IEE Publication, No. 395, pp. 241-6. Stylios, G., Wan, T.R. and Powell, N.J. (1996), “Modeliing the dynamic drape of garments on synthetic humans in a virtual fashion show”, International Journal of Clothing Science and Technology, Vol. 8 No. 3, pp. 95-112. Terzopoulos, D., Platt, J., Barr, A. and Fleischer, K. (1987), “Elastically deformable models”, Computer Graphics, Vol. 21, 4 July. Volino, P. and Thalmann, N.M. (1994), “Efficient self-collision detection on smoothly discretized surface animations using geometrical shape regulatory”, EUROGRAPHIC 94 Proc., Vol. 13 Part 3, pp. 155-66. Volino, P. and Thalmann, N.M. (1997), “Interactive cloth simulation; problem and solutions”, from Desktop to Webtop: Virtual Environments on the Internet www and Networks, International Conference. Volino, P., Courchesne, M. and Thalmann, N.M. (1995), “Versatile and efficient techniques for simulating cloth and other deformable objects”, Computer Graphics Proceedings, Annual Conference Series, pp. 137-44. Werner, S. (1993), Vibrations of Shells and Plates, 2nd ed., Marcel Dekker, Inc. New York, NY.
A new collision detection algorithm 49
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50 Received March 1996 Accepted August 1997
Lightweight wool garment wrinkle performance A wear experiment C. J. Salter, A.F. Roczniok CSIRO Division of Wool Technology, Ryde, NSW, Australia and
L.G. Stephens Biometrics Unit CSIRO IAPP, Delhi Road, North Ryde, NSW, Australia Introduction Wool fabrics have traditionally shown superior wrinkling performance when compared to other natural fibres such as cotton and linen. Recently there has been an increased use of wool in lightweight garments. The wrinkles that form in lightweight and thin fabrics tend to be sharp and may not recover as well as they usually do in bulkier fabric constructions. Wear trials are the most direct method for accurately establishing the wrinkling performance of fabrics. Fabric wrinkling during wear is in practice due not only to a combination of fabric properties such as fabric construction, fibre stress relaxation, and fibre content, but also to wear variables such as the wearers themselves, the garment fit, the care procedures and ambient conditions during wear. In order to determine the relative contributions of these wear factors to the final wrinkling performance separately from the more traditionally considered fabric properties, some control over them is needed until their action and mechanisms are adequately understood. Background Wear trials have been conducted in the past to establish and quantify the effect of some of the important characteristics in the process of wrinkling in wear. Work has been carried out on the effect of fabric composition (Looney, 1969; Phillips, 1982) and treatments applied to the fabric (Makinson, 1970), as well as the effect of fabric temperature and regain during wrinkling (Sørensen and Høg,
International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 50-63. MCB University Press, 0955-6222
Support for this project was provided by the Australian woolgrowers and the Australian Government through the International Wool Secretariat and CSIRO. The authors particularly appreciate the assistance of Mr P.G. Minazio from IWS (Biella) and Mr J. Mills from IWS (Ilkley) for the sourcing and supply of the fabrics from the wool industry, and also Fletcher Jones Pty Ltd, of Australia for the sourcing of fabric. The garments were tailored by Rundles Ltd. We are also indebted to Mr J. Nilon from CSIRO Division of Wool Technology, Geelong Laboratory for his useful comments on the commercial fabrics used in the trial. Finally, thank you to the wearers and observers for your time and effort put into this trial as without your help it would not have been possible. © CSIRO, Australia, published under licence.
1971). The changes in wrinkle performance due to the ageing of wool during wear (Mohar et al., 1987) as well as the effect of the wear and laundering (Wilkinson and Hoffman, 1959) have also been studied. Wear trials have also been compared directly with different test methods (Bostwick and Kärrholm, 1965; Lako and Veer, 1962; Matsuoka et al., 1984). In most of these trials the garments were worn during the normal activities of the wearers, without any control of the ambient conditions or activity of the wearer. In many cases the design of the trials did not attempt to control the care of the garment during wear. In most cases, where the wrinkle performance of worsteds was examined, the fabrics weighed more than 200gm –2 and sometimes over 300gm–2. Information on heavier weight fabrics was gained from these trials. However, there is little information available relating to the wrinkle performance of lightweight wool fabrics. Also, owing to the large costs associated with conducting wearer trials, the statistical design was often compromised. Generally either limited numbers of garments and wearers were used, or the subjective evaluation of the garment appearance was completed by only two or three observers. The garment’s wrinkled appearance was assessed by comparison to either another garment in the trial, or to a set of three dimensional wrinkle standards (AATCC, 1984). This allowed a judgement on garment performance in that particular trial to be made in comparison with another garment, or a split side garment, but did not provide quantitative information on wrinkle severity. Wear studies which have considered the effect of ambient conditions during wear have shown the large influence ambient conditions have on the resultant wrinkling performance. Sørenson and Høg (1971) measured the temperature and moisture regain profiles during normal and controlled wear. They found considerable variations in the monitored temperature and relative humidity from day to day, and season to season for the two fabrics tested. They showed that there was a positive correlation between the rated wrinkle appearance of garments and the temperature decrease which occurred in the garments after sitting. These temperature changes were significantly influenced by the room temperature, which in turn affected the temperature of the wearer’s skin and the temperature of the body/cloth microclimate. The moisture regain was found not to increase or change during sitting, and to decrease or remain constant during recovery in the experiments conducted. This current study used six wearers and six lightweight wool fabrics to determine the effects of wearer, fabric, condition of wear, and pressing on the resultant wrinkle performance, with garments worn in controlled ambient conditions for two-hour sessions. Experimental Design A balanced cyclic design (John, 1986) was used for this experiment which allowed four aspects of wear to be tested and is shown in Table I.
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Fabric Wearer
1
2
3
4
5
6
1 2 3 4 5 6
1 2 4 3 3 2
4 2 3 1 3 4
3 4 2 4 2 1
2 1 3 1 4 4
1 3 4 2 1 2
4 3 1 2 1 3
Notes: Code 1 Table I. 2 The cyclic design used in the wear trial with the 3 four treatments defined 4
Treatment 20°C 40% rh – no pressing 20°C 40% rh – pressing 25°C 75% rh – no pressing 25°C 75% rh – pressing
Six wearers tested the performance of six lightweight wool fabrics in two controlled ambient environments. The wear sessions were two hours in duration. Figure 1 shows the detailed structure of the wear protocol. The assessment of the garment wrinkling was made by photographing the garments at specific times after wear. Ten observers assessed the wrinkled appearance of the photographs, using a method that quantified the amount of the wrinkling. The factors wearer, fabric, condition of wear, and pressing, were defined at the beginning of the trial. This cyclic design allowed each factor to be estimated from each wearer testing a garment made from each fabric so that 36 combinations of wearer by fabric were used rather than the 144 combinations required in a full factorial design. However, only one interaction effect could be estimated using this design, i.e. condition of wear * pressing. Wearers Six male wearers were chosen for the trial. A brief qualitative description of the wearers is given in Table II. No measurements of the physiological changes of the wearers were made before, during or after the wear periods. Shimizu et al. (1993) have since shown that the sweat rate of sitting wearers increases over 25 minutes in ambient temperatures of 30°C, and similarly decreases in conditions of 20°C. Consequently, it was assumed that some physiological changes in the wearers would occur during the wear sessions, but only those that would usually occur in normal wear. Fabrics Six plain weave fabrics were chosen for the wear trial, their features are described in Table III. Manufacturing and finishing information was available for fabrics 1 and 6, as they were experimental fabrics. Qualitative comments on the probable finishing routes for the commercial fabrics have been provided.
Wrinkle performance
Garments Tailored Garments Pressed
Garments Aged 6 days (20°C 65%rh)
53 Garments Worn – Session 1 25 minutes sit Pressed stored overnight (20°C 65%rh)
Not Pressed stored overnight (20°C 65%rh)
5 minutes stand 25 minutes sit 5 minutes stand
Garments Worn – Session 2
25 minutes sit Pressed stored overnight (20°C 65%rh)
Not Pressed stored overnight (20°C 65%rh)
5 minutes stand 30 minutes sit
Garments Worn – Session 3
Assessment 1 hour
Figure 1. A flow chart outlining the wear protocol
Assessment 4 hours Assessment 24 hours
Wearer
Description
1 2 3 4 5 6
Small Tall Large Small Large Tall
Waist – cuff (cm)
Waist (cm)
97 110 91 103 96 109
84 95 126 78 110 87
The mean fibre diameter measurements were made on fibres taken from the fabrics, using Sirolan Laserscan. The fabrics were made up into tailored trousers of a plain, unpleated classical design as supplied to retail stores. The trousers were not dry cleaned during any stage of the trial, but were prepared
Table II. Garment sizes and description of wearers
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Fabric
Description
1
2/46 21 µ wool, 2/50 Lycra, sirospun, 2% Lycra in warp and weft, light grey colour, crab, scour, piece dyed, tenter, crop, pressure decatised, 194gm–2 19.1 µ 100% wool, green, singles weft yarns, probably piece dyed, commercial fabric, 139gm–2 19.9 µ 100% wool, taupe, colour woven, probably pressure decatised, commercial fabric, 163gm–2 21.7 µ (mean) 20% polyester, 80% wool, black, commercial fabric, 161gm–2 19.6 µ 100% wool, checked, high twist yarn, probably pressure decatised, commercial fabric, 177gm–2 2/46 µ wool, sirospun, light grey colour, crab, scour, piece dyed, tenter, crop, pressure decatised, 190gm–2
2
54
3 4 5
Table III. Fabric characteristics
6
for the trial by pressing. This procedure is discussed below. After pressing, the trousers were hung from the waist in a standard conditioned laboratory (20°C, 65%rh) for six days prior to wear. When not being worn the trousers were stored in this environment. Conditions of wear Two ambient air conditions were chosen for the wear trial which could be expected to occur during the normal wear of garments made from these types of fabrics. The first condition, 20°C, 40 per cent rh, reproduced a typical office wear situation, the second condition, 25°C, 75 per cent rh, represented a fairly average spring day in Australia, or a representative average condition for a European summer day. The experiment was conducted in the Clothing Climate Chambers at CSIRO Division of Wool Technology, Ryde, Australia, which were specifically designed for comfort evaluation trials. Pressing In normal wear and care, garments tend to be pressed to improve or restore their appearance after wear. In this trial, each garment was worn for three sessions, over three consecutive days. This procedure was tested by pressing some garments after wear, prior to the next day’s wear, while others were not. All pressing in the trial was done as described in the pressing protocol. Pressing protocol Pressing was the only process used to standardise the garments prior to wear. There were three issues considered in the definition of the protocol: (1) The pressing action was to be as short as possible, as pressing in industry is done to achieve a good appearance in the shortest amount of time. (2) The pressing time had to be long enough to remove all of the wrinkles from wear sessions.
(3) The pressing operation had to be reproducible and the same for all garments. Table IV summarises the pressing procedure adopted for all pressing that occurred during the experiment. Garment wear Wear sessions were conducted in the mornings from 8 a.m.-11 a.m. Wearers tested each garment in three separate wear sessions over three consecutive days. They sat on canvas chairs for 25-minute periods, followed by five-minute periods of standing or walking. No restriction was placed on the way wearers sat, and some chose to cross their legs. The activity of the wearers was not regulated and they mainly talked, read, wrote, or used a computer. Except for wearers 1 and 2, all wearers sat for only one session per day. Wearer 1 sat two sessions a day for fabrics 3 and 5, and wearer 2 sat two sessions a day for fabrics 2 and 4. In the third week of the trial there was a malfunction of the climate rooms and five garments had to be retested after two wear sessions had been completed. In this case the garments were pressed and stored for one week and then treated as new.
Step
Action
1 2 3 4
5 seconds buck steam, head open, steam pressure 400kPa 5 seconds buck steam, head closed, steam pressure 400kPa 5 seconds, bake with no steam, head closed 5 seconds vacuum, head open
Assessment of wrinkles At this time there is no instrument or method to assess garment wrinkling objectively and so the assessment of the wrinkled trousers was done subjectively. As the garment wrinkles recover with time, it is physically impossible to get a significantly large number of assessors to rate the garments at the same time. The wrinkle performance of the garments was recorded by taking black and white photographs at several time periods during the recovery sections of the wear cycle. These times are identified in Figure 1. Using a viewing board, as set out in AATCC test method 128 (1984), garments were hung from the waist against a green background. The light source was incident from above casting a shadow over the garments to highlight the wrinkles. A camera was placed 1.2m from the hanging garment and photographs of the back view of the entire garment were made. A fine grained black and white film was used (Ilford PANF 50). The photographs of the back view were processed into A4 size prints on semi-matt paper. Each print was labelled with a random three digit number from 100 to 999 to identify it uniquely.
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Table IV. Pressing protocol
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Figure 2. The question sheet used in the subjective assessments of the photographs. The line in both questions was 150mm long
Ten observers were asked to view the prints of the garment. The question sheet given to the observers is shown in Figure 2. Two questions were asked for each photograph viewed, one on the wrinkled appearance of the garment, the other on its appearance acceptability. Two photographs of the AATCC three dimensional replicas (1 and 5), were supplied to define the ends of the scale “not wrinkled at all” and “extremely wrinkled”, in question 1. Observers were asked to use these as the reference points of the scale rather than their own personal idea of what the wrinkling intensity was at these extremes. To get the observers familiar with the task there was a training period where they were shown a variety of the photographs and asked to rate their appearance using the question sheet. The same information was given to all observers. When they asked questions about the features of the photographs they were not generally answered if they were specifically about the garment wrinkles. They were referred to use the reference photographs to make their assessment. The photographs were viewed by all observers in a standard conditioned laboratory (20°C, 65 per cent rh) under the same lighting conditions, where the 12 photographs were viewed at one sitting to prevent observer fatigue. The scores were collated by measuring the distance the mark made on the scale from the left hand side. These values were then used in the analysis.
Question 2 in Figure 2 was designed to establish how acceptable the observer found the wrinkled appearance of the garment. There were no guidelines provided on how to answer this question. The scores from this question were obtained in the same way as for question 1. A second set of photographs, showing only the back of the left knee at 1-2 hours recovery, was also tested. This was to establish if the observers had been assessing the behind the knee wrinkles or a full garment appearance parameter. A5 size prints were made and only the first question in Figure 1 was asked, as the results from the A4 size prints showed that the two questions were highly correlated to each other. Analysis The results obtained from the ten observers were averaged and then analysed using analysis of variance. The experimental design was not completely orthogonal. The factors of wearer and fabric were orthogonal as were the other factors of condition of wear and pressing. However, the factors of wearer and fabric were not orthogonal to the other two factors. The experimental design was balanced if the factor of wearer was fitted prior to the other factors of fabric, condition of wear and pressing. Fitting the wearer factor first was reasonable as the wearers were treated as a blocking factor in the experiment. The loss of information for the condition of wear and pressing factors was only small, the efficiency of estimation was 92.6 per cent. Recovery time was also analysed to determine the recovery relationship with the subjective assessments of wrinkles. It is well established that wool recovery behaviour is a viscoelastic phenomenon which occurs over logarithmic time. Initially several different analyses were completed on the data, which estimated the sources of variation in the trial at the specific recovery times. Time was treated as a factor with levels (0, 1, 2) corresponding closely to the logarithm of time to the base 5 (i.e. log5(time)). Three specific fabric contrasts were identified at the beginning of the trial: (1) Fabrics 3 and 5, two commercially produced pure wool fabrics, one of which was claimed by the manufacturer to perform better with respect to wrinkling. (2) Fabrics 1 and 6, two fabrics matched except for the inclusion of a small amount of lycra in fabric 1. (3) Fabric 4 with fabrics 3 and 5, a wool/polyester blend fabric compared with two pure wool fabrics. All of these were specifically tested for in the analyses. There were two data points missing from the data set and were treated as missing data in the analysis using substituted (estimated) values. Two different methods of calculating the significance of means were used, the Fisher’s protected least significance difference test (FPLSD) and Bonferroni’s method (BLSD) (Snedecor and Cochran, 1989).
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Results and discussion The analysis including time as a variate is given in Table V. There was a significant difference between the three assessment times tested and the resultant wrinkle performance, with the performance improving as the recovery time increased. The linear component of the time accounted for the largest amount of the variation in this factor, as is seen in the large value for the sum of squares term. It reflects that the photographs had a logarithmic feature which was most likely to be the change in the wrinkled appearance of the garments, and indirectly verified that the observers assessed a wrinkling feature of the garment. The quadratic component was also significant, but accounted for a much smaller proportion of the variation. The analyses at the individual times also provided similar results to these. The results from the assessments using the A5 photographs of wrinkling (behind the knee) of the trousers, produced similar results to the assessments using A4 photograph in terms of the significant factors and the extent of their significance. Only the results for the assessments using A4 photographs are presented here. The analysis of question 2 showed that there was a direct correlation between the acceptability of the wrinkled garment (question 2) and the wrinkled appearance of the garment (question 1), with poor appearance being synonymous with low acceptance. The nature of the experimental design meant that it was not possible to extract the fabric rankings for the different wearers, conditions of wear, pressing or their interaction, as the design was not completely orthogonal.
Source of variation (Wearer.occasion).stratum Wearer Fabric Fabric 5 vs fabric 3 Fabric 1 vs fabric 6 Fabric 4 vs (average of fabrics 5 and 3) Deviations Conditions of wear Pressing Conditions of wear * pressing Residual
Table V. ANOVA for wear analysed with time transformed to log5
(Wearer.occasion).time.stratum Log5(time) Linear Quadratic Residual Total
DF
5 5 1 1 1 2 1 1 1 22 2 1 1 70 107
SS
MS
F
36,670.2 6,734.0 10.52 28,356.1 5,671.2 8.86 4,407.9 4,407.9 6.89 11.1 11.1 0.02 1,970.8 1,970.8 3.08 21,966.5 10,983.2 17.16 71,253.4 71,253.4 111.34 226.9 226.9 0.35 807.9 807.9 1.26 14,079.7 640.0 5.64 15,517.7 7,758.8 11,411.7 11,411.7 4,106.0 4,106.0 8,613.8 123.4 172,522.8
63.05 92.74 33.37
P
<0.001 <0.001 0.015 0.897 0.093 <0.001 <0.001 0.558 0.273
<0.001 <0.001 <0.001
Wearer differences The results in Table V show that wearers were perceived as wrinkling the garments to different levels of wrinkle severity. The wrinkle intensity for each wearer is given in Figure 3, where the average wrinkle score is plotted for the six wearers over all three assessment times. It is interesting to note that the largest wearer (wearer 3) did not necessarily wrinkle the garments more severely than other body types. The relationship between sweat rate and humidity inside clothing has been shown by Shimizu et al. (1993) to be dependent on the ambient air conditions. The humidity inside the clothing can be influenced by the environmental humidity and sweat rate of the wearer as well as the style of clothing. The significant variation between wearers, even in this controlled wear environment, shows part of the difficulty in relating wear results to laboratory wrinkle tests.
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59
Average Score (Wrinkle Intensity) 100 90 80 70 60 50 40 30 20 10 0 1 Wearer
2
3
4
5
6
Fabric differences Fabrics were perceived by observers to have different wrinkle performance. This was shown in the 0.05 probability result in the Anova analysis (see Table V). Figure 4 shows the wrinkle intensity of each of the six fabrics. Some of the fabric scores were similar (e.g. fabrics 1 and 6) but there was clear differentiation between the performance of fabrics 5 and 2 (commercial 100 per cent wool fabrics). The types of features that differ between the fabrics were: • pattern; • colour; • yarn construction; • weight; and • probable finishing route.
Figure 3. The average wrinkle score (wrinkle intensit) for the six wearers. A high value indicates a large level of wrinkling
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Average Score (Wrinkle Intensity) 100 90 80 70
60
60 50 40
Figure 4. The average wrinkle score (wrinkle intensit) for the six fabrics. A high value indicates a large level of wrinkling
30 20 10 0 1 Fabric
2
3
4
5
6
Questions raised on why the wrinkle performance of these fabrics was so markedly different to one another cannot be adequately answered within the scope of this experiment, but the differences are likely to be due to the different fabric weights and the different finishing routes. The FPLSD showed that fabrics 3, 6, 1, and 4 had similar wrinkle appearance to one another. The BLSD included fabric 5 into this group. The specific fabric contrast of fabric 5 and 3 showed that there was a significant difference between the two fabrics at the 0.015 level. Thus fabric 5, which was claimed to have superior wrinkle performance in wear, did in fact have better performance to fabric 3. The comparison of fabric 1 and 6 showed no significant difference at all. This indicates that the inclusion of 2 per cent lycra in both the warp and weft of the fabric had no beneficial effect on the resultant wrinkling performance during wear as measured in this experiment. The last fabric contrast specifically tested for was the comparison between the wool-polyester blend fabric (4) and the two best performing fabrics (5 and 3). The results of fabrics 5 and 3 were averaged, and as can be seen from the first contrast the difference between these fabrics was significant. The difference between fabric 4 and the averaged fabrics 5 and 3 was significant at the 0.093 level. Given the results of the FPLSD and the BLSD both showing that fabric 3 and 4 were not significantly different, this difference was due to the difference between fabric 5 and 4, and 5 and 3. It also showed that the 100 per cent wool fabric performed better than the wool-polyester blend fabric. Condition of wear differences In all of the analyses that were carried out, the most significant and consistent result was the difference produced by wearing garments in two different environmental conditions. The ambient condition of 25°C, 75 per cent rh consistently produced poorer wrinkle performance in garments, compared to the milder ambient conditions of 20°C 40 per cent rh. Figure 5 shows the
Average Score (Wrinkle Intensity) 90
Wrinkle performance
80 70 60
61
50 40 30 20 10 0 20°C 40%rh Condition of Wear
25°C 75%rh
average wrinkle severity for the two conditions of wear, averaged over all of the other variates in the model. It was not possible to determine, in this experiment, whether the effect was due to ambient temperature, ambient relative humidity or a combination of both temperature and relative humidity. The effect of pressing between wears and the condition of wear and pressing interaction There was no statistical difference between the perception of wrinkles of garments that had been pressed and those that had not during the wear cycle. For wool fabrics there are two competing mechanisms influencing the effect. Wool fabrics have been tested to show that with increased ageing time prior to wrinkling, recovery improved. Garments have been shown to de-age with one day of wear (Mahar et al., 1987) and no further effects of de-ageing were noted. This may indicate that ageing of the garments took place after this time, so that in the present study garments that had not been pressed between wear sessions may have aged longer than those that had been pressed between wear sessions. The de-ageing effects of wear could be due to pressing, the effects of wear causing strain on the fabrics, change in temperature and relative humidity of the garment during the wear cycle. As the quantitative effects of these parameters have not been measured in this trial it is not possible to say how much they have affected the result, but it is likely that there has been some cancelling out of the effects in this trial. The condition of wear * pressing interaction was not found to be significant at any of the time intervals examined. This too may be due to many mechanisms co-exerting their influence on the final wrinkled appearance of the garments. Conclusions This wear trial has highlighted a number of important features of wrinkle performance. Fabrics made solely from wool can behave differently in actual
Figure 5. The different wrinkle intensities produced at the two ambient wear conditions of 20°C, 40% rh, and 25°C, 75% rh
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wear situations, showing that there are some properties other than fibre type that lead to differences in wrinkle performance. This has not been quantified before in wear trials, owing to the many parameters that have a confounding influence on wear and recovery conditions. Because these have not been successfully controlled or accounted for, the different behaviour, if it has been present, has been obscured. This specifically designed trial with controlled wear and recovery conditions and a statistically efficient design has enabled smaller differences in wrinkle performance to be detected than were previously observed. This was shown through the measurable differences between 100 per cent lightweight wool fabrics 5 and 2. The conditions under which a garment was worn played a major part in the wrinkle performance of that garment. This has been observed even for the relatively small difference in ambient conditions used in this trial, 25°C, 75 per cent rh and 20°C, 40 per cent rh. Further work A further experiment was carried out (Salter et al., 1995) to determine differences in wrinkle performance for different fabrics under different ambient temperature and humidity conditions. It was designed with specific reference to identifying the influence of ambient temperature and relative humidity on the resultant wrinkle performance. References AATCC Test Method 128 – 1980 (1984), “Wrinkle recovery of fabrics – appearance method”, Technical Manual AATCC, Vol. 59, p. 320. Bostwick, C.O and Kärrholm, E.M. (1965), “Wrinkling and wrinkle recovery of wool fabrics determined by wear tests and laboratory methods”, American Dyestuff Reporter, 24 May, p. 392. Genstat, NAG Ltd, Mayfield House, 256 Banbury Road, Oxford. John, J.A. (1986), Cyclic Designs, Chapman and Hall, London. Lako, J. and Veer, L.S. (1962), “The assessment of crease resistance and crease recovery”, Proceedings of the Textile Institute, Vol. 53, p. 99. Looney, F.S. Jr (1969), “The influence of construction variables on the wrinkling characteristics of Dacron®/Wool Suitings”, Proceedings of the Textile Wear Test Symposium, Raleigh, NC, October. Mahar, T.J., De Jong, S., Dhingra, R.C. and Postle, R. (1987), “Physical ageing and annealing in fibres and textile materials: part IV – ageing during wear of apparel wool fabrics”, Text. Res. J., Vol. 57 No. 12, p. 697. Makinson, K.R. (1970), “Wearer trials conducted to assess the effects of DOWFAX 2A1 on worsted suits and other worsted garments”, CSIRO Division of Textile Physics Laboratory Note, SN/23. Matsuoka, H., Niwa, M. and Nagase, S. (1984), “On evaluation methods for wrinkling by using moire topography”, J. of the Japan Res. Assoc. for Text. End Uses, Vol. 25 No. 34, (in Japanese – translation available).
Phillips, D.G. (1982), “The assessment of wrinkling in the laboratory and in wear”, in Kawabata, S., Postle, R. and Niwa, M. (Eds), Proceedings of the 1st Japan/Australia Symposium on Objective Measurement, Textile Machinery Society of Japan, Osaka, Japan, p. 75. Salter, C.J., Roczniok, A.F. and Postle, R. (1995), “Controlled wear – a method of determining wrinkle performance”, 9th International Wool Textile Research Conference, Biella, Italy, 28 June-5 July. Shimizu, H., Shimizu, Y. and Yuge, O. (1993), “Influence of different coverage clothing on temperature and humidity inside clothing due to changes in air conditions”, J. of the Japan Res. Assoc. for Text. End Uses, Vol. 34, p. 238. Snedecor, G.W. and Cochran, W.G. (1989), Statistical Methods, 8th ed., Iowa State University Press. Sørensen, T. and Høg, J. (1971), “Determination of temperature and moisture regain changes in wool fabrics which are wrinkled during wear”, Applied Polymer Symposium, Vol. 18, p. 981. Wilkinson, P.R. and Hoffman, R.M. (1959), “The effects of wear and laundering on the wrinkling of fabrics”, Text. Res. J., Vol. 29 No. 8, p. 652.
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64 Received October 1995 Revised June 1996 Accepted October 1997
Strength reduction in sewing threads during high speed sewing in industrial lockstitch machine Part II: Effect of thread and fabric properties G. Sundaresan, K.R. Salhotra and P.K. Hari Department of Textile Technology, IIT, New Delhi, India Introduction During high speed sewing, the needle thread is subjected to stresses that adversely influence both its sewing and seam performance. The severity of these stresses depends on the tensile and other properties of the needle thread and its interaction with the machine elements, the fabric and the bobbin thread. A good sewing thread should give satisfactory sewing and seam performance. The sewing performance of threads is generally assessed by studying the dynamic tension and the strength reduction of the thread. Crow and Chamberlain (1969) were two of the earliest researchers to investigate thread strength reduction. They studied the strength reduction of different sewing threads and the effect of certain machine and process parameters on strength reduction. Though the study provided information on the strength loss in sewing threads under different conditions many of their observations such as inconsistency in the change in the percentage of elongation and insignificant change in the loop strength of sewn threads lacks explanation. Gersak et al. (1991) found that the strength reduction of sewing thread depends on the tightening tension, the thread fineness, needle size and the number of plies of fabrics. Recently, Gersak (1991) studied the changes in the tensile properties of the thread caused by dynamic loading. Nevertheless, the mechanism of strength reduction in sewing threads is not clear. The effect of thread structural and surface properties in enhancing its strength retention ability and that of certain fabric properties on the severity of strength loss needs to be studied.
International Journal of Clothing Science and Technology, Vol. 10 No. 1, 1998, pp. 64-79. MCB University Press, 0955-6222
The authors are grateful to the following for their help during the course of study. M/s Coats Viyella India Ltd, Bangalore and M/s Bhilwara synthetics for providing the sewing threads required for the study; Mr D.C. Sharma, SEM Lab for the SEM study of the threads; Professor S.M. Ishtiaque, Department of Textile Technology and Professor Sneh Anand, Mr S. Biswas, Centre for Biomedical Engineering for the help and suggestions to do the cross-sectional studies. ©G. Sundaresan, K.R. Salhotra and P.K. Hari, 1995.
An understanding of the above mechanisms will not only be useful for the sewing thread manufacturer to engineer the thread with specific properties but also for the garment manufacturer to select threads and appropriate machine settings to have an optimum sewing and seam performance. The present study is, therefore, envisaged to investigate these aspects of thread strength reduction. The mechanism of strength reduction of sewing threads is discussed in Part I of this paper (Sundaresan et al., 1995). The changes in the structural compactness of the thread has been found to be the dominant factor causing its strength reduction. This paper discusses the effect of fabric parameters like its tightness and the thread structural parameters like fibre length and fineness, number of plies and coefficient of yarn-metal friction on the extent of strength reduction. Fabric tightness factor Fabric tightness factor indicates its firmness and has been found to be useful in predicting certain important properties like its weavable limit and elasticity (Galuszyuski, 1981; Seyan and El-Shiekh, 1994. It is also found to affect the needle penetrating force during high speed sewing. Similarly, the frictional force acting on the thread surface during its movement through the fabric and the bending strain on the loops of the stitch is expected to depend on fabric tightness. It is, therefore, important to study the influence of fabric tightness on thread strength reduction. For this purpose, one paper and three fabrics have been selected for the study. In the case of paper a permanent hole will be made by the needle as it pierces and hence both the lateral force acting on the thread surface and the bending strain on the stitch loop are expected to be nearly absent, while in the case of fabrics the intensity of these forces will depend on its tightness factor. Thread structural parameters The thread structural parameters are expected to play an important role in the loss in thread strength in view of the fact that the strength loss is mainly due to its structural disintegration. The influence of some of the important parameters can be described as follows. Twist in spun yarns has the primary function of binding the fibres together through interfibre friction; higher twist also gives the thread better fatigue resistance. Hence, an optimum twist could minimise strength loss. The number of plies in the thread can influence the extent of strength loss owing to the contribution of better transverse force build-up through inter-ply pressure. Accordingly, more plies should reduce the strength loss. Similarly, a comparison of the relative difference in strength reduction of cotton and polyester spun threads would be useful in view of the significant difference in fibre length and uniformity of these fibres. In addition to lower strength, the mean length and uniformity of cotton fibres is, in general, expected to be lower than that of polyester fibres.
Strength reduction in sewing threads 65
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The severity of abrasion between the thread and the sewing elements of the machine depends on the yarn-metal friction. A thread with higher coefficient of friction is, therefore, expected to suffer higher strength reduction. Materials and methods Commercial cotton and polyester spun sewing threads with different structural and tensile properties were selected for the present study. The details of these threads are given in Table I. In order to assess the degrading effect of fabric constructional parameters one paper and three fabrics were selected for sewing (details as in Table II). The fabric tightness factor [T] was calculated using the following expression derived by Seyam and Aly El-Shiekh (1994): T = ta / m (1) where ta = actual thread density, e tm = (e – i ) πd / 4 + 2id
(2 )
Twist Tenacity Elongation Initial modulus Coefficient Thread Fibre Fineness Factora Direction Mean CV Mean CV Mean CV of code used tex ply/single cNtex–1 (%) (%) cNtex–1 (%) friction
Table I. Properties of sewing threads
PC120 PD120 PS100 BH100 PC80 CN50 CZ60 CS60
PES PES PES PES PES Cotton Cotton Cotton
13.9 × 2 9.4 × 3 11.1 × 3 16.0 × 2 12.3 × 3 12.3 × 3 14.7 × 2 14.7 × 2
56.3/34.6 44.2/16.6 47.3/32.5 36.1/24.4 53.7/32.6 47.2/38.0 56.1/34.7 60.7/32.9
z/s z/s z/s z/s z/s z/s z/s s/z
34.7 8.3 32.3 9.3 31.7 11.0 36.8 7.4 33.8 7.3 23.7 6.1 22.1 9.5 24.3 7.3
6.3 4.3 3.8 4.0 3.7 8.6 8.3 9.8
341 263 194 216 248 413 270 355
10.8 6.3 9.8 10.6 5.9 12.8 15.2 20.6
0.134 0.113 0.139 0.179 0.103 0.157 0.168 0.177
Note: a Twist factor = (tex) ⁄ × twist per cm 1
2
Fabric
Weave
Tex
FA FB FC
Plain Plain 2/1 twill
10.7 10.7 39.4
Warpa Ends (cm–1) 41 41 23
Notes: Table II. Fabric particulars
12.4 16.2 17.6 14.3 15.2 5.4 7.0 6.8
a FA and FB: polyester twisted filament; b FA and FB: polyester textured filament;
FC: cotton spun FC: cotton spun
Tex
Weftb Picks (cm–1)
Tightness (%)
17.2 17.2 37.0
29 17 19
96.0 76.0 80.8
e is the number of threads per weave repeat i is the number of weave intersections in the repeat d is the yarn diameter given by (3) d = 1/{29.3(φ ρ N) ⁄ } N is the count in cotton system ρ is the fibre density in gcm–3 The packing factor φ in equation (3) is assumed as 0.75 for the twisted polyester filament warp, and 0.55 for both the polyester textured and cotton spun yarns (Hearle, 1969; Seyan and El-Shiekh, 1994). A Singer industrial lockstitch machine (model 191D 200AA) was used for these trials. It was run at 4,000 stitches per minute with singer needle No.16 and the stitch dial number 2.5. The other sewing parameters and conditions were similar to those mentioned in Part I of the paper (Sundaresan et al., 1995). The seam balance ratio has been calculated as the ratio of the length of the needle thread to that of the bobbin thread per unit seam length (Mahar et al., 1989). The needle thread tension for all the threads except CN50 and BH100 was 80cN, while it was 85cN for CN50 and 65cN for BH100. The bobbin thread tension was 25cN for all the threads. The procedure adopted for the assessment of thread strength reduction, fibre denier and structural damages including cross-sectional studies has also been described elsewhere (Sundaresan et al., 1995). The thread yarn-metal friction was estimated using the Zweigle friction tester with the yarn and chart speed as 15.8m/min and 150mm/min respectively for a testing time of 10 minutes. 1
Strength reduction in sewing threads
2
Results and discussion Effect of fabric tightness Analysis of the strength reduction of polyester threads (Table III) sewn with paper and the three fabrics, FA, FB, and FC reveals an interesting trend. Contrary to expectation, the reduction is higher for these threads when sewn with fabric FB, whose tightness factor is the lowest among the three fabrics than with FA, the fabric with highest tightness factor. While the strength loss is significantly higher in PC120, a 2-ply thread, it is slightly high in PC80 and insignificant in PD120 thread. The seam balance ratio and the thread consumption of some threads for all the fabrics are given in Table IV. Both the factors are lower for all the threads when sewn with FB than with paper and FA. This should be the reason for the higher strength reduction since lower thread consumption will subject the thread to extra cycles of stresses. It can further be seen that the significance of strength reduction depends on the extent of decrease in thread consumption and the seam balance ratio. This explains the higher strength reduction in PC120 thread. Additionally, lower abrasion resistance of PC120 thread owing to its 2-ply structure may also be contributing to the higher loss. Higher loss in the initial modulus, Figure 1, and more fibre slippage in the initial region, Figure 2, of the stress-strain curves for the PC120 thread sewn with fabrics supports this.
67
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Table III. CV% of and percentage change in tensile properties of the sewn thread
Thread code
Breaking elongationa Red. CV (%)
Tenacity Red. CV (%)
Sewn on paper PC120 14.0 PD120 10.4 PS100 8.8 BH100 27.5 PC80 20.5 CN50 21.7 CZ60 24.7 CS60 39.3 Sewn on fabric Fabric FA PC120 17.6 PD120 11.9 PC80 18.9 CN50 30.6 CZ60 28.0 CS60 48.5 Fabric FB PC120 20.3 PD120 12.6 PC80 20.7 CN50 28.5 CZ60 23.0 Fabric FC PD120 11.0 CZ60 25.6
Toughness Red. CV (%)
8.3 7.6 8.3 10.2 8.1 7.7 9.5 10.4
15.7 –7.6 1.8 14.8 7.4 –28.1 8.7 12.1
6.9 5.1 7.1 8.6 6.2 6.6 11.5 15.2
14.2 68.9 63.2 45.1 48.1 66.5 66.3 63.3
11.4 30.1 37.6 39.9 16.8 18.8 48.6 40.3
27.3 13.9 17.0 38.0 28.7 16.1 45.8 56.3
12.7 10.3 11.6 15.2 11.5 12.8 15.6 17.5
9.2 6.8 7.3 12.7 8.7 10.6
9.0 –11.2 12.1 0.0 3.7 –5.5
6.7 5.8 5.9 12.9 10.8 11.3
49.5 81.9 58.7 61.3 70.6 79.9
23.3 44.7 31.4 36.6 43.4 46.2
29.9 15.6 30.1 45.2 47.2 58.8
13.1 9.3 9.7 19.2 13.9 16.5
9.5 8.8 7.0 8.2 8.5
0.0 –8.0 0.0 –22.1 4.0
8.5 5.4 5.2 12.9 9.2
71.0 70.4 65.3 82.4 67.7
45.0 33.7 32.9 43.3 30.8
28.4 15.2 24.4 37.1 40.3
14.1 11.1 8.7 12.8 14.0
7.2 8.2
–8.5 3.9
5.8 10.1
75.9 71.9
39.7 48.0
14.3 44.4
9.3 14.1
Note: a a “–” sign indicates increase in the elongation-at-break
Paper
Table IV. Thread consumption and seam balance
Initial modulus Red. CV (%)
Thread code
LT cm
SB %
PC120 PD120 PC80 CN50 CZ60
2.85/2.83 2.98/2.98 3.02/2.95 3.01/2.90 3.00/3.00
100.7 100.0 102.3 104.0 100.0
Fabric FA LT SBLT cm % 2.92/2.92 2.98/2.86 3.10/3.00 2.98/2.90 3.05/2.90
100.0 104.2 103.3 102.8 105.2
Fabric FB SBLT SB cm % 2.55/2.61 2.82/2.84 2.79/2.86 2.80/2.75 2.73/2.82
Notes: SB: seam balance LT: needle thread/bobbin thread consumed per 10 stitches
97.7 99.3 97.3 102.0 97.6
Fabric FC SBLT SB cm % – 3.24/2.85 – – 3.05/2.88
– 113.7 – – 105.6
Strength reduction in sewing threads
Tenacity (cN/tex) 40.0
30.0
69 20.0
10.0
0 0 4.0 Strain (%)
8.0
12.0
16.0
20.0
Key : Parent : Sewn with paper : Sewn with fabric FA : Sewn with fabric FB
Figure 1. Stress-strain curves for PC120 threads
Tenacity (cN/tex) 1.5
1.0
0.5
0 0 0.2 Strain (%)
0.4
0.6
Key : Parent : Sewn with paper : Sewn with fabric FA : Sewn with fabric FB
0.8
1.0
1.2
Figure 2. Initial region of the stress-strain curves for PC120 threads
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Fabrics FA and FB are made from similar yarns with the same warp thread spacing except for the lower pick density in FB than in FA (Table II). Therefore, the bending stiffness of FB is expected to be lower than that of FA. FC, is a 2/1 twill fabric with coarser cotton rotor spun yarns and is therefore also expected to have higher bending stiffness than FB. It is known that fabric bending property is an important factor influencing its movement during sewing and puckering of the seam (Lindberg et al., 1960). Therefore, the reduction in the stitch length and thread consumption in FB should be due to its lower bending stiffness. Seams sewn on fabric FB also exhibit severe puckering with considerable differential feeding of the bottom and top layers. Cross-sectional analysis: Thread cross-sectional study is of special interest since it could reveal the structural damages that had taken place in threads sewn with paper and different fabrics. The typical cross-sections of the PC80 sewn threads are given in Plate 1. While the change in cross-sectional shape is minimal with damages mostly restricted to the surface in the case of the thread sewn with paper, there is significant change in threads sewn with fabrics FA and FB. The individual plies have been relatively displaced leading to the opening of the structure with distinct change in the cross-sectional shape.
(a)
Plate 1. Cross sections of PC80 threads: (a) sewn with paper; (b) sewn with fabric FA; (c) sewn with fabric FB
(b)
(c)
The strength loss in cotton threads, however, exhibits sensitiveness to the tightness factor in spite of the reduction in stitch length and thread consumption when sewn with FB. The loss in all the three cotton threads is lowest with paper and highest with FA; the loss for those with FB and FC falling between these two values. This can be attributed to the difference in abrasion resistance, fibre strength and length properties of cotton threads as compared to the polyester threads. A detailed analysis of the strength reduction in cotton threads reveals an important aspect of the thread ply structure. The strength loss for CZ60 sewn with paper is higher than that for CN50, the coarser thread. This can be attributed to the higher loss of fibre strength in CZ50 owing to the higher specific loading during sewing. Analysis of the loss in the initial modulus for both the threads highlights the importance of three-ply structure rather than the thread size with regard to its abrasion with fabric cross threads and various sewing elements, especially the needle. The strength loss in CN50 is maximum when sewn with FA though the loss in initial modulus is lower than that sewn with FB. Analysis of the stress-strain curve, Figure 3, indicates that the thread failure is mostly preceded by isolated fibre breaks, suggesting the presence of severe weak spots in some of the fibres. The breaks are also found to initiate from the interlacing point of the needle thread in the stitch. Scanning electron microscope (SEM) examination, Plate 2, of the thread interlacing point in the stitch shows distinct transverse cracks in the fibres indicating the presence of excessive bending stress at these points. This must be due to the combined effects of tight fabric construction and larger thread diameter. Conversely, the strength loss is nearly the same in the case of FB in spite of the higher loss in modulus; the occurrence of isolated breaks is less with comparatively less
Strength reduction in sewing threads 71
Tenacity (cN/tex)
20.0 Isolated fibre breakages
15.0
10.0
5.0
0 0 1.0 2.0 Strain (%)
3.0
4.0
5.0
6.0
7.0
Figure 3. Typical stress-strain curves for CN50: sewn with fabric FA
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severity, Figure 4. This suggests the absence of such severe weak spots in the fibre present in threads sewn with FA. The geometry of the needle thread at the intersection also varies with the material used for sewing. The thread is folded closely in the case of FA indicating higher bending stress while the crimp arc is wide with little crimp in the case of paper. Higher tightness factor in FA leaves less space for the loops of the stitch so that they take high curvatures. The variations in the failure of threads sewn with a particular fabric suggests significant variation in the severity of the damages. The CV percentage of the initial modulus of the sewn threads is also found higher.
Plate 2. SEM photograph of the interlacing point of needle thread with cracks in fibre – CN50 sewn with fabric FA
Tenacity (cN/tex)
20.0 Isolated fibre breakages
15.0
10.0
5.0
Figure 4. Typical stress-strain curves for CN50: sewn with fabric FB
0 0 1.0 2.0 Strain (%)
3.0
4.0
5.0
6.0
7.0
Higher loss in CZ60 with FC for smaller increase in tightness as compared to fabric FB suggests that the frictional property of the component yarns of fabric also affects strength reduction. Thread fractography The failure of CN50 sewn threads often shows segregation in the break of the plies as against the catastrophic breaks of the parent thread, suggesting significant difference in the strength of the individual plies. The severity of this type of break is extreme in threads sewn with FA while it is much less in threads sewn with paper. The thread break is, generally, initiated by the failure of the weaker component, as can be seen in Figure 3, and mostly at the interlacing point of the thread in the stitch. The scatter of the strength of the plies is also wide. The first ply breaks well in advance and the remaining two plies share the load thereafter. The broken ends exhibit distinct breakage of the three plies with longer breakage zone than the parent thread, as shown in Plate 3. The propagation of break is different in PC80, the thread with same resultant count as CN50. The plies break close to each other suggesting insignificant difference in the ply strength. This can be attributed to less fibre damages owing to the higher strength of polyester fibres. Further, the twist in the thread is less as compared to CN50, which gives freedom for fibre movement during bending, thus reducing the bending strain. The occurrence and severity of the above mentioned type of breaks is less in finer threads possibly owing to lower bending strain on these threads in the stitch.
Strength reduction in sewing threads 73
Plate 3. SEM photograph of the broken end of CN50 thread sewn with fabric FA
Effect of fibre type The strength reduction of cotton threads is, generally, higher than the polyester threads. In most of the cases, the reduction in initial modulus and toughness of cotton threads is also higher with comparatively severe fibre slippage. The above difference between cotton and polyester threads is less in the case of paper and it widens when sewn with fabrics. This can be attributed to the poor abrasion resistance of cotton threads. While the loss in initial modulus, mostly, gets compensated in sewn polyester threads as the tension builds up irrespective of the magnitude of the initial loss,
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there is cumulative loss or little compensation in cotton threads. Since the strength reduction in sewn threads is partly due to non-contribution of a part of some fibres to the yarn tension, an increase in fibre length restricts the pullout of surface fibre ends from the structure. This increases the resistance to fibre slippage thus proportionately reducing fibre slippage in polyester threads.
74
Effect of twist Analysis of the strength reduction of polyester threads shows the dependence of the loss in initial modulus and the severity of fibre slippage on the twist factor (Table I) of the parent thread except in the case of BH100. PC120, the thread with the highest twist factor, exhibits lowest reduction in modulus with shorter slip period, Figure 2, indicating well developed resistance to slippage. Higher twist in PC120 maintains its structural integrity thus preventing the loosening of the structure due to cyclic loading. Comparing PD120, PS100 and PC80, all three-ply threads, the loss in initial modulus is higher in PD120 followed by PS100 and then by PC80. The difference in loss between PD120 and PC80 persists when sewn with fabrics though PD120 is a finer thread. It can also been seen from Figures 1, 5 and 6 that while the initial loss in modulus is compensated in PC120 and PS100 sewn threads, it is maintained until failure in PD120. It is also interesting to note that the elongation-at-break of the polyester sewn threads varies widely and depends on the loss in the initial modulus and the extend of fibre slippage and hence the twist in the parent thread. In cotton threads the trend is not very clear owing to simultaneous variations in the number of plies, amount and direction of twist. Effect of number of plies Comparing threads of the same linear density but of different ply numbers, the strength loss is more for PC120 than for PD120 though the loss in initial modulus of PD120 is higher. The fibre denier in both the threads is 1.1 dtex. The strength loss increases in the case of PC120 when sewn with fabrics while it remains nearly consistent throughout in the case of PD120. On the other hand, loss in PD120 remains consistent throughout except in the case of FA where it is higher. This could be due to the combined effects of lower twist and high fabric tightness factor because of which there could be more structural disintegration, especially at the interlacing point in the stitch. Effect of thread size The thread size in relation to the needle size has been found to be an important factor affecting the thread strength reduction (Gersal and Knee, 1991). Analysis of the percentage strength reduction of PD120, PS100 and PC80 sewn with paper does not exactly reflect this trend. The loss is highest, 20.5 per cent, for PC80, followed by PD120, 10.4 per cent and then PS100, 8.8 per cent. Though PD120, PS100 and PC80 are all three-ply threads there is significant difference in their structural composition. The first two are spun from 1.1 dtex and the last one from 1.7 dtex fibres. Therefore, the number of fibres in the cross
section is approximately the same for PD120 (254) and PC80 (223), while it is higher for PS100 (300). Additionally, the twist factor is the lowest in PD120 among the three, followed by PS100 and PC80 with ascending order of twist factor. It is the combination of these factors that must be enabling better strength retention in PS100. The loss in initial modulus and the rate of tension build-up in these threads as evident from Figures 5 and 6 confirms this. Therefore, it seems that the structural property of the thread is more important rather than its size with regard to its abrasion with the needle.
Strength reduction in sewing threads 75
Effect of thread-metal friction and twist direction The loss in strength among all the polyester spun threads is the highest (27.5 per cent) in BH100, the thread with the highest coefficient of thread-metal friction . It is approximately three times higher than that in PS100, the thread of same size. The difference in the number of plies and the amount of twist between the two threads may also be contributing to the higher strength loss in BH100. The loss in the elongation-at-break of BH100 is also higher. The reduction in fibre strength was found to be only 8.5 per cent. Larger reduction in strength and elongation percentage of the thread in spite of the comparatively less reduction in fibre strength and elongation suggests that the strength reduction is mainly due to severe structural damages owing to higher frictional Tenacity (cN/tex) 40
30
20
10
0 0 5.0 Strain (%)
10.0
15.0
20.0
25.0
Key : Parent : Sewn with paper : Sewn with fabric FA : Sewn with fabric FB : Sewn with fabric FC
Figure 5. Stress-strain curves for PD120 thread
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Tenacity (cN/tex)
40.0
76
30.0
20.0
10.0
0 0 5.0 Strain (%)
Figure 6. Stress-strain curves for PS100 and BH100 threads
10.0
15.0
20.0
25.0
Key : BH100 Parent thread : BH100 Sewn with paper : PS100 Parent thread : PS100 Sewn with paper
coefficient. The stress-strain curves, Figure 6, of the sewn thread also supports this. Larger deposits of fibre dust were also observed under the throat plate during the sewing of BH100 owing to the stripping of fibres from the surface of the thread by parts like the throat plate because of higher friction. It is known that the linear density of the thread reduces after sewing as a result of such actions (Crow and Chamberlain, 1969). Similarly, CS60, having reverse twist direction (S/Z), exhibits higher strength reduction than CZ60. The difference in the severity of structural damage can be seen in the SEM photographs, Plates 4 and 5, of the sewn CZ60 and CS60. Though the surface fibres have been severely pulled out in CS60, there is no significant difference in the loss in initial modulus between the two threads. This may be due to the higher twist in CS60 as a result of which the core may not be disturbed much. Further, longer length of fibre pull-out may lead to the complete noncontribution of these fibres to the thread strength as a whole. It is known that the rubbing of the bobbin hook against the thread tends to open the twist and fray the thread if the twist direction is counter to the rubbing direction (Solinger, 1961). It is this rubbing action that causes the opening of the structure in CS60. The failure of the CS60 sewn threads closely resemble that of sewn CN50 thread with even more severity when sewn with the fabric FA, Figure 7. This
Strength reduction in sewing threads 77 Plate 4. Sem photograph of CZ60 threads sewn with paper
Plate 5. Sem photograph of CS60 threads sewn with paper Tenacity (cN/tex) 30.0
20.0 Isolated breakages
10.0
0 0 2.0 Strain (%)
4.0
Key : Parent thread : Sewn with paper : Sewn with FA
6.0
8.0
10.0
Figure 7. Stress-strain curves for CS60 thread
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shows that the relative strength level of the plies is high even when sewn with paper. This must be due to the severe abrasion to which the thread is subjected in the machine owing to the reverse twist direction and higher yarn-metal friction (Table I). Conclusion The results show that the abrasion resistance of the sewing thread is an important factor as far as its ability to retain the strength during sewing is concerned. Structural parameters like fibre length and fineness, number of plies and twist influence strength retention after sewing. Fabric tightness factor seems to influence strength reduction only in certain threads under specific conditions, indicating that the thread structural and fibre properties play a more dominant role. It is also clear that the changes in the elongation-at-break of the thread depends on the loss in the initial modulus and hence the loosening of the structure. This, in turn, depends on the twist and other structural properties of the thread. However, the lateral constraints due to the locking of the needle and bobbin thread and that of the support by fabric cross threads may provide sufficient additional force to compensate for the reduction owing to the structural openness at the stitch interlocking point. Hence the magnitude of strength reduction due to the structural damage may not be reflected in the seam strength in a significant manner in all the cases. This must also be the reason why the knot strength of the sewn threads carried out by Crow and Chamberlain (1969) did not show any significant strength reduction. Nevertheless, the reduction in the tensile properties of the fibres is expected to influence the seam strength. This will depend on the ability of the fibre to withstand the different stresses acting on it under the dynamic loading condition that exists during sewing. References Crow, R.H. and Chamberlain, N.H. (1969), The Performance of Sewing Threads in Industrial Sewing Machines, Clothing Institute Technological Report No. 21, The Clothing Institute, London. Galuszyuski, S. (1981), “Fabric tightness: a coefficient to indicate fabric structure”, Journal of the Textile Institute, Vol. 72 No. 1, pp. T44-9. Gersak, J. (1991), “Rheological properties of the thread: their influence on dynamic loads in the sewing process”, International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, pp. 6-12. Gersak, J. and Knez, B. (1991), “Reduction in thread strength as a cause of loading in sewing process”, International Journal of Clothing Science and Technology, Vol. 3 No. 4, pp. 6-12. Hearle, J.W.S. (1969), “One dimensional structures: yarn geometry”, in Hearle, J.W.S., Grosberg, P. and Backer, S. (Eds), Structural Mechanics of Fibres, Yarns and Fabrics, Wiley-Interscience, New York, NY, pp. 96-7. Lindberg, J., Westerberg, L. and Svenson, R. (1960), “Wool fabrics as garment construction materials”, Journal of the Textile Institute, Vol. 51, pp. T1475-93.
Mahar, T.J., Ajiki, I. and Postle, R. (1989), “Fabric mechanical properties relevant to clothing manufacture: part I – structural balance, breaking elongation and curvature of seams”, International Journal of Clothing Science and Technology, Vol. 1 No. 2, pp. 5-10. Seyam, A. and Aly El-Shiekh (1994), “Mechanics of woven fabrics: part IV – critical review of fabric degree of tightness and its applications”, Textile Research Journal, Vol. 64 No. 11, pp. 653-62. Solinger, J. (1961), Apparel Manufacturing Analysis, Textile Book Publishers, Inc., New York, NY, p. 65. Sundaresan, G., Hari, P.K. and Salhotra, K.R. (1995), “Strength reduction in sewing threads during high speed sewing in an industrial lockstitch machine: part I – mechanism of thread strength reduction”, International Journal of Clothing Science and Technology, August.
Strength reduction in sewing threads 79
Penetration force of fabric by a needle
A predictive model for the penetration force of a woven fabric by a needle
91
Stepan V. Lomov St Petersburg StateUniversity of Technology and Design,St Petersburg,Russia Introduction The force applied to a sewing needle while penetrating a woven fabric affects the important features of the sewing process, such as needle temperature, danger of fabric rupture and needle breakage, etc. There is a good amount of experimental data on the needle penetration force (Galuszynsli, 1986; Garbaruk, 1975; Khan et al., 1970; Matthews and Little, 1988; Orlov, 1985), but fewer attempts to develop a predictive algorithm for it. In a recent paper, Stylios and Xu (1995) have proposed a mathematical model to compute the penetration force for an optimisation of the needle profile. The authors address the problem with the help of a general solution for a circular hole expanding in elastic plate, and produce the resulting equation in a form
Received August 1996 Revised and accepted December 1997
(1) where F is the penetration force, δ r – radial displacement of threads by a needle, β – inclination angle of a needle surface, µm – coefficient of friction needle-fabric, and A is a factor depending on mechanical properties of a fabric. The latter is related to the tension strain developed in threads as they are deflected by a needle, but in the final form the force value is given in arbitrary units; it is used for a comparative study of needle profiles, and not for producing an absolute value of a force, which can be compared with experimental data. The theory of Stylios and Xu was further developed by them (Stylios and Xu, 1998) to describe the mechanism of sewing damage in knitted fabrics. The aim of the present paper is to propose a mathematical model of a woven fabric deformation during an interaction with a needle and to compute the penetration force as a function of warp and weft mechanical properties, weave pattern and fabric sett to provide a predictive model for the factor A in equation (1). It is based on the theory of resistance of woven fabric to local deformation (Lomov, 1995); some results of needle piercing simulations are cited also in (Lomov and Truevtzev, 1995). The author would like to acknowledge support of work reported here by Prof. N.N. Truevtzev (St Petersburg State University of Technology and Design) and Dr R.J. Harwood (De Montfort University, Leicester). © S.V. Lomov
International Journal of Clothing Science and Technology, Vol. 10 No. 2, 1998, pp. 91-103. MCB University Press, 0955-6222
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General approach There are four major sources of fabric resistance to the penetrating needle: (1) membrane stress resulting from the fabric bending in the direction of the needle motion; (2) friction between the needle and the fabric (directly included in equation (1)); (3) threads resistance to a displacement in the vicinity of the needle, resulting from threads resistance to bending and friction between warp and weft threads; (4) threads tension owing to their elongation after the displacement. Membrane stresses are discussed and the predictive model for them is given in Garbaruk (1975). These stresses determine the resistance force before the needle’s tip pierces a fabric; after that their influence is diminished. We will not consider membrane stresses here; the other three components will be included in the analysis. With this assumption the basically three-dimensional problem of fabric deformation under the needle is reduced to two dimensions; here we follow the approach of Stylios and Xu (1995): starting with the model for forces associated with yarn displacement in the fabric plane and acting parallel to it, and then considering the simple geometrical scheme to compute the force normal to this plane caused by an inclination of needle surface and friction (see equation (1)). The scheme of the model construction consists of the following steps: (1) consider the general case of a threads displacement within the woven structure and compute the force resisting it (following Lomov, 1995); (2) assuming the simplest – central – placement of the penetrating needle relative to woven cell of a plain weave, compute displacement of threads induced by the needle and then apply the results of step 1; (3) compute threads elongation and tension forces owing to their displacement near the needle; to perform this, first find the length of the deformed part of a thread, i.e. conditions of the clamping of the thread, displaced by the needle, within a woven structure (using the data on friction between warp and weft threads computed in step 2); (4) combine the results of steps 2 and 3 to produce formulae for the resistance force of needle penetrating a plain weave fabric; (5) choose a physically sound coefficient to transform the result for plain weave to resistance force for other weaves. Local deformation of an elementary cell We shall consider the elementary cell of a woven fabric (Figure 1) to compute a force resisting small displacement of a weft thread δx. Let pWe be weft yarns spacing. Subscripts “Wa” and “We” will be used for warp and weft respectively;
formulae for the warp displacement can be obtained after swapping of these Penetration force subscripts. The value of δx is small, so we shall neglect terms of the order of fabric by a (δ x/pWe)2. needle The total force resisting the weft yarn displacement BB′ (see Figure 1) is owing to: (1) change of warp bent shape in the region ABC in the xz plane; 93 (2) weft bending DB→DB’; (3) friction between warp and weft. y(weft) z A
B
B’
B
h /2 Wa
C
δx
B’
z (x,p ) 0 We –p We
p Wa
δx
0
p We
x
z (x,p ) 1 We
A
D
p We
C
–h /2 Wa
x(warp) (a)
(b)
Change of warp bent shape To compute the force resisting the change of warp bent shape we will compute the variation of bending energy associated with this change, which is equal to the work done by this force, and then evaluate the force itself. Before the deformation the middle line of the warp thread had the shape z = z0 (x) (Figure 1b); using the spline approximation for this function (Lomov, 1990), one can write (2)
(3) where hWa is the warp crimp height. Let us compute the increment of the warp yarn bending energy after the displacement δW1. An important simplifying assumption is the constancy of crimp height in the displacement. Then the new middle line of the warp can still
Figure 1. A local displacement of a thread: (a) a weave cell; (b) centre line of the warp yarn AB before the deformation (dashed) and after it (solid)
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be evaluated via spline expression, equation (3), with the different p and shifted zero point for x:
(4)
94
If δz is the difference δz(x) = z1(x) – z0(x), then for a set fabric (5) where B1 is the bending rigidity of the warp, κ is a curvature. For the small δx
Computing the partial derivatives from equations (2-4) and neglecting the terms of the order (δx/pWe)2, we will obtain after the integration in equation (5) (6) then the equation for the resistance force FbWa = 2δW1/δx resulting from the yarn bending gives (7)
Weft bending The bending of the weft yarn is similar to the deformation mode described by Leaf and Sheta (1984); using the equation from this paper, we can compute the resistance force FbWe resulting from the weft bending (8) and the total force owing to bending is Fb = FbWa + FbWe
Friction Penetration force The frictional force can be evaluated if the force of normal pressure between of fabric by a threads is known. The latter is generated by transversal forces owing to threads needle bending. The transversal force in the warp and weft yarns bent in the spline shape Z(x) with the curvature κ(x) is
95
From equation (3) this is computed to get
After the displacement the additional transverse force is developed: δ Q = BWaδ(δκ)/ds, where δκ is the curvature of the δ z(x); from equation (4) we can compute
Now the frictional force (9)
Total force From equations (7-9) we get the final expression for the displacement resistance force for a small displacement of a weft yarn
(10)
For example, for the square fabric with pWa = pWe = p, BWa = BWe = B and with h/p = 0.4, δx/p = 0.1 (typical values), we will get Fb = 2.9B/p2, Ff = 3.9B/p2.
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Resistance to needle penetration – plain-weave fabrics Displacement-induced force We consider an interaction of a fabric with a needle with the maximum crosssection diameter D (non-circular shapes of a needle can be treated as it is done in Stylios and Xu, 1995) which is applied symmetrically to a fabric unit cell (Figure 2). The maximum displacements of warp and weft thread are
For a plain weave, all four corners of the cell will resist to the displacement, and the maximum total force acting on a needle F d will be (see Figure 2b with F = Fd, F’ = FWa or FWe):
D
d We
p Wa
δx Wa
d Wa
δx We
p We
(a) N F F’
Figure 2. A scheme for the computation of penetration force: (a) needle placement within a weave cell and threads displacement; (b) forces acting on the needle
β
(b)
µN
(11) where µ m is the coefficient of friction yarn-metal, F We is computed from equation (10) with δx = δxWe, FWa is computed from equation (10) with swapped subscripts and δx = δxWa, β is a current inclination of needle surface (compare with equation (1)). In the reasoning above we considered only weft deflection with the intersecting warp thread remaining straight and vice versa. However, when the needle is piercing the fabric, threads move simultaneously; their spacing in the vicinity of the needle is enlarging; the maximum spacing is pWam = pWa + δxWa, pWem = pWe + δxWe. When computing forces FWa and FWe in equation (11), one should account for this change. It is done with the simple approximation
Penetration force of fabric by a needle 97
(12) Tension-induced force To compute the elongation of a thread associated with its displacement one must make an assumption concerning the point of its clamping within the woven structure. Let T be the warp thread tension, and let us assume that the thread is clamped at points B and B’ at distance Y from the needle’s centre (Figure 3). Assuming linear behaviour of a thread for a small elongation and the straight thread line AB, tension T can be computed as (13) where E is Young modulus of the thread.
A T
T δx
γ
B
Y
B’
Figure 3. A scheme for computing a tensile component of threads resistance
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Thread will be clamped at intersection B if: (14)
98
where Q is a normal force of warp and weft interaction in a weave structure (see equation (9)). The deformation ε and tension T can be computed from equations (13) and (14), but there is no need to do this to obtain the resistance to needle penetration, because the force in the fabric plane (see Figure 2) is
and force acting on a needle Ft will be (as in equation (11))
The total resisting force is (15) where FWa, FWe, and Q are computed with equations (10), (11) and (14), with the approximation, equation (12), applied to all three force components. Non-plain weaves The bending of yarns and its variation during the displacement plays the principal role in the formulae of the previous section. In the non-plain weaves the intensity of bending is diminished and the freedom of yarns to deviate from their original positions is increased. One can, of course, conduct the similar calculations for some geometrical model of a yarn in the general weave structure, but because of the strong approximate assumptions used in such models, the equal accuracy level can be achieved if we use the corrective factor in the above equations for the plain weave: (16) where Fplain is the force computed for the plain weave. The factor k must be equal to one for the plain weave, and to zero for the “weave” with no bending (warp above, weft below); for a given weave k represents the comparative intensity of bending and freedom of yarns. Skliannikov (1974) proposed a socalled “linkage factor” C, which has similar properties and was successfully used in the yarn crimp models in woven fabrics (Lomov, 1993). The value of C is computed from the number of “links” (bent yarn intervals) in the weave pattern (Table I). As an approximation we will set k = C in equation (16).
Comparison with published data for woollen fabrics Penetration force S. Galuszynsli (1986) has presented experimental data on the dependence of of fabric by a maximum (first peak) penetration force on worsted wool fabric parameters and needle needle diameter. Weave Plain Twill 2/2 Sateen 5/2 Twill 2/6 Sateen 8/3 No weave
C
99
1.0 0.653 0.417 0.350 0.208 0.0
Table I. Linkage weave coefficient
Source: Skliannikov (1974)
The following estimations were used to simulate these experiments: (1) bending rigidity was measured for similar worsted wool yarns in the textile materials laboratory of St Petersburg State University of Technology and Design using IZ-3 equipment (Truevtzev and Kivipelto, 1991) and values B = 1.2 ± 0.1 cNmm2 for 40 × 2 tex yarn and B = 1.5 ± 0.1 cNmm2 for 50 × 2 tex yarn were obtained; (2) friction coefficient wool-wool was measured for the surface of woollen woven fabric to be µ = 0.4 ± 0.1; (3) to measure the friction coefficient wool-chromium coated needle, the tension of a thread enveloped n times around a needle was measured and friction coefficient was computed with the Euler equation P2/P1 = exp. (µm2πn) to yield the value µm = 0.2 ± 0.05; (4) to evaluate the crimp height, thread dimension was estimated as (17) where ρ is fibre density, η is a compression coefficient, η ≈ 0.9 for twisted worsted yarns (Skliannikov, 1974), and then crimp height for warp and weft was estimated with equations derived from the minimum bending energy conditions:
(18) These estimations were used in the formulae above together with the values of fabric sett and threads linear density given in Galuszynsli (1986). The results are compared in Figure 4 with the experimental data presented in the form used in Galuszynsli (1986), i.e. maximum force versus product of fabric tightness and mass (both quoted in Galuszynsli (1986)).
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Comparison with experimental data for cotton fabrics To produce experimental data for cotton fabrics, samples were woven from single cotton yarns 34 tex (warp) and 50 tex (weft) using weaves shown in Table I. Fabric sett was 24 threads/cm for warp and 15, 16 and 18 threads/cm for weft. Bending rigidities of yarns and coefficients of friction were measured as described above to obtain B1 = 0.23 ± 0.05 cNmm2; B1 = 0.27 ± 0.05 cNmm2; µ = 0.3 ± 0.1; µm = 0.2 ± 0.1. Compressed thread dimensions in a fabric were measured on photographs of cross-sections to obtain d1 = 0.16 ± 0.02mm, d1 = 0.18 ± 0.02mm ( η 1 = η 2 = 0.7 ± 0.1); evaluation with equation (17) gives d1 = 0.15mm, d2 = 0.18mm. Warp and weft crimp height was also measured on photographs; Table II compares measured and evaluated with equation (18) crimp heights. Equation (18) tends to overestimate warp crimp because of unaccounted yarn relaxation. F,cN
Figure 4. Maximum penetration force versus product of fabric tightness and mass. Grey regions – Galuszynsli’s data (Galuszynsli, 1986), lines – prediction with the present model. Needle diameter 1 – D = 1mm, 2 – D = 0.75mm
1
1200 2
600
0 80
140
220
300
txm,g/m2
The chromium-coated needle with maximum diameter D = 1mm was used. The surface angle near the maximum needle diameter is β = 1.5°. Maximum penetration force was measured on the Instron equipment. When needle moves slowly (1mm/s) against the fabric sample, the peak force registers just before the rapid decrease of force when the maximum diameter location of needle is reached and fabric contacts the cylindrical needle surface. This measurement yields the “static” penetration force which corresponds to the formulation of our model. For every fabric sample 10 measurements of penetration force were made with the fabric shifted horizontally between them; needle point was positioned every time within a weave cell. The measured and predicted penetration forces are shown in Table III. Discussion Approximate as it is, the present model gives reasonably good predictions of the maximum penetration force, especially when parameters of the fabric structure and threads mechanical behaviour are measured carefully for the sample under
P1 threads/cm
P2 threads/cm
24 24 24 24 24 24 24
15 16 18 18 18 18 18
Plain
Twill 2/2 Sateen 5/2 Twill 2/6 Sateen 8/3
Measureda h1, mm h2, mm 0.25 0.24 0.21 0.21 0.20 0.21 0.20
Estimated with (18) h1, mm h2, mm
0.09 0.10 0.13 0.13 0.14 0.13 0.14
0.27 0.26 0.24 0.24 0.24 0.24 0.24
0.06 0.07 0.09 0.09 0.09 0.09 0.09
Note: aError range ± 0.02 mm; crimp heights were corrected within this range to satisfy the equation h1 + h2 = d1 + d2 = 0.34 mm
Penetration force of fabric by a needle 101
Table II. Measured and estimated crimp height for cotton fabrics
consideration. It also proves to be able to provide predictions in a case when evaluations of these parameters must be used – which is usually the case in practice. The comparison with the experimental data above shows that the model qualitatively and for some extent quantitatively describes trends of penetration force change versus change of needle diameter, threads spacing and fabric weave. It should be noted that in the latest case, the dependence of penetration force on fabric weave, the approach used is very approximate; nevertheless, the linear dependence of penetration force on linkage factor C was approved by the experiment. This corresponds to the correlation found by Galuszynsli (1986) for needle piercing and with similar correlations of the mechanical behaviour of fabrics with weave in tension (Lomov, 1993) and bending (Lomov and Truevtzev, 1994).
Weave Plain
Twill 2/2 Sateen 5/2 Twill 2/6 Sateen 8/3
P1, threads/cm
P2, threads/cm
Measured maximum penetration force, cN
24 24 24 24 24 24 24
15 16 18 18 18 18 18
22 ± 22 30 ± 5 41 ± 5 25 ± 5 12 ± 3 11 ± 3 7±5
Estimated maximum penetration force, cN Crimp height Crimp estimated height with (18) measured 30 34 43 28 18 15 9
25 32 42 27 17 15 9
Table III. Measured and estimated penetration force for cotton fabrics
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When using the model proposed here, care should be taken not to overestimate its precision. The main sources of errors, besides the uncertainty of thread dimensions and friction coefficients, are: (1) the assumption of the central position of a needle within a fabric cell; (2) possible compression of threads caused by needle pressure; and (3) forces caused by interaction of threads in the neighbouring weave cells. Conclusion The proposed algorithm for the computation of maximum needle penetration force further develops the Stylios and Xu (1995) formula, equation (1), to introduce the direct dependence of penetration force on fabric structural parameters and warp and weft geometrical and mechanical properties. It uses the approach to the simulation of local deformation of woven fabric proposed by the author (Lomov, 1995); this approach accounts for the thread resistance to crimp change and friction forces when the thread is shifted from its original position in the fabric structure as the result of its interaction with a needle. The resistance of threads to tension caused by the needle pushing them from their straight-line paths is also accounted for; conditions of a thread clamping within the woven structure allows the deformed thread length to be evaluated. The resulting formulae, equations (10-15) give the dependence of needle penetration force for a plain-woven fabric on the following parameters: needle diameter and surface angle; warp and weft spacing, dimensions, crimp height and bending rigidity; friction coefficients thread-thread and thread-needle. Threads dimensions and crimp height can be roughly estimated by equations (17-18). For a non-plain-woven fabric the linear dependence of penetration force on the fabric linkage factor C (Skliannikov, 1974) is suggested. The comparison with the published and specially measured penetration force data proves the predictive ability of the model to be qualitatively accurate and quantitatively reasonable. References Galuszynsli, S. (1986), “Effect of fabric structure on fabric resistance to needle piercing”, Textile Research Journal, Vol. 56 No. 5, pp. 339-40. Garbaruk, V.N. (1975), “Textile materials piercing by a needle”, Izvestia Vuzov. Technologya Legkoi Promishlennosti, No. 5, pp. 85-90 (in Russian). Khan, R.A., Hersh, S.P and Grady, P.L. (1970), “Simulation of needle-fabric interactions in sewing operations”, Textile Research Journal, Vol. 40 No. 6, pp. 489-98. Leaf, G.A.V. and Sheta, A.F. (1984), “The initial shear modulus of plain woven fabrics”, Journal of the Textile Institute, Vol. 75 No. 3, pp. 157-83. Lomov, S.V. (1990), “The description of a yarn shape in a fabric with splines”, Izvestia vuzov. Technologiya Tekstilnoy Promishlennosti, No. 6, pp. 49-52 (in Russian). Lomov, S.V. (1993), “Automatized design of multi-layered woven structures, Parts I-III’, Izvestia vuzov. Technologiya Tekstilnoy Promishlennosti, No. 1, pp. 40-4, No. 2, pp. 47-50, No. 3, pp. 42-5 (in Russian). Lomov, S.V. (1995), “Resistance of woven fabrics to local deformations: computational prediction”, Vlakna a textil, Vol. 2 No. 2, pp. 49-54.
Lomov, S.V. and Truevtzev, A.V. (1994), “Evaluation of the thread-to-fabric bending rigidity ratio”, Izvestia vuzov. Technologiya Tekstilnoy Promishlennosti, No. 4, pp. 13-17 (in Russian). Lomov, S.V. and Truevtzev, N.N. (1995), “A software package for the prediction of woven fabrics geometrical and mechanical properties”, Fibres & Textiles in Eastern Europe, Vol. 3 No. 2, pp. 49-52. Matthews, B.A. and Little, T. (1988), “Sewing dynamics, Part I: Measuring sewing machine forces at high speed”, Textile Research Journal, Vol. 58 No. 2, pp. 383-91. Orlov, V.A. (1985), “An investigation of needle-piercing strength of technical fabrics”, Tekstilnaya Promishlennost, No. 12, pp. 59-60 (in Russian). Skliannikov, V.P. (1974), The Optimisation of the Structure and Mechanical Properties of the Woven Fabrics Made of Chemical Fibres, Legpromizdat, Moscow (in Russian). Stylios, G. and Xu, Y.M. (1995), “An investigation of the penetration force profile of the sewing machine needle point”, Journal of the Textile Institute, Vol. 86 No. 1, pp. 148-63. Stylios, G. and Xu, Y.M. (1998), “The mechanism of sewing damage in knitted fabrics”, Journal of the Textile Institute. Truevtzev, A.V. and Kivipelto, V.G. (1991), “Measuring of yarn bending rigidity”, Izvestia vuzov. Technologiya Tekstilnoy Promishlennosti, No. 1, pp. 71-7 (in Russian).
Penetration force of fabric by a needle 103
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104 Received October 1997 Revised and accepted January 1998
Studies on handle of microdenier polyester filament dress materials B.K. Behera Department of Textile Technology, Indian Institute of Technology, New Delhi, India and
S. Chowdhry and M. Sobti Institute of Home Economics, University of Delhi, New Delhi, India Introduction The most exciting and important development in man-made textiles for decades is microfibres. The term microfibres is now firmly established in textile terminology and has found its way in to all conceivable types of functional fabrics and fashionable articles (Lewis, 1993; Sobti, 1995). The effect of finer single filament count on yarn and fabric properties can be used with advantage to obtain fashionable fabrics. Microfabrics satisfy the trend towards soft, lightweight fabrics with varied surface characteristics (Murata, 1993). The combinations of microfilament warp and cotton weft are widely used in the fashion sector. These fine yarns are eminently suitable for strengthening light weight supple silk fabrics. The success of microfabric in the market is because of properties like lower stiffness, drapability, softness, watertightness, windproofing and permeability to water vapour. Thus, functional requirements are satisfied without any loss of aesthetic requirements. Despite all these advantages, it is realised that the handling of a microfilament is comparatively difficult during warp preparation and weaving (Baltensperger, 1993; Holme, 1994). Twisting and sizing are the two popular methods for achieving desired weavability of the filament yarn. However, fabric properties are greatly influenced by type of preparation and fineness of the filament. Sizing of microdenier filaments needs special care to prevent them from both mechanical and thermal damage (Holme, 1993; Rozelle, 1993). The twisting of filaments improves weavability but damages the yarn by making it harsh and hard. This project aims at determining the effect of twist vis-à-vis sizing and fineness of the single filament on the hand value of polyester fabrics to be used for dress materials.
International Journal of Clothing Science and Technology, Vol. 10 No. 2, 1998, pp. 104-113. © MCB University Press, 0955-6222
Methodology Sample preparation Ten polyester fabric samples were prepared on water-jetloom, out of which eight were microfilament fabrics and two were normal filament fabrics. These fabrics were produced using both twisted and sized yarn as per details given in
Table I. In this Table CD represents coarse denier and MD represents microdenier filament fabrics. Warp Sample Total No. of no. denier filaments DPF
Weft Total No. of denier filaments DPF
Ends/ Picks/ inch inch
TPM Warp Weft
Handle of microdenier polyester
Crimp Warp Weft GSM
105 CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
50 50 80 80 80 80 80 80 80 100
36 36 100 100 100 100 100 100 136 200
1.38 1.38 0.8 0.8 0.8 0.8 0.8 0.8 0.58 0.5
75 75 80 80 80 80 80 80 80 100
36 36 100 100 100 100 100 100 136 200
2.08 2.08 0.8 0.8 0.8 0.8 0.8 0.8 0.58 0.5
120 150 92 88 80 80 80 120 50 88
80 84 84 94 72 76 76 88 52 88
0a 0a 0a 1,200 1,200 1,200 1,200 1,200 800 0
1,000 1,000 0 1,600 1,600 1,600 1,600 1,600 1,200 130
3.85 5.1 5.45 1.0 10.4 4.1 6.6 5.7 4.7 7.1
1.05 66 0.8 74 0.9 84 9.2 80 12.7 91 12.8 61 13.2 90 9.0 95 10.3 125 14.8 115
Note: aFlat sized yarn; CD = coarse denier; MD = micro-denier
Processing of fabrics The ten fabric samples were processed using the following sequence, using standard procedures in industrial production systems. Scouring. The grey fabric was first scoured to remove the size before dyeing of fabrics. Scouring was done in a jet dyeing machine. Heat setting. Fabric was heat set at 170-180° for 30 seconds to improve dimensional stability. Dyeing. Dyeing was done with high concentrations of dye at 125°C in a jet dyeing machine. Peach finish. This is a special finish given to microfilament fabrics. Finishing emensing is done to increase bulk, to improve handle and wear properties of fabrics. The emery process imparts to the fabric slightly napped, peach like surface and soft handle. Fabric evaluation Evaluation of fabric constructional parameters. Constructional parameters such as thread density, areal density and crimp were determined using standard techniques. Evaluation of mechanical properties Crease recovery. The test was conducted on a Monseto crease recovery tester in which crease recovery angle was measured. Drape. The test was performed on a Eureka drape tester. Drape coefficient was calculated as below: Drape co-efficient (F%) = (As – Ad)/(AD – Ad) where, AD = the area of the specimen. Ad = the area of the supporting disk. As = the actual projected area of the specimen.
Table I. Fabric construction particulars
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Evaluation of fabric low stress mechanical properties A Kawabata fabric evaluation tester was used for this purpose. Tensile, shear, bending, compression and surface characteristics were measured as follows. Tensile and shear testing: sample size (20 × 20 cm); maximum tensile strain 100 per cent; maximum shear strain (shear force at 0.5 degrees); tensile strain rate 0.2mm/sec; shear strain rate 0.417mm/sec. Testing of compression properties: rate of compression 1mm/50sec; maximum compression load 0.5g./cm. Testing of bending behaviour: rate of curvature 0.5 cm/sec.; clamp interval 1cm; fabric speed 1mm/sec. Testing of surface properties: maximum sweep 3cm; vertical load on surface roughness detector 0.5g; weight of surface roughness detector 10g. Other test parameters were kept standard as specified by the KES instrument. Details of low stress properties with their symbol obtained from the Kawabata system are given in Table II. Results and discussion The constructional details given in Table I show that the fabric areal density ranges from 65-125 g./sq.m., keeping in view that the fabrics are suitable for ladies’ dress materials. The end use being the same for all ten fabric samples, the effect of warp preparation method and filament denier on low stress mechanical properties and hand behaviour are studied.
Test
Low stress properties
Notation
Tensile test
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
EM LT WT RT G 2HG 2HG5 B 2HB LC WC RC MIU MMD SMD W
Shear test
Bending test Compression test
Surface characteristics Table II. Fabric mechanical attributes
Fabric construction
Extensibility Linearity Tensile energy Tensile resilience Shear stiffness Hysteresis at 0.5° shear angle Hysteresis at 5° shear angle Bending rigidity Hysteresis of bending moment Linearity of compression thickness curve Compressional energy Compressional resilience Coefficient of friction Mean deviation of MIU Geometrical roughness Weight/unit area Fabric thickness
Effect of filament denier and twist on mechanical properties of fabrics Intermingling, twisting and sizing are three different warp preparation techniques used to make flat filament yarn weavable. The two former methods damage yarn surface characteristics, whereas the sizing increases the cost of yarn preparation substantially. Twisting, especially, adversely affects other mechanical properties such as bending and shear. Zero twist/flat sized filament yarn keeps the yarn structure undisturbed and hence produces a smooth fabric surface. Conversely, fine denier filament produces a fabric with supple feel. The effect of filament denier and preparation methods on fabric mechanical properties are shown in Table III. The results show that fabrics made out of conventional denier filament show poor drapability, compared with microdenier filaments. Fabric produced from lowest denier per filament gives lowest drape ratio, i.e. highest drapability. This is owing to low bending resistance which is due to smaller diameter of the microdenier yarn. The marginal increase in drapability in case of finest denier fabric samples (MD8) is due to higher total denier of warp and weft yarn used for this fabric. Crease recovery angle is lowest for finest denier fabric (MD8). However, no clear trend has been observed between conventional and microdenier fabrics, due to large variations in twist in warp and weft yarn for these fabrics.
Sample no. CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
Drape ratio (%) 61.66 60.54 59.63 53.90 52.52 50.27 52.88 54.03 50.64 56.09
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Crease recovery (degrees) Warp direction Weft direction Average 143.5 134.6 138.1 146.0 148.2 154.0 160.4 142.2 146.15 144.9
162.6 162.2 150.7 161.0 150.2 168.7 169.0 157.2 152.2 138.9
153.05 148.4 144.4 153.5 149.35 161.35 169.7 149.2 149.15 141.9
Effect of filament denier and twist on the low stress mechanical properties of the fabric Tensile properties. Low stress tensile parameters such as extensibility (EM), linearity of load extension curve (LT), the tensile energy (WT) and tensile resilience (RT) for various fabrics are shown in Table IV. The results show that extensibility of microdenier filament fabrics is substantially higher than that of normal denier fabrics. However, the linearity of load elongation behaviour of various fabrics does not exhibit any significant difference at low-stress levels.
Table III. Drape and crease recovery
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Table IV. Tensile properties
Sample no.
EM (%) Warp Weft Mean
CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
0.60 1.35 2.20 0.62 3.47 2.91 2.34 1.56 1.85 1.88
0.74 0.99 2.25 2.95 5.45 5.21 4.07 3.28 3.63 3.24
0.67 1.17 2.22 1.78 4.40 4.06 3.20 2.42 2.74 2.56
LT Warp Weft Mean 0.96 0.80 0.96 0.99 0.86 0.80 0.85 0.85 0.86 0.94
0.98 0.94 0.98 0.77 0.80 0.79 0.83 0.85 0.77 0.88
0.97 0.87 0.97 0.88 0.83 0.80 0.84 0.85 0.82 0.91
WT (g. cm2) Warp Weft Mean
RT (%) Warp Weft
Mean
0.14 0.27 0.53 0.15 0.75 0.59 0.50 0.33 0.40 0.44
75.08 78.14 65.37 59.72 59.38 47.49 56.24 59.79 50.02 60.23
71.50 75.97 64.42 59.86 57.81 49.91 56.46 58.51 52.19 62.88
0.18 0.23 0.55 0.57 1.09 1.03 0.85 0.70 0.70 0.71
0.16 0.25 0.54 0.36 0.92 0.81 0.67 0.51 0.55 0.57
67.93 73.80 63.47 60.00 56.25 52.04 56.68 57.24 54.37 65.54
The tensile energy which indicates the mobility of the body of the fabrics is higher for all microdenier filament fabrics as compared to normal denier filament fabrics due to zero twist in normal filament warp yarn and comparatively low twist in the weft of these fabrics as compared to the microdenier fabrics. The zero twist reduces surface friction giving rise to higher mobility of threads in the fabrics. The result for tensile resilience clearly indicates that the normal denier but twistless/low twisted filament fabric gives significantly higher resilience as compared to microdenier high twisted filament fabrics. On close examination it may be observed that the tensile resilience of both normal denier fabrics, i.e. CD1 and CD2 is higher in warp direction than the weft direction due to the zero twist filament being used in warp threads. Bending and shear properties. The bending and shear properties, particularly at low stress level, play an important role due to the interference of frictional restraint at the cross-over points of the warp and weft threads. Therefore, low stress bending and shear properties are not only dependent on the stiffness of the material, but also dependent on the surface geometry of the warp and weft yarn. The bending and shear properties in terms of bending rigidity and bending hysteresis and shear rigidity and shear hysteresis are given in Table V. The results show that bending rigidity and bending hysteresis are highest in normal denier fabrics than microdenier filament fabrics, even though the latter have more twist in both warp and weft yarn. This is because microdenier filament has low stiffness, owing to smaller diameter and shows that bending rigidity is more dependent on diameter than on twist of the yarn. The overall bending rigidity and hysteresis are lowest for MD7 which is lower than MD8 due to fewer threads and low total denier yarn, in both warp and weft. The results for shear rigidity and hysteresis show the same pattern as bending properties. This is probably due to total higher denier and a greater number of filaments being present in both warp and weft yarn, which prevents
Sample no.
B Warp Weft Mean
CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
0.03 0.02 0.02 0.02 0.0082 0.0058 0.0095 0.02 0.011 0.02
0.02 0.02 0.03 0.0048 0.0063 0.0044 0.0070 0.0097 0.0089 0.24
0.02 0.02 0.01 0.011 0.0072 0.0051 0.0082 0.0150 0.001 0.27
Warp
Bending 2HB Weft Mean
G Warp Weft Mean
0.01 0.0095 0.0145 0.0185 0.0044 0.0037 0.0082 0.0115 0.0114 0.0196
0.0089 0.0049 0.0115 0.0049 0.0035 0.0030 0.0051 0.0094 0.0075 0.0117
0.45 0.36 0.44 0.22 0.29 0.22 0.031 0.28 0.28 0.66
0.01 0.0072 0.013 0.0116 0.0039 0.0033 0.0066 0.0104 0.0064 0.0157
0.35 0.40 0.29 0.33 0.46 0.45 0.25 0.24 0.29 0.29 0.23 0.22 0.29 0.3 0.26 0.27 0.29 0.28 0.62 0.64
Warp 0.81 0.24 1.31 0.07 0.42 0.18 0.32 0.26 0.26 0.94
Shear 2HG 2HG5 Weft Mean Warp Weft 0.24 0.88 1.37 0.21 0.41 0.28 0.37 0.31 0.42 1.06
0.53 0.16 1.34 0.14 0.42 0.23 0.35 0.29 0.34 1.0
1.5 1.08 1.75 0.25 0.61 0.28 0.73 0.55 0.59 2.65
1.01 0.78 1.85 0.38 0.69 0.39 0.72 0.55 0.77 2.6
Mean 1.2 0.93 1.8 0.32 0.65 0.34 0.73 0.55 0.68 2.63
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Table V. Bending and shear properties
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the slippage at intersection points, resulting in higher shear resistance. High shear resistance in microdenier filament is mainly due to twist in the yarn which prevents mobility of yarn. Surface properties. The surface properties in terms of surface friction (MIU and MMD) and surface roughness (SMD) are given in Table VI. From the Table it may be seen that surface friction does not show any fixed trend with filament denier as well as the twist level in warp and weft. However, the surface roughness result clearly shows that fabrics made out of finest denier give lowest surface roughness. It may be concluded that the range of dpf being used in fabric samples is not sensitive enough to result in a significant difference in surface properties while measuring surface properties on the Kawabata system. Sample no.
Table VI. Surface properties
CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
MIU Warp Weft Mean 0.24 0.19 0.22 0.26 0.27 0.25 0.27 0.23 0.27 0.23
0.26 0.26 0.22 0.22 0.27 0.24 0.28 0.22 0.25 0.22
0.25 0.22 0.2 0.24 0.27 0.24 0.27 0.22 0.26 0.22
MMD Warp Weft Mean
SMD Warp Weft
Mean
0.013 0.012 0.018 0.028 0.019 0.01 0.014 0.027 0.022 0.025
8.52 7.004 2.971 10.163 3.50 5.382 3.667 5.029 8.864 3.139
5.37 4.915 2.729 9.894 4.177 5.703 3.519 4.723 8.528 2.894
0.013 0.014 0.013 0.106 0.022 0.017 0.02 0.02 0.016 0.019
0.013 0.013 0.016 0.022 0.02 0.018 0.017 0.024 0.019 0.022
2.22 2.826 2.488 9.624 4.854 6.025 3.371 4.428 8.198 2.649
Fabric compressional properties. The compressional behaviour of an apparel fabric has a very good correlation with fabric hand value. The higher the compressibility, the better hand value is expected from the fabrics. The compressibility of the fabric mainly depends on the bulk density, thickness, fabric weight and other fabric construction parameters. The compressional properties such as linearity of compression thickness curve (LC), compressional energy (WC) and compressional resilience (RC) measured on Kawabata instruments (KES-FB 3) are given in Table VII. The results show that the linearity of the compression thickness curve is higher for low twisted coarse denier filaments, compared to high twisted but fine denier filaments. Compressional energy is highest for fine denier filament fabrics, compared to coarse denier fabrics. However, compressional resilience is found to be highest for low/zero twisted coarse denier filament fabrics. Effect of filament denier and twist on hand values of the fabric For evaluation of hand values of ladies’ dress materials, six different primary hand values such as koshi (stiffness), hari (anti-drape stiffness), shinayakasa
Sample no. CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
Compression LC WC RC 0.8 0.824 0.622 0.794 0.684 0.723 0.695 0.665 0.783 0.625
0.007 0.006 0.028 0.011 0.022 0.012 0.012 0.01 0.018 0.022
111 119 59 90 74 96 74 96 79 69
Thickness (mm)
Weight (mg/cm2)
0.207 0.199 0.361 0.318 0.434 0.33 0.315 0.389 0.462 0.375
6.57 7.4 8.43 7.97 9.11 6.14 8.97 9.53 12.47 11.5
Handle of microdenier polyester 111
Table VII. Fabric compressional properties
(flexibility), fukurami (fullness and softness), shari (crispness) and kishimi (scroop) are estimated in the Kawabata system and given in Table VIII. The results show that fabrics made from normal denier filament give highest koshi and hari values, indicating that these fabrics are very stiff and have enough anti-drape stiffness as compared to all microdenier filament fabrics. This is mainly due to the large diameter of the coarser filament which gives high stiffness values. Conversely, normal denier filament fabrics also give lowest shinayakasa, compared to microdenier filament fabrics. It is interesting to note from the table that the normal denier filament fabric gives highly negative primary hand value with respect to fukurami, which gives an idea about fullness and softness of the fabric. The fukurami values for all other microdenier fabrics are either negative to some extent or with poor primary Sample no. CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
Koshi
Hari
Shinayakasa
Fukurami
Shari
Kishimi
7.34 7.58 4.85 5.63 4.18 4.17 4.74 5.54 4.87 5.04
7.68 7.30 5.20 5.03 3.41 3.15 4.49 5.82 4.15 5.73
3.81 4.17 5.80 5.52 7.03 6.89 0.06 4.68 6.17 5.15
–5.28 –6.42 3.32 –2.76 1.07 –3.38 –2.29 –2.86 0.02 1.92
6.35 7.03 4.45 8.26 6.27 7.57 0.57 8.13 7.07 5.18
2.91 2.10 4.05 2.89 2.46 1.39 1.55 1.96 2.44 2.84
Notes: Primary hand expressions and their meanings – koshi = stiffness/firmness; shari = crispness; hari = anti-drape stiffness/hardness; fukurami = fullness/softness; shinayakasa = flexibility/softness; kishimi = scroop
Table VIII. Primary hand values
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hand values. The primary hand values with respect to shari value (crispness) are not significantly affected by changing the twist level and denier of the yarn. Similarly, in the case of kishmi, no clear-cut trend in the primary hand value has been observed by varying the filament denier and twist. In order to obtain total hand value (THV), only three primary hand values such as koshi, numeri and fukurami are estimated for men’s winter and summer shirting fabrics and the results are given in Table IX. The results show that the koshi values are comparatively higher for coarser denier fabrics due to reasons explained earlier. The fukurami values are also found to be marginally lower for normal denier filament fabrics compared to microdenier filament fabrics. The numeri values do not show any significant difference among various fabric samples. On overall examination, it may be observed that the twistless/low twisted coarse denier filament fabrics give comparable/slightly better total hand values compared to microdenier and high twisted filament fabrics.
Sample no.
Table IX. Primary and total hand values
CD1 CD2 MD1 MD2 MD3 MD4 MD5 MD6 MD7 MD8
Koshi 5.98 6.00 5.20 4.63 4.23 3.66 4.48 5.01 4.64 5.70
Primary hand values Numeri Fukurami 4.88 5.06 5.66 4.60 5.28 5.83 5.53 4.87 4.94 4.83
4.64 4.90 5.81 4.53 5.69 5.85 5.65 5.08 5.14 5.40
Total hand values (THV) Winter Summer 3.05 3.12 3.36 2.68 2.73 2.42 2.97 2.98 2.83 3.14
3.32 3.41 3.54 3.00 3.01 2.93 3.29 3.24 3.14 3.26
Notes: Primary hand expressions and their meanings – koshi = stiffness/firmness; numeri = smoothness; fukurami = fullness/softness
It may be concluded that although microdenier filament fabrics provide softness and fullness effect as compared to the coarse denier fabrics, the twist in the yarn neutralises the rich handle value of these yarns. Therefore, the conventional coarse denier filament fabrics with very low or zero twists offer comparable performance as far as fabric handle value is concerned. However, weaving of zero/low twisted filament yarn is usually undertaken after the sizing process, which increases the cost of weaving, but at the same time gives very good fabric hand value. Conversely, weaving of zero twist microdenier filament yarn is more difficult than that of zero twist conventional denier yarn. Similarly, the sizing of the microfilament yarn is also difficult as these yarns are delicate, and more prone to stretch and filamentation. Twisting of microdenier yarn reduces its novelty completely. However, if the fabric handle and comfort are the
main requirement one can achieve better hand value either by using zero twist coarse denier-sized yarn or low-twist microdenier yarn. The hand value can further be improved by using microdenier zero twist sized filament yarn. Conclusions Microdenier filament fabrics give a better drape property than normal denier filament fabric. Microdenier filament fabrics also give better hand values. On average, the total hand values of twistless/low-twisted coarse denier filament fabrics are comparable or slightly better than those of microdenier and hightwisted filament fabrics. Twisting of micro-denier yarn mars its novelty effect resulting in reduction of fabric hand value. Sizing of microdenier filament yarn is the most important process to realise the speciality effect of this yarn. References Baltensperger, J. (1993), “POY process for microfibres”, Int. Text. Bulletin, Vol. 39, p. 25. Holme, I. (1993), “Microfibres”, Textile Month, December, p. 36. Holme, I. (1994), Synthetic Fibres, Vol. XXIII No. 2, p. 13. Lewis, M. (1993), “Forecasting fibres and fabrics”, Textile Month, June, p. 13. Murata, T. (1993), “Finest microfibre for garments”, Int. Text. Bulletin, Vol. 32, p. 42. Rozelle, W.N. (1993), “How to size microdenier yarn”, Textile World, November, p. 51. Sobti, M. (1995), “Studies on handle of microdenier polyester filament fabric”, MSc thesis, University of Delhi, India.
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Towards the virtual garment: three-dimensional computer environments for garment design C.H.M. Hardaker and G.J.W. Fozzard Department of Textiles and Fashion, School of Design and Manufacture, De Montfort University, Leicester, UK Introduction Computer aided design (CAD) is a recognised tool in the clothing industry, with many commercial systems available to assist in the design process. Essentially, these systems provide the designer with two separate environments for either conceptual design or pattern cutting, grading and marker making. Both of these are 2D and while integrating well into their respective stages of the design process, it can be seen from observation of manual methods that garment design should not be restricted to two dimensions. Manual methods rely on the development of a physical prototype, whereby the first patterns developed from a stylised fashion sketch are used to create a sample garment. This physical modelling technique enables the designer to assess the true silhouette and proportions of the garment from any viewing position. Taking CAD forward to provide the designer with a virtual prototyping system in 3D has been an internationally active research area over recent years. Despite being commonplace in many design disciplines, the development of such working methods for the clothing industry still continues to present a considerable research challenge. This paper discusses the development of 3D CAD methods. The traditional use of 3D in the garment design process is examined and a popular view of a 3D CAD system is introduced. In this context, the major research issues involved in developing such a system are considered and the approaches taken by key researchers are introduced. While commercial 3D systems do exist, and partly address the features of the hypothetical system, published information is scarce. Where any information has been found the systems have been evaluated. The paper concludes by highlighting the current limitations and proposes potential future developments.
International Journal of Clothing Science and Technology, Vol. 10 No. 2, 1998, pp. 114-127. © MCB University Press, 0955-6222
The need for 3D working methods The garment design process is highly specialised, requiring a combination of design creativity and technical pattern making skills, as well as a thorough knowledge of fabric performance. Typically for mass-produced garments, a
design concept starts as a 2D sketch accompanied by fabric swatches. This is interpreted by a pattern cutter, who assesses the proportions and balance of the design and the fabric handle to develop the first set of patterns. Flat pattern cutting is widely used because of its sizing accuracy and the speed with which complicated designs can be constructed[1]. Basic garment patterns, derived from statistical measurements are adapted “on the flat” in line with the concept design using measurements from a size chart and heuristic knowledge. The design is assessed in 3D when the first set of patterns are made up into a sample garment or toile. The importance of a physical prototype is confirmed by [1], who states “it can be difficult to relate flat pieces of paper to a design, which is basically sculptural when completed”. Alternatively the toile can be developed directly on the dress stand using the modelling technique[2-4]. The design evolves through pinning and draping fabric on the stand and allows the garment to be assessed in 3D throughout the development process. Foundation-wear design presents further difficulties. Here fit is most critical with the garment shaped to support and mould the soft tissues of the human form. This involves developing accurate patterns, which are highly dependent on the fabric being used. A great deal of prototyping is involved with sample garments fitted to life models, assessed, altered and re-fitted several times before a satisfactory fit is achieved[5]. It can be seen that the need for 3D working methods during the garmentdesign process is of extreme importance for visualisation; to assess the design, fabric suitability and the accuracy of developed patterns. Although CAD systems are available to aid the process, the existing technology provides, predominantly two separate 2D working environments for fashion sketching and pattern design. Towards virtual prototyping The popular concept of a 3D CAD system to provide a design environment for garment development uses a range of interactive tools around a simulated 3D body form. It is envisaged that the concept design would evolve as a toile garment rather than a 2D sketch, providing a true representation of the garment, which can be viewed from any angle. Further, if the system incorporated a suitable fabric model, it would be possible for the designer to assess how a particular type of fabric would interact with the 3D body form. This fabric model may include links to objective data and surface visualisation techniques to allow a fabric design or structure to be superimposed on the garment. To complete the visualisation the virtual garment could be animated and assessed on a moving figure. The link to pattern development would occur through the flattening of the computer-generated panels, reducing the need for the pattern maker to interpret the design sketch. Automatic grading may follow, where the sample size panels could be extrapolated to fit a set of different sized body forms. There could also be implications for bespoke design with the use of personalised 3D body forms
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as design templates. It is also envisaged that a system may be capable of a “dressing” approach where it would accept 2D pattern shapes from a conventional CAD system which could then be assembled into a garment and draped on the body form for assessment and adjustment. To summarise, the popular notion of a 3D CAD system is seen as both a garment visualisation and pattern making tool and could offer the following capabilities: • A true visualisation of a garment design from concept to production displayed in a static 3D view, which can be rotated to view the model from any angle. • Fabric models provide a realistic simulation of drape with links to objective measurement data. • Texture mapping enabling fabric surface design and structure to be shown. • Dynamic visualisation and animation. • Automatic development of patterns using 3D to 2D flattening algorithms. • Automatic development of grades using sized body forms. • A dressing facility, where conventionally-designed pattern shapes are assembled into a garment and viewed on a 3D body form. • Bespoke design through the use of personalised 3D body forms. Development of the 3D CAD method On examination of the popular notion of a 3D CAD system for garment design it is possible to identify the key research areas that contribute to the development of such a system. These are indicated in Figure 1. The following section considers these issues and reviews the approach taken by key research teams; contributions made by allied research are also discussed. Body form data A mannequin is a requirement of both manual and computer aided 3D working methods. Most of the systems investigated use a digital representation of a conventional dress stand. This can be generated by using a 3D digital digitiser to determine and record the co-ordinates of the intersections of points on the stand at set latitudes and longitudes. A surface can then be created using this array of co-ordinates. Although this provides an accurate model, the digitising process is painstaking and for accuracy can only be performed on an inanimate object. The possibly of using real body data has been realised[6,7], by the development of the Loughborough Anthropometric Shadow Scanner (LASS). This system uses a bank of video cameras to observe a profile line cast on to the body surface by a projector unit. There are several stages of rotation, each capturing a vertical profile, which are mathematically processed to regenerate
6. Interface design
5. Pattern visualisation (2D to 3D), garment assembly
1. Body form data
Interactive 3D CAD system
4. Pattern generation (3D to 2D)
2. Garment creation philosophy
3. Fabric model
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Figure 1. Key research issues in the development of a 3D CAD system
the body surface. A model of the female body form developed using the LASS system is shown in Figure 2. These body forms have implications for both the mass production and the bespoke sections of the clothing industry. The LASS system has been used in an extensive sizing survey to develop accurate size standards for the mass production sector[8]. Parametric body forms have been developed which could be used as standard mannequins in a 3D CAD system for both prototyping and grading. At the opposite end of the spectrum, personalised body form scans
Figure 2. A female body form developed from an LASS scan
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have enormous potential in bespoke design enabling the designer to design and fit a garment to a 3D representation of the customer’s own body form. Garment creation philosophy Several approaches to creating a virtual garment have been proposed: (1) A “first principles” approach, whereby the design is originated on screen[9]. (2) Adaptation of a 3D garment block (the work of Matsuura has been developed into a commercial system, the Asahi 3D)[10,11]. (3) Visualisation and development of conventionally designed 2D patterns in a 3D environment[12-14]. (4) The use of an expert system to interpret the fashion sketch to produce a 3D visualisation[15]. The system developed by Hinds and McCartney[9] adopts a “first principles” approach, providing a tool-kit for the designer to create a garment as a series of connected panels around a mannequin. The main tool is a cursor that can move over the mannequin surface (achieved by mapping the 3D co-ordinates of the body form to a 2D reference frame). This is used to define points along the edges of each garment panel. A further degree of freedom is provided to enable a surface offset to be specified. This offset is defined as the length of the surface normal from the body to a point above the body surface and used to specify garment “fit”. When creating a garment panel the “fit” or offset is specified for each point positioned along the edges of the panels. Once the points along an edge are specified, the edge is generated using curve-fitting algorithms. A panel is defined by generating a surface bounded by a series of edges, with this surface following the contours of the underlying body form. A typical 3D garment developed on this system is shown in Figure 3. The use of 3D blocks was proposed to produce pattern shapes for tailored garments[10,11]. The system proposed by Matsuura[10] enables the designer to make alterations to the 3D block using an interactive cursor. A cross-sectional view is provided to check the fit of the garment by viewing the amount of ease or fullness at any latitude on the body. Rasdomakhin[11] presents the user with a range of 3D block patterns in a variety of sizes. These blocks are adapted to form a stylised garment by the definition of over 100 shape-forming parameters, e.g. garment height, material thickness, shoulder length allowance, shoulder pad height. Once these parameters are specified the system operates as a parametric program and generates a surface that satisfies these parameters and follows the underlying body form. The design is displayed as a 3D wireframe image, which can be altered as necessary by adjusting the parameters. Style lines and further details can be added interactively and are automatically positioned on the garment surface. Another approach advocates the use of conventionally designed 2D pattern shapes[12-14]. Noting the prevalence of the flat pattern cutting method in the mass-production sector of
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Figure 3. A typical garment designed using the Hinds and McCartney system
the industry, this approach takes pattern shapes from a 2D CAD system and simulates them in a 3D working environment. In addition, Okabe[14] provides a wide ranging tool-kit, allowing the designer to adapt shapes in the 3D environment and automatically generate the new patterns. The designer has the option to design in 2D or 3D and choose the method most suited to the task in hand. Visualisation of normal stress in garment panels is used as a measure of body contact pressure and can be used to assess garment fit. Although not strictly a 3D CAD system, Ito et al.[15] postulate a radical approach to the use of 3D methods. The aim here is to develop an automatic system that would take an initial concept sketch of a garment and generate a set of patterns as well as providing a computer visualisation of the garment in 3D. The system has no 3D tool kit; any alterations to the design are made to the original sketch, which can then be re-processed. It is envisaged that this system would utilise an expert system to convert the information from a designer’s stylised sketch and a corresponding extensive garment description database into a visualisation and set of patterns. The fabric model The close relationship between garment design and fabric selection means that fabric representation is a particularly important part of any garment design system. Fabric is a complex media to model, owing to its anisotropic nature and the need to simulate large deformations. It is considered as a research area in its own right, with contributions from the textile engineering, computer graphics and 3D CAD development research disciplines. The textile engineering approach concentrates on the relationship between fabric structure and objective measurement data. In contrast, the computer graphics approach
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treats fabric as a deformable object, to develop visually realistic cloth animations; the requirements for accurate interpretation of mechanical properties and real-time performance are of secondary importance. A combination of these approaches is needed for the 3D design environment. The fabric model is required to predict the shape of draped fabric in real time with links to mechanical data to enable different fabric types to be modelled. Dynamic capabilities, although desirable, are not necessary in the early stages. Major research contributions are summarised in Table I. It can be seen that over the last two decades two distinct types of model have been proposed and can be classified as either geometric or physical. The geometric model considers the shape that fabric takes using mathematical functions. As such, it is most suited to representing simple shaped objects. The nature of the approach also means that fabric objective measurement data cannot be incorporated and as such have limited use in 3D CAD development. Physical models, however, describe the shape of fabric by the use of differential equations, derived from mechanics or elasticity theories and incorporate mechanical properties. In addition to shape prediction, an effective fabric model needs to predict and provide solutions for collision detection. As fabric drapes, it will collide with the underlying body form (fabric-body collision) and also with itself when generating folds (fabric-fabric collision). An early approach used a very thin force field around the body surface to avoid collisions[26]. This was developed further with algorithms to enable fabric to “cling” to the underlying body form[27]. Current research shows the speed of collision detection has been improved[28] by taking advantage of a fabric’s surface regularity to build a surface hierarchy within the fabric model. To date, research from the computer graphics community arena has produced some successful animations[28,29]. Although concentrating on the visual appearance of the model, the systems incorporate physical data and can perform dynamic drape with collision detection algorithms to model fabricbody and fabric-fabric interactions. However, the computational time required for these simulations is prohibitive and, at present, cannot be achieved in real time. Such models have great potential for fashion design and also in the development of the virtual catwalk[30] and virtual retail environments[31]. The fabric models used in prototype 3D CAD systems vary. While some systems use a geometric approach, other researchers have used physical models to develop simulations for real time static drape. The finite element method is used to incorporate elasticity and bending data from testing systems such as the Kawabata Evaluation System[10,12-14]. Using minimum energy algorithms and fabric-body collision detection routines based on occupancy tree methods, these models provide a real-time simulation of static drape in fabric panels positioned on the body form. Different fabrics can be simulated by entering new mechanical data or in some systems choosing from a selection of preprogrammed fabrics. Texture mapping is also included in most systems enabling the garment to be visualised with a fabric print or texture
Research team
Year(s)
Research area
Approach
Model type
Shanahan, W.J. et al.[16]
1978
Textile engineering
Sheets, shell and plate theory used to define a matrix of elastic parameters for a fabric sheet. Later enhanced by Lloyd, who incorporated non-linearity and used finite element methods to simulate fabric deformation
Physical
Weil, J.[17]
1987
Computer graphics
Fabric is considered to be a rectangular weave of threads, each of which is inelastic and Geometric is suspended from fixed points. Each thread in the fabric is considered to fall into catenary curve. A relaxation algorithm is used to define folds in the fabric
Terzopoulus et al.[18]
1987
Computer graphics
Fabric is modelled as a grid with points connected to each other by units consisting of springs, dashpots and plastic slip units. These units obey local stress strain rules. Interactions with solid objects are also considered
Physical
Aono[19]
1990
Computer graphics
Model is based on elasticity theory. Fabric simulations are the result of an impulse force applied in the fabric plane which produces a succession of “waves” through the cloth. Anisotropy and viscoelasticity can be incorporated to enhance realism
Physical
Hinds and McCartney[9]
1990
CAD tools
Garment panels are represented by a bi-cubic B-spline function
Geometric
Kunili and Gotoda[20]
1990
Computer graphics
Singularity theory is used to identify wrinkle deformation primitives (the geometric approach). These are then applied to a “fabric” network (the physical approach)
Physical/ geometric
Collier et al.[21]
1991
Textile engineering
Fabric is modelled as a non-linear small strain, large displacement problem and analysed using finite element methods
Physical
Okabe et al.[14]
1992
CAD tools
Finite element approach is used and solved using minimum energy algorithms
Physical
Breen et al.[22]
1994
Textile engineering
A fabric microstructure is proposed which consists of “particles” with a particle representing a threads’ crossing point. Relationships between particles are formulated and used to determine deformations
Physical
Stylios et al.[23]
1995
Textile engineering
The deformable bar-node and lumped parameter concepts are used to model complex deformation of fabrics
Physical
Chen and Govindaraj[24,25]
1995 1996
Textile engineering
Fabric is assumed to be continuous and orthotropic medium and modelled as a non-linear, large displacement problem using flexible shell finite elements. Later work investigates the effects of various fabric characteristics on drape
Physical
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Table I. Approaches to fabric modelling
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superimposed. A “draped” garment from the Ashai system developed by Matsuura is shown in Figure 4.
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Figure 4. A “draped” garment, designed using the Ashai 3D system
Pattern generation Automatic pattern generation is seen as a major aim of a 3D CAD system, requiring a method to develop the 2D pattern shapes from the 3D prototype. A flattening process described in [9] was developed, using a similar method to Efrat[32]. This process considered the doubly-curved surfaces, developed as an assembly of triangular platelets. By selecting groups of these platelets as strands of the pattern, each group can be flattened on to a 2D plane. This meant that the resulting pattern contained darts, which are then approximated to provide a more realistic pattern shape. Okabe et al.[14] propose a method for pattern generation that mainly relies on the assessment in 3D of dressed panel shapes. Noting that the process to flatten a curve surface to a plane is different from the process to force a plane into the curved shape of a garment, they propose that a 3D to 2D transformation is only used to develop an approximate panel shape. Cylindrical mapping is used to derive the panel shape which is refined after meshing and re-simulating the position of the panel in 3D space.
Visualisation of 2D pattern shapes Visualisation of conventionally designed patterns in 3D is seen to be an important aspect of a 3D CAD system[12-14]. This garment creation philosophy requires both a “dressing” algorithm to translate 2D panels into 3D and a garment assembly routine to join the separate panels together to create the garment. Dressing is implemented by Fozzard and Rawling, by the use of a flattening technique. The 3D mannequin is flattened, using a similar method to that used by Hinds and McCartney[9] to develop their 2D pattern shapes. The resulting 2D representation of the body form is mapped to a digitising tablet, thus enabling 2D movement on the tablet to be translated into 3D movement of a cursor on the surface of the body form enabling garment panels to be positioned on the body form. Okabe at al.[14] propose a method for assembling a garment that enables seaming characteristics to be included. This is done by assigned “seaming” characteristics to the edges of the pattern that are to be sewn together and positional lines, such as the waistline, neckline, arm hole, etc. A finite element mesh for each pattern shape is generated so that that there are an equal number of divisions along the edges to be sewn together. These edges are merged using the “seaming” characteristics to create a 3D cylindrical object leaving the unseamed edges left open. The upper unseamed edge will correspond to a positional line on the body, for example the waistline for a skirt. “Dressing” the 3D body form is achieved by superimposing the appropriate 3D body co-ordinates along that upper edge. The garment can then be “draped” over an underlying solid body form using the same mesh definition and minimum energy criteria. Interface design The success of a 3D CAD system does not depend only on the efficacy of the resultant garment, the way the designer interacts with the system is also important. On evaluating the development of 2D CAD tools, it can be seen that there has been a great deal of emphasis placed on the designers’ interaction with the system. Recent developments incorporate input devices that mimic manual processes. In sketching systems, the use of a cordless pen and tablet enables the designer to draw freely on the tablet and see the results on screen. Lectra’s Graphic Instinct takes this a stage further, with input devices comprising an A3-sized electronic drawing area and a cordless pen, which allow the designer to sketch directly on-screen. Similar input devices are part of pattern design systems such as the Gerber Silhouette, where full-size paper patterns can be adapted using traditional tools on an electronic pattern cutting table using a cordless pen. Modifications to patterns are displayed on a monitor. These systems enable the designer to work with full-size patterns, as opposed to at-scale on a monitor. Engineering and architectural 3D CAD systems provide the user with a variety of view-ports, typically consisting of front, back, left, right, top, bottom and isometric with options to zoom, pan and rotate the model around any axis. This method has been adopted by many 3D CAD developers for their prototype
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systems. However, as with the early 2D systems there is room for improvement and it would appear that work on innovative tool kits is taking place[31]. The 3D CAD systems reviewed in this section are summarised in Table II. Discussion It can be seen from the research to date that the systems developed do go some way to achieving the goals set out above. One key goal was to provide a virtual prototyping environment to enable the designer to visualise a garment prior to making a physical sample. A number of systems have been proposed which provide the tools to develop 3D garments with further links to objective measurement data and “drape” algorithms. These virtual environments provide a forum for the design to be assessed on-screen and go some way towards simulating the conventional design environment. However, assessing fabric suitability is a major part of the need to prototype and thus the accuracy and realism of the fabric model is paramount. Some models developed for 3D CAD link to mechanical data and perform static drape with collision detection; however, these models at present are limited when compared with the work of the computer graphics arena. Here, fabric models incorporate routines to simulate dynamic drape, fabric-body and fabric-fabric collision detection and wrinkling; they also require a vast amount of computational time and so cannot be run real time. However, it can be envisaged that given suitable improvements in hardware capabilities, that the addition of such fabric models to a 3D CAD system would enable fabric to be visualised “falling” over a static or even dynamic body form. It should be noted that although some complex fabric models have been developed, none can provide any tactile response to date. In the meantime, it is possible that the need for complex fabric models may be circumvented by the developing CAD systems for different functions. For example a system developed for the design of close-fitting garments would not need drape modelling features; a system for visualisation and merchandising would not need links to pattern development. In addition to appraising fabric suitability, assessing garment fit is also a major part of the prototyping process. In many systems garment fit is considered to be the normal distance between the body surface with the designer specifying the fit required during the design process by selecting a suitable offset distance. This is a significant departure from convention, where the pattern cutter uses standard measurements and a high level of heuristic knowledge to create the first patterns for a style to convey the type of fit required. The development of pressure sensitive deformable body forms is a further way to improve the assessment of garment fit[33] and would offer benefits particularly in the design of close fitting garments. Pattern generation is seen as a key goal of 3D CAD. Research to date demonstrates that most systems rely on some form of conventionally-designed 2D panel. This may be a 2D block that is converted to a 3D form or a styled 2D pattern visualised in 3D. It would appear from the work to date that the pattern
System developers
Garment fit considerations
Aim
Body form data
Fabric model
Pattern generation
Garment assembly
Hinds and McCartney[9]
To provide an interactive 3D environment for garment design
Use of digitised stands. Later revisions include LASS body forms
Uses rigid 3D surfaces, no objective fabric data
Flattening techniques are employed to convert a 3D B spline surface to a 2D plane
No seam characteristics included. Seams are positioned where surfaces meet
Achieved by adjusting garment panel offset by underlying body form
Fozzard and Rawling[12,13]
To provide a 3D environment for garment visualisation, particularly aimed at the prototyping stage. Pattern modification taking place in 2D
Use of LASS body forms
Uses simple FE model. Provision made for objective data
Not applicable
Not completed for whole garment assembly
Not considered. Fabric “drapes” over body form. No feedback on pressure
Okabe et al.[14]
To provide a flexible system with both 2D and 3D environments for pattern making and garment visualisation
Uses digitised stands
Uses FE model, with provision for objective data
Use of a minimised energy surface to develop 2D pattern shape
Seaming characteristics are defined and used in “assembly”
Visualisation of normal stress in garment panels is used to estimate contact pressure
Ito et al.[15]
To generate patterns Uses digitised and a garment stands visualisation from the original fashion sketch
Uses rigid 3D surfaces, no objective fabric data
Flattening techniques are employed to convert a 3D B spline surface to a 2D plane
Not considered
Algorithms are set up to interpret designers’ fit descriptors
Matsuura[10]
To provide a commercially viable 3D visualisation environment and pattern making system
Uses FE model, with provision for objective data
Not considered
Fit can be assessed by a cross-sectional view
Uses rigid 3D surfaces, no objective fabric data
Not considered
Fit is specified by using appropriate measurements for parameters
Uses digitised stands
Rasdomakhin[11] To generate a set of Uses digitised patterns using 3D stands garment blocks, using parametric programming methods
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Table II. Features of the 3D CAD reviewed
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development is most successful when adapting these pre-defined shapes, although no evaluation has been presented. Currently, there are two commercially available 3D CAD systems being marketed, Design Concept 3D developed by Computer Design Inc. (CDI) in the USA and the Asahi 3D developed by the Asahi Chemical Industry Co. Ltd in Japan. At the time of publishing Asahi 3D has 25 users in Japan. At this stage it is difficult to judge the potential impact of 3D CAD; however, it is accepted that the need for creativity and technical pattern making skills will be crucial. With the development of improved interfaces including, possibly, the use of virtual reality techniques, current working practices may change. The need for the concept sketch may diminish; there may also be a shift from flat pattern cutting to the modelling method of pattern making. However, it is likely that the use of some physical prototyping will continue for the forseeable future. References 1. Aldrich, W., Metric Pattern Cutting, Blackwell Science Ltd, Oxford, 1994. 2. Cloake, D., Fashion Design on the Stand, B.T. Batsford Ltd, London, 1996. 3. Mee, J. and Purdy, M., Modeling on the Dress Stand, BSP Professional Books, Oxford, 1987. 4. Shoben, M. and Silberberg, L., The Art of Dress Modell ing: Shape within Shape, Butterworth-Heinemann Ltd, Oxford, 1992. 5. Hardaker, C.H.M. and Fozzard, G.J.W., “The bra design process – a study of professional practice”, International Journal of Clothing Science and Technology, Vol. 9 No. 3, 1997. 6. Jones, P., West, G., Harris, D. and Read, J., “The Loughborough anthropometric shadow scanner”, Endeavour, Vol. 13 No. 4, 1989, pp. 162-8. 7. Jones, P., Li, P., Brooke-Wavell, K. and West, G., “Format for human body modeling from 3D body scanning”, International Journal of Clothing Science and Technology, Vol. 17 No. 1, 1995, pp. 7-16. 8. Jones, P. and Hunt, M., British Women’s Size Survey Age 17 to 69 Years, HUMAG Group, University of Loughborough, Loughborough, 1987. 9. Hinds, B. and McCartney, J., “Interactive garment design”, The Visual Computer, Vol. 6, 1990, pp. 53-61. 10. Matsuura, S., “Shape visualisation method and apparatus”, German Patent No. 43 01 698, 1993. 11. Rasdomakhin, N., “Three dimensional clothing design”, Internal report, St Petersburg University of Technology and Design, 1995. 12. Fozzard, G.J.W. and Rawling, A.J., “Simulation of dressing and drape for garment CAD”, Proceedings from the 6th International Forum on CAD, 1991, pp. 157-62. 13. Fozzard, G.J.W. and Rawling, A.J., “CAD for garment design – Effective use of the third dimension”, Proceedings of the Eighth National Conference on Manufacturing Research, 1992, pp. 183-9. 14. Okabe, H., Imaoka, H., Tomiha,T. and Niwaya, H., “Three dimensional apparel CAD system”, Computer Graphics, Vol. 26 No. 2, 1992, pp. 105-10. 15. Ito, I., Kawauchi, K. and Odagaki, C., “Three dimensional computer system for automatic pattern making and simulation”, Journal of the Textile Machinery Society of Japan, Vol. 38 No. 3, 1992, pp. 68-77. 16. Shanahan, W.J, Lloyd, D.W. and Hearle, J.W.S., “Characterising the elastic behaviours of textile fabrics in complex deformation”, Textile Research Journal, Vol. 48, 1978, pp. 311-42.
17. Weil, J., “The synthesis of cloth objects”, Proceedings, SIGGRAPH ’86, Computer Graphics, Vol. 20 No. 4, 1986, pp. 44-54. 18. Terzopoulus, D., Platt, J., Barr, A. and Fleischer, K., “Elastically deformable models”, Computer Graphics, Vol. 21, 1987, pp. 205-14. 19. Aono, M., “A wrinkle propagation model for cloth”, Proceedings, Computer Graphics International ’90, Springer-Verlag, Toyko, 1990, pp. 96-115. 20. Kunii, T.L. and Gotoda, H., “Singularity theoretical modeling and animation of garment wrinkling formation processes”, The Visual Computer, Vol. 6, 1990, pp. 326-36. 21. Collier, J.R., Collier, B.J., O’Toole, G. and Sargand, S.M., “Drape prediction by means of finite element analysis”, Journal of the Textile Institute, Vol. 82 No. 1, 1991, pp. 96-107. 22. Breen, D.E., House, D.H. and Wozny, M.J., “A particle-based model for simulating the draping behaviour of woven cloth”, The Textile Research Journal, Vol. 64 No. 11, 1994, pp. 663-85. 23. Stylios, G.K., Wan, T.R. and Powell, N.J., “Modeling the dynamic drape of fabrics”, International Journal of Clothing Science and Technology, Vol. 3, 1996, pp. 95-112. 24. Chen, B. and Govindaraj, M., “A physically based model of fabric drape using flexible shell theory”, The Textile Research Journal, Vol. 65 No. 6, 1995, pp. 324-30. 25. Chen, B. and Govindaraj, M., “A parametric study of fabric drape”, The Textile Research Journal, Vol. 66 No. 1, 1996, pp. 17-24. 26. Lafleur, B., Magnenat Thalmann, N. and Thalmann, D. ,“Cloth animation with self-collision detection”, in Kunii, T.L. (Ed.), Modeling in Computer Graphics, Springer-Verlag, Tokyo, 1991. 27. Liu, J., Ko, M. and Chang, R., “Collision avoidance in cloth animation”, The Visual Computer, Vol. 12, 1996, pp. 234-43. 28. Volino, P., Magnenat Thalmann, N., Jianhua, S. and Thalmann, D., “The evolution of a 3D system for simulating deformable clothes on virtual actors”, Computer Graphics and Applications, September 1996, pp. 42-50. 29. Carignan, M., Yang, Y., Thalmann, N.G. and Thalmann, D., “Dressing animated synthetic actors with complex deformable clothes”, Computer Graphics, Vol. 26 No. 2, 1992, pp. 99-104. 30. Gray, S., “Formula for a fashion show”, Bobbin, January 1994, pp. 54-8. 31. Gray, S., Rosella, A. and Aitken, R., “Virtual fashion”, http://www.ntu.ac.uk/fas/ vr.html, June 1997. 32. Efrat, S., “The development of a method for generating patterns for garments that conform to the shape of the human body”, PhD thesis, Leicester Polytechnic, 1982. 33. Hardaker, C.H.M., “Simulation of band tightness using finite elements”, internal report, De Montfort University, 1997.
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Seam pucker simulation S. Inui and T. Yamanaka
128 Received April 1997 Revised October 1997 Accepted March 1998
International Journal of Clothing Science and Technology, Vol. 10 No. 2, 1998, pp. 128-142. MCB University Press, 0955-6222
National Institute of Materials and Chemical Research, Tsukuba, Ibaraki, Japan Introduction Seam pucker is one of the most important factors of sewing. We proposed an objective evaluation method for seam pucker in the context of automatic sewing system (Inui and Shibuya, 1992) but seam pucker is subjectively evaluated by persons for practical use. In the subjective evaluation, a set of photographs (AATCC Technical Manual, 1985) has been usually utilized as a practical standard to discriminate rating of seam pucker. The photographs are based on seam pucker samples in five different ratings. Recently, a set of solid replicas (JIS, 1994) was made for a reference of seam pucker evaluation in the Institution of Japanese Industrial Standards (JIS), and now it is proposed by the International Standardization Organization (ISO). In both cases, photographs and replicas, the original seam pucker samples were made from the same fabric. However, the appearance of seam pucker is different according to the mechanical properties of fabrics, and in some cases, it is difficult to assess the rating of seam pucker compared with the standard. When we investigate the effects of mechanical properties of fabrics on the appearance of seam pucker in experiments, it is difficult to control experimental conditions, e.g. we cannot source fabrics where only one mechanical property differs but all the other mechanical properties are constant. We proposed here a simulation for the shapes of seam pucker on fabric strip. In simulations, the mechanical properties of fabric can be easily controlled. The seam pucker simulation We reconstructed the computer program of the apparel computer-aided design (CAD) system (Okabe et al., 1992) developed by Imaoka and Okabe et al. and utilized it for the simulation of seam pucker. In the simulation, virtual elastic sheet is treated as fabric and it is divided into triangular elements. The elastic energy due to deformation of the virtual fabric is calculated from the stress generated within the fabric. The stress is calculated from the strain which is determined from the shapes and the formation of the triangular elements. The nodes of those triangles are moved to minimize the energy. The stable shape of fabric is determined by the shapes and the formation of triangular elements in the minimal energy state. The seam pucker sample for the simulation is not a pile of two fabrics but, for simplicity, one strip of virtual fabric. The dimension of a virtual fabric © S. Inui and T. Yamanaka
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Figure 1. The division of virtual fabric
strip is 50mm × 400mm. The method of the division of the fabric into triangular elements is shown in Figure 1. In the division, two triangles make a square, based on the structure of fabric consisting of the warp and the weft. The size of a square is 2.5mm by 2.5mm. The centre line of the fabric strip is regarded as the seam line. The initial conditions for the simulation are that the fabric is flat and the length of the fabric strip is shortened from the original fabric strip length to form seam pucker. The shortening of the length of the fabric strip as an initial condition corresponds to material puckering. The material puckering is defined to be the ratio of the length of the fabric strip, after sewing, compared to the length of the fabric strip before sewing. During the simulation, almost all the nodes of triangles are moved according to movement and deformation of the triangles to minimize the energy. The length of the triangle sides on the seam line are kept constant during simulations. Relationship between mechanical properties and appearances of seam pucker Many factors influence the mechanics which create seam pucker. The factors are classified into two groups. One of the groups consists of the factors relating to conditions of the sewing machine, such as upper and lower thread tension, pressure of presser foot, size of needle, sewing speed, etc. The other group relates to factors about properties of fabric and thread, such as
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bending, shear, friction, thickness, structure, etc. There were theoretical and experimental approaches to the instability of overfeed seam by Amirbayat and collaborators (Amirbayat, 1990; Amirbayat and McLaren Miller, 1991; Amirbayat and Norton, 1990). They derived a group of dimensionless values. These values are related to energy terms calculated from the status of seam and give remedies to avoid seam instability. According to the analysis, the conditions for seam stability are as follows: (1) Minimum thread tension. (2) Greater inextensibility of sewing thread. (3) Finer stitch length. (4) Thicker fabric. (5) Greater bending stiffness of fabric. (6) Greater extensibility of fabric. The condition (1) is not considered here. The condition is related to dynamic sewing processes of sewing machines. Seam pucker is formed in the dynamic sewing processes, e.g. fabric is overfed, local distortion of fabric has occurred and the distortion is fixed by stitches. After dynamic sewing processes, the shape of seam is fixed with thread, which is the restrictive condition of the seam status. In the simulation, the shape of the seam is determined statically with that restrictive condition because it is difficult to simulate the dynamic sewing processes. The condition (2) relates to the mechanical property of thread. Stylios and Lloyd pointed out that the balance of thread and fabric bending stiffness is related to seam pucker (Stylios and Lloyd, 1990). In practice, when the pile of fabric strips is sewn together, the shape of the seam is determined by the force balance of thread and fabric. In the simulation, thread is not explicitly considered and the shape of the seam is determined with the mechanical properties of fabric and material puckering. In this case, the force of virtual fabric is assumed to be balanced with the force of imaginary thread. The condition (3) concerns the setting of sewing machines. Stitch length is not an explicit condition of the simulation. The virtual fabric for the simulation is divided into triangular elements. The sides of the triangles on the seam line are straight and the length of those sides is kept constant during calculation. Although the triangle elements are brought to the simulation for the sake of calculation, the sides of triangles on the seam line are regarded to be related to stitch length. The size of triangles is not varied in the simulations. The condition (4) relates to one of the dimensions of the fabric. It was shown that seam appearance definitely improved when fabric thickness increased (Stylios and Lloyd, 1989a; 1990) but the virtual fabric for the simulation is ideal, being zero thickness. The conditions (5) and (6) are about mechanical properties of fabric. Mechanical or structural properties of fabric are complex and correlate with each other and the effect of a specific
property can not be easily extracted from experiments. In the case of simulation, the properties of fabric can be determined without restriction and can extract the effect of a specific property. The symbols of properties are explained in Table I. The conditions relating to mechanical properties of virtual fabrics for the simulations are shown in Table II. The ranges of properties were determined from our data of more than 200 fabrics and a reference (Hand Evaluation and Standardization Committee, 1975). There, Poisson ratio and torsional stiffness were not measured. We defined that the value of Poisson ratio for tensile is 0.8 (Young and Hindson, 1997) and that for bending is neglected. It is said that torsional stiffness can be calculated by the following formula (Kilby, 1963; Mori and Lloyd, 1994): τ = F45° – (Fu + Fv)/4 We assumed that F45° = 0.6 ((Fu + Fv)/2) and then τ becomes 0.1 ((Fu + Fv)/2). Five groups of simulations about single stitch seam puckers were executed. Figures 2a-2c show the results of the simulations, C-1 to C-3. In these simulations, all the properties are the same but material puckering is different. As the material puckering of the simulation becomes smaller, the size and the number of wrinkles of the virtual fabric are increasing. This is the same situation which occurs when using real fabric.
Symbol
Description
Unit
Tu,Tv Tuv Mu, Mv Muv Eu, Ev G Fu, Fv τ εu , ε v εuv κu, κv κuv νu, νv U Si fi r ω
Tensile stress Shearing stress Bending couple Torsional couple Tensile rigidity Shearing rigidity Bending rigidity Torsional rigidity Tensile strain Shearing strain Bending strain Torsional strain Poisson ratio Potential energy Area of ith triangular element Force on jth apex Torsion vector of surface Unit vector to the direction of thickness
(gf/cm) (gf/cm) (gf cm/cm) (gf cm/cm) (gf/cm) (gf/cm) (gf cm2/cm) (gf cm2/cm) (–) (–) (cm–1) (cm–1) (–) (gf cm) (cm2) (gf) (cm) (–)
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Table I. List of symbols
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Table II. Specifications of the simulation
Figure 2a. Seam pucker simulation with the condition C-1 where material puckering is 0.98
τ
νu
νv
0.03 ↑ ↑
0.003 ↑ ↑
0.8 ↑ ↑
0.8 ↑ ↑
0.15 ↑ ↑
0.15 ↑ ↑
0.015 ↑ ↑
↑ ↑ ↑
↑ ↑ ↑
↑ ↑ ↑
0.6 ↑ ↑
0.6 ↑ ↑
0.6 ↑ ↑
↑ ↑ ↑
↑ ↑ ↑
1,000 20,000
↑ ↑
0.15 ↑
0.15 ↑
0.015 ↑
↑ ↑
↑ ↑
5,000 ↑
0.2 3.5
↑ ↑
↑ ↑
↑ ↑
↑ ↑
↑ ↑
Simulation no.
MP
Eu
Ev
G
Fu
Fv
A-1 A-2 A-3
0.98 0.94 0.90
5,000 ↑ ↑
5,000 ↑ ↑
0.9 ↑ ↑
0.03 ↑ ↑
B-1 B-2 B-3
0.98 0.94 0.90
↑ ↑ ↑
↑ ↑ ↑
↑ ↑ ↑
C-1 C-2 C-3
0.98 0.94 0.90
↑ ↑ ↑
↑ ↑ ↑
D-1 D-3
0.94 0.94
1,000 20,000
E-1 E-3
0.94 0.94
5,000 ↑
Notes: MP = material puckering; ↑ = the value is the same as above
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Figure 2b. Seam pucker simulation with the condition C-2 where material puckering is 0.94
Figure 2c. Seam pucker simulation with the condition C-3 where material puckering is 0.90
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Figure 3a. Seam pucker simulation with the condition A-1 where Fu,v are 0.03 gf cm2/cm and τ is 0.003 gf cm2/cm
Figures 3a-3c show the results of the simulations, A-1, B-1 and C-1. In these simulations, all the properties and material puckering, except bending stiffness, are the same. It can be seen that the period of wrinkles on the virtual fabric extends as the bending stiffness increases. It is the same situation as real fabric that many small wrinkles are formed on “soft” fabric because it is easily deformed and on “rigid” fabric small wrinkles can not be formed and wrinkles grow larger. It is shown that bending stiffness is one of the major factors of seam pucker and this result relates to the condition (5) regarding seam stability. From theoretical analysis, Amirbayat derived the condition (6) that rigidity of fabrics affects stability of seam pucker. From the result of experiments, the correlation coefficient between the index of the rating of seam pucker and membrane modulus of fabric was about 0.28 to 0.29 and it was concluded that extensibility is not significant (Amirbayat and Norton, 1990). Figure 4 shows the results of the simulations, D-1, B-2 and D-3. In these simulations, only tensile modulus differs. The tensile modulus of D-1 is one-twentieth of that of D-3 and the virtual fabric of the simulation D-1 is more extensible than B-2 or D-3. In this simulation, the period of wrinkles is longer and the size of those is greater than those of B-2 or D-3. The result of the simulations means that when tensile modulus is small, local distortion can easily occur and the seam pucker rating is severe for extensible fabrics.
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Figure 3b. Seam pucker simulation with the condition B-1 where Fu,v are 0.15 gf cm2/cm and τ is 0.015 gf cm2/cm
Figure 3c. Seam pucker simulation with the condition C-1 where Fu,v are 0.6 gf cm2/cm and τ is 0.6 gf cm2/cm
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Figure 4a. Seam pucker simulation with the condition D-1 where Eu,v are 1,000 gf/cm
Figure 4b. Seam pucker simulation with the condition B-2 where Eu,v are 5,000 gf/cm
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Figure 4c. Seam pucker simulation with the condition D-3 where Eu,v are 20,000 gf/cm
The simulation, in which the shearing modulus was changed, was also executed, but the results were almost unchanged. Stylios and Lloyd reported that shearing modulus is one of the most important factors of structural jamming seam pucker (Stylios and Lloyd, 1989a; 1990). Structural jamming seam pucker is related to the density of fabric and local distortion can occur only by penetration of needle and thread (Stylios and Lloyd, 1989b). They concluded that the local distortion is related to shearing modulus, i. e. when shearing modulus is small, the fabric allows severe local distortion and seam pucker is easily formed. In the simulation, shearing modulus does not affect seam pucker as much, because the factor of fabric density and local distortion by the penetration of needle are not considered. Comparison with real seam puckers The goal of this simulation is to predict the shape of seam pucker. To check the validity of the simulation, some real fabrics were sewn and compared with the results of the simulations. The mechanical properties of the fabric are shown in Table III. Tensile and shearing moduli were measured with KESFB1, EMT is used for tensile modulus and G is used for shearing modulus. Bending stiffness was measured with KES-F2 and B is used for bending stiffness. The value of Poisson ratio is the same as that used in the simulation
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above and torsional stiffness is calculated in the same way as above. The fabric was chosen because the bending stiffness is minimal and seam pucker is easily formed. Seam pucker samples are usually made of two strips of fabrics, but samples were made of one strip of fabric, in the same manner as used in the simulation. The centre of the strip of the fabric was sewn with single needle sewing machine (JUKI DDL-5580). Seam pucker samples containing different material puckering were obtained. The simulation was executed using the same conditions as the real samples. One of the samples, with the condition of material puckering 0.984, is shown in Figure 5. The seam lines of real samples are wavy; this is because the samples consist of one strip of fabric and the mechanical properties of the fabric and the thread may not be balanced. The shape of the seam line cannot be simulated because thread is not explicitly involved in the simulation. The shape of seam line was
Fabric no.
MP
Eu
Table III. 143 0.984 11,800 Mechanical properties of Note: MP = material puckering the real fabric
Figure 5. A seam pucker sample made from real fabric
Ev
G
Fu
Fv
4,068
0.535
0.0376
0.0227
τ 0.00302
νu
νv
0.8
0.8
measured with a laser displacement meter. The measuring instrument consists of a pulse motor-driven stage and a laser displacement meter controlled by a computer. The heights of the points on the seam line were measured at the pitch of 0.5mm. From the measured data, initial shape shown in Figure 6 was made and used as the initial condition of the simulation. In the initial shape, every point on the line perpendicular to the seam line is the same height as the point on the seam line. The shape of the seam line is fixed during iterations of the calculation as the boundary condition of the simulation. One of the results of the simulation of real seam pucker samples corresponds to Figure 5 and is shown in Figure 7. The rating of seam pucker of the simulated virtual fabric is a little less severe than the real one, but the simulation accurately reflected the shape of the fabric with seam pucker.
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Summary The simulation, which calculated the shape of seam pucker, was performed. In the simulation, some properties, such as material puckering, bending stiffness, tensile modulus and shearing modulus of fabric were varied. Material puckering affected the rating of seam pucker. Among the mechanical properties, bending stiffness most affected the appearances of seam pucker. Tensile modulus affected little, and shearing modulus did not affect the appearance of seam pucker. Some samples were made from a real fabric for
Figure 6. The initial state for the simulation with the same properties as the real sample shown in Figure 5
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Figure 7. The result of the simulation with the same properties as the real sample shown in Figure 5
comparison. A simulation with the same properties as the real fabric sample was carried out and a similar shape to the real sample was obtained. References American Association of Textile Chemists and Colorists (1985), AATCC Technical Manual, p. 115. Amirbayat, J. (1990), “An energy approach to the instability problem of overfed seams, Part 1”, International Journal of Clothing Science and Technology, Vol. 2 No. 1, pp. 21-5. Amirbayat, J. and McLaren Miller, J. (1991), “Order of magnitude of compressive energy of seams and its effect on seam pucker”, International Journal of Clothing Science and Technology, Vol. 3 No. 2, pp. 12-17. Amirbayat, J. and Norton, M.L. (1990), “An energy approach to the instability problem of overfed seams, Part 2”, International Journal of Clothing Science and Technology, Vol. 2 No. 2, pp. 7-13. Hand Evaluation and Standardization Committee (1975), The Standardization and Analysis of Hand Evaluation, p. 49. Inui, S. and Shibuya, A. (1992), “Objective evaluation of seam pucker”, International Journal of Clothing Science and Technology, Vol. 4 No. 5, pp. 24-33. Japanese Industrial Standards L 1905 (1994), “Methods for assessing the appearance of seam pucker on textiles”. Kilby, W.F. (1963), “2-planar stress-strain relationship in woven fabrics”, J. Text. Inst., Vol. 54 No. 1, pp. T9-27.
Mori, T. and Lloyd, D.W. (1994), “Measuring the twisting rigidity of woven fabrics”, Textile Res. J., Vol. 64 No. 7, pp. 397-405. Okabe, H., Imaoka, H., Tomiha, T. and Niwaya, H. (1992), “Three dimensional apparel CAD system”, Computer Graphics, Vol. 26 No. 2, pp. 105-10. Stylios, G. and Lloyd, D.W. (1989a), “The mechanism of seam pucker in structural jamming woven fabrics”, International Journal of Clothing Science and Technology, Vol. 1 No. 1, pp. 511. Stylios, G. and Lloyd, D.W. (1989b), “A technique for identification of seam pucker due to fabric structural jamming”, International Journal of Clothing Science and Technology, Vol. 1 No. 2, pp. 25-7. Stylios, G. and Lloyd, D.W. (1990), “Prediction of seam pucker in garments by measuring fabric mechanical properties and geometric relationship”, International Journal of Clothing Science and Technology, Vol. 2 No. 1, pp. 6-15. Young, F.S. and Hindson, W.R. (1977), “An examination of a ‘wide-jaw’ test for the determination of fabric Poisson ratios”, J. Text. Inst., Vol. 68 No. 9, pp. 299-302. Appendix The simulation program utilized here was coded by authors from the beginning, but the theoretical model of the simulation was based up on the work of Okabe et al. (1992). The processes executed in the program are as follows: (1) The shape of fabric is defined in two-dimensional plane. (2) The fabric is divided into triangular elements. (3) An initial three-dimensional shape is given. (4) The nodes of triangular elements are displaced and the minimal value of potential energy is searched. The state of the minimal potential energy gives the shape of fabric because real fabric is always in the minimal energy state when it is stable. The potential energy is due to internal and external forces. The internal force is originated from deformation of fabric. The deformation of fabric is given as strain. We assume linear relationship between stress and strain. Gravity is treated as an external force in the model but it is neglected here. The total potential energy of fabric is expressed as:
The first term corresponds to the tensile energy. This term comes from the relationship between stress and strain as follows where u means the direction of warp and v means the direction of weft.
The above relationship is expressed in matrix notation as: (T) = A . (ε) The second term is the bending energy. This term also comes from the relationship between stress and strain as follows:
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This relationship is expressed as: (M) = B . (κ) For computation, the integration of potential energy is descretized as follows:
The force acting on the node of a triangular element is derived as gradient of the energy.
The nodes are moved according to the force. The strains are defined as follows where ru means the derivative of r to the direction u:
These strains are calculated from the shape of triangular elements in two-dimensional plane without strain and the co-ordinates of nodes of triangular elements in three-dimensional space. Then the shape of fabric can be obtained as the coordinates of nodes of triangular elements in the minimal energy state.
Communication On-line measurement of fabric-bending behavior: background, need and potential solutions
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Naiyue Zhou and Tushar K. Ghosh College of Textiles, North Carolina State University, Raleigh, North Carolina, USA Introduction In recent years, there have been growing demands in the textile and apparel industries for higher manufacturing speed, better product quality and wider application of automation. With the obvious advantages associated with this trend, the challenges posed by these new technologies must be considered. Higher speed and quality usually mean less tolerance for error, and therefore require better process control. At higher speeds, a machine or even a single delivery making an off-quality product is likely to have significantly greater impact on overall product quality[1]. Additionally, with fewer machines and higher production rates, machine downtime becomes an expensive proposition. Therefore, traditional off-line measurement systems can no longer meet this quality monitoring task due to their end-of-the-line inspection, limited sampling and time-consuming nature. On-line, real-time measurement systems have to be employed, which are not only useful in quality monitoring but also in process control. There are numerous examples of the application of on-line monitoring techniques in the textile and apparel industries, e.g. sliver and yarn uniformity measurement[1,2], color inspection of dyed fabrics[3], inspection of non-woven web’s surface properties[4], and quality inspection of sewn seams[5], etc. However, very little work[6,7] on the on-line measurement of fabric mechanical properties has been reported. This is probably due to the fact that fabric mechanical properties, normally can only be evaluated through the responses of the fabric to the applied forces or moments, and therefore are difficult to measure without interfering with the dynamic production line. Another likely contributing reason is that the potential applications of such a system have not been thoroughly studied. The latter is probably the determining factor. The authors would like to acknowledge the support of the National Textile Center of the USA for funding this project.
International Journal of Clothing Science and Technology, Vol. 10 No. 2, 1998, pp. 143-156. © MCB University Press, 0955-6222
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As a first step, the primary objectives of this study are: • to examine the principles of existing off-line methods and their potential in on-line application, and • to modify or develop a simple measurement principle which can be used to evaluate fabric bending behavior on-line. The measurement principle being sought must be based on sound theoretical principles, and should be validated experimentally so that the potentials and limitations of the system can be studied. The work being reported here is part of a larger effort to develop on-line measurement systems for all the basic mechanical properties and surface behavior of fabrics. Although only fabric bending behavior is the subject of this report, the potentials of on-line measurement of fabric mechanical properties as a whole will be discussed in order to justify the utility of such a system. Background and rationale The characterization of fabric mechanical properties has traditionally been undertaken by destructive tests (e.g. tensile, tearing, bursting, wear, etc.). Only since the beginning of the 1970s, has there been a strong interest in the study of fabric behavior under low stresses[6]. The research interest in this area was initiated by Kawabata[8] and his associates, is an effort to identify the interaction between fabric hand values and the mechanical properties of fabrics. Their work led to the development of the well-known Kawabata’s Evaluation System (KES) for the measurement of fabric low-stress mechanical and surface properties. The initial thrust of this development was aimed at characterizing fabrics through various “hand values”, which were calculated from various parameters of measured low-stress mechanical behavior of fabrics. Since then, the low stress mechanical behavior of fabrics has been related to drape, formability and tailoring properties, garment appearance and seam pucker, mechanical stability and shape retention, wrinkle recovery and crease retentions, etc. of fabrics[5-12]. Recently, another system, FAST (Fabric Assurance through Simple Testing)[13] has been developed to carry out similar tests on fabrics. The fabric characteristic parameters obtained through these tests have been widely used to identify potential problems in apparel manufacturing[11,14-16]. Both KES and FAST systems are off-line methods and require careful sample preparation and testing. An automated on-line measurement system will enable continuous measurement of these characteristic parameters without the lag-time and tedious and time-consuming sample preparation. Another likely, and maybe more beneficial use of the proposed system, is in process control. Increasing automation in apparel manufacturing demands precise control of fabric handling, cutting, sewing and pressing operations. The various control parameters of these operations have been shown to depend on the mechanical and surface properties of the fabric being processed[17-19]. Since material handling takes the largest percentage of time needed in apparel
manufacturing, fabric behavior during this process has been studied by many researchers[9,20-23]. Although the specific problems they dealt with may vary, they all used fabric mechanical properties as inputs to theoretically simulate fabric deformation for designing equipment to handle a variety of fabric types and sizes. In robot-based automatic sewing stations, a basic operational requirement is to push fabric panels across smooth surfaces and into a sewing machine without buckling the fabric. Computational techniques for predicting the onset of fabric buckling, mainly determined by fabric mechanical properties, are currently employed for the programming of robotic sewing systems[6,9]. If an on-line measurement system is available, the dynamically measured fabric mechanical property values, along with other parameters, can be immediately sent to control the robotic feeding systems to avoid the onset of buckling. In this context, a quick, non-destructive and continuous fabric property measurement system is clearly superior to the traditional off-line methods. It can provide realtime measured mechanical property values in a production environment, which could lead to the automatic adjustment of machine parameters. A successful implementation of on-line mechanical property measurement system in the paper industry further highlights the significance of the proposed system. Nearly all end-use applications of paper leaving the mill involve strength specifications of one kind or another. These so-called strength parameters (e.g. burst, tensile, tear, etc.) are all usually obtained by destructive tests which, therefore, can not be directly measured on-line. Unfortunately, this may lead to the production of large quantities of sub-standard products before the problem is recognized. To solve this problem, an on-line system capable of measuring certain mechanical properties of paper, i.e. extensional stiffness, bending stiffness and shear stiffness has been developed[24-28]. The principle is based on the measurement of ultrasound velocities through moving paper, and subsequent use of empirical relations to calculate these properties. Based on the experiments performed on mill samples, excellent correlation between on-line and laboratory measured values has been demonstrated[28]. In addition to recording the mechanical properties continuously in real time, the system can be used to recognize likely problems in the manufacturing operation as well as track the effects of subtle changes in process variables, leading to better process control[25,27]. The general principle of this system can certainly be applied to the fabric finishing and non-woven production process, which share so much in common with paper manufacturing. As a matter of fact, an effort to apply the above technology on the non-woven webs has been reported[29]. The benefit of an on-line measurement system, therefore, can be readily seen in two areas, to identify potential problems in subsequent processes, e.g. apparel manufacturing and in process control. The full potential of the proposed system may not be exploited today, but it will be worthwhile in the near future if the present technological trends continue.
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Characterization of fabric bending Like other mechanical properties, the bending behavior of a woven fabric directly influences its performance. It is well known that fabric bending rigidity, along with its shear rigidity, determines the drapability of a fabric. Research results show that fabric bending rigidity is also an important contributor to the fabric’s formability[30], handle[31], buckling behavior, wrinkle-resistance and crease-resistance[10]. In the case of industrial fabrics, such as air-supported structures or fabric-reinforced flexible-composite conveyor belts, the bending behavior of the fabrics is critically important[32]. Two parameters that characterize the fabric bending behavior are its bending or flexural rigidity and the hysteresis, a measure of fabric’s ability to recover. For common engineering materials the bending behavior is assumed to be linear and the bending rigidity is defined as the constant of proportionality between applied moment and curvature. Bending of fabrics, however, is generally non-linear. Woven fabrics are made of large numbers of fibers that may have considerable freedom of motion, relative to each other, within the fabric structure. As a result, the fiber strains which develop during bending are considerably lower than those which develop in bending of corresponding solid sheet materials. With this mobility, the potential flexibility of the fibers can be realized and the fabric structure will, in turn, have a low bending rigidity[33]. The inter-fiber friction associated with the fiber movement is believed to be the major cause of fabric non-linear bending behavior. Figure 1 shows a typical bending-hysteresis curve for a woven fabric[34]. In bending a fabric, fibers are under pressure and cannot slip past each other without setting up a frictional resistance. When the bending time is large
M (moment)
K (curvature) 0
Figure 1. A typical bendinghysteresis curve of woven fabric
enough to overcome the frictional resistance, then actual bending of the fabric occurs. The presence of frictional resistance in bending implies the hysteresis when the fabric is allowed to recover its original unbent configuration. If an initially straight fabric is bent, the bending time does not suddenly change. Instead, it follows a smooth transition curve, as shown in Figure 1. Similarly, when a bent fabric is unbent, the change of bending time is also continuous. Based on these considerations, the fabric bending rigidity is defined as the couple required to bend a fabric strip of unit width to unit radius of curvature under pure bending condition[35], if the relationship is assumed linear. If not, it is defined as the rate of change of bending time with respect to the curvature, i.e. dM/dK, and certainly is not a constant. It will be relatively high during the initial bending of a straight fabric and at the onset of unbending process, and remains approximately unchanged in between. For the sake of simplicity, fabric bending rigidity is traditionally represented by a single constant with certain assumptions attached to each specific testing method. This situation has already produced considerable inconsistency among the bending rigidity values measured by different methods and problems in interpreting those values[36,37]. Therefore, understanding the principles of existing off-line testing methods and the ways to interpret the data is fundamental to the development of the proposed on-line system, and will be discussed in the following section. Potential measurement systems Development of any new instrument for characterizing fabric bending behavior must begin with the examination of existing systems and their principles. Principles of a potential on-line measurement system may already exist in some fashion, or any of the existing systems may be adapted for on-line measurement. Over the past 60 years, there has been considerable interest in the development and application of instruments to measure fabric bending behavior. Many instruments based on, seemingly, a variety of principles have been proposed and developed. However, based on their basic principles of working, these systems can be placed in two broad categories. The first group is concerned with the measurement of forces, moments or energy while applying a prescribed bending deformation. These instruments are generally designed to produce the moment-curvature relationship of fabrics. The second group involves the measurement of fabric deformation under its own weight. Commercial developments in these two groups are represented by KES and FAST systems, respectively. Instruments for measuring the time-curvature relationship Although the effort to develop bending testers in this group started in the early 1930s[35], it was not until the late 1950s[38] that the requirement for the pure bending of a fabric sample during the measurement process was finally recognized and, almost, satisfied. In pure bending the curvature along the fabric
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is maintained constant while it changes as a function of time. The bending rigidity of the fabric is then obtained directly from the measured momentcurvature relationship. A common problem shared by most of the early testers was the clamping system. Inappropriate clamping of the two ends of a fabric sample by traditional grips renders pure bending of the fabric impossible. To apply pure bending at least one of the clamps has to rotate about its own axis in addition to the movement along a prescribed arc in order to maintain uniform curvature all over the sample. Eeg-Olofsson in 1959[38] proposed a mechanism to achieve this. The device is shown schematically in Figure 2. In it, the test specimen is held by two vertical clamps c1 and c2, of which c1 is fixed and c2 floats on mercury. The clamp on the float carries an electrical coil. This coil can rotate about a vertical axis in a magnetic field. When current flows, the coil rotates and a moment is applied to the fabric by clamp c 2. In order to apply only bending moment to the sample, the float with clamp c2 must be able to move sideways. The radius of curvature of the sample depends on the bending moment, which can be changed by varying the current through the coil. As the moment is proportional to the current, it is easily determined by means of an ordinary milliameter. The curvature is read from the angle of deflection of the coil. As a result, for the first time in history, a complete curve of fabric bending moment-curvature relation was recorded.
axis of the moving coil c2
Figure 2. Diagram of Eeg-Olofsson’s tester (plan view)
c1 float
test specimen
glass bowl with mercury
Livesey and Owen, in 1964[34], proposed a bending tester to apply almost constant curvature condition over the sample with a simple mechanism as shown in Figure 3. The fabric specimen AB is held at one end in a revolvable clamp C, which is pivoted at O and carries an index reading against a scale of degrees, D. The other end of the specimen is attached to a long, light-weight pendulous arm P, which under the action of gravity, provides a couple in the specimen. Provided the specimen is sufficiently short (normally 0.5cm) relative to the diameter of the scale and the distance from the specimen to the center of gravity of pointer (P), the curvature is proportional to the angle α and the
couple to sinθ. By moving clamp C clockwise and counterclockwise in steps, a complete cycle of fabric bending-hysteresis curve can be recorded. Although the instrument is quite simple in principle, the experimental process is tedious and time consuming. In 1966, Owen[39], and Abbott and Grosberg[40] independently adapted Livesey and Owen’s tester to the Instron tensile tester. Hence, a direct plot of the bending-hysteresis curves became possible. In 1967, Owen[41] improved his tester by providing two scales for direct reading of bending moment and curvature.
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D C
O
A B
α θ P
Figure 3. Diagram of Livesey et al.’s tester
The bending tester developed by Isshi[42] in 1957 can be viewed as the predecessor of today’s bending tester in Kawabata’s Evaluation System (KES)[43]. For the first time in Isshi’s bending tester the two clamped ends of a fabric sample were positively controlled so that the whole specimen could be bent in an arc of constant curvature K, while the curvature was changed continuously. In Isshi’s design, shown schematically in Figure 4, one end of the sample is fixed in clamp at O, the other end Q is driven to execute a translation as well as rotation so that the sample is always in a uniform circular arc. The curvature of the sample is then obtained from the angle indicated by a pointer fixed on the moving clamp. The fixed clamp is mounted on a shaft prevented
Q
M
1/K O M
Figure 4. The principle of Isshi’s tester
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from rotation by a torsion spring. As the sample is bent, the fixed clamp will cause a slight rotation of the torsion spring. This rotation is then reversed by another spring through a linkage system until it is nullified. The deflection of the second spring for nullification can be recorded and is directly related to the bending moment on the clamp. In 1968, Popper and Backer[44] proposed a modification of Isshi’s tester. In this tester, the fabric sample is bent about a vertical axis in order to avoid the influence of its weight. A synchronous motor was used to drive the moving clamp to enable continuous measurement. Moreover, the moment exerted on the sample was measured by a transducer, and the output signal of the transducer can be fed directly into a recording unit. Introduced in 1972, upgraded in 1978, and followed by some more recent automation, the well-known KES pure bending tester, developed by Kawabata[43], could be considered as a further modification of Isshi’s and Popper and Backer’s work. Although a complete cycle of fabric bendinghysteresis curve can be recorded automatically on an X-Y recorder, and its electronic signal processing system is much more sophisticated, its fundamental measurement principle remains virtually unchanged. Methods based on measuring fabric deformation under its own weight In an attempt to characterize fabric hand, Peirce in 1930[35] introduced the principle of “cantilever” method to measure fabric bending behavior. In this method the fabric is made to deform under its own weight as a cantilever and the cantilever-length necessary to produce a predetermined deflection-angle is measured. The cantilever method uses the engineering principles of beam theory and the fabric is assumed to be a linearly elastic. As shown in Figure 5, a 1in. wide strip of fabric is moved forward to project as a cantilever from a horizontal platform. As soon as the straight line connecting the edge of the platform and the leading edge of the fabric makes an angle of 41.5° to the horizontal, the cantilever length, l, is measured. Fabric bending length, c, a measure of the interaction between the fabric bending rigidity and weight is defined as:
The choice of deflection angle, θ, as 41.5° is primarily based on the ease of calculation of bending length as half of the cantilever length. The equation for bending length was derived by Peirce based on the elastic theory and corrected
Figure 5. Peirce’s cantilever tester
θ (41.5°)
by experimental data[35]. Finally, the fabric bending rigidity, B, is calculated from bending length, c, and fabric weight per unit area, w:
The cantilever method developed by Peirce has been used by numerous researchers and a number of commercial testers have been developed based on this principle. The most recent development is by the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. CSIRO’s “Fabric assurance through simple testing” or FAST system[13] includes a bending tester that is based on the principle of Peirce’s cantilever test and uses a light beam to detect the deflection angle. Apart from the cantilever method, the folded loop method shown in Figure 6 has been reported by a number of researchers[45,46]. The method consists in folding a strip of fabric back on itself and measuring the height of the loop. Stuart and Baird[45] are among the first to use this method in measuring fabric bending length. They found that bending length, as defined by Peirce, is proportional to the height of the folded loop, as expressed by the following relationship: bending length = 1.10 loop height.
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loop height
Figure 6. Folded fabric loop
In other words, stiff fabrics will produce higher loop than limp fabrics. Stuart and Baird conducted folded loop and cantilever tests on number of woven fabrics, the average difference between the values given by the two methods was merely 0.06 cm. Therefore, they suggested that the folded loop test could be used as an alternative to the cantilever method for measuring fabric bending length. More recently, this method was employed by Cassidy et al.[46] in developing their “Bending box” in order to measure the bending property of knitted fabrics, which are usually difficult to measure by the cantilever method, due to their tendency to curl. They measured loop height as a measure of fabric bending behavior. The test results show that for knitted fabrics, the reproducibility of the results is significantly better for their method than for the cantilever method. But the physical meaning of the loop height, as they admitted, was not quite clear. Hence, they could not use the method to obtain values for the fabric bending length. For fabrics which are very limp, or very stiff, or tend to curl and twist, Peirce[35] recommended additional methods to measure their bending length.
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They are generally referred to as hanging loop methods, among which two are well known: heart loop and pear loop methods. As shown in Figure 7, a fabric specimen of known width and length is formed into a heart-shaped or pearshaped loop by placing two ends together and suspended from a horizontal bar. The bending length, c, can be obtained by measuring the loop height, the distance from the point of suspension to the bottom of the loop, and then substituting into Peirce’s equations derived from his elastic model. Peirce suggested in his paper that the heart loop method was probably more satisfactory for materials which were very flexible. Conversely, the pear loop method was found to be more satisfactory for stiffer fabrics. However, according to Winn and Schwarz’s[47] later comparative study, for a wide range of materials, both as to thickness, hardness, and stiffness, the heart loop method could be used satisfactorily throughout. They also indicated that values obtained for bending length, as measured by hanging loop methods, were dependent on the lengths of the fabric strips tested. Consequently, measured values of bending length and, thereby, calculated bending rigidity values depend on sample lengths. This undesirable variability is obviously a consequence of the assumption of linear bending behavior. In addition Winn and Schwarz[47] reported that for the heart loop method, once a specimen length of certain magnitude has been reached, any increase of the specimen length beyond that does not involve a change of bending length. This is a further advantage of the heart loop method. The pear loop method was found to be relatively less sensitive to fabric bending behavior[35]. It is probably due to the higher level of bending in fabrics in the heart loop than that in the pear loop. Intuitively, the higher the extent of bending of a fabric, the more sensitive the loop shape to the fabric bending behavior.
loop height
loop height
Figure 7. The heart loop and pear loop methods Heart Loop
Pear Loop
Finally, it must be pointed out that the cantilever and all other test methods in this category measure fabric bending length, not bending rigidity. Since the bending rigidity values obtained from these systems is based on the linear elastic assumption, the calculated bending rigidity B is an overall indicator of its bending behavior. Substantial errors in measurement could be produced due to the neglect of significant effects of frictional resistance to bending that exist in most fabrics. Nevertheless, the bending rigidity obtained by the FAST system turns out in practice to be satisfactory and correlates well with KES
values[15]. Experimental results also indicate that the FAST bending tester can give excellent correlation with a subjective evaluation of fabric[37]. Adaptability to on-line systems In comparing the working principles and their ability to characterize fabric bending behavior, it is obvious that the first group of bending testers are able to provide a complete description of fabric bending behavior including bending rigidity and hysteresis as a function of curvature. However, they are relatively complicated and more importantly, measuring the forces or moments during the bending deformation require predetermined manipulation of test sample, which is likely to make their on-line implementation very difficult, if not impossible. For the second group, the measurement principle is quite simple. Their general principle is based on characterization of fabric loop shape, formed under the force of gravity. Therefore, non-contact measurement techniques can easily be applied. This is an obvious advantage for the design of an on-line measurement system. In addition, these or other loop shapes can be formed by fabrics moving through processing lines. It should be noted that none of the systems discussed above can be directly adopted for on-line use, nevertheless, the principle behind the fabric loop methods will provide a good starting point for the present research. Although the measured result is affected by the fabric’s linear elastic assumption, the values are sufficiently accurate for most purposes[13,37]. Concluding remarks The utility and relevance of an on-line system to characterize fabric bending behavior has been discussed. The various principles of measuring fabric bending behavior, reported in the literature, have been reviewed. It is likely that automation and control will shape the future textile/apparel complex and necessitate real-time characterization of material properties. The review of the existing off-line measurement methods indicates that the principle of the proposed on-line method should be based on the characterization of fabric loop shapes. The ease of loop formation with least interference with dynamic manufacturing lines is an obvious advantage. However, a major drawback of this principle is that only the overall bending rigidity is obtained; it does not allow characterization of nonlinear bending behavior. Furthermore, fabric sample size influences the measured values. To find fabric loops theoretically reasonable and practically feasible for the on-line measurement, analytical models must be utilized to study the relationship between the measured characteristic parameters of shapes and fabric bending rigidity. Problems such as loop sensitivity, the influence of fabric nonlinear bending behavior on the measured results, and the difference between static and dynamic loops should also be studied.
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References 1. Thomas, R.K., “You need on-line and off-line testing for top yarn quality”, Textile World, Vol. 143 No. 1, 1993, pp. 50-1. 2. Caban, J.C., “Denier control – on-line or off-line?”, Fiber Producer, Vol. 10 No. 6, 1982, pp. 407.
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3. Best, R.H., “On-line color monitoring”, American Dyestuff Reporter, Vol. 68 No. 11, 1979, pp. 34, 67-8. 4. Graf, J.E., Enright, S.T. and Shapiro, S.I., “Automated web inspection ensures highest quality nonwovens”, Tappi Journal, Vol. 78 No. 9, 1995, pp. 135-8. 5. Clapp, T.G., The On-line Inspection of Sewn Seams, NTC research brief, 1995. 6. Stylios, G., Textile Objective Measurement and Automation in Garment Manufacture, Ellis Horwood, Chichester and New York, NY, 1991. 7. Panarusky, M., On-line Measurement of Fabric Stiffness, Master’s thesis, North Carolina State University, Raleigh, NC, 1992. 8. Kawabata, S., The Standardization and Analysis of Hand Evaluation, (2nd ed.), The Textile Machinery Society of Japan, Osaka, Japan, 1980. 9. Gershon, D. and Grosberg, P., “The buckling of fabrics during feeding into automatic sewing stations”, Journal of Textile Institute, Vol. 83 No. 1, 1992, pp. 35-44. 10. Textile Institute, Studies in Modern Fabrics, Textile Institute and Contributor, UK, 1970. 11. Shishoo, R.L., “Importance of mechanical and physical properties of fabrics in the clothing manufacturing process”, International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 35-42. 12. Postle, R., Kawabata, S. and Niwa, M. (Eds), Objective Evaluation of Apparel Fabrics, Textile Machinery Society of Japan, Osaka, Japan, 1983. 13. Fabric Assurance by Simple Testing, Instructional Manual, CSIRO Division of Wool Technology, Australia, 1990. 14. Curiskis, J.I., “Fabric objective measurement: 5, Production control in textile manufacture”, Textile Asia, Vol. 20 No. 10, 1989, pp. 42-57. 15. Bona, M., Modern Control Techniques in the Textile Finishing and Making-up, Universidade do Minho, Portugal, 1990. 16. Curiskis, J.I., “Fabric objective measurement: 5, Production control in textile manufacture”, Textile Asia, Vol. 20 No. 10, 1989, pp. 42-57. 17. Ono, E., Nishikawa, S., Ichijo, H. and Aisaka, N., “New robot hand for cloth handling”, SenI Gakkaishi, Vol. 48 No. 9, 1992, pp. 501-6. 18. Govindaraj, M., Chen, B. and Koechling J., “Fabric properties as control factors for flexible apparel production systems”, International Journal of Clothing Science and Technology, Vol. 4 No. 2/2, 1992, pp. 34-8. 19. Shishoo, R.L., “Importance of mechanical and physical properties of fabrics in the clothing manufacturing process”, International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 35-42. 20. Brown, P.R. III, Buchanan, D.R. and Clapp, T.G., “Large-defluxion bending of woven fabric for automated material-handling”, Journal of Textile Institute, Vol. 81 No. 1, 1990, pp. 1-14.
21. McWaters, S.D., Clapp, T.G. and Eischen, J.W., “Automated apparel processing: computer simulation of fabric deformation for the design of equipment”, International Journal of Clothing Science and Technology, Vol. 6 No. 5, 1994, pp. 30-8.
On-line measurement
22. Eischen, J.W. and Kim,Y.G., “Optimization of fabric manipulation during pick/place operations”, International Journal of Clothing Science and Technology, Vol. 5 No. 3/4, 1993, pp. 68-76. 23. Seyam, A. and Sun, F. “Manufacturing technology for apparel automation – lay-up module, Part II: the impact of fabric properties on the gap length between two slats”, International Journal of Clothing Science and Technology, Vol. 6 No. 1, 1994, pp. 5-13. 24. Baum, G.A. and Habeger, C.C., “On-line measurement of paper mechanical properties”, Tappi Journal, Vol. 63 No. 7, 1980, pp. 63-6. 25. Habeger, C.C. and Baum, G.A., “On-line measurement of paper mechanical properties”, Tappi Journal, Vol. 69 No. 6, 1986, pp. 106-11. 26. Vahey, D.W., “An ultrasonic-based strength sensor for on-line measurements”, Tappi Journal, Vol. 70 No. 3, 1987, pp. 79-82. 27. Chase, L.M. and Lantz, K.G., “On-line measurement and control of strength properties”, Tappi Journal, Vol. 71 No. 2, 1988, pp. 75-8. 28. Vahey, D.W., “Correlating the on-line measurement of ultrasonic velocity with strength properties”, Tappi Journal, Vol. 71 No. 4, 1988, pp. 149-52. 29. Bhat, G., “Sonic velocity of melt blown nonwoven webs”, INDA Journal of Nonwovens Research, Vol. 4 No. 3, 1992, pp. 26-8. 30. Lindberg, J., Waesterberg, L. and Svenson, R., “Wool fabrics as garment construction materials”, Journal of Textile Institute, Vol. 51, 1960, pp. T1475-1492. 31. Hallos, R.S., Burnip, M.S. and Weir, A., “The handle of double-jersey knitted fabrics, Part II: polar profiles”, Journal of Textile Institute, Vol. 81 No. 1, 1990, pp. 15-35. 32. Ghosh, T.K., Batra, S.K. and Barker, R.L., “The bending behavior of plain-woven fabrics, Part I: a critical review”, Journal of Textile Institute, Vol. 81 No. 3, 1990, pp. 245-53. 33. Popper, P., The Effect of Friction and Fiber Mobility on the Bending and Unbending Behavior of Cotton Structures, doctoral thesis, Massachusetts Institute of Technology, MA, 1966. 34. Livesey, R.G. and Owen, J.D., “Cloth stiffness and hysteresis in bending”, Journal of Textile Institute, Vol. 55 No. 10, 1964, pp. T516-530. 35. Peirce, F.T., “The ‘handle’ of cloth as a measurable quantity”, Journal of Textile Institute, Vol. 21, 1930, pp. T377-416. 36. Abbott, N.J., “The measurement of stiffness in textile fabrics, Part I: a comparison of five methods of laboratory evaluation”, Textile Research Journal, Vol. 21 No. 6, 1951, pp. 435-41. 37. ASTM Annual Book of Standards, Vol. 7 No. 1, “Textiles-yarns, fabrics, and general test methods”, Philadelphia, ASTM, 1993, pp. 356-59. 38. Eeg-Olofsson, “Some mechanical properties of viscose rayon fabrics”, Journal of Textile Institute, Vol. 50, 1959, pp. T112-132. 39. Owen, J.D., “An automatic cloth-bending-hysteresis tester and some of its applications”, Journal of Textile Institute, Vol. 57 No. 9, 1966, pp. T435-438. 40. Abbott, G.M. and Grosberg, P., “Measurement of fabric stiffness and hysteresis in bending”, Textile Research Journal, Vol. 36 No. 10, 1966, pp. 928-30.
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41. Owen, J.D., “An improved manually operated cloth-bending-hysteresis tester”, Journal of Textile Institute, Vol. 58 No. 11, 1967, pp. 589-91. 42. Isshi, T., “Bending tester for fibers, yarns and fabrics”, Journal of Textile Machinery Society of Japan, Vol. 3 No. 2, 1957, pp. 48-52. 43. Manual for Bending Tester KES-FB2-L, Katotekko Co. Ltd, Kyoto, Japan.
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44. Popper, P. and Backer, S., “Instrument for measuring bending-moment curvature relationship in textile materials”, Textile Research Journal, Vol. 38 No. 8, 1968, pp. 870-4. 45. Stuart, I.M. and Baird, K., “A new test for bending length”, Textile Research Journal, Vol. 36 No. 1, 1966, pp. 91-3. 46. Cassidy, T., Cassidy, C., Cassie, S. and Arkison, M., International Journal of Clothing Science and Technology, Vol. 3 No. 5, 1991, pp. 14-19. 47. Winn, L.J. and Schwarz, E.R., “Flexibility and drape as measurable properties of fabric”, Textile Research Journal, Vol. 10 No. 1, 1939, pp. 5-16.
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The fabric pressing performance and its role in predicting the appearance of men’s wool suit jackets Pier Giorgio Minazio IWS, Technical Centre, Biella, Italy Introduction Tailoring is regarded as successful when a garment of good appearance is produced efficiently. Garment appearance is the result of a number of inputs including the materials and actual processing parameters. Two main processes are used to manufacture a garment from individual fabric pieces; sewing and pressing. The ability of a fabric to be pressed into a sharp crease or a flat seam can be assessed by pressing a swatch of the fabric and measuring the angle of the crease formed. As the performance of the fabrics under examination can be masked by variations in the steam press conditions, instrumentation was developed to press and measure the creases in a consistent manner. This paper describes that instrumentation and test method. The result of the pressing performance test is an angle, the smaller the angle the better the pressing performance. This measured crease angle has been related to seam appearance [1]. Round trials have been conducted using both the steam press and the new instrumentation to set and measure creases. The results are reported here. The pressing performance of a fabric is an aspect of appearance that has not previously been related to an objective measurement. The new fabric test method developed as a result of collaboration between the CSIRO Division of Wool Technology in Australia, IWS (Biella Technical Centre) and the Italian industries, enables the prediction of the propensity of a fabric to produce “blown seams” after pressing. Seam blowing refers to a phenomenon whereby the pressed seam in a garment does not remain flat but has a rounded or “blown” appearance. Because garment manufacturers would like to reduce the need to produce sample garments for all new fabrics, this can only be done by successfully predicting garment appearance from measurable fabric properties. Two components of appearance for men’s suit jackets have been identified; seam pucker and seam blowing, which are important to the general garment appearance.
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 182-190, MCB University Press, 0955-6222
Experimental and method The fabric pressing performance was assessed using the newly developed setting jig[2] and IWTO draft test method[3]. The pressing apparatus is shown
in Figure 1. The crease angles are measured using an optical device shown in Figure 2. Small samples of fabric are folded across either warp or weft threads and constrained at standard conditions (20°C, 65 per cent Rh). The samples are then placed in the setting jig, where their temperature is raised and then lowered in a repeatable manner. After the samples are removed from the jig and released from their constraint, they are allowed to recover under standard conditions for 24 hours before the crease angles are measured. The instrumentation was designed to mimic the action of a steam press. The new instrumentation has been tested in four round trials. Trial I was conducted using the steam press to set the creases. Trial II was conducted using a protractor and magnifying glass to measure the crease angle and Trial III employed a newly developed optical projection device. Trial IV used an improved sampler holder, which presented the crease angle for measurement suspended over a piece of thin plastic instead of the creased sample lying with one arm in contact with the bench. The round trials are described in Table I. In addition, 25 fabrics were selected from the new suiting fabrics submitted for summer season. The fabrics were either pure wool or wool rich blend and
The fabric pressing performance 183
Figure 1. Pressing performance crease setting device
Figure 2. Pressing performance crease angle measurement instrumentation
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Trial
Equipment
Fabrics
Laboratories
I
Steam press
4 lightweight pure wool
6 industry and research
II
Setting jig
6 pure wool
5 research
III
As II plus optical bench
12 light weight pure wool
12 industry and research
As III with hanging sample presentation
As III
As III
Table I. Details of round trials on the pressing IV performance test method and instrumentation
ranged in weight from 130g/m2 to 228g/m2 (see Table II). The 25 fabrics were converted to suits on a sample-garment production line. The suits were of conventional construction using a fused interlining in the front panels. In this construction all the seams in the weft direction are fused and therefore their appearance is not dependent on the fabric properties. The seams on the back and sides of the garment are not fused and are in the warp direction, therefore the fabric properties along these seams are the most important for garment appearance. In a garment using interfacing which is sewn in place, rather than being glued as is the case with fused interfacings, fabric properties along all of the seams contribute to the garment appearance. Seams and properties in the weft direction, i.e. weft formability (F-2) and warp crease angle (CA-1), will become important, especially as they are located on the front of the garment. The suit jackets were judged for appearance while draped over a tailors mannequin in a room with ample ambient light and supplemented with additional illumination from above. At the same time photographs were taken of all jackets, under the same lighting conditions, as a record and for subsequent appraisal. The appearance characteristics assessed were seam pucker and seam blowing. Results and discussion Pressing performance test method and instrumentation A summary of the results of the round trials, analysed according to ISO Standard 5725[4], and also using a two way analysis of variance[5], is given in Table III. The results of the analysis are presented as the critical differences at the 0.05 error level for the average of measurements on three separate crease angles.
Fabric ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Trial
Subjective rating Blown Seam seams pucker 4 2.5 3 5 4.5 4 4.5 5 4.5 4.5 4 4.5 4 4.5 4 3 3 4 4 3.5 4 4.5 3.5 5 4
Wt (g/m2)
4.5 2 3 5 4.5 5 5 5 5 4 3.5 5 4 4.5 4 4 3 4 3.5 3.5 4 4.5 4 5 5
159 130 141 166 153 228 169 185 169 162 145 164 150 152 140 142 160 161 127 140 150 161 155 197 173
Fabric properties F-1 F-2 (mm2) (mm2)
CA-2 (°)
0.28 0.21 0.30 0.39 0.27 1.34 0.48 0.43 0.71 0.42 0.14 0.49 0.26 0.34 0.25 0.31 0.30 0.51 0.25 0.20 0.28 0.46 0.35 0.88 0.57
18.5 91.0 35.5 13.5 14.0 14.0 17.5 4.5 10.0 17.0 19.5 11.0 19.5 15.5 21.5 24.5 24.0 22.5 15.5 24.5 13.5 9.5 21.5 13.5 20.5
Within laboratory
0.48 0.37 0.24 0.56 0.34 0.17 0.18 1.02 0.47 0.74 0.27 0.74 0.26 0.61 0.33 0.43 0.39 0.64 0.34 0.23 0.56 0.89 0.24 0.92 0.72
The fabric pressing performance 185
Table II. Subjective assessment and important objective properties
Between laboratory
Laboratory
Fabrics
I
6
4
11.7°
29.3°
II
5
6
4.6°
11.4°
III
12
12
2.7°
7.7°
IV
12
12
3.5°
7.1°
Table III. Results of round trials
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Table IV. Subjective scouring scale
The new jig was designed to mimic the steam press and the resulting crease angles correlate well with the results from pressing creases on a steam press. However, because of the variability in the performance between steam presses the absolute values of crease angle are rarely the same. The results of Trial I, in Table I, show the problems of steam press variability. However, Trials II, III and IV show an improvement in agreement both within and between laboratories. Trials III and IV used the optical measuring device shown in Figure 2 and were conducted in industry, research and educational laboratories. The variety of laboratories participating ensured that the results are a good estimate of the type of reproducibility, between laboratories, that will be achieved by the instrumentation in use. As far as the trial with the fabrics is concerned the garments were subjectively assessed and ranked under two categories: seam blowing and seam pucker. The rating range from a severe problem, scoring one, to no problem, scoring five. The scale is described in Table IV. Table II shows the average scores given to each fabric for each category of fault. The garments were photographed from a number of angles. Figures 3 and 4 illustrate the range encountered in this trial for each category of problem. Seam blowing, as illustrated in Figure 3, occurs when a fabric does not accept a sharp crease during pressing. This results in billowing seams and rounded garment edges. The centre back seam is often the most noticeable location of this problem. Figure 3a shows the worst occurrence of this problem for this trial with a score of 2.5 and figure 3b shows an example of a garment with no problem, trial score 5. Seam pucker is a common problem encountered during making up. There are a number of possible contributing factors such as machine and sewing parameters and operator skill levels. Fabric characteristics also influence the occurrence of seam pucker. Seam pucker is often observed along the centre back and the side panel seams. Figure 4a shows the worst example encountered during this trial, with a score of two, and figure 4b shows a garment with no seam pucker problem, with a score of five. Fabric properties were measured on the FAST equipment[6] and using the press-test instrument[3]. These properties were then compared with the assessment of appearance provided by the expert judges. Table II gives
Score
Description of level of problem
5
None present
4
Small amount
3
Definitely present
2
Notable
1
Severe
The fabric pressing performance 187
Figure 3. Seam blowing in the garment (a) is the worst fabric (No. 2). (b) is the best fabric (No. 24)
Figure 4. Seam pucker in the garment (a) is the worst fabric (No. 2). (b) is the best fabric (No. 4)
the FAST properties of warp and weft formability, F-1 and F-2 respectively, for each fabric as well as the fabric weight and the pressing performance, given as the weft crease angle value, CA-2. The measurement of crease angle in the press-test can be directly related to seam blowing in the final garment. Figure 5 is a graph of the subjective assessment score for seam blowing plotted against the weft crease angle for all
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Subjective Assessment 5.5 5 4.5 4
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3.5 3
Figure 5. Subjective assessment of seam blowing against weft crease angle
2.5 2 0 5 10 15 20 Weft Crease Angle
25
30
35
40
fabrics. The larger the crease angle the worse is the rating of seam blowing. As an example, consider the jackets shown in Figure 3: fabric No. 2 has a crease angle of 31°, whereas fabric No. 24 has a crease angle of 13.5°. A smaller crease angle produces a flatter, crisper seam. A fabric with a large crease angle tends to have “blown” seams, and hence a worse appearance. Formability is the fabric property most often linked to seam puckering; the lower the formability the more likely seam pucker is to occur. In finished garments the seams being judged for appearance have been pressed. In this case there is the influence of both formability and pressing performance. The fabrics in Table II which exhibit seam pucker generally also exhibit seam blowing. From the data in Table II it can be shown that a positive correlation exists between increasing warp formability and improving seam appearance (r2 = 0.32). However, a function to predict seam pucker using formability and crease angle determined using multiple regression was found to give a better fit to the data. The relationship was shown to be: Seam pucker rating: 5.10 + 1.03 (F-1) – 0.0744 (CA-2) (1) 2 This equation has the correlation of: r = 0.66, and therefore improves the ability to predict seam pucker. Figure 6 shows warp formability plotted against weft crease angle. Each point is labelled with the subjective assessment of seam pucker. Fabrics with F-1 less than 0.5 display a range of seam pucker ratings, from five (no seam pucker) at low crease angle to two or three (definite seam pucker) at high crease angle values. Multiple regression analysis was also used to determine the relationship between seam blowing, formability and crease angle. The relationship was shown to be: Seam blowing rating: 5.49 + 0.069 (F-1) – 0.0816 (CA-2) (2) with a correlation of r2 = 0.71.
The fabric pressing performance
Warp Formability 1.6 5
1.4 1.2 1 0.8 0.6 0.4 0.2
5 5 54 5 4 55 5 45 4 5 4 4 43 5545 454 34 5 3 33
189 2
3
0 0 5 10 15 20 Weft Crease Angle
25
30
35
40
In equation (2), for seam blowing, because of the relative values of F-1 and CA2 the contribution of the formability term is not significant. In equation (1), for seam pucker, the contribution of the formability term is more significant but still smaller than that of the crease angle. This indicates that while crease angle is important for both appearance characteristics, formability is only important for seam pucker. The values of the coefficients of formability and crease angle in each of the two equations reflect the importance and role of the two measurements in determining these two fabric properties. The value of the formability coefficient is about 15 times higher in the regression equation for seam pucker than it is in that for seam blowing. This, as expected, is due to the important role that the property of formability has in the generation of seam pucker. There is, however, only a 10 per cent difference in the coefficient of crease angle between the two equations. The measure of fabric pressing performance by crease angle is therefore important for the prediction of the general appearance of seams, including both seam blowing and seam pucker. It appears that some seam pucker in a pressed garment, as predicted by low fabric formability, can be removed if the fabric has a good pressing performance. It should be noted that there are aspects of both seam blowing and seam pucker that are unrelated to fabric properties. These relate to the steam press operating parameters, the sewing parameters and the skill of machine operators. When these are controlled, or at least consistent, the level of confidence in the ability of the measurements of formability and seam blowing to predict garment appearance is high. Conclusion An instrument has been developed which can be used to measure pressing performance of fabrics both within a laboratory for product development and also between laboratories. The test method and instrumentation are more repeatable and reproducible than using steam presses for the pressing deformation.
Figure 6. Warp formability plotted against weft crease angle
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Pressing performance, as measured with the crease angle test, is a separate property which must be assessed in order to predict garment appearance. Mechanical and dimensional properties together with pressing performance provide information that can be used to predict a garment’s performance. Weft crease angle and warp formability have been shown to be strongly related to important aspects of the appearance of a finished garment for highquality, tailored, men’s suits. Since the appearance of garments is judged after both sewing and pressing both properties are necessary in order to give a reliable guide to the finished appearance. The measurement of the crease angle in the press-test relates to the level of seam blowing. The fabric formability relates to the potential level of seam pucker; however, seam puckering is reduced for fabrics with good pressing performance. References 1. Biglia, U., Roczniok, A.F., Fassina, C. and Ly, N.G., “The prediction of garment appearance from measured fabric properties”, Proceedings of Textile Objective Measurement and Automation in Garment Manufacturing, Bradford University, UK, July 1990. 2. Rosenblad-Wallin, E. and Cednas, M., Clothing Res. J., Vol. 2, 1974, pp. 115-21. 3. IWTO Draft Test Method (IWTO No 42-92) Crease Pressing Performance Test, 1981. 4. ISO Standard 5725, Precision of Test Methods: Determination of Repeatability and Reproducibility by inter-Laboratory Tests. 5. Snedecor, G.W. and Cochran, W.G., Statistical Methods, 6th edition, Iowa State University Press, Ames, Iowa, USA, 1967. 6. SIROFAST Instruction Manual, CSIRO Division of Wool Technology, Sydney, 1992.
Numerical evaluation of fabric construction parameters
Evaluation of fabric construction
˘ ˘ Milena ZibernaSujica and Andreja Pinteri˘c University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia
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1. Introduction The new product development in the industry is mostly based on empirical obtained values of fabric construction parameters. For a new fabric a large number of trial samples has to be made. To avoid that, the fabric construction work has to be supported by theoretical knowledge in the sense of construction parameter numerical values prediction. Our research work refers to the fabrics for under-linen and fabrics for bed linen. 2. Theoretical bases of fabrics construction Fabrics construction signifies the adjustment of fabrics construction parameters for the subject of project requests for a particular fabric application area. The fabrics construction and its structure respectively is defined by the following parameters: • fibre structure and its properties (fibre type; fibre mixture; geometrical, physical, mechanical and chemical properties); • yarn structure and its properties (yarn type; geometrical, physical, mechanical and structural properties; technological parameters of spinning); • fabric geometry (weave, density, working-in, weight, physical, mechanical fabric properties); • fabric patterning; • technology of fabrics production. Those fabrics structure parameters, which can be numerically evaluated, are important for the announcement of the properties of a new fabric. 2.1 Selection of fibre type 2.2 Yarn parameters 2.2.1 Yarn fineness 2.2.2 Yarn porosity factor – p Yarn thickness is influenced by density, which is defined with yarn porosity factor p. The value p = 1 indicates that the yarn consists of fibre substance only. This is only valid for monofilaments. The yarn porosity factor values were taken from the literature[1, Picture 7].
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 191-200, MCB University Press, 0955-6222
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2.2.3 Yarn flexibility factor – f In a fabric weave the threads are bending with a different degree of flexibility. The empirically obtained values are given in [2, Picture 9]. 2.2.4 Yarn volume coefficient – v Volume coefficient of a yarn: (1) where v = yarn volume coefficient Tt = yarn fineness ρ = specific fibre density (g/cm3) p = yarn porosity factor 2.3 Parameters of fabrics geometry 2.3.1 Yarn diameter – d Yarn diameter is classified as a basic fabric construction parameter. Yarn diameter is influenced by many parameters. At the fabric geometry analyses a circular yarn diameter is presupposed. The yarn diameter is defined by the equation[1]: (2) where d = yarn diameter Tt = yarn fineness p = yarn porosity factor ρ = specific fibre density (g/cm3) 2.3.2 Weave – weave factor Most fabrics properties depend on the selected weave. The weave type influence on the fabric setting is numerically evaluated with the weave factor – V. The value of plain weave is 1. The factor values for other weaves must be calculated [2]. When the yarn construction in both thread systems is equal, the factor value is calculated:
(3) where
Evaluation of fabric construction
V = weave factor R
= size of the pattern repeat
f
= yarn flexibility factor
z
= minimal thread displacement in a weave
a
= number of double crossings in a repeat of pattern
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When the warp and weft threads are different, the factor value is calculated: (4)
(5) 2.3.3 Fabric setting Most fabric functional properties are influenced by the thread setting. Fabrics setting is defined as the number of warp and weft threads to a determined unit of length. We encounter the following settings: •
setting of a finished fabric (Gg);
•
setting of a grey fabric (Gs);
•
set of warp threads (Gogr);
•
set of weft threads (Gvt).
which are related: Gg > Gs > Gogr > Gvt In the new product development the fabric setting has to be previously defined. The calculations of grey fabric setting will be presented as follows. Those fabric settings which cannot be exceeded at a determinate weave and yarn fineness are termed as maximal fabric setting (Gm). Relative fabric density (Grel) is expressed in percentage of the maximal fabric setting. The calculation of grey fabric setting is based on an ideal quadratic fabric with the following characteristics: •
it is made out in plain weave;
•
the number of warp and weft threads in the repeat of pattern is equal;
•
the warp and weft yarn fineness is equal;
•
the working-in is equal in both thread systems.
First the setting constant C of the quadratic fabric has to be defined. This constant indicates the maximal setting of a fabric, which has the warp and weft
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thread fineness of 1,000 tex; p = 1; ρ = 1,0 g/cm3. The value of the constant C can be calculated by equations: (6)
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(7) (8) (9) where r = distance between two threads (mm) d = yarn diameter (mm) ρ = specific fibre density (g/cm3) p = yarn porosity factor C = setting constant In the calculation of the maximal setting of an optional fabric, the following has to be taken into consideration: • specific fibre density; • yarn porosity factor; • weave factor; • thread fineness. The influence of specific fibre density and yarn porosity factor is expressed in the basic setting – e: (10) where e = basic setting (in plain weave) ρ = specific fibre density (g/cm3) p = yarn porosity factor C = setting constant Basic setting e value is valid in plain weave. The basic setting in any other weave is calculated considering the weave factor V: (11) where eV = basic setting in determinate weave e = basic setting (in plain weave) V = weave factor
2.3.3.1 Fabric setting in equal threads in both thread systems Maximal fabric setting and setting of a grey fabric are calculated considering the yarn fineness and the relative fabric density:
Evaluation of fabric construction
(12) (13) where Grel = relative fabric density (per cent) Gm = maximal fabric setting eV = basic setting at determinate weave Gs = setting of a grey fabric V = weave factor Tt = yarn fineness (tex) 2.3.3.2 Fabric setting in different yarn fineness in warp and weft If the yarn fineness of a particular thread system is different, we have to calculate the basic setting separately for warp and weft system. The arithmetical mean represents those values, which would be taken into consideration in equal threads in both thread systems[2]. (14)
(15) where e = basic setting eo = basic warp setting ev = basic weft setting Tto = warp yarn fineness Ttv = weft yarn fineness The warp and weft setting in a grey fabric is expressed by equations: (16)
195
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(17) where Go = warp setting Gv = weft setting Grel = relative fabric density (per cent) Tto = warp yarn fineness Ttv = weft yarn fineness 2.3.4 Working-in The theoretical calculation of warp and weft working-in is defined[3,4]: (18) (19) where εo = warp working-in εv = weft working-in lor = length of the warp repeat lvr = length of the weft repeat lno = length of the warp yarn in a repeat lnv = length of the weft yarn in a repeat The length of the warp and weft repeat is calculated: (20) (21) The length of the warp and weft yarn in a repeat is calculated: (22)
(23) where
Evaluation of fabric construction
a = number of double crossings in a repeat of pattern v = yarn volume coefficient Grel = relative fabric density (%) R = size of the pattern repeat d = yarn diameter g = yarn setting in a grey fabric u = number of single crossings in a repeat of pattern
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2.3.5 Fabric weight For comparison of two different fabrics, the weight of a square meter of fabric is used. Next equations represent the calculation of those values[5]: (24) (25)
(26) When the yarns in both thread systems are equal, the equation is simplified: (27) where Mkvad Grel ε Tt a z f R v p ρ
= weight of a square meter of fabric (g/m2) = relative fabric density (per cent) = working-in (per cent) = yarn fineness (tex) = number of double crossings in a repeat of pattern = minimal thread displacement in a weave = yarn flexibility factor = size of the pattern repeat = yarn volume coefficient = yarn porosity factor = specific fibre density (g/cm3)
3. Practical example for calculation of construction parameters For practical presentation of construction parameter calculation we selected two fabric samples, which differentiate in application area, yarn fineness, weave and relative fabric density. Fabric samples belong to two groups (Table I)[6]:
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(1) 1st sample: fabric for under-linen; and (2) 2nd sample: fabric for bed linen. Parameters, needed for the construction parameter calculation, are represented in Table II. Yarn porosity factor is defined from Picture 7 [1], yarn flexibility factor is defined from Picuture 9[2]. Yarn parameters and fabric geometry parameters were obtained by the following equations: ⇒ yarn volume coefficient; ⇒ yarn diameter; ⇒ weave factor; ⇒ setting constant; ⇒ basic setting; ⇒ maximal setting; ⇒ grey fabric setting; ⇒ working-in; ⇒ repeat length; ⇒ yarn length in a repeat; ⇒ fabric weight. 4. Results and discussion The calculated construction parameters are represented in Table III[6]. Special attention to following construction parameters has been taken:
Table I. Sign and description of fabric samples
Raw material
Weave
1
100 per cent cotton
plain
2
100 per cent cotton
satin
1
2
12 12
14 17
Yarn porosity factor – p
0.85
0.85
Yarn flexibility factor – f
0.8
0.8
Specific fibre density – ρ
1.52
1.52
Repeat of pattern
2/2
5/5
Fineness (tex) – warp – weft
Table II. Collective table of given parameters
Yarn diameter (mm) warp weft Yarn volume coefficient warp weft Weave factor warp weft Fabric setting grey fabric setting warp weft maximal fabric setting warp weft relative fabric density (per cent) warp weft common fabric density (per cent) Working-in (per cent) Kienbaum warp weft Measured value warp weft Fabric weight (g/m2) calculated measured
•
•
•
1
2
0.11 0.11
0.12 0.13
3.05 3.05
3.29 3.63
0.904 0.904
1.33 1.36
48.4 30.0
50.6 28.0
47.9 47.9
66.7 63.2
101.0 101.0 79.5
76.0 44.0 57.8
4.2 11.5
2.3 6.9
7.0 5.2
5.0 5.2
101.1 100.2
122.7 124.1
Fabric setting. The results of fabric setting calculations are as expected. The relative density for the first sample is: warp 101.0 per cent; weft 62.6 per cent and for second sample: warp 76.0 per cent; weft 44 per cent. The common relative density of the first sample is 79.5 per cent and for the second sample 57.8 per cent. Working-in. The results of working-in calculations do not meet the expectations in some cases. In the first sample we can notice a large movement of weft working-in values in the positive sense (11.5 per cent), and in the second sample a negative movement (2.3 per cent) of calculated warp working-in values is noticed. Fabric weight. The weight values for a square meter of fabric, calculated by Kienbaum, are very close to the measured values. The calculated weight value for the first fabric sample is 101.1g/m2 and the measured
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Table III. Calculated values of fabric construction parameters
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value is 100.2 g/m2. For the second fabric sample the calculated value is 122.7g/m2 and the measured value is 124.1g/m2. From the parameters comparison of both fabric sample we can conclude that the differences between them are a consequence of different project requests.
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5. Conclusion In terms of predicting the construction parameters we can conclude: • Theoretically calculated values of fabric settings, represented in the Table III, meet the project requests for a particular fabric group. • In working-in results some differences between the calculated and the practical values can be noticed due to the influence of a large number of parameters on warping-in. • Theoretically obtained fabric weight values indicate a high rate of agreement with measured values. Because of the fabric structure complexity only the most important construction parameters were taken into consideration. The further research work has to include other parameters of fabric structure (fabric thickness, fabric porosity) which also influence the fabric properties. References 1. Kienbaum, M., “Gewebegeometrie und Produktentwicklung”, Melliand Textilberichte, No. 10, 1990, pp. 737-42. 2. Kienbaum, M., “Gewebegeometrie und Produktentwicklung”, Melliand Textilberichte, No. 11, 1990, pp. 847-54. 3. Kienbaum, M., “Gewebegeometrie und Produktentwicklung”, Melliand Textilberichte, No. 8, 1991, pp. 615-19. 4. Kienbaum, M., “Gewebegeometrie und Produktentwicklung”, Melliand Textilberichte, No. 12, 1991, pp. 987-90. 5. Kienbaum, M., “Gewebegeometrie und Produktentwicklung”, Melliand Textilberichte, No. 3, 1992, pp. 237-42. 6. Prelo˘znik, A., “Modeli konstruiranja tkanin”, Diplomsko delo, Univerza v Mariboru, Fakulteta za strojni˘stvo, Maribor, 1997.
Simulation of drape behaviour of fabrics Hartmut Rödel, Volker Ulbricht, Sybille Krzywinski, Andrea Schenk and Petra Fischer
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Dresden University of Technology, Dresden, Germany Introduction In the textile and clothing industries, because of the increasingly individual and customer-oriented production, the sample collections of the firms extend more and more, whereas the quantities for the production decrease. At present, the stage of product development and product preparation of clothes requires approximately three times the stage of consumption. In order to compensate the resulting greater efforts in the product preparation and to react more quickly and flexibly to latest fashion, the use of complex CAD-CAM solutions is a must. Today there exist a lot of design programs with various software tools, a wide choice of designing functions are at the disposal of the designer. Connected with sketching-systems, so called two-and-a-half dimensional presentation programs can give an optical impression of how the colours, motifs and materials look on a scanned model. Steps of production preparation such as pattern construction, grading system, pattern planning and pattern optimization and the automated cutting are realized by computer assistance[1]. However, of the CAD-systems available on the market (with two exceptions) the systems work only two-dimensionally and the material behaviour and the material parameters are not taken into account. Both these aspects are required for the three-dimensional display of a model with regard to the draping in order to give the designer and model maker a real impression of the model. The three-dimensional display of a two-dimensional pattern construction on a dummy or vice-versa, a development of a threedimensionally constructed model into the two-dimensional level, would be the optimal possibilities to examine the correct fitting and the form of a model, when the specific material parameters are taken into account. Consequently, the main focus in international research is to investigate the fundamentals of three-dimensional handling of fabrics. For that, a prerequisite is the implementation of algorithms for the simulation of draping of ready-made clothes. The specific material parameters such as the significant material parameters must be taken into consideration. Therefore, more detailed treatment of physical and mechanical properties and their correct mathematical and physical formulation is of interest. This paper is the result of an interdisciplinary research project of the Institute for Mechanics of Solids and the Institute for Textile and Ready-Made Clothing Technique at Dresden University of Technology. It is the aim of this
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project to describe and simulate the deformation behaviour of flexural fabrics (especially of woven fabrics). The deformation theory developed in the Institute for Mechanics of Solids and its application for fabrics is utilized as a practicable approach for the simulation of the drape behaviour. Investigations on the description of the material behaviour and of its properties are a further necessary focal point. Drape behaviour In the mechanical consideration of deformability of fabrics, on principle, two directions are distinguished. The first one deals with the deformational behaviour of fabrics when covering defined surfaces. This application requires a nearly wrinkle free draping of the fabric, as for example when upholstering. Here, the extensibility, i.e. the force-extension relation in case of tensional strain with the corresponding modulus, are significant material parameters. There is a large number of research works in this field. The second one is the drape behaviour of the fabric. The behaviour of nonresistance of fabrics to bending without external force only under the influence of the true specific weight results in a three-dimensional deformation. A possibility to determine objectively the drape behaviour is the draping experiment (Figure 1) carried out using the drape meter developed by Cusick[2] and the calculation of the resulting drape coefficient D in percent. The drape image is characterized by the area, the form and amplitude of the folding, the number of foldings and their position with regard to warp and weft direction. Figure 2 shows two drape images of different woven fabrics. According to Cusick, the drape coefficient is defined by the surface relations of the drape image, the supporting disk and the surface of the cut. So far, many planimetering activities were required. The other characteristic features are not taken into account. For the more efficient evaluation of the tests, the drapemeter measuring device was coupled with a video camera and image processing systems. This measuring method was developed by the Department of Textiles
Figure 1. Drape experiment
Simulation of drape behaviour of fabrics 203
D = 60.0%
D = 37.9%
of the University of Gent, as reported in [3]. Hence, the previous subjective errors made when measuring were minimized and the drape coefficient was determined within considerably less time. By image processing, new possibilities of evaluation of the drape image were created. Fabrics are classified on the basis of the drape coefficient. A high drape coefficient indicates a small deformation whereas a small drape coefficient marks great deformations and more waves. For these deformations, the specific material properties of the fabric are decisive. To simulate the drape behaviour, so far, mainly fabrics were investigated, because they can be simulated most simply, they are predominant in outer garments and the total testing technique described at present is mainly oriented to fabrics. Therefore, our investigations only refer to fabrics. The fabrics geometry is defined by the fabrics parameters such as weave structure, the number of warp/weft nodes, the fineness of threads, the density of threads in warp and weft. The often viscoelastic behaviour of the material is determined by the material (the fibrous material used), the properties of the individual fibre and by the fabrics geometry. In the three-dimensional deformation investigated in greater detail by Amirbayat and Hearle[4] as double or complex curvature, for the deformation bending and shear properties are decisive. Bending stiffness of fabrics is mainly based on the stability of fibres in the thread. Shear stiffness is a unit of measurement of the ease with which threads are gliding across each other at deformation. For both properties, the thread spacing and crimp in the structure of the fabric is decisive. Besides the bending stiffness and shear stiffness, the drape behaviour of a fabric is determined by the weight/unit area, whereas properties of compression of the surface (friction and roughness) and extension properties have no considerable influence. At present, a system of devices developed according to Kawabata[5] is used internationally for the investigation of mechanical characteristics of fabrics. This system includes measuring devices which are mainly suitable for
Figure 2. Drape image
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measuring insignificant quantities of compression, friction and roughness, of bending stiffness and extension. Geometric fundamentals The basis for the determination of the drape behaviour is a geometric model adapted to the drape test and shown in Figure 3. Two configurations are related – the non-deformed configuration and the deformed configuration – where the independent values of the model are purely geometric values. Kinematic assumption of the illustration without strains is taken into consideration in the formula for the geometric description of the deformed configuration[6]. To describe the geometric model, independent boundary curves are r0 (– v) and → r1 (v); introduced with the local vectors and → (1) (2) Both local vectors are in one plane z = constant each, in a distance of h to each other. The ruled surface; (3) is formed with the standardized local vector; (4) – and the coordinate u ∈ [0,h]. Functional dependence of the parameters –v = v(v) of the boundary curves should be determined in such a way that the ruled
u h v
r0
y r1
Figure 3. Geometric model
x
v
surface becomes a torse. When the Gauss curvature is zero, the special Simulation of condition of torse which is adapted to the problem is: drape behaviour of fabrics (5) with
205 (5a) Analysing equation (5), two surfaces exist of which the deformed geometry is composed – a tangent surface and a conical surface (Figure 3). r0 and → r1 various equations can be set up. According to (1) for the vectors → One possibility is the description of the boundary curves by the following functions (Figure 4): (6) (7) with
As a result, the description of the surface is ensured as required to simulate the drape behaviour. By the function f n (v), a waviness is defined which is superimposed to the lower boundary curve. Calculation of the isometric mapping with constant lengths of the threedimensional geometry results from the condition that the quadratic line
Figure 4. Simulation of drape image
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elements of the three-dimensional and the two-dimensional geometry are identical[7]. To simulate the real experiment two constraints prove to be nessesary. Material equation and potential For the description of the material properties isotropic, linear-elastic material behaviour is taken as a basis. In the description of surfaces without strains, deformation takes place exclusively by change of curvature of the surface. Only bending moments occur which are linked with the change of curvature κ by bending stiffness B. For the solution, the principle of the minimum of the elastic total potential is taken as a basis. It is assumed that the fabric specimen comes down from the planar starting position into that state, where there is a minimum elastic total potential. The task to determine the extreme value for the elastic total potential together with strain energy Wf and external energy Wa can be set up as follows[8]: (8) As the model chosen was introduced as a simply curved one and without strains for strain energy follows: (9) with
Analogously to the assumptions, the integral includes only bending parts. Surfaces without strains are neglected deliberately in order to exclude that when calculating energy, these parts will be predominant caused by numeric inaccuracies with regard to strain energy and the results are distorted. During the drape test, no external forces or momentums occur. Consequently, external energy Wa is determined only from the own weight of the material. It is introduced with: (10) with (10a) where by z the difference of the vertical distance of both configurations is designated. The variable Fga is the force of weight per unit area determined experimentally. The elastic total potential:
Simulation of drape behaviour (11) of fabrics with the parameters to be varied of the radius of the lower boundary curve r1, amplitude of wave a, number of waves, and overhanging h for the description of the deformed geometry. The minimum is determined by computer assistance and by means of a searching strategy according to Gauss-Seidel.
207
Summary In Figure 5, the three-dimensional description of the drape is shown which is generated by means of the simulation model presented. It is in good coincidence with the experiment concerned (Figure 1). In the future the research work has the aim to improve the description of the material behaviour. Therefore it is necessary to consider in the model the shear stiffness too. Summarizing it can be stated that when compared with FEM, a minimum of independent values is sufficient to reach an efficient basic approach of the problem[9]. In addition, very much calculation time can be saved with this method.
Figure 5. Simulation of the drape behaviour
References 1. Kirchdörfer, E. and Mecheels, J., “Wohin steuert CAD in der Bekleidungsindustrie?”, Bekleidung und Wäsche, Vol. 14, 1987, pp. 8-15. 2. Cusick, G.E., “The measurement of fabric drape”, Journal of the Textile Institute, Vol. 59 No. 6, 1968, pp. 253-60. 3 Vangheluwe, L. and Kiekens, P. “Time dependence of the drape coefficient of fabrics”, International Journal of Clothing Science and Technology, Vol. 5 No. 5, 1993, pp. 5-8. 4. Amirbayat, J. and Hearle, J. “The anatomy of buckling of textile fabrics, drape and conformability”, Journal of the Textile Institute, Vol. 82 No. 1, 1989, pp. 51-70. 5. Kawabata, S. and Niwa, C. “Fabric performance in clothing and clothing manufacture”, Journal of the Textile Institute, Vol. 80 No. 1, 1989, pp. 19-50.
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6. Landgraf, G., Modler, K.-H., Ulbricht, V. and Ziegenhorn, M., “Differentialgeometrische Beschreibung abwickelbarer Flächen”, Informationstechnik it, Vol. 33 No. 2, 1991, pp. 77-82. 7. Kreyszig, E., Differentialgeometrie, Akademische Verlagsgesellschaft Geest & Portig K.-G., Leipzig, 1957. 8. Landgraf, G., Ulbricht, V., Nestler, R. and Krzywinski, S., “Möglichkeiten der Übertragbarkeit der Methode der doppelt gekrümmten Flächen und der im Maschinenbau gewonnenen Ergebnisse auf die Konfektionsindustrie”, Wissenschaftl. Zeitschrift der TU Dresden, Vol. 44 No. 6, 1992, pp. 79-85. 9. Schenk, A., “Berechnung des Faltenwurfs textiler Flächengebilde”, TU Dresden, Dissertation, 1996.
Selection of suitable sewing needle using machine learning techniques Zoran Stjepanovi˘c
Selection of suitable sewing needle 209
Faculty of Mechanical Engineering, Maribor, Slovenia, and
Helena Strah Lisca, Sevnica, Slovenia Introduction The selection of a suitable sewing needle was proved to be one of the most important parameters in production of garments’ joints. Good knowledge of types and properties of processed textile materials is needed in order to select the appropriate sewing needle. One should also be acquainted with types and quality of sewing needles[1]. It must be considered that finishing processes prior to sewing have an important influence on sewing ability of textile material. Here, appropriate finishing agents have an important role. A great number of different textile materials are being used for production of garments. Therefore, the garment producers strive for a simple and effective selection of a suitable sewing needle without previous testing on material. The problem of material damage as a consequence of unsuitable needle selection is especially important in the production of knitted goods. Namely, such a material is exposed to deformations during sewing which is shown as damage of loops of knitwear[2]. These spots tend to enlarge at wearing and maintenance of the garment. Importance of selection of suitable sewing needle The sewing needle is one of the basic elements that directly contribute to seam formation. Its role is to penetrate through the textile material with the point, push away the threads and transmit the sewing thread under the throat plate. Each sewing needle has two characteristic notations. The first notation is used to describe the needle system. Fineness of the needle is marked with the second notation. Additional notation describes the point shape of a needle. Needle thickness is an important parameter and should correspond to thickness, respectively surface mass of sewing material and sewing thread. Good adjustment of the above sewing parameters is essential for good seam quality[3,4]. The authors wish to express their thanks to Dr Aram Karali˘c for preparing a special version of a programme that allowed processing of a great number of discrete attributes’ values in learning examples.
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A suitable sewing needle is very important for quality assembly of garments’ parts. Good knowledge of kinds and properties of processed textile materials as well as of types of sewing needles is needed in order to select the appropriate sewing needle for a certain material. Very important for understanding of the complex sewing process is also research in the areas of acquisition and control of sewing parameters[5,6]. The problem of material damages as a consequence of unsuitable sewing needle is above all present during sewing of knitwear. Because of specific positions of yarns, built in fabric and knitwear, mainly mechanical and thermal damages occur during the sewing process[7,8]. The fabric is composed of two rectangular thread systems. When the needle penetrates the fabric, the yarns are being pushed away. Even if the yarn is damaged, the consequences are not heavy since other yarns support it. Damaged spots extend only a little. In knitwear, the yarns are connected with loops. The needle point pushes away the loops during needle penetration. The wrapping angle is here considerably greater than in the previous case. While the penetration force during sewing the fabric is being distributed on four yarns, it is concentrated only on one yarn in the case of sewing the knitwear. Different shapes of needle points are used in sewing. First of all the shape of a needle point depends on processed material. The needle with a normal needle point (notation R or without notation) can be used for sewing of the majority of textile materials. The needle point is lightly rounded and during the penetration through the material pushes away the threads without damaging the material. Needles with rounded or ball points (notation SES for light ball point and SUK for medium ball point) are used for knitwear processing. The needle point pushes the thread loops away effectively since no thread damages are allowed because of possibility of loop bursting. Elastic materials with built-in elastic threads require special heavy ball points (notations SKL and SKF). Damage of material appears when the needle penetrates into the layer(s) and hits the thread. The thread can be damaged or even broken if the needle is too thick. Damages can be seen in materials produced from natural or man-made fibres as holes that spread across the elastic thread system. They can have different forms and can be clearly visible under the microscope. Bursting of fibres mostly appears in materials made from cotton while thermal damages are characteristic of synthetic fibres. Friction between the sewing needle and textile material that acts at high sewing speeds causes heating of the needle. The needle temperature can exceed the fibre’s melting point, which results in material damage. Needle size has a decisive role on the appearance of material damage. An oversized needle can cause bursting of threads or tension around the stitch area, which results in too large holes. For that reason as fine a needle as possible should be used. However, one must pay regard also to considerable vibrations of finer needles at high sewing speeds, which can result in frequent needle fractures. Type and fineness of a sewing thread also influences the size of a sewing needle.
Artificial intelligence and machine learning Artificial intelligence is a discipline that deals with development of methods, techniques and tools for solving the complex logical problems with help of computers. Above all, logical problems on symbolic level are meant here which can be for the time being better solved by a human. The methods of artificial intelligence represent the imitation of mental processes of a human, i.e. his intelligent behaviour, considering that these processes can be presented as processing of symbols at an abstract level[9]. Expert systems are computer programs that imitate the behaviour of the expert in a particular, usually narrow area, where exact algorithmic solutions do not exist or experience plays an important role in problem solving. Here, special knowledge from particular fields is needed. Therefore, expert systems are also known as knowledge-based systems. They should simulate those elements of humans’ way of problem solving that represent intelligence: inference, estimation, decision making using unreliable and incomplete information, and ability to explain decisions (answering the questions, such as “how?” and “why?”). The conventional way of learning, where the expert for methods of artificial intelligence builds the knowledge base on the basis of a dialogue with domain expert, represents a bottleneck in expert system building. Machine learning can be treated as an alternative way to build up the knowledge base. The essence of machine learning techniques is development of algorithms that are able to build up the knowledge base from data which alone do not represent the knowledge that could be used in an expert system. However, they represent principles from which it is possible to learn rules and relations that are valid for a certain domain. There exist several forms of learning[10]: • Learning by being told – a conventional way of learning where a teacher formally presents knowledge to be learned. • Learning by discovery – learning on the basis of unstructured observations or by performing experiments, where no teacher is involved. • Learning by analogy – inference on the basis of already comprehended knowledge that can be applied on similar problems. • Learning by reformulation – gaining the new knowledge by adaptation of gained knowledge. • Learning from examples – learning of relations and rules that are hidden in examples. Learning from examples presents the most researched kind of learning in artificial intelligence. This kind of learning is also called inductive learning. In the last ten years it has produced many good results. Machine learning from examples The principle of learning from examples can be explained using two sets of objects[10]. Let U be the universal set of all objects that can appear in a certain
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domain. A concept C can be formalised as a subset of objects in U. For any object X in U we can recognise if it belongs to subset C if we learned before the concept C. As an example we can take block structures. The universal set U represents all possible structures. Arches are subset C of U containing all the arch-like structures. To learn to recognise the objects from subset C, we need the positive examples (objects from subset C) and negative examples (objects that do not belong to subset C). The task of learning from examples is to find the definition of objective relation which will be complete and consistent. The first condition means that the objective relation covers all positive examples while the second condition means that it does not cover any negative example. In case we deal with a noisy domain (when examples are unreliable), the conditions can be softened in order to avoid too specific definitions. In such cases it is allowed that learned definition covers also a certain number of negative examples. Regarding language for describing objects and concepts we distinguished between two kinds of machine learning from examples: attributebased learning and relational learning. Attribute-value learning. In this kind of learning we use attribute descriptions to present examples and learned concepts. Examples are presented by attribute values which could be numerical or non-numerical (discrete) and connected class value. Learned concepts can be presented as decision trees or IF-THEN rules. Two problems occur when the domain is noisy – that means that we deal with uncertain and uncomplete data: • induced tree unreliably classifies new objects; and • induced tree tends to be large and therefore hard to understand. The above problems can be overcome with employment of pruning of uncertain parts of a particular decision tree. In such a case leaves do not contain class values but probabilities of possible classes (leaves with those classes appear in continuation of the appropriate branch of the original, complete tree). Pruned decision trees are also called probability trees. In attribute-value learning the whole information considering a particular domain is included in learning examples. Therefore, such a learning can be treated as incomplete because it is not possible to use the relational pre-defined knowledge of a certain domain. As a consequence, attribute-based learning can not be applied when relations exist between learning examples that should be taken into account. Relational learning. Relational learning is an alternative to attribute-based learning. Such systems use more expressional languages to present learning examples and learned concepts. While the task of attribute-based learning is learning of classes out of learning examples, the task of relational languages is learning of logical definitions coming from examples and pre-defined knowledge. Description of learning examples is similar to those used in attribute-based learning with the difference that certain examples do not belong to a particular
class. Rather, they belong to a relation we would like to learn. Pre-defined knowledge is expressed in the form of relations or logical definitions, written in one of the languages for programming in artificial intelligence, for example in PROLOG. Experimental A program for automatic induction of regression trees Retis[11,12] was used as a tool for selection of a suitable sewing needle on the basis of a build-in algorithm and collected learning set. The knowledge, extracted from examples, is presented in the form of regression trees. The learning examples must be described with attributes. Each attribute has a certain set of values. We distinguish between two kinds of attributes: • continuous attributes: their value can be any real number; and • discrete attributes: their value can be from some pre-defined (usually small) set of non-numeric values. The program enables declaration of a continuous class value. For that reason in this particular case, it can be used only to predict the fineness of the sewing needle. The needle point shape must be defined on the basis of experience. Following the purpose of the research, the suitable learning examples were prepared first. Suitable fineness of a sewing needle was determined then using the programme Retis. For that purpose we collected all available data about materials and sewing needles. Those data resulted from previously produced underwear, where the right choice of a sewing needle was confirmed by good product quality and high productivity. Different kinds of materials (up to three) that are included in produced women’s underwear were presented as components. In this way, 15 attributes were gained. From this set, continuous attributes were: ⇒ pl_masa1 (surface mass of the 1st component); ⇒ gost_dol1 (density in length direction, 1st component); ⇒ gost_sir1 (density in width direction, 1st component); ⇒ pl_masa2 (surface mass of the 2nd component); ⇒ gost_dol2 (density in length direction, 2nd component); ⇒ gost_sir2 (density in width direction, 2nd component); ⇒ pl_masa3 (surface mass of the 3rd component); ⇒ gost_dol3 (density in length direction, 3rd component); ⇒ gost_sir3 (density in width direction, 3rd component). Discrete attributes were: ⇒ tip_vbod (stitch type notation); ⇒ komp_1 (material of the 1st component);
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⇒ komp_2 (material of the 2nd component); ⇒ komp_3 (material of the 3rd component); ⇒ mat_suk (material of the sewing thread); ⇒ fin_suk (sewing thread fineness). The value we are looking for is sewing needle fineness, described under class name Needle_Fineness. Basic information on applied components includes material type, surface mass and densities in length and width directions. Except the material type the above parameters were treated as continuous attributes. Sewing machine type, respectively stitch type, used for a certain sewing technological operation to join a particular combination of components, was described with a discrete attribute tip_vbod (stitch type notation). This attribute is influential in sewing needle selection. All attributes were collected in a file used for domain description named NEEDLES.RDO. Learning examples contained the following ten stitch types: (1) 301 (straight lockstitch); (2) 304 (zig-zag lockstitch); (3) 308 (zig-zag double lockstitch); (4) 301/301 (double needle straight lockstitch); (5) 504 (three-thread chainstitch); (6) 514 (four-thread chainstitch); (7) 103 (blind stitch); (8) 602 (four-thread covering chainstitch); (9) zatrjevanje (fixing); (10) pent (for tie attachment). Another file, NEEDLES.RDA was established to describe 193 collected examples. Each learning example contains a class value and attribute values that determine class value. On the basis of learning examples the program builds the regression tree. In order to evaluate the appropriateness of suggested sewing needle fineness, we carried out sewing of selected samples using different sewing needles. Samples were produced from pure cotton knitwear with a surface mass of 125 gm-2. This component was described in learning examples as bpletx. Samples were sewn in both loop directions. The influence of applied sewing needle on material damage appearance in stitch area was investigated using the electronic stereomicroscope. Material was sewn with and without sewing thread. The microscope was connected to a personal computer which enabled visualisation and magnification of pictures.
Results and discussion The programme for automatic induction of regression trees Retis builds a regression tree on the basis of a special learning algorithm. A special evaluation function enables selection of a suitable fineness of a sewing needle for processing a certain material type. A part of a regression tree for described domain is presented in graphical form in Figure 1. Because of a great number of values of discrete attributes this graphical presentation is not complete; otherwise the figure would not be clear. For a complete interpretation of a regression tree we should also look at the textual form of the same tree. This form of representation shown in Figure 2 contains all the values of discrete attributes while graphical representation should be used for better notation of a tree form. In graphical presentation, the nodes are labelled as ellipses and leaves as rectangles. Each node contains a test
Selection of suitable sewing needle 215
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Figure 2. Textual form of a part of regression tree
NODE: Weight=193.00 Mean=75 Error= 6 [ KOMP_3 in { ellokc2, okrc1, sarmes, alisa1, till1 } ] NODE: Weight=84.22 Mean=72 Error= 5 [ KOMP_1 in { elplet1, elplet3, elplet9, elplet11, bplet1, bplet2, bplet3, bplet4, bplet5, bpletx, elpletz1, elpletz2, elpletz3, saten1, saten2, ellokc4, ellokc10, elcipka2, fibkas, markm } ] NODE: Weight=58.40 Mean=72 Error= 4 [ KOMP_2 in { elplet3, elplet11, bplet2, bplet4, bpletx, elpletz1, elpletz2, saten1, saten2, ellokc1, ellokc2, ellokc6, ellokc8, vezbord2, okrc1, okrc3, elast1, fibkas, gumit, markm, till1, tkanina } ] NODE: Weight=38.87 Mean=70 Error= 1 [ KOMP_2 in { elpletz1, saten2, vezbord2, till1 } ] NODE: Weight=7.98 Mean=68 Error= 2 [ KOMP_3 in { sarmes } ] NODE: Weight=4.79 Mean=68 Error= 2 [ TIP_VBOD in { 301, 514, 103 } ] NODE: Weight=2.49 Mean=66 Error= 2 [ PL_MASA3 <= 54 ] LEAF: Weight=1.00 Mean=65 Error= 0 [ 54 < PL_MASA3 ] LEAF: Weight=1.49 Mean=67 Error= 3 ENDNODE
that considers value of a certain attribute. Leaves contain class values. Interpretation of a tree begins in a root. Clear graphical presentation of a whole regression tree would be almost impossible because of a great number of values of discrete attributes. However, evaluation of new examples is simplest when clear graphical presentation of a tree is available. This is possible when we have a smaller number of discrete attributes and their values. The analysis showed an average absolute error of ± 5 in cases when two components were used. However, acceptable error would be within the range of ± 1. Similar results were achieved in cases when only one component was used. That confirmed the fact that the program takes average values of attributes when it operates the unknown values. Best results in prediction of sewing needle fineness were achieved when all three material components were used. In ideal cases, the absolute error was 0 or at the most ± 1. Those results are acceptable and applicable. In underwear sewing we mostly deal with a different number of material layers, depending on style and sewing technological operation. As others, also the program for automatic induction of regression trees Retis has some limitations regarding the number of attributes and values of discrete attributes. The improved version of the program Retis V2.16.6.b (1996) enables processing of up to 16 attributes. The number of values of discrete attributes is limited to 56. In the discussed case this limitation is disadvantageous because it sets an
obstacle in widening the learning examples set. New collections, namely, are in most cases connected with new materials which have new properties that should be described as attributes in learning examples. Appropriate selection of a suitable sewing needle fineness should take into account as much information as possible. The new possibility of artificial intelligence usage in the field of garment manufacture technology was investigated with application of machine learning from examples. The selection of a suitable sewing needle using machine learning techniques could be further improved with application of a more suitable program that would be able to deal with an even greater number of discrete attributes and their values. Also the set of learning examples could be expanded. Ideally, the absolute error in prediction of a sewing needle fineness should be within the range of ± 1. Certain improvements could be achieved also by another accession to expression of attributes and presentation of learning examples. Analysis of sewing problems, above all knitwear damages, showed that the number and size of loops bursting increased with the sewing needle size. Knitwear damages were hardly seen on samples, sewn using the fine needle size Nm 65. We also observed samples, sewn with sewing needles of same size but different point shapes. Less visible were damages on samples, sewn with the sewing needle ending with a needle point SUK. Analysis also showed a considerable influence of a sewing thread. Namely, damages are more obvious on samples sewn with the thread. Knitwear was less damaged where sewing was performed in direction of loop rows. That confirmed the fact that it is reasonable to consider the direction of loop columns and rows. Conclusions Producers improve sewing needles more and more. Hardiness and strength can be achieved that allow the needle to puncture even the metal plate without fracture. The needle is designed and treated in that manner to avoid friction and thus is connected to overheating in sewing process. However, the sewing problems cannot be entirely abolished, and many of them are connected with a right choice of a sewing needle. Sewing materials are changing and their quality and properties are not constant, so they influence the sewability. Knowledge of textile materials and consideration of experience gained with their processing can essentially contribute to selection of the suitable sewing needle. Computer hardware and software can be successfully used for selection of a suitable sewing needle. Specificity of such application requests a suitable program which would be able to select a suitable sewing needle on the basis of data and knowledge sets and parameters that describe material and technological process. A program for automatic induction of regression trees Retis was put to the test for that purpose. This software was successfully used in the past for prediction of continuous classes in many real domains. Applied improved version of the program still set some limitations regarding the number of discrete attributes and their values. Reliable results of selection of
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sewing needle fineness were achieved in cases when all the attributes of a new example were known and input into the system. Less reliable results were achieved when only one or two layers of material were processed. References ˘ c, L.D., “ Presentation of sewing needles at IMB ’93”, Tekstilec, Vol. 36 No. 11-12, 1993, 1. Zuni˘ pp. 405-8. 2. Strah, H., “Right choice of sewing needle considering material type and seam quality”, Diploma work, Faculty of Mechanical Engineering, Maribor, 1996. 3. “Needles for avoiding specific sewing problems”, Internet URL: http://www.schmetz.com/. 4. “Brother Europe Products Index; industrial sewing machines”, Internet URL: http://www.brother.com/. 5. Rocha, A.M., Araujo, M.D., Lima, M.F. and Ferreira, F.N., “Acquisition and control of sewing parameters”, Proceedings of Textile Process Control 2001, UMIST, Manchester, 18-20 April 1995. 6. Dorrity, J.L. and Olson, L.H., “Thread motion ratio used to monitor sewing machines”, Proceedings of Textile Process Control 2001, UMIST, Manchester, 18-20 April 1995. 7. Ujevi´c, D., “Problems of knitwear production”, Tekstil, Vol. 41 No. 1, 1992, pp. 19-23. 8. Ujevi´c, D., “Loop damages in knitwear sewing”, Tekstil, Vol. 40 No. 10, 1991, pp. 465-70. 9. Jezernik, A., C˘ ep, J., Dols˘ ak, B., Golob, B., Hren, G., Stjepanovic, Z., Ulaga, S. and Ulbin, M., “Computers in construction and production processes” (in Slovene), Faculty of Technical Sciences, Maribor, 1992. 10. Bratko, I., Prolog Programming for Artificial Intelligence, Addison-Wesley, 1990. 11. Karali˘c, A., Retis – Users’ Manual, Joz˘ef Stefan Institute, Ljubljana, 1991. 12. Karali˘c, A., “Machine learning of regression trees from incomplete data”, MSc dissertation, (in Slovene), University of Ljubljana, 1991.
Simulation of sewing machine mechanisms using program package ADAMS ˘ c Lojen Darja Zuni˘
Simulation of sewing machine mechanisms 219
Faculty of Mechanical Engineering, Maribor, Slovenia Introduction Stitch formation process depends on a lot of parameters. It represents a dynamic and unstable process. Already minor changes in initial conditions have important influence on seam quality. The influence of particular sewing parameters on stitch formation process should be defined. This is important to reach the appropriate seam quality. Also the definition of the influence of modification of sewing machine elements on thread loading is important. Such a problem is connected with defining and modelling of kinetic and kinematic properties in stitch formation process. This could be done by simulation of sewing machine mechanisms and their interaction with sewing thread and fabric. Theoretical base It is a complex task to follow the stitch formation process and to define important parameters like dynamic thread tension and needle penetration force. For this purpose different researchers have developed measuring equipment for measuring some parameters[1-4] or to follow them with help of simulation[5]. With simulation of sewing machine activity it is possible to analyse the motion of parts and mechanisms and get the information about motion – displacements, velocities and accelerations. Simulation also enables fast presentation of mechanism modification. In reality such modifications are almost impossible because of time and financial factors. In this contribution the simulation of needle bar mechanism and analysis of data, which could be used for calculation of a needle penetration force, are presented. Influence of some parameters on needle penetration force Needle penetration force depends on a lot of parameters such as material and sewing thread characteristics, sewing needle and sewing velocity[1,6,7]. High penetration force may result in sewing damage, needle breakage or thread break. Therefore it is necessary to know the value of penetration force and the influence of different parameters.
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The needle penetration force can be calculated in different ways: as a function of a normal force and friction coefficient, where the normal force is expressed with the resistance force of a yarn[8] or as a function of normal force and needle profile and speed[6]. The movement of a sewing needle depends on a characteristic movement of a whole mechanism, which moves needle bar or needle. Therefore it is not possible to express velocity of a needle bar with the sine function. This would be possible if the moving crank was endless. Values of needle velocity in the penetration area could be obtained with the help of simulation of needle bar mechanism movement. Mechanism simulation with ADAMS The simulation modelling process usually results in idealisation of real mechanisms. The chosen simplification removes unnecessary complexity of a model. Program package ADAMS (automatic dynamic analysis of mechanical system) is one of simulation systems for mechanisms. ADAMS enables kinematic, static, and dynamic analyses as well as simulation of mechanisms and their motions. This system enables modelling and verification of models. It can be used for solving equations for kinetic, static, quasistatic and dynamic simulations. The behaviour of any mechanism is governed by a system of equations: six first-order dynamic equations for each part, six first-order kinematic equations for each part, a single algebraic constraint for each motion constraint, a single algebraic equation for each scalar force component and any number of userdefined algebraic or first-order differential equations. Methodology The analysis of needle bar movement in dependency of main shaft velocity was carried out. Furthermore, the influence of length modification of needle bar mechanism on needle velocity was studied. The lockstitch sewing machine was studied. Movement of a sewing needle was analysed taking into account four velocities of the main shaft: (1) n1 = 1,000rpm; (2) n2 = 2,000rpm; (3) n3 = 3,000rpm; and (4) n4 = 4,000rpm and at three positions of the needle. The foreseen penetration area was placed 1mm over the stitch plate (h1), 0.5 mm over stitch plate (h2), and in the stitch plate level (h3). By investigation of influence of needle bar mechanism modification the length of input link and moving crank: ∆ l = ± 2mm were varying, so the sum of both lengths was constant. Mechanism marked A had dimensions like a real
sewing machine, while mechanisms marked B and C were modified. The length of input link of mechanism B was shorter for 2mm and moving crank was longer for 2mm. The length of input link of mechanism C was longer for 2mm and moving crank was shorter for 2mm. The simulation was done with the program package ADAMS. The work was performed following the next steps, Figure 1: • modelling of needle bar mechanism, Figure 2; • modification of length of input link and moving crank; • kinematics analysis; • animation of mechanisms movement; • drawing diagrams of displacement, velocity and acceleration in dependence of velocity of the main shaft and mechanisms modification.
Simulation of sewing machine mechanisms 221
Results Results of analysis present the maximum velocities and accelerations when the sewing needle moves in up and down direction (Table I). Curves for all three observed parameters: displacement, velocity and acceleration, at the main shaft velocity n1 = 1,000rpm, are shown in Figure 3.
Identify model
Design/ / Model (ADAMS/View)
Test / Analyse (ADAMS/Solver) ADAMS Visualise/Investigate (ADAMS/View)
Desired Perfomance
Yes
End of analysis
No
Figure 1. Working steps with ADAMS
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thread take up lever
input link moving crank
222
needle bar
Figure 2. Needle bar mechanism model
y z
x
Furthermore, the velocity and acceleration are shown in the area where the needle penetrates the material. This is a case in positions h1, h2 and h3, by all the three mechanisms A, B and C (Table II). The influence of length modification of needle bar mechanism on velocity and acceleration of needle in the penetration area is represented in Table III (at n1 = 1,000rpm). Figure 4 shows the influence of length modification on velocity and Figure 5 on acceleration of sewing needle in position h1.
Table I. Maximal velocity and acceleration of sewing needle movement in dependence of velocity of the main shaft and mechanism modification
Velocity
vmax/ms–1
amax/ms–2
–amax/ms–2
A
n1 n2 n3 n4
1,752 3,507 5,258 7,014
229,25 916,79 2,062,70 3,667,00
–123,64 –494,58 –1,113,00 –1,975,50
B
n1 n2 n3 n4
1,514 3,028 4,542 6,056
193,12 772,52 1,738,20 3,090,10
–113,59 –454,40 –1,022,50 –1,817,60
C
n1 n2 n3 n4
2,005 4,008 6,012 8,016
268,25 1,072,70 2,413,20 4,290,80
–137,23 –548,34 –1,234,00 –2,193,60
a/ms–2 390
v/ms–1 1.800
v
I 260
Simulation of sewing machine mechanisms
I/mm 19.0
9.5
0.900 a
223
0.0
130
0.0
0.0
–0.988
–9.5
–130
–1.798
–19 0.0 0.026349 0.052698 0.079047 0.105395 t/s
vp/m s–1
Velocity
h1
Penetration area h2 vp/m s–1 ap/m s–2
ap/m s–2
Figure 3. Diagram of displacement, velocity and acceleration of the needle bar
h3 vp/m s–1 ap/m s–2
A
n1 n2 n3 n4
–1,740 –3,480 –5,216 –6,958
–23,544 –94,181 –208,92 –376,14
–1,746 –3,492 –5,237 –6,981
–17,214 –68,722 –156,86 –278,80
–1,750 –3,500 –5,249 –6,998
–10,725 –42,289 –94,293 –172,41
B
n1 n2 n3 n4
–1,472 –2,941 –4,413 –5,884
–38,537 –154,40 –340,78 –611,15
–1,483 –2,966 –4,446 –5,933
–32,625 –131,61 –259,59 –522,26
–1,491 –2,987 –4,477 –5,975
–27,175 –108,93 –237,79 –433,36
C
n1 n2 n3 n4
–2,003 –4,007 –6,011 –8,015
–1,484 –14,932 –33,459 –55,866
–2,003 –4,007 –6,010 –8,015
+5,803 +16,932 +36,376 +60,951
A
–2,002 +13,842 –4,001 +45,532 –6,005 +102,79 –8,007 +182,96
B
C
Penetration area
vp/m
h1
–1,740
–23,544
–1,472
–38,537
–2,003
h2
–1,746
–17,214
–1,483
–32,625
–2,003
+5,803
h3
–1,750
–10,725
–1,491
–27,175
–2,002
+13,842
s–1
ap/m
s–2
vp/m
s–1
ap/m
s–2
vp/m
s–1
ap/m
s–2
–1,484
Table II. Velocities vp and accelerations ap at needle penetration in dependence of velocity of the main shaft at different needle positions in penetration area
Table III. The influence of length of input link and moving crank of needle bar mechanism on velocity and acceleration in penetration area (at n1 = 1,000 rpm)
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Needle velocity / ms–1 0 2000 –11000
3000
4000
3000
4000
–2 –3
224
–4 –5
Figure 4. The influence of modification of input link and moving crank on velocity of sewing needle (position h1)
–6 –7 –8 –9
Key A B C Main shaft velocity / rpm
Needle acceleration /ms–2 0 1000 2000 –100 –200 –300
Figure 5. The influence of modification of input link and moving crank on acceleration of sewing needle (position h1)
–400 –500 –600 –700
Key A B C Main shaft velocity / rpm
Discussion From results the values of velocities and accelerations of sewing needle regarding the velocity of the main shaft can be seen (Table I and Table II). Also the velocities and acceleration at different positions in penetration area were investigated. In this area (1mm) the velocity is almost constant, only a slight decrease was perceived, while the acceleration was changing considerably (Table II). The length modification of input link and moving crank on the velocity and acceleration of sewing needle in the penetration area showed that the lowest velocities were reached using mechanism B (Figure 4). On the other hand, the greatest accelerations were detected in this case (Figure 5). Conclusion The values of velocities and accelerations during the movement of a sewing needle are very high. That proves that the sewing thread is exposed to very high loading. At higher velocities of the main shaft the velocities of a needle are certainly also higher. This is the case at the maximum values and also in the penetration area. The construction of a needle bar mechanism has also an important influence on velocity and acceleration values. Already minor changes of length of input link and moving crank are reflected on velocities of a needle bar or needle. Achieved information on velocities could be used for calculation of the penetration force of a needle. Higher main shaft velocity results in higher velocity of a sewing needle in the penetration area. Also the penetration force is higher in that case. The simulation enables to analyse and represent the movements of sewing machine mechanisms, which are in reality difficult to follow. For that reason use of such a simulation is reasonable also for educational purpose. References 1. Rocha, A.M., Ara´ujo, M.D., Lima, M.F. and Ferreira, F.N., “Acquisition and control of sewing parameters”, Textile process control 2001, Proceedings, International Conference, University of Manchester, 1995. 2. Ferreira, F.B.N., Harlock, S.C. and Grosberg, P., “A study of thread tensions on a lockstitch sewing machine”, International Journal of Clothing Science and Technology, Vol. 6 No. 1, 1994, pp. 14-19; Vol. 6 No. 5, 1994, pp. 26-9, 39-42. 3. Gotlih, K. and ˘Spaner, M., “Razvoj merilnega sistema na ˘sivalnem stroju”, V: Zbornik referatov Simpozij obla˘cilno in˘zenirstvo ’94, Tehni˘ska fakulteta Maribor, 1994, pp. 74-81. 4. Ujevi`c , D. and Knez B., “Prebodne sile ˘siva´cih igala u procesu s˘ ivanja odje´ce”, Tekstil, Vol. 42 No. 7, 1993, pp. 394-9. 5. Klavins, A. and Priednieks, V., “Monitoring and control system (MCS) for lockstitch formation”, Textile process control 2001, Proceedings, International Conference, University of Manchester, 1995. 6. Stylios, G. and Xu, Y.M., “An investigation of the penetration force, profile of the sewing machine needle point”, Journal of the Textile Institute, Vol. 86 No. 1, 1995, pp. 148-63. 7. Ger˘sak, J. and Knez, B., “Odredivanje probodnih sila s˘ iva´cih igala u procesu ˘sivanja odje´ce”, Tekstil, Vol. 34 No. 10, 1985, pp. 759-68.
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Viscoelastic parameter determination for a yarn Karl Gotlih
226
University of Maribor, Faculty of Mechanical Engineering, Institute for Textile and Garment Manufacture Processes, Maribor, Slovenia Introduction The observation and control of the manufacturing processes in the garment industry are strongly connected with knowledge about the material which is used in these processes. The basic element of all materials used in the garment industry is the yarn. The yarn could be the element of the fabric or it is the thread. The yarn is in a special form woven into the fabric. The strength of the fabric is dependent on the strength of each yarn and hardly on the interactions between the yarns in the fabric. To get better knowledge about the sewing processes it is of great importance to know the mechanical models of each used material. The present research is focused on the determination of mechanical properties of a yarn. A lot of researchers deal with the yarn model. The main idea of all of them is that the yarn is a heterogen and anisotropic material[1,2], with a complex structure. The modelling of such structures is complicated and dependent on the specific problem which we want to investigate. Our research in the “Laboratory for Garment Engineering” is focused also on the analysis and the synthesis of the occurrences in the sewing process. We are interested in the interactions of the garment made of yarns, the thread and the sewing machine. To investigate these interactions we study the models of the influencing components.
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Rheological model The basis for choosing the rheological model is the stress-strain experiment of a real yarn. This experiment gives us the response of the real yarn with respect to the loading force. The diagram which we get with the stress-strain experiment is the personal card of the mechanical properties in the length axis of the yarn. With respect to the yarn response we choose the rheological model, for which we could guarantee that it will represent the behaviour of the real yarn well enough. The yarn is a solid with viscoelastic properties. We search for the model between two bound rheological models. On the one side is the Maxwell’s rheological model of a linear viscose liquid. On the other side is the Hook’s linear elastic material. Between these two extreme models there is
the model of the viscoelastic yarn. Each rheological model, simple or complicated, is built from simple elements. These elements are spring, damping element, friction element and backlash. The characteristics of these elements are linear or rather non-linear. From the stress-strain diagrams of this kind of material we see that the model elements should be non-linear. This non-linear behaviour is dependent on the hetherogen and anisotropic structure of the yarn. Such complicated mechanical models of the material with non-linear elements are tedious for practical use. They require great experimental effort and also great computational amount in the parameter determination process. With respect to the given facts we choose the linear Kelvin-Voigt model[3], as it is shown in Figure 1.
Viscoelastic parameter determination 227
Parameter identification for the linear material model To determine the values of the parameters of the viscoelastic model of the yarn the tear experiment was done. The difference between the standard tear experiment and our experiment is that we are interested in the time behaviour of yarn. The result of the tear experiment is not the stress strain curve but the stress or the tear force-time curve and the specific deformation-time curve. These special measurements have been made on special measuring equipment which was constructed in the laboratory for garment manufacturing[4]. With the use of this special equipment the history of the tear experiment with respect to time is done. The measuring values which we get are in ASCII form prepared for further numeric or statistic processing. With the use of approximated polynomial expression the tear stress with respect to time is written: (1) and the specific deformation with respect to time is: (2)
E
ε(†)
D
F(†)
Figure 1. Kelvin-Voigt linear material model
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With these two polynomial expressions the history of the tear experiment of the yarn is given exactly. These two polynomial expressions or these two functions are the basis for the determination of the linear rheological model of the material. For the chosen linear rheological model from Kelvin and Voigt, Figure 1, we can get the mathematical model in the form: (3) The constants D (the linear damping constant) and E (the elasticity of the model) (3), should be so developed that the response of the yarn model will be nearly the same as the real response of the yarn for the same loading conditions (1). These two constants will be calculated with the parameter identification method. The main idea of the method is the minimisation of the area between the curve, which represents the specific deformation of the real yarn with respect to time and the curve, which represents the specific deformation for the yarn model for the same loading conditions (1). The design parameters in this minimisation process are the Kelvin-Voigt parameters D and E. The minimisation process is shown in the flow chart, Figure 2. With respect to the chosen Kelvin-Voigt model and the procedure shown in Figure 2 the initial values of the design parameters must be chosen. The design parameters D and E must be bounded, because if one of them becomes the value zero the chosen rheological model changes its nature. The box in the flow chart, Figure 2, headed “optimisation” is the symbol for the optimisation procedure. We use a standard routine from the numerical library IMSL[5], which is connected with the Powerstation Fortran for PC Bounds of the design parameters
Initial values of the design parameters Einitial in Dinitial
Emin ≤ E ≤ Emax Dmin ≤ D ≤ Dmax
Optimisation (quasi Newton’s method and gradients with finite differences)
Eoptimal τ
Figure 2. Flow chart of the optimisation procedure eM = εM (model of yarn) in eT = εT (real yarn)
Doptimal
τ
(eT)dt – (eM)dt
min( 0
2
)
eM, eT (%) 14,0 12,0 10,0 8,0 6,0 4,0 2,0 0,0 0,0
0
with respect Emin ≤ E ≤ Emax Dmin ≤ D ≤ Dmax
2,0
4,0
6,0
8,0
t(s) Key
eM (%)
eT (%)
computers under Windows platform. The optimisation procedure uses the quasi-Newton method. The gradients which are used in the sensitivity analysis are computed with the finite difference method. The cost function I (4), in the optimisation procedure, Figure 2, has the form:
Viscoelastic parameter determination
(4)
229
and represents the square difference between the area under the curve of the real response of the real yarn eT and the model of the yarn eM. The model will describe quite well the real yarn at the same load conditions if the difference between these two curves will be as small as possible. Example To show the effects of the method undertaken for the rheological yarn model parameters determination we take a cotton yarn from a plain woven fabric[6]. The fabric has 295 warp threads per 10cm and 270 weft threads per 10cm. Each thread is spun from 100 per cent cotton. Each thread, warp and weft, has the cross area of 19.5 tex. The tear force of the yarn is 270 cN at the specific elongation 6.34 per cent. The yarn has 942 spins on one meter. The spin direction is Z. The tear experiment of the yarn is shown in the diagrams in Figure 3 ( the strength of the real yarn with respect to time), Figure 4 (specific deformation of the real yarn with respect to time) and Figure 5 the standard diagram (strength-specific deformation of the real yarn). With respect to the equations (1) and (2) we find with numerical approximation polynomial expression, which will (with the smallest quadratic violence) describe the diagram in Figure 3 well enough. With the use of software tools from the EXCEL for WINDOWS for the PC we compute the constants of the approximated polynomial expression. The polynomial expression has the form: (5a) with the average square violation: (5b) In the same manner we find the polynomial expression for the diagram, Figure 4. The constants of the polynomial expression are calculated with the same program tools EXCEL. The polynomial expression for the specific deformation-time relation is: (6a)
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Strain (cN/tex) 14,0
12,0
230
10,0
8,0
6,0
4,0
2,0
Figure 3. Strain of the real yarn with respect to time
0,0 0,0 2,0 Time (s)
4,0
6,0
8,0
6,0
8,0
Specific deformation eT (%) 6,0 5,0 4,0 3,0 2,0 1,0
Figure 4. Specific deformation of the real yarn with respect to time
0,0 0,0 2,0 Time (s)
4,0
Viscoelastic parameter determination
Strain (cN/tex) 14,0
12,0
231
10,0
8,0
6,0
4,0
2,0
Figure 5. Strain-specific deformation relation of the real yarn
0,0 0,0 2,0 4,0 Specific deformation (%)
6,0
with the average square violation: (6b) With respect to Figure 2 the optimisation procedure was developed. For the initial values of the design variables Einitial = 0.01 and Dinitial = 0.01 and the bounds of these design variables are: (7) The non-linear programming method which use the quasi-Newton’s method with the numerical calculation of the gradients with the finite-difference is used, IMSL[5]. The optimal values of the design variables are calculated after 46 iterations. The cost function, which was minimised in the optimisation process (4), has at the beginning the value Iinitial = 19.4. After the optimisation process the design parameters have the values Eoptimal = 2.24755 and Doptimal = 0.01. The cost function has the value Ioptimal = 1.60878.
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The mathematical expression of the Kelvin-Voigt’s model is: (8) The comparison of the properties of the real cotton yarn with the properties of the linear Kelvin-Voigt’s model of the solid material is shown in Figure 6.
232
Discussion In this work a possible way for the modelling of yarn for the analysis of processes in the textile and clothing industry is shown. The real yarn, as shown in Figure 5, has a non-linear characteristic. The rheological model which will exactly describe the real yarn is complicated and non-linear. The parameters of such a complex model are not constants. They are functions of deformations, and the change of deformations the speed of deformations. The determination of such a model is very complicated and time consuming. The results are complicated mathematical functions with which the manipulation in complex models is very complicated. In Figure 6 we see that the chosen linear model with constant parameters has a good coincidence with the response of the real cotton yarn[6] as shown in comparison to the measured values. The search of the constant values of the model elements was done with the use of non-linear optimisation method. The non-linear optimisation process has some difficulties as sensitivity analysis, the problem of local minimum and
Strain (cN/tex) 16,0 14,0 12,0 10,0 8,0 6,0 4,0
Figure 6. Comparison of the mechanical properties of the real cotton yarn with the linear KelvinVoigt model
Key 2,0
Real yarn Kelvin-Voigt model
0,0 0,0 2,0 4,0 Specific deformation (%)
6,0
8,0
the global minimum and the problem of the initial values of the design variables. Conclusions In the work, an approach to modelling the rheological model of hetherogen anisotropic material is shown. The approach shown is based on optimisation method which is used in the parameter identification. The computed values of the constant of the rheological model of the yarn show with respect to Figure 6 good coincidence of the model with the real response of the yarn. In the future, the work must be focused on non-linear material models which will give better results with respect to the hetherogen and anisotropy nature of the investigated materials. With respect to the outstanding viscosity component of the rheological models we hope to get better coincidence of real material at time dependent problems. References 1. Zhong, C., “A nonlinear viscoelastic model for describing the deformation behavior of braided fiber seals”, Textile Res. J., Vol. 65 No. 8, 1995, pp. 461-70. 2. Frydrych, I., “Relation of single fibre and bundle strengths of cotton”, Textile Res. J., Vol. 65 No. 9, 1995, pp. 513-21. 3. Morton W.E., Physical Properties of Textile Fibres, Published by The Textile Institute, Manchester, 1993. 4. Gotlih, K., “Analysis of rheological properties of seams under dynamic loads”, The 3rd Asian Textile Conference, Proceedings, Vol. II, Hong Kong, 1995, pp. 847-53. 5. IMSL/library, Microsoft Fortran Powerstation Professional edition, Ver 4.0. ˘ 6. Gotlih, K. and Zunic ˘ Lojen, D., “The relation between the viscoelastic properties of the thread and the sewing needle penetration force”, The 78th World Conference of The Textile Institute, Proceedings, Vol. III, Thessaloniki, 1997, pp. 133-48.
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Investigation of operative logical movement groups in garment sewing Zvonko Drag˘cevi´c, Dubravko Rogale and Ljiljana Trgovec University of Zagreb, Faculty of Textile Technology, Department of Clothing Technology, Zagreb, Croatia Introduction In contemporary garment production processes a great deal of attention is being paid to the duration of the technological operations involved. Clothing engineers are under a constant pressure to shorten the operations, as productivity, production capacity and, finally, the production costs of every item of clothing depend upon it. In performing this task, the methodology of work study, predetermined times and workplace engineering are employed in most instances. Quite often, the above methods do not yield timely or adequate precise results. This is why the researchers at the Department of Clothing Technology, Faculty of Textile Technology, University of Zagreb have been trying to develop measuring analytical methods and equipment for the purpose of determining optimal logical movement groups for the operatives in the process of garment sewing. This paper describes the basic idea and the first results achieved. Besides defining optimal logical movement groups, the equipment developed can also be used to investigate the following: • optimal methods of work; • ergonomically designed workplaces; • biomechanical phenomena occurring in the process of sewing (investigation of masses, geometry and dimensions of the parts of limbs and their impact on the speed of movement, or technological operations, investigation of dynamic movements and stresses on certain parts of the operative’s body, as well as of dynamic anthropometry); and • as an addition to the measuring equipment for work study, used to avoid the error on the part of the person making the recordings, it makes more detailed recording and further analysis possible and repeatable. The measuring equipment and method used to determine logical movement groups are based on the usage of a two-plane video system, with adequate cameras and video recorders, and with a special software package for the analysis of the video recordings. The equipment is completely compatible with
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 234-243, MCB University Press, 0955-6222
The measuring equipment described has been developed in the course of the scientific research project 117003, financed by the Ministry of Science and Technology of the Republic of Croatia.
the measuring system for processing parameter determination, and together Investigation of they represent an original system for the investigation of man-machine operative logical interactions in garment production processes (see Figure 1)[1-4]. movement Measuring equipment The measuring equipment consists of two video cameras which make recordings in two planes: vertical and horizontal. A continuous video recording of all the positions and changes of position for of the operative’s limbs during garment sewing is obtained in this way, both as groundplan or sideview presentations. Video camera SABA Colour Camera CVC 76SL is used for sideview presentation. It can be equipped with an additional generator SABA VTG 70, enabling the insertion of the date, optional text and time on the recording, with the possibility of reading ± 0.1s. The camera is linked to a video tuner SABA VTU 683, and to a video recorder SABA PVR 6083. The upper camera is SABA Camcorder CM 11, used for the groundplan presentation. It is smaller and much lighter, so it can easily be mounted on a fixed holder above the operative and the workplace. It is controlled by a remote controller SABA Compact Movie CMF 1. This camera has an in-built timer used to control the duration of the recording and to insert the data notations. Both cameras are equipped with microphones for the purpose of making audio-recordings with the data concerning the operative, the code and name of the operation in question, the code number of the operation and commentary. The cameras are extremely sensitive and can function in the illumination range of 10 to 3,000 lx. A multi-plane light source that can be seen by both cameras is used for measuring synchronisation. The light source is activated at the start of the recording for 1.0s, using a timed impulse maker KPGK 308. The light impulse thus obtained represents a
235
video output video input computer monitor e.g. video camera
e.g. VCR
e.g. TV
PC
miroVIDEO 10/20TD live
Figure 1. Linking of one recording channel periphery components to the video card
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marking point, necessary for the synchronisation of both recorders in a later simultaneous analysis of the recordings. Video recorders SABA VR 6855, a personal computer, software packages and a colour printer are used to process the video recordings made. The computer employs a Pentium MMX processor with the frequency of 166Mhz and with 64MB RAM memory. The computer should have all the conventional periphery elements, together with a special interface for the processing of video recordings miroVIDEO 10/20TD live, tt. miro Computer Products AG, Braunschweig, Germany. It has 2MB dynamic RAM memory, graphic processor Tseng Labs ET 4000 W32p and video processor Tseng Labs VIPER, and uses a composite video input on Cinch top tension connector of 1.0Vpp. Figure 1 shows the manner of linking periphery components for one recording channel. The system is compatible with PAL, NTSC i SECAM standards and has the resolution of 1,280 × 1,024 pixels, 16.7 mil. colours (24 bit colours) and the speed up to 30 frames per second. The graphic card is supported by two software packages intended for a direct usage with the video card – Video Editor & Capture and Image Editor, both produced by tt. Ulead Systems, Inc., California, USA. The recording processed by these two softwares can be later processed by the universal software such as CorelDRAW! 6, or CorelPhoto-Paint. The pictures processed can be printed by HP 6L laser printer or ink-jet colour printer Epson Stilus Colour 600. The other equipment includes special markers, positioned on the head, shoulders, arms, hips and legs of the operative, as well as a special elastic band with marking notations, following the operative’s spine. Marking points are made of special fluorescent paint, of the colour in contrast with those in the workplace environment, so a colour filter can separate them and contrast them to the other colours at the workplace. The markers are thus clearly seen on the video recordings and are quite adequate for further processing and scientific analyses. A measuring test plate is constructed with the system. Its dimensions were 0.7 × 1.0 metre and it has a grid with 10 × 10cm squares, used to compare the presentations on video recordings and to measure the length of the movements, or the distance of the referral points at the workplace. Measuring and results The measurements at the workplace are started by recording so called referral positions of the operative sitting. In this phase, the sideview camera is 2.54m away from the working area, 75cm above the floor level (at the height of the operative’s abdomen), while the upper camera is 1.89m above the working area, and the central axis is above the sewing needle position. Camera positions are not altered during recording, and automatic focusing and zooming are disengaged, as they would alter the recording geometry and contribute to measurement errors. The visibility of nine markers, attached to the operative’s head and used to analyse head rotation and eye view shift, is also checked at the
referral position, as is the visibility of 11 markers on the operative’s spine, used Investigation of to analyse body movements and spinal column curvature angles, 21 markers on operative logical arms and 12 on legs, used to analyse the movements of the limbs in garment movement sewing. Figure 2 shows a sideview presentation of the referral position, and Figure 3 a groundplan of the position, together with the markers attached to the operative’s body. 237 Referral position recording is also necessary for the recording of the measuring control plate with the grid on it. Figure 4 shows such a recording.
Figure 2. Sideview presentation of the referral position with the measuring control grid positions for the upper camera
Figure 3. Groundplan presentation of the referral position with the measuring control grid positions for the sideview camera
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Figure 4. Recording of the measuring control plate using the sideview camera
Practical experience in developing the system has indicated the advantages of recording the measuring control grid plate at three different positions for each camera: (1) for the upper camera (Figure 2): Position a – grid at the level of the operative’s temple; Position b – grid at the level of the sewing machine head; and Position c – grid at the level of the sewing machine working area. (2) for the sideview camera (Figure 3): Position a – grid at the distance to the sewing machine main shaft belt; Position b – grid at the distance to the sewing needle position; and Position c – grid at the distance to the edge of the sewing machine working area. The recording of the grid at various distances and heights is necessary because of the video camera optics roundness, and also to enable corrections to be made (the operative’s body parts that are nearer seem to be proportionally larger and for some applications of the equipment and measuring methods described dimensional corrections are essential). Practical construction and grading of the equipment show that optimal results can be obtained with three recordings of the grid, provided most of the markers are inside the limits of the positions described for recording. All the other positions can be corrected using
numerical interpolation methods, and only the positions in which the markers Investigation of are outside of the recording zone are corrected employing numerical operative logical extrapolation method. movement The following, second, step is a continuous measuring using the system that has been graded. Video and audio recordings are stored on video tapes, and, as convenient comparations are possible, measuring of processing parameters or 239 some other interesting characteristics of the garment sewing operations is often done at the same time. The third step is processing and analysis of the video recording. This step can be performed later, in laboratory conditions, and be repeated as necessary. Video recordings can be analysed frame by frame, or individual shots can be taken according to precisely chosen intervals indicated by the timer marks from the timer generator. In both cases, synchronisation by a light impulse through the timed impulse generator is important for the initiation of the analysis. The frame chosen is frozen and its processing from this moment onward is done employing the software package provided. Graphic filtration and separation of the fluorescent colour of the markers, together with fading of the other colours on the recording, is the first phase. Unnecessary details are thus faded out, and markers are emphasised, presenting the key points for the analysis. After that, key analytical points are connected by straight lines, so as to obtain a simplified skeleton, appropriate for further scientific analysis. Figure 5 shows a sideview of the operative and the workplace, with the markers
Figure 5. Sideview of the operative with the markers pointed out and key points connected
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Figure 6. Groundplan recording of the operative with the markers pointed out and key points connected
Figure 7. The sideview of a simplified skeleton
pointed out, and key points connected, and Figure 6 shows the same recording in groundplan. The fourth step is the separation of the simplified skeleton and basic details of the workplace and blotting out all the other unnecessary details. Figure 7 shows the sideview and Figure 8 the groundplan of a completely separated simplified skeleton with the basic elements of the workplace. Presentations of a simplified skeleton and basic parts of a workplace can quite well serve to measure and analyse various movements of any part of the operative’s body, in any given interval.
Investigation of operative logical movement 241
Figure 8. The groundplan of a simplified skeleton
By connecting the frames of a simplified skeleton from the same intervals, chain marker systems can be constructed, and they are a perfect starting point for the analysis of the body movements in garment sewing. Figure 9 shows a chain marker system, symbolising the operative’s body in movement. Using the chain marker system (Figure 9) as a starting point it is possible to perform movement speed analyses, arm reaches measurements etc., but for the purpose of the investigations in the project 117003, the most important are the analyses of optimal movement groups. Clothing engineers are well aware of the limitations of work study methods, especially prominent in modern industrial processes where short operations are the rule. Not even the methods of predetermined time yield satisfactory results, as they are extremely thorough and of long duration when it comes to applying them in an analysis.
Figure 9. Chain marker system
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The equipment described here is the basis and starting point of the investigations aimed at precise determination of optimal logical movement groups for sewing machines. Optimal logical movement groups will be investigated for the sewing machines of: • minimal technical level (no automatic control functions); • medium technical level (with conventional electronic control, simple electromechanical and pneumatic components and pneumatic components and simple microcomputers); and • high technical level (more complex processing microcomputers and sophisticated servo-mechanisms). Using the equipment described and chain market systems, optimal movement groups will be defined for each of the above groups of technical complexity of machines. The movements defined will be those performed in: • taking and laying-off the workpiece; • putting the workpiece in a proper position; • lifting and lowering the pressure foot; • needle positioning; • initial and final seam stitching; • thread cutting; and • performing other characteristic technological operations. Definition of optimal movement groups will make the procedures of setting standards for the duration of operations, technological operation structure, machine and labour utilisation, much faster, optimal working methods will be easily defined and standardised and proven movement groups will be used, which will grant the application of sound ergonomical principles in performing the technological operations in question and in workplace engineering. Key advantage of the equipment and the method for determining optimal movement groups presented here, is the availability and relatively low cost of the research equipment, and the fact that they can most successfully be employed when there is not sufficient time to perform a long-term analysis, or when the changes in production are so swift that reaction times are too short for an analysis. Conclusions The measuring equipment presented and the method proposed for grouping and standardisation of logical movement groups is especially important for the investigations of contemporary clothing technology processes. The equipment enables an effective analysis to be performed and logical movement group to be defined and promptly used. Instead of an analysis of often more than 100 different movements, when employing predetermined times method, the method proposed can easily define an appropriate method of work and standards for its application, analysing on average less than ten logical
movement groups. Properly sorted and standardised logical movement groups, Investigation of besides being ready for swift application, make quick working methods and operative logical workplace designing possible, using a high level of contemporary movement multidisciplinary knowledge. References 1. Rogale, D. and Drag˘cevi`c, Z., “Portable computer measuring systems for automatic process parameter acquisition in garment sewing processes”, International Conference IMCEP ’97’ Maribor, 1997, pp. 47-55. 2. Rogale, D., “Determination of processing parameters of clothes sewing technological operations”, PhD thesis, Faculty of Textile Technology, University of Zagreb, April 1994. 3. Rogale, D. and Drag˘cevi`c , Z., “Methods of measuring process parameters on designed workplaces in clothing industry”, International Conference ICED, Vol. 3, Dubrovnik, pp. 1689-96. 4. Rogale, D., “Garment sewing processing parameters: determination using numerical methods and computers”, IJCST, Vol. 7 No. 2/3, 1995, pp. 56-60.
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Study of the yield point of the thread Jelka Ger˘sak
244
University of Maribor, Faculty of Mechanical Engineering, Institute of Textile and Garment Manufacture Processes, Maribor, Slovenia Introduction Knowing of yield point or stress of thread in flow point as found between elastic and plastic deformations is important for the thread design parameter optimisation or for the planning of appropriate visco-elastic properties of thread. These properties must assure overcoming of the fastest changes of motion and with this connected loadings of thread in the sewing process[1-3] without greater change in the strength of thread or visco-elastic properties. Although there are well known methods for determining yield point, as the Meredith’s, the Copland’s constructions[4], and the numerical method DINARA[5], which has its basis in the determination of visco-elastic parameters of thread through the interpolation of the curve, which is constructed with the Lagrangian’s interpolation terms, a practical method which enables exact numerical determination of yield point is not known. On the basis of intensive research and study of thread behaviour in the sewing process and the evaluation of visco-elastic parameters of thread, the study of yield point on the basis of numerical evaluation of the curve stressdeformation σ (ε) is shown in this paper.
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 244-251, © MCB University Press, 0955-6222
The yield point Study of yield point or stress in flow point takes a complex area and can be interpreted with visco-elastic properties of thread. Visco-elastic behaviour of fibre is not causal. It is determined with complicated anisotropic formation of molecules on which the macroscopic mechanical deformation acts. Mechanical properties of fibre can be interpreted on the basis of differential mesh model[6], which is built of crystalline and amorphous area of arranged chains of molecules, oriented in the longitudinal axis of fibres and connected together with intermolecular joints. Movement of macromolecules and with this connected deformation depends on the values of acting forces and changes with time and temperature. If fibre is loaded with a force, deformation, which is a thermodynamic change of the state and is manifested in the elongation form, appears. The appeared deformation can be dependent on the intensity of loading or the duration of acting forces, elastic or plastic. The elastic deformation is given with the Hooks law: Ex = (dσx/dt)
(1)
and the plastic deformation is given with the Newtonian viscosity law: σx = ηx (dx/dt)
(2)
where:
σ = stress; η = viscosity; E = elastic modulus; x = deformation. Mechanical properties of thread can be shown on the basis of a modified model which has the basis in the constitution of fibres and their connection through the cohesive forces into thread spun yarns. With the fact that thread takes the geometry of line formed fibres, which are connected into yarn with cohesive forces, a modified thread model can be designed. The modified thread model is combined from yarns, which are joined through cohesive forces and oriented under defined angle with respect to the longitudinal axes. They are combined from ordered with respect to the longitudinal axes of yarn oriented and with cohesive forces connected spun fibres. On the basis of given model it can be supposed that for thread the same processes appear. When thread is loaded with a constant loading that causes a deformation which grows slowly with the time. An opposite force appears in the structure of thread against the loaded force. The relation between the tension loading and the deformation of thread is experimentally determined and it is used, with the fineness of thread, for the construction of the stressdeformation curve σ(ε). From the given curve it can be seen that in the initial phase in the region of the steep angle of σ ( ε ), the stress σ is increasing proportionally with deformation ε, which is given with the term: σ = E.ε (3) and corresponds to the elastic deformation. When stress in this phase decreases, the deformation is totally reversible. We could say that this region is a proportional region or the region of elastic deformations. When this border is extended, stress is not proportional to the relative deformation any more. The curve changes and deformation increases faster as stress. The deformation, as it appears, is not totally elastic any more. It is plastic-elastic or visco-elastic. The point in which this charge appears is the yield point. For the exact determination of the position Meredith[4] has introduced the yield point as a point at which the tangent on the curve stress-deformation is parallel with a line which connects the initial point of the curve with the tear point of the curve, Figure 1. Copland[4] has used a different construction. He has defined yield point as a point which is the crossing of tangent on initial steep and the tangent which has the smallest slope of the curve, Figure 2.
Study of the yield point of the thread 245
246
Specific stress
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Break Yield point
Yield stress
Figure 1. Meredith’s construction of yield point
Strain
Specific stress
Yield strain
Break
Yield stress
Figure 2. Coplan’s construction of yield point
Yield point
Yield strain
Strain
Opposite to the introduced methods is yield point developed with the computer program DINARA[5] computed numerically in the point of the curve stressdeformation σ(ε), where the third derivative is σ’’’ = 0. With respect to the given methods for determining yield point, all three methods determine the area of yield stress in the region, where the change of direction of curve σ(ε) is significant. Yield point determined with the Meridith’s and Coplan’s construction, which differs with respect to their construction, does not assure determination of real value of yield stress. Numerical method from program DINARA theoretically assures determination. Disadvantage of the mentioned method is in the constructing of mean curve σ(ε), which has the basis in Lagrangian interpolation terms and does not assure enough smooth approximated curve. With respect to the given facts and the disadvantages shown in the present paper the construction of mean curve σ(ε) which guarantees enough smooth approximation curve and also smoothness of derivatives of curve as the basis for yield point determination is shown.
Approximation curve construction The starting point for the construction of approximation curve stressdeformation σ(ε) is curve tension force-elongation Fs(ε). The dependence of curve tension force-elongation, function Fs(ε), can be determined with respect to a great number of measurements. Results of measurements are many twins (εk, Fs(ε)), k = 1,2, … ,n which can be treated as many points on a plane, where Fs(ε) represents the mean of measured values of tension force with respect to specific elongation εk. The construction of mean approximation curve stress-deformation σ(ε) can be developed with respect to the curves of results of measurements of tear forceelongation which was manually read for twins force F and associated elongation ε directly from the curve tension force-elongation or from written numeric values of twins (εk, Fs(ε)) at measurements. In both cases the results of tension force must be transformed with regard to the fineness of thread into a form independent of thread dimension, which can be determined with the term: (4) where: σs = stress in thread in cNtex–1, Tt = fineness of thread in tex, Fs = tension force in thread in cN. In further work the construction of mean curves σ(ε) with the use of B-spline approximation and the approximation with polynomial expression is shown. Evaluation of visco-elastic parameters of thread For the construction of mean curve σ(ε) which as a smooth curve fits well enough to the real curve, B-spline approximation with least squares is used. The mean curve, which appears with B-spline approximation or the gluing of cubic parabolic parts is given in the form: (5) f(x) = c1N1(x) + c2N2(x) + … + cpNp(x) where: Ni(x); i = 1,2, … , p normalised cubic B-spline, ci(x); i = 1,2, … , p are the coefficients. Cubic parabolic parts are smoothly glued in the points, where abscissae are knots. For this reason a computer program, named VILSUK, was developed. This program, on the basis of numerical values for curve stress-deformation σ(ε), calculates the parameters for evaluation of the visco-elastic properties[7]. The developed approximation function and its derivatives σ(ε)′, σ(ε)′′ and σ(ε)′′′, are shown in Figure 3.
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Tension (cN/tex) 300
first, second, third derivative 5
σ’
240
248
4
180
3
120
2 σ
60
1
0
σ’’
–60
Figure 3. Graphical representation of curves σ(ε), derivatives σ(ε)’, σ(ε)” and σ(ε)’” and integral for PES thread with program VILSUK
0 –1
σ’’’
–120
–2
–180
–3
–240
–4
–300
–5 0
1
2
3
4
5
6
7 8 9 10 Extension (%)
11
12
13
14
15
16
The analysis of developed results for numerical validation of visco-elastic properties of thread with program VILSUK shows at the very beginning that approximation with B-splines with least squares is appropriate, but later we found that it was not reliable enough, because of small non-smoothness of curve of the second derivative and in small manner perceived non-smoothness in the third derivative. This fact does not assure totally real values from the mathematical point of view. Because of the perceived small non-smoothness of the second and third derivative of mean approximation curve σ(ε), further mean approximation curves where constructed of the degree of polynomial expression 4,5,6,7,8,9 and 10. On the basis of these polynomial expressions of curve σ(ε) between real values, from numerical written mean curves or measured values, and values from approximation of mean curves in polynomial expression a comparative analysis was done. The obtained results show in comparison for the polynomial expressions from the fourth to the tenth degree that the best approximation is given for the ninth degree. This approximated polynomial expression gives the best smooth curve which fits almost perfectly the measured values and guarantees smoothness of the curve and also the derivatives. With regard to the chosen approximated polynomial expression the computer program was modified and was named VILSUK-P. This program now with respect to the numerical values of curves stress-deformation σ ( ε ) calculates mean approximated curve in the form of polynomial expression of the ninth degree and also calculates for the visco-elastic properties evaluation needed parameters. Chosen approximated curve in polynomial expression of the ninth degree and the derivatives σ(ε)’, σ(ε)” and σ(ε)’” are shown in Figure 4. This
Tension (cN/tex) 40
4
35
2
Study of the yield point of the thread
30 0 25 –2
20 Key polynomial 1. derivative 2. derivative 3. derivative k 0
15 10
–4
–6
5 –8 14,3
13,6
12,8
12,1
11,4
10,7
9,3
8,6
7,8
7,1
5,7
5,0
4,3
3,6
2,9
2,1
1,4
6,4
Extension (%)
10,0
–5
0,7
0,0
0
–10
Figure shows the curvature of the basic curve additionally to stressdeformation curve and mentioned derivatives. Besides the graphical representation of mean approximation curve and their derivatives, program VILSUK-P also gives the polynomial expression and enables the calculation of characteristic mechanical and visco-elastic properties of the analysed thread. The characteristic visco-elastic properties of thread from Figure 4, shown in approximation curve σ(ε) and given with the polynomial expression of the ninth degree: σ(ε) = –0.074036314 + 3.366333849x + 1.390668171x2 – 1.33707633x3 + 0.44076768x4 – 0.07679775x5 + 0.007834181x6 – 0.000470163x7 + 1.53978E-05x8 – 2.1265E-07x9 with the value R2 = 0.999970555, are given in Table I. Discussion With respect to the research performed and undertaken methods for evaluation of visco-elastic properties of thread or determining yield point of thread it is shown that this point can be determined just on the basis of appropriate and exactly enough accurate approximation curve σ(ε). On the basis of elastic modulus E, Figure 4, which gives at the elongation the resistance for further deformation in thread, it is shown that at the initial phase in the region of slope of curve σ(ε) elastic modulus increases fast. This fast
249 Figure 4. Graphical representation of approximated curve σ(ε) in the form of polynomial expression of the ninth degree, derivatives σ(ε)’, σ(ε)” and σ(ε)’” and curvature of curve for PES thread calculated with program VILSUK-P
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Name of property Tear stress σ Tear elongation ε
250
Work at tear Ap Stress in flow point σy
Computed value
cNtex–1
37.36624
%
16.1809
MJ
321.1742
cNtex–1
5.0483
Elongation in the flow point εy
%
1.3862
Work to the flow point Ay
MJ
9.91875
cNtex–1
3.95294
%
0.47561
cNtex–1
1.31965
%
3.46304
Elastic modulus E0 Elongation ε0 Table I. Visco-elastic properties of analysed PES thread, with fineness 28.97 tex
Unit
Elastic modulus E1 Elongation ε1 Elastic modulus E2 Elongation ε2
cNtex–1 %
3,.3757 12.05378
increasing of elastic modulus E is the consequence of the force which acts on the thread, because at the beginning the loads at first stretch the unstretched fibres in the direction of acted loading. Because fibres spun into thread are more stretched and for the elongation a greater force is needed, what we see in the maximum elastic modulus E ( the value E 0), and appear when the second derivative is σ” = 0. This value of elastic modulus decreases very fast because acting loading causes the first movements into thread spun fibres and represents the yield point σy. From this moment stress is not proportional to relative elongation or the deformation any more. The curve is turned, the curvature is K = 0. At this moment the maximum change of velocity of elastic modulus is reached (the maximum of the second derivative, and σ’”= 0). From the moment, when the first flowing of fibres spun into thread appears, the tension force does not change, as it is represented in the decreasing of elastic modulus. This value is decreased to value E1 when the flowing and the deformation of fibres spun into thread is the biggest. From this moment the tension force causes greater stresses of fibres spun into thread. They are highly stretched and cause transversal pressure to the stretch direction. Because of the transversal forces, which are the result of the radial compression of the fibres, a greater force is needed for the stretching. This can be seen in greater stress and the increasing of elastic modulus. At the moment when fibres spun into thread do not hold the loading, the thread breaks (value E2). Conclusion On the basis of research performed it can be seen that the starting point for the evaluation and the study of visco-elastic properties of thread is a well constructed approximated curve stress-deformation σ(ε).
The results of the analysis show that the best approximated curve, which guarantees the real mean curve σ(ε) is the polynomial expression of the ninth degree, which fits to the measured values well and also guarantees smooth curve and its derivatives. From the numerical evaluation of visco-elastic properties of thread it can be seen that the yield point can be determined on the basis of known course and the velocity of change of elastic modulus and appears in the modulus when the greatest change of elastic modulus is reached. Yield point σ y, which represents the border between elastic and plastic deformation, must be determined in the region, where the curve turns down, where curvature is K = 0 or at the moment when the third derivative σ’” = 0. At this moment the greatest change of velocity of elastic modulus is reached. References 1. Ger˘sak, J. and Knez, B., “Untersuchungen über die Größe der Belastung des Nähfadens während des Stichbildungsprozeßes”, Bekleidung + Wäsche, Vol. 40 No. 16, 1988, pp. 37-41. 2. Ger˘sak J. and Knez, B., “Reduction in thread strength as a cause of loading in sewing process”, International Journal of Clothing Science and Technology, Vol. 3 No. 4, 1991, pp. 6-12. 3. Ger˘sak, J., “Dinami˘c ko naprezanje konca kao posljedica tehnolo˘ski uvjetovanih sila u procesu oblikovanja uboda”, Tekstil, Vol. 40 No. 5, 1991, pp. 213-22. 4. Morton, W.E. and Hearle, J.W.S.H., Physical Properties of Textile Fibres, 3rd ed., The Textile Institute Manchester, Manchester, 1993. 5. Buko˘sek, V., “Ra˘cunalni˘cko vrednotenje viskoelasti˘cnih lastnosti vlaken”, Tekstilec, Vol. 36 No. 12, 1983, pp. 24-9. 6. Gruber, E., Polymerchemie, Dr Dietrich Steinkopff Verlag, Darmstadt, 1980. 7. Ger˘sak, J., “Evaluation of rheological properties of a thread using numerical methods”, International Journal of Clothing Science and Technology, Vol. 9 Nos 2/3, 1991, pp. 236-40.
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Objective evaluation and prediction of properties of a fused panel Simona Jevs˘nik and Jelka Ger˘sak University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia Introduction Evaluation and prediction of properties of a fused panel often present a difficult task because its properties depends on variety shell fabrics and fashion requirements, choice of fusible interlinings and fusing parameters. The expert’s selection of suitable fusible interlining and fusing parameters is based on experience, but it is not always reliable and accurate. How this problem can be avoided by using a system for automatic knowledge acquisition to make the right selection of fusible interlining and fusing parameters will be presented with this contribution. Machine learning as an alternative way to build the knowledge base helps experts in the decision-making process. At the same time the acquired knowledge is presented and the time for technical preparation of production is shortened.
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 252-262, MCB University Press, 0955-6222
Quality requirements of a fused panel During clothing manufacture processes it is very difficult to harmonise fashion and quality requirements of a produced garment. The most important questions are: will the produced garment fulfil aesthetic and functional requirements, will it be resistant to washing and dry cleaning as well as will the shape during wear be stable? The required properties of the textile surface as the assembling element of certain garments can be reached by stabilisation with the suitable adhesive interlining that can be fixed on the surface of a clothing part. A fused panel as a joined composite has specific properties with respect to the shell fabric and interlining. These properties take consequences in interactions, i.e. behaviour of the shell fabric and interlining in fused panel[1]. From this point of view it is important, for the selection of interlining, to know mechanical and physical properties of the fused panel and the built-in shell fabric and the interlining. A fused panel as a joined composite arises on the basis of joining the interlining with the shell fabric in the stabilisation process. To get the joint, the softened thermoplastic substance is, with the action of pressure, partly
impressed into the shell fabric and the interlining. The interlining is connected to the shell fabric. The strength of the connection depends on the adhesive forces between the thermoplastic substance and the fibre, or the shell fabric, respectively, and the cohesive forces in the thermoplastic substance as a polymer[2]. A fused panel that is a basic assembling element of sophisticated quality of the garments must influence the improvement of the appearance, the applicable properties and also mechanical properties. In this category there are: • hand value; • quality and strength of the connection; • flexibility; • shape stability; and • the duration of the joint. Influence of the type and quality of fusible interlining on the quality of produced garments The quality and the type of the shell fabric, as the main assembling element of a garment, are chosen already in the phase of garment model design and is considered as a constant[2]. This means that the required properties of the produced garment depend on known mechanical properties of the chosen shell fabric as well as on correct selection of fusible interlining. For this reason the fusible interlining has not just great influence on the hand value, but also on the aesthetic appearance, functionality, shape or model stability and on the final use of the garment. The selection of the appropriate interlining can be defined on the basis of adjusting to[1]: • base characteristics which determine the properties of the certain interlining in the process of stabilising and the final use; and • mechanical properties of the fusible interlining with respect to the used shell fabric. The basic properties, which determine the properties of the fusing interlining, are the type and the structure of the supporting material as a substrate and the type and deposit of the thermoplastic substance. Type and the structure of the supporting material, which is chosen with respect to the surface fabric weight and the thickness of this fabric, determine the mechanical properties of the interlining and influence the final applicable properties of the fused panels of garments. The type of thermoplastic substance that has the function of a joining connection element between the supporting material – interlining and the shell fabrics – dictates its use. The applicability depends on melting-point, melting-index, granulation, as well as on resistance against washing and drycleaning.
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Figure 1. The basis TDIDT algorithm
Expert systems and machine learning An expert system can be defined as computer program, which can present knowledge and perform decisions in the area where exact algorithm solutions do not exist. Here, an important role in problem solving has experience. The base components of an expert system are: knowledge base, which contains expert knowledge in defined area, inference engine, which uses the knowledge from knowledge base and user interface for communication between user and the system. To design a knowledge base of an expert system is very specialised and responsible work because it must include entire expert knowledge from the specific area[3]. In general two ways exist to build a knowledge base: (1) assembling of knowledge using human experience; and (2) assembling of knowledge with a system for automatic knowledge acquisition from a given set of examples. The essence of machine learning is construction of algorithms that are able to build the database which reflects the principles of a certain scientific field. Machine learning from a given set of examples with tree structured regression method is used for building of regression trees algorithm which belongs to the family of TDIDT algorithms (top-down induction of decision trees), The basis TDIDT algorithm could be presented in the following way, shown in Figure 1[4]. The tree structured regression is a technique which serves to find the functional dependence between dependent variables y and independent variable xi. This dependence is presented in the form of a regression tree. RETIS is a system for automatic knowledge acquisition from a given set of examples. The induced knowledge is represented by regression trees with a different degree of pruning. When learning regression trees with RETIS, examples have to be described with a set of attributes. Each attribute has its possible set of values. There are If all learning examples belong to a single class then terminate with a leaf labelled with that class else begin on the basis of the learning set choose the most informative attribute (using the entropy measure) for the root of the tree and partition the learning set into subsets according to the values of the selected attribute; for each value do recursively construct a subtree with the corresponding subset of examples end
two kinds of attributes: continuous attributes (their value can be any real number) and discrete attributes which can have a value from some predefined set of values. Each example has also associated class value, which is continuous and represents the quantity we want to learn. Therefore, the program actually learns a function y(x1 … xn), which approximates the relationship between the values of the attributes and the value of the class. Each internal node of a regression tree contains a test on a value of an attribute. According to the result of the test, interpretation of the tree proceeds to the left or to the right subtree of the node. A leaf prescribes a value to a function, approximated by the regression tree. The quality of the constructed tree is measured by the mean squared error R of a tree T, defined with; (1) where: N = number of testing examples; = the actual value of the class of the ith example; yi → x = the value of the ith example; → y( xi) = a value of the class estimated by a regression tree. To enable comparison of the quality of several trees, possibly from different domains, one uses the relative mean squared error, defined as: (2) The mean squared error of the tree is normalised by the mean squared error of the predictor, which always predicts the mean value of the training example set. Methodology On the basis of analyses of achieved results of shell fabrics, fusible interlining, fusing parameters and bond strength of fused panels a set of examples was constructed with the purpose of predicting the bond strength of a fused panel. To construct the learning and testing set of examples 60 woollen shell fabrics were used. Those fabrics were stabilised with three different fusible interlinings. The shell fabrics had different surface fabric weight, weft and warp density, colour and weave; 21 shell fabrics were in plain weave, P 1⁄1 , 28 were in twill weave, K 1⁄2 and 11 were in twill weave, K 2⁄2. Fusible interlinings differed in raw material, weave and type of adhesive (Table I). Experimental work The learning set of examples was constructed to give a rule for prediction of bond strength of fused panel.
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256
Table I. List of properties of used interlinings
Code of fusible interlining FI-2
FI-1
FI-3
28% PA 72% CV
100% Co
100% Co
twill
plain
plain
Adhesive type
PA
VTPE
PA
Warp density (yarns/cm)
11
24
24
Weft density (yarns/cm)
13
13
10.5
Warp linear density (Tt/tex)
4.4
20
17
Weft linear density (Tt/tex)
36
20
17
Surface fabric weight (m/gm–2)
75
85
65
Weave
For a constructed learning set of examples the following were carried out: • analyses of mechanical and physical properties of shell fabrics and fusible interlinings. The measurements have been performed on FAST system for objective evaluation of properties of shell fabrics; and • analyses of quality and bond strength of fused panel by DIN 54 310. Fusing of shell fabrics and fusible interlinings was performed on a continuous press machine. Fusing parameters i.e. temperature, time and pressure of fusing, have been determined on the basis of previous testing and achieved quality of fused panel (Table II). Recommendations of manufactures of interlining have been also considered. All measurements were carried out at standard testing conditions (20±2°C and 65±2 per cent RH).
Fusing parameters Fusing temperature T/°C Table II. List of selected fusing parameters
Fusing pressure p/Ncm–2 Fusing time t/s
FI-1
Code of fusible interlining FI-2
FI-3
135
125
130
3
2
4
15
8
10
The learning set contained 165 examples. The value of the class was described with 21 attributes, out of which 16 were continuous and five discrete. A list of attributes used is given in Table III. Regression tree for prediction of bond strength of fused panel was constructed using the program RETIS and learning set of examples. Comparison between predicted values and measured values of bond strength was made on the basis of testing a set of examples which contained 15 examples.
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Results The results of research for prediction of bond strength of analysed shell fabrics regarding the applied fusible interlining and fusing parameters are given in a form of: • results of measured values of bond strength of fused panels; • graphical presentation of regression tree for prediction of bond strength; and • analyses of comparison between predicted and measured values of bond strength. The achieved values of measured bond strengths are shown in Table IV.
Name of attribute
Type of attribute
Fusing_temp C Fusing_press Ncm2 Fusing_time_t Weave_SF Warp_density_SF-1_yarns/cm Weft_density_SF-2_yarns/cm Linear density_SF-1_TEX Linear density_SF-2_TEX Thickness_SF_MM E100-1_SF_% E100-2_SF_% RS-1_SF_% RS-2_SF_% Type_FI Type_of_resin Forms_of_adhesives Raw_material_FI Warp_density_FI-1_yarns/cm Weft_density_FI-2_yarns/cm Linear density_FI-1_TEX Linear density_FI-2_TEX
continuous continuous continuous discrete continuous continuous continuous continuous continuous continuous continuous continuous continuous discrete discrete discrete discrete continuous continuous continuous continuous
Table III. List of used attributes of predicted class
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Table IV. Average values of bond strength of fused panel
Code SF-01_FI-1 SF-02_FI-1 SF-03_FI-1 SF-04_FI-1 SF-05_FI-1 SF-06_FI-1 SF-07_FI-1 SF-08_FI-1 SF-09_FI-1 SF-10_FI-1 SF-11_FI-1 SF-12_FI-1 SF-13_FI-1 SF-14_FI-1 SF-15_FI-1 SF-16_FI-1 SF-17_FI-1 SF-18_FI-1 SF-19_FI-1 SF-20_FI-1 SF-21_FI-1 SF-22_FI-1 SF-23_FI-1 SF-24_FI-1 SF-25_FI-1 SF-26_FI-1 SF-27_FI-1 SF-28_FI-1 SF-29_FI-1 SF-30_FI-1 SF-31_FI-1 SF-32_FI-1 SF-33_FI-1 SF-34_FI-1 SF-35_FI-1 SF-36_FI-1 SF-37_FI-1 SF-38_FI-1 SF-39_FI-1 SF-40_FI-1 SF-41_FI-1 SF-42_FI-1 SF-43_FI-1 SF-44_FI-1 SF-45_FI-1 SF-46_FI-1 SF-47_FI-1 SF-48_FI-1
Bond strength F/N/5cm2
Code
Bond strength F/N/5cm2
Code
10.84 10.08 10.78 10.02 7.12 9.8 8.32 10.77 10.78 9.32 9.64 10.76 9.71 9.77 8.03 8.87 8.8 8.65 8.61 8.91 10.73 10.4 10.05 9.99 9.25 8.67 7.47 9.24 7.76 7.27 8.46 7.1 8.67 8.73 8.14 11.21 10.77 10.42 12.49 10.94 12.07 12.42 12.84 12.6 12.76 10.62 12.04 13.82
SF-01_FI-2 SF-02_FI-2 SF-03_FI-2 SF-04_FI-2 SF-05_FI-2 SF-06_FI-2 SF-07_FI-2 SF-08_FI-2 SF-09_FI-2 SF-10_FI-2 SF-11_FI-2 SF-12_FI-2 SF-13_FI-2 SF-14_FI-2 SF-15_FI-2 SF-16_FI-2 SF-17_FI-2 SF-18_FI-2 SF-19_FI-2 SF-20_FI-2 SF-21_FI-2 SF-22_FI-2 SF-23_FI-2 SF-24_FI-2 SF-25_FI-2 SF-26_FI-2 SF-27_FI-2 SF-28_FI-2 SF-29_FI-2 SF-30_FI-2 SF-31_FI-2 SF-32_FI-2 SF-33_FI-2 SF-34_FI-2 SF-35_FI-2 SF-36_FI-2 SF-37_FI-2 SF-38_FI-2 SF-39_FI-2 SF-40_FI-2 SF-41_FI-2 SF-42_FI-2 SF-43_FI-2 SF-44_FI-2 SF-45_FI-2 SF-46_FI-2 SF-47_FI-2 SF-48_FI-2
7.11 7.43 7.08 7.81 7.23 6.81 7.06 6.95 7.02 6.21 7.37 6.96 6.82 6.89 6.03 7.56 7.26 6.32 5.98 6.31 6.96 7.75 7.23 7.21 7.65 7.18 6.53 6.22 6.61 6.47 6.08 6.31 6.28 6.24 6.11 9.19 11.89 8.76 10.89 10.84 11.04 11.75 9.84 10.53 12.47 10.68 11.27 13.38
SF-01_FI-3 SF-02_FI-3 SF-03_FI-3 SF-04_FI-3 SF-05_FI-3 SF-06_FI-3 SF-07_FI-3 SF-08_FI-3 SF-09_FI-3 SF-10_FI-3 SF-11_FI-3 SF-12_FI-3 SF-13_FI-3 SF-14_FI-3 SF-15_FI-3 SF-16_FI-3 SF-17_FI-3 SF-18_FI-3 SF-19_FI-3 SF-20_FI-3 SF-21_FI-3 SF-22_FI-3 SF-23_FI-3 SF-24_FI-3 SF-25_FI-3 SF-26_FI-3 SF-27_FI-3 SF-28_FI-3 SF-29_FI-3 SF-30_FI-3 SF-31_FI-3 SF-32_FI-3 SF-33_FI-3 SF-34_FI-3 SF-35_FI-3 SF-36_FI-3 SF-37_FI-3 SF-38_FI-3 SF-39_FI-3 SF-40_FI-3 SF-41_FI-3 SF-42_FI-3 SF-43_FI-3 SF-44_FI-3 SF-45_FI-3 SF-46_FI-3 SF-47_FI-3 SF-48_FI-3
Bond Strength F/N/5cm2 8.94 8.81 9.07 7.08 6.77 7.42 7.99 8.80 7.88 9.03 7.39 7.68 7.50 7.22 8.52 8.14 9.70 9.17 8.83 7.87 9.05 8.18 8.74 8.96 8.66 8.96 9.21 9.28 9.54 9.53 9.56 9.65 9.35 8.59 7.66 8.44 8.38 7.32 9.61 9.33 8.83 8.97 8.91 9.05 8.49 9.18 10.21 12.31 (Continued)
Code SF-49_FI-1 SF-50_FI-1 SF-51_FI-1 SF-52_FI-1 SF-53_FI-1 SF-54_FI-1 SF-55_FI-1 SF-56_FI-1 SF-57_FI-1 SF-58_FI-1 SF-59_FI-1 SF-60_FI-1
Bond strength F/N/5cm2 11.35 11.97 11.23 9.02 10.97 10.68 9.31 9.85 10.76 9.08 11.82 12.21
Code
Bond strength F/N/5cm2
Code
Bond Strength F/N/5cm2
SF-49_FI-2 SF-50_FI-2 SF-51_FI-2 SF-52_FI-2 SF-53_FI-2 SF-54_FI-2 SF-55_FI-2 SF-56_FI-2 SF-57_FI-2 SF-58_FI-2 SF-59_FI-2 SF-60_FI-2
10.75 14.03 12.64 6.71 7.58 7.51 7.39 6.48 7.17 7.4 10.75 8.93
SF-49_FI-3 SF-50_FI-3 SF-51_FI-3 SF-52_FI-3 SF-53_FI-3 SF-54_FI-3 SF-55_FI-3 SF-56_FI-3 SF-57_FI-3 SF-58_FI-3 SF-59_FI-3 SF-60_FI-3
10.39 11.95 11.50 8.26 9.14 8.18 8.08 7.34 8.06 7.27 9.30 9.08
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Table IV.
The regression tree, written in file TRDN.TRT, was constructed from the file with learning examples TRDN.RDA and file with domain definition TRDN.RDO. The regression tree contained from 16 nodes and 17 leaves. A part of a regression tree for prediction of bond strength is shown in Figure 2. The testing of the quality of the regression tree was carried out with 15 randomly selected examples (Table V). Furthermore, the comparison between predicted and measured values of bond strength was elaborated with linear coefficient correlation. Linear correlation coefficient is shown in Figure 3.
RE-2_OT_% =<0.6
>0.6 Density_yarn_SF-2_yarn/cm
11.53±1.28 =<21.90
RE-1_OT_% =<4.3
=<3.3
9.92±0.97
10.29±0.55
=<30.22
8.19±1.06
>30.22
8.93±0.84
>3.3
Linear_density_SF-1_tex =<17.75
Density_yarn_SF-2_yarn/cm >4.3
Fusing_press_Ncm–2
>21.90
>17.75 9.16±1.09
8.96±0.40
Figure 2. Part of a regression tree for prediction of bond strength of fused panel
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Table V. Comparison between predicted and measured values of bond strength
Figure 3. Correlation between measured and predicted values of bond strength
Measured value of bond strength F/N/5cm
Predicted value of bond strength F/N/5cm
7.81 6.21 7.26 7.23 6.61 8.76 10.53 9.64 8.91 7.47 12.49 12.28 15.65 5.56 12.21
6.78 6.78 6.78 8.54 7.38 6.36 11.05 9.92 9.16 8.19 12.14 11.05 13.08 8.19 10.29
Predicted value of bond strength. F/N/5cm 16 14 12 10 8 6 4 4 6 8 10 12 14 16 Measured value of bond strength F/N/5cm
18
Discussion From achieved results of measurement of mechanical and physical properties of shell fabrics it can be seen that all the shell fabrics have a high relaxation shrinkage, which has a negative influence on dimension stability of garment parts. Relaxation shrinkage is higher in weft direction than in warp direction in all shell fabric; however, it stays within permitted borders. Results of research work indicated that the same fusing parameters do not ensure the same bond strength even if the same shell fabric fused with identical fusing interlining has been used. At the same time also weave, weft and warp density of shell fabric have influence on bond strength. The reason is different influence on the adhesive forces to the shell fabric and different heat conductibility of analysed shell fabrics.
It can be seen that the bond strength of the fused panel has values within the range of 7 to 14 N/5cm for twill K1⁄2 and K 2⁄2 weave and that it is higher than for plain weave P 1⁄1 which values are within the range of 6.2 to 10.8 N/5cm. Also the fusible interlining structure influenced the bond strength. It can be seen from results that fused panels fused with interlining noted as FI-1 had in average for 1.3 N/5cm higher bond strength than fused panel fused noted as FI3 and for 1.9 N/5cm when compared with the fusible interlining noted as FI-2. Furthermore, the results of bond strength of fused panel showed that all the values were within allowed border. The fused panel of shell fabric in twill weave K 2⁄2 had an average for 3 N/5cm higher bond strength than the shell fabric in twill K1⁄2 and plain weave P 1⁄1. When predicting the bond strength of woollen shell fabrics and fusible interlinings using the regression tree we simply follow the values of attributes in nodes and read the class value in the leaf. For example, the predicted bond strength 10.36±0.93 N/5cm is achieved for a fused panel in weave twill with the following properties: • warp density: 30.19 yarns/cm; • weft density: 24 ˘st. yarns/cm; • warp linear density: 19.23 tex; • weft linear density: 14.29 tex; • thickness of shell fabric: 0.382 mm; • extensibility in warp, E100-1: 1.8 per cent; • extensibility in weft, E100-2: 3.6 per cent; • relaxation shrinkage in warp direction, RS-1: 3 per cent; • relaxation shrinkage in warp direction, RS-2:3.4 per cent; the fusible interlining had the following characteristics: • type of fusible interlining: twill • adhesive type : PA • form of adhesive: random • material of fusible interlining: 31% PA, 69% CV • warp density: 11 yarns/cm • weft density: 13 yarns/cm • warp linear density: 4.4 tex • weft linear density: 36 tex fused following fusing conditions under the: • fusing temperature: 135°C • Fusing pressure: 3 Ncm–2 • fusing time: 15s.
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Achieved results of bond strength predicted with regression tree showed that they are more reliable in the case of new fabrics than new interlining. The reason lies in the greater number of different shell fabrics included in the learning set of examples. Coefficient of correlation between measured values of bond strength and those predicted using the testing set was 0.87, which showed good agreement between them. Conclusions Owing to the interacting influence of shell fabric, fusible interlining and fusing parameters evaluation of properties of fused panel was based on investigation and subjective evaluation. Application of machine learning from a set of examples using program RETIS proved to be a successful and promising technique. It also indicated some important direction for future activities. References 1. Ger˘sak, J., “Proper evaluation and quality choice of fusible interlining DWI reports”, Deutschland Wollforschungsinstitut an der Technischen Hochschule Aachen, Aachen 1996, pp. 499-506. 2. Ger˘sak, J., “Objective evaluation of heat-set garment parts”, Tekstil, Vol. 46 No. 4, 1997, pp. 193-203. 3. Stjepanovi´c , Z., “Employment of information technologies in spun yarn production – a case study”, The 78th World Conference of the Textile Institute in association with The 5th Textile Symposium of SEVE and SEPVE, Vol. 1, 1997, pp. 271-85. 4. Karali˘c , A. and Cestnik, B., “The Bayesian approach to tree-structures regression”, Proceedings of ITI-91, Cavtat, 1991.
Clothing engineering based on objective measurement technology Sueo Kawabata
Clothing engineering
263
The University of Shiga Prefecture, Hikone, Japan and
Masako Niwa Nara Women’s University, Nara, Japan A review of the investigation on the objective evaluation of suiting quality and tailoring process control on the basis of the objective measurement of fabric The total hand value (THV)[1,2] High quality garments must be comfortable to wear and have a beautiful appearance. This comfort depends on the fabric quality. For many years, fabric hand judgement has been used in textile mills for evaluating the fabric quality, which is directly related to the garment quality. This subjective method of judgement has been transferred to the objective method in the last two decades and the objective method is becoming a powerful tool for developing the engineering used in manufacturing high quality fabrics. The objective system expresses the fabric hand with three or four (in the case of mid-summer suiting) components of primary hands. This expresses the fabric characteristics such as stiffness, smoothness and fullness according to their intensity over a range of hand value, from 10 (strong feel) to 5 (average) and to 1 (very low feel). The fabric quality is expressed with the total hand value, or THV, from 5 (excellent) to 3 (average) and to 1 (poor). These scores are originally based on the subjective judgement by experts in textile mills. The objective evaluation system evaluating these hand values was then developed around 1975. In this system, fabric mechanical parameters are measured and transformed into the primary hand values, then these primary hand values are converted into the total hand value with transforming equations. Figure 1 shows the “Hand chart” for (a) winter/autumn suiting and (b) mid-summer suiting. The three primary hands and THV are shown on the normalized scale. A shaded zone is a good zone and excellent suiting falls into this zone. The suiting for which THV is higher than 4 is very high quality, and 3.6 is high quality from our experience. Tailoring control chart[3] Following the development of objective measurement of fabric hand, a new technology was developed from the apparel engineering side that applies objective measurement technology. The precise measurements of the non-linear mechanical properties of fabric in low load regions were developed for the
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 263-272, MCB University Press, 0955-6222
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Hand Value STIFFNESS (KOSHI)
264
2
SMOOTHNESS (NUMERI)
0
FULLNESS (FUKURAMI)
4
1
0
1
T.H.V. AIR RESISTANCE
3
2
6
3
2
4
3
1
0.1
5
7
5
4
6
5
2
7
6
3
0.2 0.3 0.4 0.5 –3σ –2σ –σ
8
7
9
8
9
10
8
9
10
4
1
2
5
3
σ
0
10
6
4 5 2σ
10 3σ
(a) H.V.
Hand Value KOSHI
1
HARI
Figure 1. Criteria for hand: (a) the hand chart for winter/autumn suiting, (b) for mid-summer (tropical) suiting. The shaded zone is the good zone
1
SHARI FUKURAMI
2 0
1
T.H.V. AIR RESISTANCE
2
1
0.1
4
3 1
2
3
2 3
4
5 5
3
6
4
0.2 0.3 0.4 0.5 –3σ –2σ –σ
7
7
5
4
2
6
6
5
8 7
6
3
2 0
σ
9
9 8
10
9
7
4
1
8
10 8
9
5
3
4 5 2σ
10 3σ
(b)
objective measurement system of the fabric hand. This objective measurement technology was soon transferred to apparel manufacturers. First, men’s suit manufacturers tried to apply the mechanical parameters to tailoring process control. At that time, in the 1970s, suit manufacturing was shifting from the former craftsman system to an industry. People work on the production line and one worker manufactures part of a suit. This system was quite different from the traditional craftsman system of suit making. The engineers who control the new line system found that the production line produces excellent suits from excellent suiting. However, some poor suits were also produced with the excellent suiting, even with the same production line and the same workers. The engineers investigated the reason and found that the line must be controlled based on the fabric mechanical properties. Ito experimented with a control system. Operation instructions were sent to the workers on the production line based on fabric mechanical properties. In some cases, those fabrics which are difficult to use to make suits were rejected for tailoring prior to production. Ito
made clear criteria for this rejection and he also made a chart for tailoring process control with Kawabata based on the mechanical parameters of the tensile and shearing properties of suiting, as shown in Figure 2[3]. This chart indicates a zone of “non-control”. If a fabric’s mechanical parameters representing the tensile and shearing properties fall into this zone, the workers on the production line may continue their tailoring operation without any special notice. If any one or more parameters are outside of this zone, a special instruction for tailoring operation is sent to the related workers on the line. In this chart, a good zone is also shown as a shadowed zone. This good zone was derived from Ito’s experience from the tailoring viewpoint and Kawabata’s analysis through a combination of Ito’s experience. It was found that some parts of this good zone were outside of the non-control zone. This means that the tailoring of good suiting is difficult in general, and careful attention is necessary in the tailoring process. After this control technique was found, the line production system could start production of high quality suits. Before this, the line production system could not process high quality suiting because of unpredictable troubles.
CONTROL ZONE
LT:
0.5
0.55
NON-CONTROL ZONE
0.58
0.6
50
55
0.65
3
60
65
70
75
DIF. CUT.
3.5
4
4.5
5
DIF. O.F. DISCOMF. EM2:
0.7
DIF. O.F.
DIF. CONT. POOR APP. EM1:
5.5
DIF. O.F.
6
POOR SHAPE RET. DIF. O.F.
DIF. CUT.
2
DIF. O.F. 4 DISCOMF.
EM2/EM1
6
1
8
2
3
GOOD APP.
10
15
SEWING CONT.
DIF. O.F. G:
0.4
0.5
0.6
0.7
0.8
0.9
0.8
1
1.0
1.2
DIF. O.F.
POOR APP. 2HG5:
1.5
265
CONTROL ZONE
GOOD APP. COMF. RT:
Clothing engineering
1.8
2
2.5
3
POOR APP. COMFORT, GOOD APP. Key DIF:difficult, O.F.;overfeed, APP;appearance, COMF:comfort
DIF. O.F.
Figure 2. Criteria for mechanical comfort (tailoring control chart). This chart is commonly applied to winter/autumn and midsummer suiting. The shaded zone is the good zone. Data plotted on this chart show the changes before ( ) and after ( ) re-finishing of a worsted sample
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In addition to the process control, this good zone gave us important information regarding the good suiting from the view of tailoring experts. These criteria of good suiting are more related to comfortable wear, which textile experts were not as concerned with and included it indirectly in fabric hand. In addition to comfortable wear, the criteria are also related to the beautiful appearance and good shape retention of the suit. Although there is some overlapping between the hand criteria and the comfort criteria, we have added comfort criteria as one of the important suiting performances as well as good hand. The “total appearance value” A systematic investigation was conducted by tailoring experts and university group to explain the relation between fabric mechanical properties and suit appearance[1,2]. The prediction of the suit appearance was necessary for selecting good suiting for manufacturing a good suit, prior to the tailoring. The subjective judgement of the suit appearance was conducted by tailoring experts using the finished suit in a factory. The mechanical parameters of the suiting used for these suits were analysed by the university group, and an equation was constructed to regress appearance quality based on the mechanical parameters of the suiting. The subjective judgement of the appearance was conducted with two or three experts in such a manner that the 20 to 30 finished suits of the same lot and made from the same suiting were hung in a line, and the experts examined these suits to evaluate their appearance. This method enabled us to connect the appearance and the property of suiting without interference from small variations in the appearance of individual suits. This procedure was repeated many times over a lot of products. The grading of the appearance was the same as the grading of THV, and is called as total appearance value (TAV). The objective evaluation of the TAV was conducted by the same two-step method, used to derive the THV. However, the construction procedure of the regression equation was modified considering applications of the equation to the textile design for a good appearance suit. Some new mechanical-parameters were used[1]. They were derived from the original KESF mechanical parameters. These parameters were selected so that they were directly related to the appearance of the suit in three categories, formability, elastic potential, and drape components. The mechanical parameters related to each of these components were grouped respectively. First, the three equations were separately constructed for each component using the same subjective TAV data, then the equation for total appearance value (TAV) was constructed from the three TAV values of the three components to regress again the subjective TAV data. Thus, the general TAV(5~1) was derived from each of the three TAV component values (5~1, respectively). Each component value is derived from the respectively grouped mechanical parameters.
The TAV equation and the three TAV components equations may be applied commonly to winter and summer suiting. Figure 3 shows a chart evaluating the appearance of the suit. A good zone is indicated by a shadowed zone on the basis of the experts’ experience. The ideal fabric Good suiting must fall in the good zones shown in Figure 1, Figure 2, and Figure 3. “Ideal fabric” is the suiting which satisfies the following criteria. The conditions for an ideal fabric are: (1) Hand. THV 4 (excellent quality), 3.6 ( high quality) (condition 1). (2) Suit appearance. TAV 4 (condition 2) (3) Mechanical comfort. The parameters for the tensile and shear properties must fall within the comfortable zone on the control chart (condition 3). We are now investigating the technical guidelines for manufacturing an ideal fabric[4,5].
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267
The sewing problems Sewing is an essential operation in garment manufacturing. The recent trend in sewing machines is higher speed sewing rather than sewing quality, spreading of
–3σ
–2σ
–σ
σ
0
2σ
3σ
Mechanical Parameter X11; logEL2
2
X12; logBS2
0.02
X13; logSS X21; logBP
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3 0.03
X31; (BS/W)1/3 X32; (SS/W)1/3
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Elastic Potential; Z2 Drape; Z3 KN(eq.10) TAV
0 0 0
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7.0
6.0
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4
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4
2 0
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Three Basic Components of Tailorability Formability; Z1
40
0.5
20 2.0
3.0
30
0.5
10
1.5
2.0
20
0.1 1.0
0.05
X22; logSP
4
6 6
4 4
6 6
Figure 3. Suit appearance criteria. This chart is commonly applied to winter/autumn and midsummer suiting. The shaded zone is the good zone. Data plotted on this chart show the changes before and after re-finishing of a worsted sample
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fabric type and property from the traditional narrow range, and small lot production. In this circumstance, a serious problem confronting apparel engineers is the seam pucker problem. Many investigations have been conducted to predict the pucker; however, we have not yet achieved any clear solution for this problem. The difficulty is in the mixed relations between several factors, including sewing machine performance, sewing thread property and fabric property. Experimental investigation is necessary because of the complexity of this problem; however, a theory is also necessary, even it is a simple theory. It may direct the experimental investigation in the right direction and make the complexity much simpler, as a guideline for experimental investigation. The present authors have attempted the derivation of a simple theory which explains the relationship between the three factors. This theory was presented at the 26th Textile Research Symposium at Mt. Fuji, August 1997[6]. The investigation is still continuing from the theoretical side and the experimental standpoint. (1) Seam shrinkage Lock stitch is considered here as a standard machine sewing stitch. As shown in Figure 4, bobbin thread tension F0 is the basic tension which forms the stitch. The needle thread picks up the bottom thread and forms the stitch. The tension of the needle thread is adjusted so that the stitch structure becomes a symmetric structure. It is assumed that the tensions of both threads are equal to F0 when the stitch is formed. The thread tension (of the two threads) compresses the stitched fabrics (of two plies) in the seam line direction, and is reduced until the thread tension and fabric compression force are in equilibrium. The equilibrium equation is: (1)
Needle Thread
Bobbin Thread
F0
Figure 4. Lock stitch and seam shrinkage
L L0
∆L
εs =(L0 – L) / L0
where Fy and Ff = thread tension and fabric compression force respectively; ey and ef = thread tensile strain and fabric compression strain respectively; Ey and Ef = thread tensile modulus and fabric compression modulus respectively. These values are not necessarily constant value, but the function of strain in general, that is:
Clothing engineering
269 Suffix 0 = value in the initial state; suffix e = value in the equilibrium state; a = effective seam width. The seam shrinkage es is defined as the shrinkage of the seam line from its original length to the shrunk length in the equilibrium state, that is: (2-1) and the thread strain in the equilibrium state is expressed by using e s as follows: (2-2) From (1) and (2), (3) Actually, (1 + εyo) ≅ 1, then (3) becomes (4) From (4), we obtain (5) This seam shrinkage causes puckering as shown in Figure 5. Although es is usually small, eye is not small in actual sewing situations, and the stress-strain relationship of the thread is usually nonlinear. The thread modulus Ey must be measured by the slope of the stress (tension)- strain curve in the region between ε yo and in the vicinity of ε ye. (2) Mechanism of seam pucker occurrence The seam line and its adjoining area are much stiffer than the other areas of fabric. The shrinkage es given by (5) is the shrinkage of this stiff zone. The seam ripples do not appear in this zone because of its stiffness, and appear in a region near the seam line, as shown in Figure 6. The shrinkage of the stiff zone causes buckling of the fabric around the stiff zone. For simplicity, consider a strip of fabric along the stiff zone in the neighbourhood of the stiff zone, and assume one dimensional Euler buckling of a thin elastic plate. The critical Euler buckling force fc (buckling occurs when ffc [7] ) is:
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Figure 5. Seam pucker caused by seam shrinkage. p is wave length and a is “effective seam width”
(6) where n : 1,2,3,..., L = length of the strip fabric; Bf = bending stiffness of fabric per unit width (Nm2/m). We may replace L in equation (6) by wave length p by the relation, p = 2L/n, then: (7) The corresponding critical shrinkage is: (8) The critical shrinkage esc is very small for standard fabrics due to the actual bending stiffness of apparel fabrics and the wave length of pucker; therefore, pucker occurs more or less in almost fabrics. The seam shrinkage es may be transferred to the fabric strip and forces the fabric to shrink. The buckling is, therefore, a forced buckling caused by seam shrinkage es under the condition that: (9) es = esc . hm wave height
Figure 6. A simplified model of seam puckering caused by seam shrinkage
p
2p
Clothing engineering
The wave length p is then determined by (7), (8), and (9), as follows: (10)
(3) The pucker level When the seam shrinkage, es, becomes larger, the seam pucker becomes more severe. The pucker level is defined in a subjective manner using pictures of standard samples puckered fabric. The degree of pucker (we call it “pucker level”) is related with wave amplitude of the buckling; however, more detail is needed to clarify this. Kawabata et al. have presented a relationship between the average amplitude of pucker wave and the subjective pucker level[8] and obtained the following relationship based on assumptions about human sensory: PV (pucker value) = A + B log y (11) A and B : constants, B at around 4, y : averaged amplitude, where the signal of wave height is processed by a 2nd order high pass filter. PV is an expression of pucker level and expressed by values from 5 (large pucker) to 0 (almost no pucker). Figure 6 shows a simple buckling model. The amplitude of triangular wave hm is given by shrinkage ls, as follows, when es is small: (12) where h m ∝ y. The relationship (11) has been confirmed by experimental investigation. It is noted that the amplitude signal was processed by a high pass filter, and this process is a kind of differential operation of the signal in the frequency region below the cut frequency. If we process the signal by the differential operation, y must be derived by the relation: (13) From (12) and (13): (14) and (12) becomes: (15) PV (pucker value) = A + B’log εs where B’ = B/2. The seam shrinkage is directly related to pucker level. In conclusion, equation (5) is important and this equation clearly expresses the relationship between thread tension, fabric mechanical property and thread mechanical property. The effective seam width a in this equation has not been determined theoretically. It is in a range slightly larger than thread width.
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Experimental verification of this problem is necessary. This theory is not a perfect theory at this stage; however, it may provide a strong guideline for further experimental investigation. Conclusions Fibres are a valuable natural resource, and the mass production of clothing consumes much energy. It is therefore important that we produce only quality garments to eliminate waste and conserve resources. The engineered production of high-quality fabrics and garments is an essential factor in the consistent production of high-quality garments. In future, we must produce only good garments and not produce poor ones, because fibres are valuable natural resources, and also mass production spends a lot of energy. For these requirements, the engineered production of high quality fabrics and garments is essentially important. Objective evaluation technology is a powerful tool for the engineered garment production. References 1. Kawabata, S. and Masako, N., “Fabric performance in clothing and clothing manufacture”, J. Textile Institute, Vol. 80, 1989, pp. 19-50. 2. Kawabata, S. and Masako, N., “Objective measurement of fabric hand”, in Raheel, M. (Ed), Modern Textile Characterization Methods, Chapt. 10, Marcel Dekker, New York, 1996. 3. Kawabata, S., Ito, K. and Masako, N., “Tailoring process control”, J. Textile Institute, Vol. 83, 1992, pp. 361-73. 4. Kawabata, S. and Masako, N., “Development of high quality apparel fabrics by means of objective measurement”, presented at the 78th Textile Institute World Conference, Thessaloniki, Greece, 1997. 5. Kawabata, S., Masako, N., Yamashita, Y. and Inamura, A. “Engineered design and manufacturing of high quality fabrics”, A Preliminary Report of the Ideal Fabric Project, Presented at the 26th Textile Research Symposium at Mt. Fuji, 3-5 August 1997. 6. Kawabata, S. and Masako, N., “Analysis of the occurrence mechanism of seam pucker, Part 1: modeling”, Presented at the 26th Textile research Symposium at Mt. Fuji, 3-5 August 1997. 7. Sechler, E.E., Elasticity in Engineering, John Wiley & Sons, New York, 1952. 8. Kawabata, S., Mori, M. and Masako, N., “An experiment on human sensory measurement and its objective measurement; case of the measurement of a seam pucker level”, Proceedings of the 25th Textile Research Symposium, 1996, pp. 165-8, and International Journal of Clothing Science and Technology, Vol. 9 No. 3, 1997, pp. 203-6.
Suitability of nonwoven fusible interlining to the thin worsted fabrics S.J. Kim and K. H. Kim
Suitability of nonwoven interlining 273
School of Textiles, Yeungnam University, Kyeungsan, Korea
D.H. Lee Korea Academy of Industrial Technology, Suwon, Korea, and
G.H. Bae Kyeungnam Wool Textile Co. Ltd, Masan, Korea Introduction The fitness of fusible interlining to the worsted fabric is very important for wearing performance[1,2]. Functions of fusible interlining in garments can be summarized as the ease of garment manufacturing due to stability of shell fabric, endowment of volume due to good formability and silhouette and shape retention of garment due to repetition of dry cleaning. Physical property of fusible interlining is changed according to kind of adhesives, adhesion state and amount of adhesives. Bubble and strike through and back phenomena due to excessive amount of adhesives occur according to adhesive conditions. So, optimum adhesive conditions and selection of relevant interlining related to shell fabric are required. Many researches[3-5] into this field have been performed about adhesive force according to material of textile, kind and amount of adhesives and adhesive conditions. There was no research related to the change of physical properties and formability of garment due to repetition of dry cleaning in thin worsted fabrics and kinds of fusible interlining. Therefore, the objective in this study is to analyze suitability of nonwoven fusible interlining to the thin worsted fabric with various fabric structural parameters. For the purpose of this study, specimens with various weft yarn twists and weft densities of thin worsted fabrics are prepared. Three nonwoven fusible interlinings with different structure which were made of nylon/polyester were used for adhering to the thin worsted fabrics. Mechanical properties of these 24 adhesive fabrics fused with three nonwoven interlinings are measured by KES-FB system for analyzing the suitability of nonwoven fusible interlinings to the thin worsted fabrics with various fabric structural parameters. Some mechanical properties of fused fabrics are analyzed and discussed with repetition of dry-cleaning of adhesive fabrics for performing effects of dry cleaning to the suitability of nonwoven fusible interlining to the shell fabrics.
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 273-282, MCB University Press, 0955-6222
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Experimental Specimens in this study are shown in Tables I and II respectively. Table I shows the characteristics of shell fabric. Table II shows the characteristics of nonwoven adhesive interlinings. Twenty-four specimens of adhesive fabrics with nonwoven fusible interlinings are prepared by combination of Tables I and II. Adhesion was carried out using a roller press machine (KCF-382) made by Keum Seong Co. Ltd. Adhesive conditions such as adhesive temperature, processing time and roller pressure were selected through examining the preliminary experiment, which indicates the temperature is 140° C, time is 9 seconds and roller pressure is 44.1 N/cm2. Laundry method by dry-cleaning was adopted from JIS-L1042J-I. Dry cleaning was carried out once, three times, and ten times. Mechanical properties of specimens such as tensile, bending, shear, compression and surface after each dry cleaning were measured using KES-FB system. Formability, elastic potential and drape of these specimens were calculated using empirical equations proposed by Kawabata and Niwa[5]. Result and discussion Change of fabric mechanical properties with the kind of nonwoven interlining and repetition of dry cleaning Figure 1 shows tensile work of warp direction with weft twist directions S and Z, weft densities and t.p.m. Tensile work of fabrics with nonwoven fusible
Density (ends/cm) No. Wp Wf
Table I. Characteristics of shell fabric
Table II. Characteristics of nonwoven adhesive interlining
1 2 3 4 5 6 7 8
34.3 34.4 30.7 30.7 30.7 25.2 25.2 25.2
30.7 30.7 36.2 36.2 36.2 28.8 24.4 26.0
Construction 3H 3H 3H 3H 3H 3H 3H 3H
Number of twist (t.p.m.) Wp Wf
Cover factor Counts (Kc) (Tex) Thickness Wp Wf Wp Wf (mm)
937Z/1200S 800Z 11.69 937Z/1200S 800S 11.69 937Z/1200S 720S 11.26 937Z/1200S 800Z 11.26 937Z/1200S 900Z 11.26 950Z/1100S 950Z/1100S 10.12 950Z/1100S 950Z/1100S 10.12 950Z/1100S 950Z/1100S 10.12
No.
Material (nylon/polyester)
Density (gauge)
Thickness (mm)
F1
80/20
18
0.22
F2
80/20
18
0.24
F3
80/20
–
0.28
11 11 13 13 13 9.2 9.8 10
2/10.42 2/10.42 2/10.42 2/10.42 2/10.42 2/12.5 1/12.5 2/12.5
1/20 1/20 1/20 1/20 1/20 2/12.5 2/12.5 2/12.5
0.4272 0.4403 0.3800 0.3898 0.4452 0.3823 0.3676 0.4023
Remark Needle punched polyester 18 ends/inch Needle punched polyester 18 ends/inch –
Suitability of nonwoven interlining
WT (Wp, gf.cm/cm2) 10 Twist Direction 9 Z
S Z
S
Z
S
8 7
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6
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58 62 66 58 62 66 58 62 66
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0 1 2 3 4 5 Number of dry cleaning (b) WT (Wp, gf.cm/cm2) 11
6
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Key interlining F1 interlining F2 interlining F3
interlining composed of nonstitch interlining (F3 in Table II) shows the difference before and after dry-cleaning compared to the other nonwoven interlinings. And those values are slightly decreased with increasing repetition of dry-cleaning.
Figure 1. Tensile energy of warp direction to the dry cleaning number: (a) twist direction (b) weft density (c) number of twist
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The tensile work of fusible interlining fabrics composed of nonstitch interlining (F3 in Table II) shows the highest value compared to the others. And it is shown that the warp tensile work of the fabrics with nonwoven fusible interlining depends on the nonwoven interlining property rather than the fabric structural parameters such as amount of t.p.m. and weft density. The tensile work of the fabrics in the weft direction with repetition of the dry-cleaning does not show any change. Figure 2 shows bending rigidity of warp direction with weft twist direction S and Z, weft density and amount of t.p.m. Bending rigidity of fabrics with nonwoven fusible interlining is decreased with increasing repetition of drycleaning. The dominent decrease of bending rigidity after first dry-cleaning is shown, after first dry-cleaning, bending rigidity is almost the same with repeated dry-cleaning. The reason why bending rigidity is decreased with repetition of dry-cleaning is thought to be the slackening of intersecting point in the fabric weave structure. And it is shown that both the nonwoven interlining property and fabric structural parameters affect the bending rigidity of interlining fabrics. But, any change of bending rigidity in the weft direction with various fabric structural parameters and the repetition of dry-cleaning was not shown. Figure 3 shows warp bending hysteresis of the fabrics with various nonwoven fusible interlinings with repetition of dry-cleaning. Warp bending hysteresis is increased with repetition of dry-cleaning. This shows very different results comparing with the study[6] which is decreased with repetition of dry-cleaning in fabric interlining. The reason why bending hysteresis is increased with repetition of dry-cleaning seemed to be due to increase of the disorder of fibres in the nonwoven interlining, and it makes friction of interfibre high. This high bending hysteresis makes garment formability deteriorate. Figure 4 shows warp shear rigidity of fabrics with various nonwoven fusible interlinings. Fabrics with non-stitch nonwoven interlining (F3 in Table II) which showed the largest value in tensile work showed the largest values in shear rigidity. And warp shear modulus of nonwoven fusible interlining fabrics is decreased with repetition of dry-cleaning. And it is shown that the decrease of shear modulus after first dry-cleaning shows the largest value; on the other hand, after first dry-cleaning, shear modulus is almost the same as dry-cleaning is repeated by three and ten times. The reason why shear modulus as shown in bending rigidity is decreased with repetition of dry-cleaning is thought to be slackening of the intersecting point of the fabric weave structure. But these phenomena were also shown in the weft direction differently in bending rigidity. Figure 5 shows weft shear hysteresis of the fabrics with various nonwoven fusible interlinings with repetition of dry-cleaning. Nonwoven interlining fabric with F2 in Table II shows the lowest value and these phenomena were also shown in the warp direction. Especially, the shear properties of the nonwoven interlining fabrics like tensile property depend on the nonwoven fusible interlining materials properties rather than the fabric structural parameters, such as twist direction, amount of t.p.m. and weft density. Low shear modulus
Suitability of nonwoven interlining
B (Wp, gf.cm2/cm) 0.4 Twist Direction 0.35
Z
S
Z
S
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S
0.3 0.25 0.2
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0.15 0.1
0 1 2 3 4 5 6 7 8 Number of dry cleaning (a) B (Wp, gf.cm2/cm) 0.4 Weft density 0.35
9
10
58 62 66 58 62 66 58 62 66
0.3 0.25 0.2 0.15 0.1
0 1 2 3 4 5 6 7 8 Number of dry cleaning (b) B (Wp, gf.cm2/cm) 0.4 Number of Twist (t.p.m) 0.35
9
10
720 800 900 720 800 900 720 800 900
0.3 0.25 0.2 0.15 0.1
0 1 2 3 4 5 Number of dry cleaning (c)
6
7
8
9
10
Key interlining F1 interlining F2 interlining F3
and hysteresis of fabrics fused with F2 nonwoven interlining make wearing and appearance performances better. Pertinence to this phenomenon is shown in Figure 6. Change of fabric formability with the kind of nonwoven interlining and repetition of dry-cleaning Figure 6 shows formability, drape and TAV with various nonwoven fusible interlining fabrics with dry-cleaning, which are calculated by regression
Figure 2. Bending rigidity of warp direction to the dry cleaning number: (a) twist direction (b) weft density (c) number of twist
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Weft density 58 62 66 58 62 66 58 62 66
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Z
S
9
10
720 800 900 720 800 900 720 800 900
0.16 0.14 0.12
Figure 3. Bending hysteresis of warp direction to the dry cleaning number: (a) twist direction (b) weft density (c) number of twist
0.1 0.08
0 1 2 3 4 5 Number of dry cleaning (c)
6
7
8
9
10
Key interlining F1 interlining F2 interlining F3
equations composed of mechanical properties of fabric, proposed by Kawabata and Niwa. Figure 6(a) shows formability of various nonwoven fused interlining fabrics with dry-cleaning. Fabrics fused by F2 interlining show the highest formability (Z1), which is due to high bending rigidity and low shear modulus and hysteresis. Figure 6(b) shows drape property of fabrics. Drape component (Z 3) of fabrics fused by F2 interlining also shows the highest value and is
Suitability of nonwoven interlining
G (Wp, gf/cm.deg) 5 Twist 4
Z
S Z
S
Z S
3 2
279
1 0
0 1 2 3 4 5 6 7 8 9 Number of dry cleaning (a) G (Wp, gf/cm.deg) 5 Weft density 4 58 62 66 58 62 66 58 62 66
10
3 2 1 0
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6
7
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3 2 1 0
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(c) Key interlining F1 interlining F2 interlining F3
increased with repetition of dry-cleaning. Figure 6(c) shows total appearance value (TAV) which is calculated by formability and drape components. It is appreciated that good formability and drape make the TAV of a garment high. Therefore, it is shown that the nonwoven fusible interlining of F2, is the most compatible for thin worsted fabrics which are made by Kyeongnam Woolen Textile Company. This demonstrates that the difference of formability of
Figure 4. Shear stiffness of warp direction to the dry cleaning number: (a) twist direction (b) weft density (c) number of twist
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S
Z
S
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7
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66
8 6 4 2
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6
7
8
9
10
2HG5 (Wf, gf/cm) 12 Number of Twist 10
720 800 900 720 800 900 720 800 900
8 6
Figure 5. Shear hystersis at ϕ = 5° of weft direction to the dry cleaning number: (a) twist direction (b) weft density (c) number of twist
4 2
0 1 2 3 4 5 Number of dry cleaning (c)
6
7
8
9
10
Key interlining F1 interlining F2 interlining F3
garment by nonwoven fusible interlining is greater than that of the fabric structural parameter. Consequently, good selection of nonwoven fusible interlining, relevant to shell fabric, is required. And comparing with the previous study[6], the effect of nonwoven fusible interlining on the garment performance is less than that of the fabric fusible interlining, especially as the bending hysteresis of the nonwoven fusible interlining fabric is increased with repetition of dry-cleaning relative to the decrease for fabric fusible interlining.
Suitability of nonwoven interlining
Z1 3.5 Weft density 3
58 62 66 58 62 66 58 62 66
2.5 2
281
1.5 1
0 1 2 3 4 5 Number of dry cleaning (a)
6
7
8
9
10
Z3 4 3 2 1 0 Twist Direction
–1
Z
–2
0 1 2 3 4 5 Number of dry cleaning (b)
S
6
Z
S
7
Z
S
8
9
10
T.A.V 5 Twist Direction Z
4
S
Z
S
Z
S
3
2
1
0 1 2 3 4 5 Number of dry cleaning (c)
6
7
8
9
10
Key interlining F1 interlining F2 interlining F3
Conclusion Tensile work, bending rigidity, shear rigidity and hysteresis of the fabrics fused by nonwoven interlining are decreased with repetition of dry-cleaning, and it makes formability and drape of fabrics better, then bending hysteresis is increased with repetition of dry-cleaning. The nonwoven fusible interlining with needle punched, F2, is more compatible than the other two interlinings for thin worsted fabrics. Good selection of fusible interlining to shell fabric is required because the difference of formability of garment by nonwoven fusible
Figure 6. Formability (Z1), drape component (Z3) and TAV values with repetition of dry cleaning (a) Z1 (b) Z3 (c) TAV
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interlining is larger than that by the fabric structural parameters such as twist and weft density, then for the effect of fabric structural parameters to the formability of garment, the fabric with nonwoven fusible interlining is relatively less effective than that of fabric fusible interlining. References 1. Lindberg, J., Waesterberg, L. and Svenson, R.,. J. Text. Inst., Vol. 51, 1960, p. 1475. 2. Postle et al., Proceedings of 3rd Japan-Austral ia Joint Symposium on Objective Measurement, The Text. Mach. Society of Japan, Kyoto, 1985. 3. Ly et al., IWTO Tech. Committee Meeting, Report 9, Paris, 1980. 4. Kawabata, S., Postle, R. and Niwa, M., Objective Measurement: Application to Product Design and Process Control, The Text. Mach. Society of Japan, Kyoto, 1985. 5. Postle, R., Kawabata, S. and Niwa, M., “Objective evaluation of apparel fabrics”, The Text. Mach. Society of Japan, Kyoto, 1983. 6. Kim, S.J., Lee, D.H., Ha, J.H. and Bae, G.H., “A study on the fitness of fusible interlining to the thin worsted fabrics with various structural parameters”, The 9th Int. Wool Textile Res., 273, Italy, 1995.
Portable computer measuring systems for automatic process parameter acquisition in garment sewing processes
Portable computer measuring 283
Dubravko Rogale and Zvonko Drag˘cevi´c University of Zagreb, Faculty of Textile Technology, Department of Clothing Technology, Zagreb, Croatia Introduction In performing the technological operations of garment sewing, it is necessary to measure a number of process parameters, so as to determine their interdependence, as it is of utmost importance for research in the field of clothing engineering. Until the invention of the first computers, most of the measurements had been done by hand, using stopwatches or some other simple measuring equipment. As individual operations are of extremely short duration, it was rather troublesome to perform the measurements and write the results down by hand, while data analysis was usually a long and tiring job. Specialised equipment for measuring the values of garment sewing process parameters, based on computer measurements and measuring data acquisition, was developed some time ago at the Department of Clothing Technology, Faculty of Textile Technology, University of Zagreb. The system was capable of simultaneously measuring a number of parameters (sewing speed, average sewing speed, maximum sewing speed attained, sewing acceleration, number of stitches in a seam etc.). It could be linked with some other measuring equipment for the investigation of other processing factors[1-4]. The system used 386 processor and DOS operative system. Its key parts were as follows: •
Hardware: personal computer, A/D converter, measuring amplifier, IC converter, connecting cables.
•
Conventional measuring equipment: oscilloscope, digital voltmeter, digital frequency tester, digital stitch counter and stabilised rectifier.
•
Software: software packages for measuring, data acquisition, result calculation, statistical and numerical data processing and for data analysis.
The measuring equipment described has been developed in the course of scientific research project 117003, financed by the Ministry of Science and Technology of Croatia.
International Journal of Clothing Science and Technology, Vol. 10 No. 3/4, 1998, pp. 283-292, MCB University Press, 0955-6222
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The most notable disadvantages of the equipment were its bulkiness, complexity and immobility, so it was appropriate for laboratory work and could not be successfully used in real mill conditions. Trying to upgrade the equipment, and eliminate its disadvantages, the department has developed a new portable system for measuring garment sewing process parameters. Besides the converter for measuring standard process parameters, other conventional measuring instruments can be linked with it, as well as measuring converters for other process measurements and movement detectors, so the system is able to determine automatically the structure of technological operations in question. Key characteristics of the portable measuring system The following prerequisites were defined in the course of the portable system development (Figure 1): • the system elements should be as light as possible, to facilitate transportation; • the system elements should be of as small dimensions as possible; • all the elements should be portable by themselves; • the requirement for modularity and possibility of system element combination should be observed; • the equipment should be completely reliable; • measurements should be precise and accurate; • measuring capacity should be high; and • it should be easy and quick to install and put in operation.
CCD1 VCR1 VCR2 CCD2 AMD IR PID
Figure 1. Key elements of the portable computer systems for measuring process parameters
ADC MMA PC
The best results have been achieved using the adjustable console with an adjustable infrared (IR) converter, passive infrared movement detectors (PID), active microwave movement detectors (AMD), multichannel measuring amplifier (MMA), analogue/digital converter (ADC) and a personal computer (PC), as can be seen in Figure 1. Two video-cameras can be mounted with the measuring equipment (CCD1 and CCD2), to be used for taking pictures in two planes, as well as video recorders (VCR1 and VCR2). The characteristics of the elements of the system are described in the chapters which follow.
Portable computer measuring 285
Console with the IR converter The adjustable console has two long three-jointed bars. One end is attached to the working surface of the sewing machine, while the IR converter is attached to the other. The console is installed behind the sewing machine head, it is not in the line of view of the operator and does not interefere with his performance. The IR measuring converter MRL 601 is situated on the adjustable top of the console, and it functions as a contactless apparatus for measuring the rotation speed of the main sewing machine, as seen in Figure 2. The best results in IR radiation reflectance are obtained when the tip of the converter is some 5 to 15mm away from the reflecting surface. Power cables for the converter and the cables for the signals detected are connected to one of the inputs of the multichannel signal amplifier (MMA). Reflecting stickers A set of four or eight stickers, under 90° or 45° is attached to the sewing machine main shaft driving belt. The stickers should be able to reflect IR radiation. They ensure that four or eight reflecting impulses are fed into the IR converter for one revolution of the shaft. If the sewing machine main shaft driving belt reflects IR radiation itself, the stickers should be able to absorb it.
Figure 2. Infrared measuring converter situated beside the sewing machine main shaft, with reflecting stickers attached
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Movement detectors The purpose of movement detectors is to register automatically the start and completition of a technological operation. Two types of detectors have been tested in the Department for Clothing Technology: (1) Passive infrared movement detector, with a two-meter range, using a Siemens IR converter PID 20. The detector consists of an IR sensor element, optical filter, parabolic mirror and an electronic signal amplifier, as shown in Figure 3. It registers IR radiation in the range of ±10° from the optical axis. The device is very light and of small dimensions (38 × 26 × 35mm), and its sole disadvantage is that there should be an unobstructed view of the zone in which the movement is detected. (2) Siemens active microwave detector SMX-1, operates using Doppler effect and microwave radiation at the frequency of 9.36Ghz ± 0.03Ghz, with the radiation force of 1mW. The movement detector is able to register the speeds of movement in the range from 0.06m/s to 22.2m/s, at distances of 0.7 to 4m. The microwave sensor is not bulky (42 × 22 × 15 mm), measured together with the SDM elements (Figure 4), but is mounted on a somewhat bigger plate, 11 × 6cm (Figure 5), together with the electronic control assembly. The advantage of this detector is that unobstructed visibility from the sensor to the operator’s hands is not necessary. It can be visually covered by any kind of obstacle transparent for microwave radiation, so it can be mounted at a concealed spot, if necessary. Both these detectors are linked to the input gates of the multichannel measuring amplifier. Multichannel measuring amplifier The purpose of the multichannel measuring amplifier (MMA) is to ensure the power conditions for the infrared measuring converter (infrared radiation
Figure 3. Parabolic mirror and sensor for passive infrared movement detector
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Figure 4. Microwave sensor with an SMD plate
diode), as well as to detect and amplify the signals reflected. The signals reflected and amplified are then guided to the signal shaper and monostable multivibrator, in order to get the signals of constant width and varying frequency. As a result of this, a DC voltage occurs in the voltage to frequency converter, it is amplified and directed to the A/D converter. The other inputs to the MMA are used for the amplification and shaping of the signal from the movement detector, as well as for the amplification of the
Figure 5. Printed circuit with a microwave sensor and electronic assemblies
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signals from the other measuring devices installed on the sewing machine. The multichannel measuring amplifier has the dimensions of 8 × 12 × 6cm. A/D converter A highly precise A/D converter of very small dimensions should be used in the portable measuring system described here. The A/D converter used in the department is 6 × 11cm (Figure 6). The converter has eight input ports for analogue signals, with the permissible voltage of 0 to 5V. Converter resolution is 12-bits, so the input signal of 5V can be divided into 4,096 parts, whereby each part has the volume of 1.22mV, which means the basic accuracy of the converter is 0.02 per cent. IC LTC 1290DCN (Linear Technology) is used for analogue/digital conversion, in which input signal multiplixer and A/D converter are integrated. High level of precision of the A/D converter is further enhanced by an extremely accurate referent voltage source (LT 1021-5), with a temperature drift of only 1ppm/K, so the temperature change of 20° to 30°C results in only 0.001 per cent change of the referent voltage. A/D converter inputs are linked through a nine-pin SUB-D converter to the multichannel measuring amplifier and COM input gate of the personal computer. A/D converter energy consumption is insignificant, so the power can be drawn from the computer and no outer source is necessary (DTR-Data Terminal Reday and RTS-Request To Send lines are used). Portable computer IMB compatible portable computer with the operative system Windows 95 or Windows NT can be used, with 486 or Pentium processor, 8MB of RAM memory, hard disk capacity of 16MB or more and at least one serial (COM) input gate, where A/D converter is linked. Measuring software packages and packages for data processing, measuring system control, storing and analysis of the processing parameters measured and calculated for garment production operations are stored in the computer memory.
Figure 6. Printed circuit of the analogue/digital converter
Two-plane video system Two video-camera system with two video-recorders can be used with the computer measuring system for the purpose of taking pictures in two planes (ground plane and side view of the work station). Video-tape analysis can be used for some detailed clarifications in process parameter analysis, and videorecorder tapes can also be analysed employing a program for video-tape processing, in order to achieve certain goals in the field of work study, work method operation etc. Results and conclusion The measuring system described, as shown in Figure 1 in its minimal configuration, uses simultaneously three A/D converter analogue input gates (sewing machine main shaft rotation speed, detection of the movement of taking the workpiece and detection of the movement of putting-off the workpiece). The system is able to measure and calculate process parameters, as well as the elements necessary for the time analysis of certain parts or the whole of the operation, key to work study investigations. The software for measuring purposes has been constructed so that the presentation on the screen can be of three kinds: (1) process presentation; (2) on-line presentation; and (3) analytical presentation. Process presentation is shown in Figure 7. The upper part of the presentation shows basic measuring instruments which show the values measured (work piece taking, foot pedal position, sewing speed, work piece laying off etc.) in a computer display of analogue instruments, digital instruments or the instruments which show the value measured in the form of a column. The lower part of the screen shows diagrams essential for the measuring procedure. The main purpose of this presentation is to check the measuring system before a sequence of measurements, and to check the accuracy of each and every instrument or measuring channel. On-line presentation is used to monitor the measuring sequence and for measuring data acquisition. This presentation is active throughout the measuring sequence. Simultaneous monitoring of all the process measuring values is performed here, and they are presented in the form of a diagram on the screen. Figure 8 shows one of the variants of the on-line presentation. The application of passive and active movement detectors makes possible the implementation of the equipment as a measuring system for automatic determination of the structure of the operations in question. If this is the case, the output signals from the movement detector become mark points denoting the start and completition of the operation. Movement detectors are able to register the movements of taking workpiece and putting it off with a high degree of accuracy and completely automatically, so there is no possibility of an error due to slowness of the human operator.
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Figure 7. Process presentation on the screen
The first diagram (Figure 8) shows the output signal from the sensor of the workpiece taking movement. The upward slope represents the initiation of the operation itself. The second diagram shows the functional dependence of the sewing speed changes upon time. The third diagram shows the position of the foot pedal, clearly indicating the neutral position of the pressure foot, its activation for the purpose of lifting the pressure foot, sewing speed regulation and sewing thread cutting sequence initiation. The fourth diagram shows the output signal from the sensor of the workpiece laying-off movement. The upward slope of the signal represents the completition of the operation. All the measuring data presented in the form of a diagram can be, after the measuring sequence has been completed, stored on the measuring system hard disk and prepared for future analysis. Analytical presentation is used for further analysis of the measuring results. All the signals measured, shown in various colours, are superimposed on the same diagram, with only the temporal axis in common for all of them. This manner of presentation is shown in Figure 9 and it makes technological operation analysis considerably easier. Analysis is also made easier by two additional markers, the positioning of which can very precisely define the time intervals and values of the parameters measured. Measuring data, permanently stored in databases, can also be used for additional numerical and statistical analyses.
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Figure 8. On-line presentation on the screen
Figure 9. Analytical presentation on the screen
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Five more input ports are free for simultaneous measuring of other parameters employing the same measuring equipment. Measuring converters for some other measurements (such as measuring converters for the detection of the workpiece shift during sewing, for monitoring the position of sewing machine pedal regulator, for measuring sewing thread tension, for measuring puncturing forces of machine sewing needles, needle temperature, etc.) can be linked to these ports. The versatility of the equipment is greatly enhanced in this way, so it can rightly be considered a universal tool for investigating processing parameters in real in-plant conditions, and, as such, a necessary part of the equipment for anybody dealing with scientific research in clothing engineering. References 1. Rogale, D., “Measuring methods and equipment for determination of process parameters of technological operations of clothing production, Tekstil, Vol. 40 No. 12, 1991, pp. 587-91. 2. Rogale, D. and Drag˘cevi´c , Z., “Methods of measuring process parameters on designed workplaces in the clothing industry”, Proceedings of International Conference on Engineering Design, Vol. 3, Dubrovnik, 28-31 August 1998, pp. 1689-96. 3. Rogale, D., “Garment sewing processing parameters: determination using numerical methods and computers”, International Journal of Clothing Science and Technology, Vol. 7 No. 2/3, 1995, pp. 56-60. 4. Rogale, D., Knez, B. and Drag˘c evi´c , Z., “Processing parameters of garment sewing operations during flat seam joining”, Tekstil, Vol. 46 No. 2, 1997, pp. 75-86.
Influence of mechanical and physical properties of fabrics on cutting process Rozalija Bleka˘c
Influence of mechanical properties 293
Elkroj Proizvodnja modne konfekcije d.d., Mozirje, Slovenia, and
Jelka Ger˘sak University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia Introduction Practical use of microcomputers’ scientific achievements in the textile industry can be seen in the area of process preparations, particularly in product development, in the preparation and pattern grading of pattern pieces, in pattern shapes preparation and optimisation, in cutting optimisation, in automatic spreading of fabrics into a lay and cutting of a lay. With modern automatic cutting machines it is possible to achieve automated technological cutting process, but in the production process some problems due to fabric processing properties and due to material mechanical and physical properties can appear. According to the above mentioned, the influence of material mechanical and physical properties on the automatic cutting of a lay into garment parts will be studied. The analysis of parameters on cutting of a lay The quality of a lay cutting into garment parts depends on interactions between the forces of the knife blade and the resistance of the fabric, and on mechanical and physical properties of the fabric. Study of knife blade technological forces in the cutting process During the cutting process, i.e. cutting of a lay into the garment parts, the material is exposed to forces which are caused by the cutting knife blade, which moves continuously in two plains, vertical in z direction and horizontal in x and y directions, this means, in the direction of pattern shapes lines (Figure 1). The force of cutting which has an effect on the material depends on: • angle of cutting αr; • velocity of knife blade vertical movement vv; this means, the frequency and the distance of knife blade movement; and • velocity of horizontal movement of cutting head vh.
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294 Figure 1. The forces which act on the knife blade in a fabric lay
vh A vs
vv
C2
B1 αb/2
C1
The angle of cutting α r, can be the same or lower than the angle of blade sharpening αb. When the blade moves in the horizontal direction, the angle of cutting αr is the same as the angle of sharpening αb. When the blade moves in the vertical direction, it passes through the cutting layer at the angle β, due to simultaneous movement in the horizontal direction. According to analysis of forces which act on a fabric lay at the vertical movement (Figure 1), it is shown that the knife blade makes the distance of s1 in the time interval of t: (1) and the distance s2 when the horizontal movement is involved: (2) The total route ss = AB2 which is done by the blade in the time interval of t, in both directions – horizontal and vertical, is shown by the equation: (3) where: αb = sharpening angle; αr = cutting angle. The total velocity of knife blade vs is given by the equation[1]: (4) where: vv = velocity of blade vertical movement; vh = velocity of blade horizontal movement.
(5)
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and second, according to work of blade due to horizontal movement at the distance s2:
295
Work of knife blade due to cutting of a lay can be presented first, according to work of the blade which penetrates through the material at the distance s1:
(6) The total work which is done by the knife blade in the process of cutting can be presented by the equation: (7) where: F1 = force of the knife blade due to its vertical movement; F2 = force of the knife blade due to its horizontal movement; Fs = force of the knife blade to the cutting lay. From the above given derivation it is possible to see that the work of the blade due to vertical movement – this means up-down movement, equation (5) – depends on the frequency of blade movement and on the force of cutting layer resistance. Work due to horizontal blade movement – this means in the direction of cutting line – depends on the angle of cutting and on the force of warp and weft yarns’ resistance against the blade (equation (6)). Total work of blade at horizontal and vertical movement (equation (7)), depends on the cutting angle αr and on the force of material resistance against the blade. Because of the force of material resistance and because of frictional forces between material and the blade, the blade deteriorates. This increases the sharpening angle αb and the angle of cutting αr, respectively. If we presume that at the blade sharpening the distances B1C1, and B2C2 remain the same at the constant work As, the work put in in material cutting after blade sharpening can be presented by the equation: (8) where: α′r = cutting angle after sharpening; Fs = the force of sharpened blade onto the cutting layer.
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The force of the sharpened blade onto the cutting layer can be presented by the equation: (9)
296 Because of blade sharpening the cutting angle increases accordingly: (10) This means that the force of the sharpened blade onto lay is F′s> Fs. The influence of material type and properties on cutting of the fabrics’ lay Fabrics are exposed to compressive and tensile stress. During the cutting, the fabrics are exposed to compressive stress which means to the force of blade onto the lay during the vertical movement and to the tensile stress which means the force of the blade due to horizontal movement onto the system of warp and weft yarns respectively which are perpendicular to the weft direction of cutting. During the cutting of fabrics into garment parts the most exposed yarns are the weft yarns, because mainly all cutting parts lay out in the warp direction. Because of this, the response of weft yarns to the force which acts due to the horizontal component of blade movement is very important. The force of fabrics’ resistance against the blade depends on velocity of blade vertical movement in the up and down direction (the frequency of knife blade), on velocity of blade horizontal movement, on fabric material composition, on fabric weave, density and fineness of the weft and the warp yarns, finishing, air permeability, and on mechanical and physical properties. The main influence on the behaviour of fabrics which are under the resistance against the compressive and tensile stress of the blade, have the elongation of fabrics, bending and shear rigidity of fabrics and fabric air permeability, which assures the adequate vacuum. When the fabrics have low air permeability, the air penetrates through the pores and causes more or less expressed deformations, which can be presented as non-smooth lines of garment parts and as a drift of warp and weft yarns. Methods According to known technological conditioned forces during the cutting of a lay, the influence of mechanical and physical properties of fibres onto an automatic cutting of lay into the garment parts were studied. Particularly, the problems which may arise, regardless of modern and advanced technology, and are the consequences of specific properties of used fabrics, were studied.
Materials For the analysis of the influence of fibres’ mechanical and physical properties onto the cutting of a lay 12 different fabrics were used (Table I). Mechanical and physical properties are shown in Table II. Research methods According to the aim of our research the influences of mechanical and physical properties on cutting of the lay were studied. For these reasons mechanical and physical properties of 12 different fabrics for women’s trousers were analysed. For all analysed fabrics tests for air permeability were carried out according to DIN 53887 [2] (Table III).
Fabric code
Weave colour
Chemical composition
TK01
Twill Yellow Twill Black Twill Grey
95% Co 5% elastane 95% Co 5% elastane 54% PES 44% volna 2% elastane 98% Co 2% elastane 65% Co 33% PA 2% PUR 60% PES 37% viscose 3% elastane 60% PES 37% visk. 3% elastane 60% PES 37% viscose 3% elastane 55% PES 45% viscose 55% PES 45% viscose 55% PES 45% viscose 55% PES 45% visk.
TK02 TK03
TK04 TK05
TK06
TK07
TK08
TK09 TK10 TK11 TK12
Plain Brown Twill Dark blue Plain Grey-green Plain White Plain Black Twill White Twill White Twill Blue Twill Green
Influence of mechanical properties 297
Yarns density in 10mm Warp Weft 33
26
33
27
24
26
34
22
56
36
16
18
16
18
16
18
57
34
57
34
57
34
57
34
Table I. Type of fabrics used
13.3
12.9
4.8
1.6
7.0
5.6
3.5
1.6
1.8
2.4
2.9
TK04
TK05
TK06
TK07
TK08
TK09
TK10
TK11
TK12
1.8
1.7
2.3
2.1
4.4
4.1
4.3
5.0
12.2
10.3
1.7 1.2
7.7
1.7
6.4
4.9 47.3
50.5
47.3
44.2
6.5 4.3
35.8
36.7
10.7
10.6
30.4
59.0
3.6 8.8
64.7
15.8
127.0
5.4
5.3
31.0
134.0
Shear rigidity G/Nm–1
0.19
0.15
0.12
0.14
0.66
1.05
0.90
0.09
0.81
1.53
8.12
6.57
0.15
0.13
0.13
0.16
0.22
0.29
0.24
0.20
0.93
0.86
5.17
4.44
Formability F/mm2 Warp Weft
0.461
0.458
0.487
0.499
0.629
0.568
0.703
0.373
0.431
0.688
1.233
1.035
Thickness h/mm
Underlined values refer to mechanical and physical properties of the fabric which caused problems in the process of cutting of a lay into garment parts.
20.4 per cent is a maximum value which can be detected by FAST measurement system. The values of fibres’ elongation are outside the instrument measurement range, so the actual values are larger.
a
4.4
9.9
17.8
TK03
5.0
44.2
>20.4 a
>20.4 a
TK02
3.7
40.8
16.0
>20.4 a
TK01
40.0
Bending rigidity B/µNm Warp Weft
Table II. Results of mechanical and physical properties measurements for used fabrics
Extensibility of fabric E100/% Warp Weft
298
Fabric code
151
156
156
151
225
217
217
130
178
207
310
286
Weight m/gm2
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Air permeability Fabric code
lm–2s–1
m3m–2min–1
TK01 TK02 TK03 TK04 TK05 TK06 TK07 TK08 TK09 TK10 TK11 TK12
77.064 52.364 360.62 54.34 36.556 276.62 261.82 267.62 54.34 54.34 54.34 54.34
4.62 3.142 21.637 3.260 2.193 16.597 15.709 16.597 3.260 3.260 3.260 3.260
Currently, the analysis of fabrics’ behaviour during the cutting into garment parts was carried out. For each fabric, five lays, which differ according to their length and according to the number of spreads, were made. When the behaviour of fabrics in the cutting process were studied, besides mechanical and physical fibres’ properties some other influences were studied as follows: • the power of vacuum; • the sharpness of knife blade respecting the frequency of knife blade sharpening; • the velocity of vertical movement or the frequency of the knife blade; and • the velocity of horizontal movement of the knife blade or the velocity of cutting. Results The research results of influences of mechanical and physical fabric properties onto the cutting of a lay are summarised according to the behaviour of fabrics during the cutting process and according to detection of line deformation (Table IV). Deformations of garment parts after cutting from the fabrics labelled as TK04 showed the deformed contours as presented in Figure 2. Discussion According to analyses of measurements of mechanical and physical properties of applied fabrics and according to results of air permeability and analysis of fibres’ behaviour during the cutting process, it can be seen that the material
Influence of mechanical properties 299
Table III. Results of air permeability measurements for fabrics used
Table IV. Results of behaviour of analysed fabrics at cutting of the lay into garment parts
TK06
TK05
TK04
TK03
TK02
TK01
Fabric code 1
KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5
249 246 244 239 239 249 248 244 249 249 395 360 239 125 132 298 324 158 178 178 476 357 234 240 242 490 489 475 251 121
40 20 10 10 8 28 30 18 10 6 15 17 8 4 5 18 22 8 20 4 22 20 15 8 3 15 9 32 8 1 7
7
9
7
8
8
25
23
25
28
32
32
6
6
4* Frequent sharpening
6
6
6
Frequency of sharpen Max length 7 0.18 0.20 0.20 0.20 0.20 0.19 0.19 0.20 0.20 0.21 0.23 0.23 0.24 0.24 0.24 0.17 0.16 0.17 0.17 0.19 0.16 0.16 0.17 0.18 0.19 0.15 0.15 0.12 0.15 0.20
Vacuum power 8
X X X X
X X X X X X X X X X
X X X
Difficult to restore vacuum
Difficult to restore vacuum
No problems at lower vacuum (Continued)
Different size of lower and upper layers
KP2 shorter for 1cm
Deformation at cutting Dimension change Frayed Snagged Unsulff. Fabric Garment edges edges vacuum lay parts Notes 9 10 11 12 13 14
300
Label of Length Blade Velocity fabric of lay Number frequency of cutting lay Lkp/cm of piles s–1 v/m min–1 2 3 4 5 6
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TK12
TK11
TK10
TK09
TK08
TK 07
Fabric code 1
KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5 KP1 KP2 KP3 KP4 KP5
475 244 230 215 123 489 251 227 471 242 487 480 234 248 123 487 239 483 120 124 487 465 248 236 249 487 188 239 473 120
59 18 11 4 1 15 5 10 20 1 11 9 5 3 1 10 4 10 2 1 18 14 8 4 1 8 4 2 8 1 7
7
7
7
7
7
2.5
2.5
2.5
2.5
2.5
2.5
Label of Length Blade Velocity fabric of lay Number frequency of cutting –1 lay Lkp/cm of piles s v/m min–1 2 3 4 5 6
6
6
6
6
6
6
Frequency of sharpen Max length 7 0.11 0.15 0.15 0.17 0.20 0.15 0.17 0.16 0.15 0.20 0.19 0.20 0.20 0.20 0.20 0.19 0.20 0.19 0.20 0.20 0.17 0.17 0.19 0.20 0.20 0.19 0.20 0.20 0.19 0.20
Vacuum power 8
X X X X X X X X X X X X X X X X X X X X
Deformation at cutting Dimension change Frayed Snagged Unsulff. Fabric Garment edges edges vacuum lay parts Notes 9 10 11 12 13 14
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Table IV.
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302 Figure 2a. Deformation of the fabric labelled as TK04 during the cutting of the lay. The contour of the garment part cut with the new blade
Figure 2b. The contour of the garment part cut with the sharpened blade
with mechanical and physical properties has a crucial influence on the quality of cutting the fabric lay into garment parts. By cutting of the fabric into garment parts for trousers where mainly all cutting parts lay in the warp direction, the quality of cutting depends mainly on the elongation of weft yarns which are mainly exposed to cutting; this means to the horizontal component of the blade movement. Some problems in the cutting process can be detected with the fabrics which have low bending rigidity (at bending rigidity lower than 5µNm). With the use of a vacuum cutting table, where the fabrics lay is fixed, this problem can be overcome. The ability of cutting lay vacuum fixing depends on the material air permeability. Material with low air permeability needs longer time to reach the
adequate pressure and during the cutting process itself it is a problem to maintain sufficient pressure. Sometimes, when we cut such a material, faults in the form of “unclean edges” can appear. Because of the low air permeability the air penetrates into the lay mainly through the cutting lines and does not penetrate into the material itself or between the folds. Power of the air flow increases due to very intensive vacuum and it is so high that some of warp and weft yarns can be snagged out from the fabrics. Such deformations can be detected mainly at slightly curved lines of cutting parts because the weave stability in the area of curved lines is lower than in the rest of the fabric and in these areas the lower forces are needed. Deformations due to low air permeability of fabrics can be shown in different dimensions of garment parts. The upper layers of the cutting lay are exposed to the force of air which penetrates through the cutting lines, the lower layers are exposed to direct vacuum which causes a slight increase in fabric stress. Because of these phenomena, the upper and the lower layers have larger dimensions compared to other layers. Layers in the middle of the cutting lay are fixed between the upper and the lower layers and are not exposed to such stress. Furthermore, it can be seen that the behaviour of analysed fabrics during the cutting process varies. Fabrics with the codes TK01 and TK02, which are highly elastic (higher than 20.4 per cent), showed some deformations of cut garment parts cutting lines. During the cutting, the system of weft yarns is exposed to horizontal component of the blade movement. Because of yarns’ elasticity the system of weft yarns resists against the blade forces and some strain of weft yarns occurs. The knife cuts the yarn when the force is so high that the yarn can not bear the force any more. The deformations are shown as the drawn out yarns from the fabric structure. Some problems during the cutting are shown at the sample coded TK04 (98 per cent Co and 2 per cent elastane blend), as well. The main influence is shown in weft yarns. The elongation in this direction is 12.9 per cent and has negative influence on the cutting quality. The main problem is to reach and to maintain the adequate vacuum. During the cutting of the lay, the vacuum decreases from 0.23 to 0.17 bar, due to the air flow which penetrates the lay through the cutting lines. Despite the material which allows high blade frequency (the frequency is 9 s–1), some deformations of the cutting lines can be seen. These deformations area consequence of yarn structure in the fabrics. The warp yarn is the twoyarn thread, and the weft yarn is spun yarn and contains elastane. Elastane in the weft yarn resists against the cutting forces. The elastane has the characteristics of rubber yarn[3] and during up and down movement of the blade some yarns attach themselves to the blade and oscillate. This phenomenon leads to deformations in the cutting lines and some yarns are drawn out from the fabric’s weave. Furthermore, it can be concluded that light fabrics with high density of warp and weft yarns, with low bending rigidity and at the same time with low air permeability, have problems maintaining sufficient vacuum. Because of unsuccessful vacuum, the cutting lay is not fixed enough and this decreases the
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force with which the cutting lay resists the forces of the blade. When small parts are being cut, deformations of upper and lower layers occur, which are shown as uneven lines. Layers in the middle are less prone to deformations because they are fixed between the upper and lower layers. During the cutting some deformations of cut garment parts due to low air permeability occur more significantly and some yarns are drawn out from the fabrics. The air penetrates through the cutting lines and yarns become loose and some yarns at the edge of cutting lines can be drawn out from the fabrics. Conclusion Mechanical and physical properties of fabrics and the study of their influence on the technological process of cutting is essential knowledge for planning and managing the cutting process. According to analysis of fabrics’ behaviour during the cutting, it is possible to see that mechanical and physical properties of fabrics and technical-technological parameters of cutting have a great influence on the technological process itself. Among mechanical and physical properties of fabrics, which have influence in the cutting process are: fabrics strain, bending and shear rigidity and air permeability. The quality of cutting depends on technical-technological parameters, such as: velocity of the knife blade, vertical movement or the frequency of knife blade, velocity of horizontal movement or the velocity of cutting and the cutting angle. References 1. Knez, B., “Tehnolo˘ski procesi proizvodnje odje´ce”, Sveu˘cili˘ste u Zagrebu, Tehnolo˘ski fakultet, Zagreb, 1990, pp. 38-57. 2. DIN 53887: Bestimmung der Luft-durchlässigkeit von textilen Flächenge-bilden, July 1977. 3. Morton, W.E. and Hearle, J.W.S.H., Physical Properties of Textile Fibres, 3rd edition, The Textile Institute Manchester, Manchester, 1993.
The dynamics of fabric drape
The dynamics of fabric drape Ron Postle and Jacqueline Rebecca Postle
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The University of New South Wales, Sydney, Australia Introduction Textile fabrics possess relatively good tailoring and draping properties. We now understand that the complex three-dimensional deformation characteristics of textile materials arise from their particular combination of basic mechanical properties such as tensile, shear, bending and compression behaviour. However, the same properties often yield the familiar problems of fabric buckling or puckering and wrinkling in tailored clothing. As we continue to extend the limits of fabrics and textile processing in terms of thin lightweight fabrics to be tailored into more casual styles of clothing than is traditional, we are introducing more problems associated with tailoring performance, draping, folding and ease of manipulation, buckling, puckering and wrinkling. The deformations of textile materials in buckling, wrinkling, folding and drape may be mathematically modelled simultaneously by considering largestrain low-stress response of the material under appropriate boundary conditions. In some instances, especially fabric drape and folding, we need to consider the weight of the fabric. Fabric weight may also be important in some situations of recovery from deformation, for example, wrinkle recovery. Moreover, it is necessary to formulate a general model of these deformations by considering the inelastic mechanisms of fabric rheology, viz. fibre viscoelasticity and interfibre friction, which are of particular significance for the initiation of fabric buckling or puckering as well as for fabric recovery from buckles, folds, wrinkles and creases. The difference between these types of deformation is often a matter of the practical situation, environmental conditions and applicable boundary conditions rather than the basic mechanisms of fabric deformation. Nonlinear mathematical model of fabric buckling and folding In this section, it is assumed initially that the fabric behaves in a linear elastic manner (i.e. the bending moment is a linear function of the fabric curvature). A mechanical model for the buckling or folding of elastic materials can be derived by balancing the fabric bending moments expressed as the nonlinear differential equation: (1a)
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where the input fabric parameters are P/B, the buckling force per unit fabric bending rigidity, and B/w, the, fabric bending rigidity per unit weight which is equal to the cube of the fabric bending length (as introduced by Peirce[1] ). These input parameters are normalised with respect to the material length, l. The dependent variable s is the normalised distance along the buckled fabric profile. The variable ψ is the angle between the tangent and the horizontal or x/l axis along the buckled fabric profile. These variables are shown in Figure 1. The boundary conditions for one half of the buckled fabric profile (the other half following by symmetry) are: at s = 0 (the inflexion point), dψ/ds = 0 and at the point of maximum curvature, s = 1 and ψ = 0 and l is the length of the buckled fabric which is twice the length of fabric from s = 0 to s = 1. The differential equation (1a) can now be solved numerically with negligibly small errors using mathematical computing and discretising along the fabric. It is convenient to normalise all parameters of the model with respect to the length l of the buckled fabric so that all input and output fabric parameters and variables are dimensionless. This procedure makes the model universally applicable to all fabric buckling, folding and wrinkling problems having the appropriate boundary conditions. For example, an increase in the value of the input parameter Pl2/B, which we refer to as the “normalised buckling force”, could in a real problem represent an increase in either the actual buckling force P, or the square of the length l of the buckled fabric profile or alternatively a reduction in the fabric bending rigidity B, or any proportionate combination of these changes. We would expect the curved fabric profile l to be small in a winkling problem, whereas this parameter would be orders of magnitude larger for fabric folding and intermediate lengths would apply for fabric buckling and puckering in garments. The computed output of this model is ψ as a function of s which can be expressed as the material profile. Figure 2 shows the computed results of the fabric buckling model using equation (1a) for the left half of the buckled fabric (the right half being symmetrical with respect to the left) initially assuming a fabric of zero weight (w = 0) for inputs Pl2/B = 10, 14, 21.2. Figure 2(a) shows the actual buckled fabric profiles where the axes are normalised with respect to the fabric length l. Figure 2(b) shows the outputs of the model: the tangent angles to the horizontal along the fabric, ψ (s). Figure 2(c) shows the curvature y l
s=1 ψ=0 ψ
Figure 1. The parameters and boundary conditions used for the mechanical model of fabric buckling
P s=0 dψ = 0 ds
0
P x l
dψ/ds along the fabric which illustrates the bending moments involved when The dynamics of multiplied by the fabric bending rigidity B. As shown, the bending moments fabric drape increase with the normalised fabric buckling force, Pl2/B. Figure 2(d) shows the change of curvature d2ψ/ds2 along the fabric. This last parameter is the most sensitive to the change in inputs and therefore the change in the shape of the buckled fabric profiles is clearly reflected in the graph of d2ψ/ds2.
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Modes of buckling As the normalised input to this fabric buckling model Pl2/B increases, either the buckling force per unit fabric bending rigidity P/B or the length of fabric being buckled l, must be increasing. As Pl2/B becomes very large, higher complex modes of buckling will occur as shown in Figure 3. The occurrence of these higher modes of buckling will therefore be favoured when a large length of very flexible fabric is buckled or folded. The graph in the top left of Figure 3 shows a fabric which has a value of Pl2/B just large enough to initiate fabric buckling (Pl2/B > 9.87). The first column of Figure 3 shows the simple classical (1,0) mode of buckling. For larger normalised buckling forces, the fabric profile will “bulge” as the ends are brought together until they touch. There is then a region of no fabric buckling 24 < Pl2/B < 247, after which the more complex (3,2) mode of buckling occurs as y l 0.4
ψ 2
0.3 1.5 (3)
(2)
0.2 0.1
(1) –0.5
dψ ds
–0.25 (a)
0.2
0.4
0.5
0 x l 0.6
0.8
–1 (1) –2
(2)
(3)
1 (1) 0.2
0.4
0.6
0.8
(b)
1
d2ψ ds2 –1
s 0.2
0.4 (1) 0.6
–2 (2)
–3
–3
(2)
–4 (3)
–4 (c)
s 1
(3)
–5 (d)
0.8
1
Figure 2. The numerically computed output of the elastic fabric buckling model described by equation (1) expressed as: (a) the fabric profiles; (b) the actual output, the tangent angle to the horizontal ψ as a function of s along the curve; (c) the curvature, dψ/ds and (d) the change of curvature d2ψ/ds2 for the left half of the buckled fabric for inputs: (1) Pl2/B = 10, (2) Pl2/B = 14 and (3) Pl2/B = 21.1
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0.1
Pl2 = 248 B Pl2 = 10 B
x l
0.3
–0.5
–0.25
–0.25
Pl2 = 1308 y B 0.05 l
0.5
–0.1
0.1
–0.5
0.25 –0.05
0.2
308
0.05
y l
0.4
y l
x l 0
0.25
x l
0.5 –0.15
Pl2 B Pl2 = 14 B
0.1
= 392 0.05
y l
0.4
x l –0.5
0.3
0.15
y l
–0.25
0.25
–0.05
0.5
–0.05 0.05
0.2 –0.1
0.1
Figure 3. Numerically computed fabric profiles describing the output of the buckling model of equation (1a) as the input Pl2/B increases: the (1,0) buckling mode in the first column, the (3,2) buckling mode in the second column and the (5,4) buckling mode in the third column
–0.5
Pl2 B
–0.25
x l 0
0.4
= 21.2
0.25
Pl2 = 1440 B
0.5
0.1
Pl2 = 448 B
y l
y l
x l
y l
–0.15
0.15
0.05
x l –0.5
0.3
–0.25
0.25
–0.05
0.5 –0.05
0.2 –0.1
0.1
–0.5
–0.25
x l 0
0.25
0.5
shown in the second column of Figure 3. The profile develops into a “meandering river” shape until the sides of the material touch. After another flat region 452 < Pl2/B < 1,308 in which no buckling occurs, the profile of the buckled fabric evolves into the (5,4) buckling mode shown in the third column of Figure 3. As the normalised buckling force Pl2/B continues to increase, more complex higher modes of buckling evolve. e.g. the (7,6), (9,8), (11,10), … modes. always with intermediate regions between each mode where no buckling occurs. Nonlinear fabric dynamics We have seen very rapid progress in recent years in the study of nonlinear dynamics when applied to many problems in physics and engineering. Examples include oceanography, ultra-short pulses in electronic communi-
cations using optical fibres, propagation of a crystal dislocation and energy The dynamics of transport in complex biological systems. The mathematical analysis of these fabric drape problems has much in common with the mechanical and dynamical phenomena associated with fabric drape, folding, buckling, wrinkling and associated fabric characteristics. We shall now formulate the mathematical model in such a way that it is 309 capable of predicting the exact configuration of a fabric draped in three dimensions. To achieve this aim, it would be a major advantage if our nonlinear differential equation (1a) could be extended from two dimensions to three dimensions and solved analytically rather than by numerical computation as has been the case in previous sections of this paper. Numerical computation of solutions of nonlinear differential equations can be very tedious especially if iteration is involved. This is the case when interfibre friction within the fabric is considered. Furthermore, numerical computation is not always stable as the solution becomes chaotic for specific values of the input parameters. Such problems would be completely overcome if we could find true analytical mathematical solutions to equation (1a) as these solutions would be universally applicable and could be directly related to the fabric mechanical properties. In the previous sections of this paper, two-dimensional buckling and folding fabric deformations have been modelled by numerical computation. We now consider three-dimensional fabric drape to be analogous to the flow behaviour in nonlinear fluid dynamics. In the same way that a two-dimensional wave forms in water and propagates along the surface, a portion of a fabric is deformed (bent, buckled, sheared or stretched) and this deformation travels yarn by yarn along the fabric evolving into a three-dimensional drape configuration. In the 1920s, Klein and Gordon derived a relativistic equation for a charged particle in an electromagnetic field, using the recently discovered ideas of quantum theory. Their Klein-Gordon equation is a mathematical generalisation of our fabric buckling nonlinear differential equation (1a). The Klein-Gordon family of equations is invariant under the Lorentz transformation, i.e. once one solution is known, an infinite number of other solutions can be found by coordinate transformations. One particular member of the Klein-Gordon family of equations, the so-called “sine-Gordon” equation reduces to (2a) Equations in the Klein-Gordon family of equations (including equation (1a)) can be obtained from this equation by coordinate transformations. The sine-Gordon equation has also been applied to mechanical models, magnetic-flux propagation in a large Josephson junction for superconductors, Bloch-wall motion in domain wall dynamics in magnetic crystals, propagation of ultra-short optical pulses in fibre optics and a unitary theory of elementary particles. The sine-Gordon equation was not considered solvable until the development of mathematical Bäcklund transformations in solitary wave
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(soliton) theory. The sine-Gordon equation is now known to be an integrable nonlinear differential equation, meaning that true analytical solutions can be found which do not become chaotic for any range of the variables x and t. These solutions are called soliton or solitary wave solutions which have the unique property of being described analytically and possessing an infinite number of conservation laws. Also, from one solution, an infinite number of other solutions can be obtained by using Bäcklund transformations (a purely algebraic procedure). Bäcklund transformations are a generalisation of surfaces of constant negative curvature. (Background on this area of mathematics can be found in[2].) They transform one pseudospherical surface into another surface of the same total curvature. On these two surfaces, the lines of curvature correspond, the line of corresponding points is tangent at these points to the surface and has a constant length, and the tangent planes at corresponding points meet at a constant angle. Let ω be the supplementary angle between intersecting geodesic lines so that our co-ordinates are polar geodesic as shown in Figure 4. The parameters α and β represent the asymptotic lines. When the transforms of a given pseudospherical surface are known, the Gauss equation becomes (2b) which is also now known as the sine-Gordon equation in “characteristic” coordinates where α = 1⁄2 (x – ct ) and β = 1⁄2 (x + ct ). If ω = φ (α,β) is a solution of equation (2b), then ω1 = φ (αm, β/m) is also a solution, where m is any constant. Therefore, from one pseudospherical surface, an infinity of other pseudospherical surfaces can be obtained by solving this equation. Lamb[3] reintroduced a classical Bäcklund transformation of the sineGordon equation (2b) that leaves the equation invariant and used this transformation to construct multi-soliton solutions. Figure 5 shows the analytical solution, known as the stationary breather which is expressed mathematically as (3)
O’’’
Figure 4. Polar geodesic parameters used in differential geometry for Bäcklund transformations
O’’
M
O
ω
O’
The dynamics of fabric drape
where
representing another solution of the sine-Gordon equation. This true analytical solution corresponds to our computed numerical solution for fabric buckling where the fabric can either buckle upwards against gravity or drape downwards with gravity as in Figure 5. As an example of the many other solutions which can be obtained using Bäcklund transformations, the two-wave or two-soliton solution:
311
(4)
for the sine-Gordon equation (2) is graphed in Figure 6 where the two curves (called kinks) undergo a nonlinear collision with phase shift µ. In summary, we can now model three-dimensional fabric drape dynamically by an integrable system of solitary wave or soliton equations from which true analytical solutions can be obtained giving all the forces and moments in the material, and the geometry of the draped fabric. Conclusion A universally applicable mathematical model is presented in this paper for twodimensional fabric buckling, folding and wrinkle deformation for which a range of numerically computed solutions have been analysed. We have described in
5 2.5 0
4
–2.5
2
–5
0 –4
–2
–2 0 2
–4 4
Figure 5. A mathematical stationary breather, an analytical solution (equation (3)) for the sine-Gordon equation (2) corresponding to our numerical solution for fabric buckling or folding in Figures 2 and 3.
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1
0 0.5
–5 –15
Figure 6. The two-solution (equation 4) of the sineGordon equation (2)
0
–10 –5 0
–0.5
mathematical detail the exact profiles of a buckled or folded fabric as well as the simple and higher order complex modes of fabric deformation which occur for different values of the fabric mechanical parameters under a given set of boundary conditions. Fabric properties considered in the mathematical analysis include the fabric bending rigidity and the bending length or bending rigidity per unit weight of the fabric. We have shown how this fundamental mathematical approach should be capable of predicting the dynamic fabric behaviour as well as the dynamic processing or tailoring behaviour of textile materials simply by solving particular examples of mathematical differential equations referred to as the Klein-Gordon family of equations. An additional quite exciting feature of this family of equations is the fact that they have been mathematically proven to have analytical solutions (called solitary wave or soliton solutions) which are applicable under all conditions and are not subject to computational difficulties associated with finding numerical solutions for highly nonlinear problems. The use of this analytical approach to fabric mechanics and dynamics provides us with a very powerful tool to formulate and solve many long-standing problems in fabric and clothing technology. References 1. Peirce, F.T., “The handle of cloth as a measurable quantity”, J. Textile. Inst., Vol. 21, 1930, p. T377. 2. Eisenhart, L.P., A Treatise on the Differential Geometry of Curves and Surfaces, Ginn., Boston, MA, 1936. 3. Lamb, G.L. Jr, “Bäcklund transformations for certain nonlinear evolution equations”, J. Math. Physics, Vol. 15, 1967, pp. 2157-65.
IJCST 10,5
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An investigation of the structure of sizing systems A comparison of three multidimensional optimized sizing systems generated from anthropometric data with the ASTM standard D5585-94 Susan P. Ashdown Department of Textiles and Apparel, Cornell University, Ithaca, New York, USA
International Journal of Clothing Science and Technology, Vol. 10 No. 5, 1998, pp. 324-341, © MCB University Press, 0955-6222
Introduction The sizing systems used to create a range of sizes for the ready-to-wear fashions sold to women in the USA are flawed in many ways. Women must try on multiple garments when shopping, and often feel that they cannot find an appropriate size[1,2]. These problems are a result of many factors including the use of a sizing system created from outdated anthropometric data, the lack of standardized size labeling, the lack of body measurements on hang tags, and the lack of sizes appropriate for the full range of variation in body type that exists in the population[3,4]. The sizing systems used for ready-to-wear clothing worldwide are created using a variety of methods ranging from trial and error to the use of elaborate statistical methods[5,6]. None of these methods addresses the problems of fitting a population in which there is a large amount of variability in many dimensions. It is possible to design sizing systems that will accommodate this variability today, given the power and sophistication of computer based calculations. A method of creating optimized sizing systems directly from the anthropometric database is used to create a series of different sizing systems. These sizing systems are designed to optimize the fit using as many variables (body dimensions) as are needed to account for the variability in the population. Therefore the resulting sizing systems will potentially fit the population better than sizing systems based on one or two dimensions only. Another advantage of the optimized sizing systems is that they can be structured in different ways by putting constraints on the system in order to simplify product development and/or distribution of the garments. However, the cost of constraining the system is that the performance of the sizing systems is compromised. It is necessary to balance the needs of providing appropriate sizes and a workable structure. In order to consider the various choices between the best fitting sizing system for the population and the best structure of the system, three optimized
sizing systems with various structures will be compared to one another and to an existing sizing system. The structure of sizing systems Sizing systems used in the design and distribution of ready-to-wear clothing are generally based on a selection of dimensions from an anthropometric study of the population for which the sizing system is designed. Key body dimensions are chosen to divide the population into size groups. The goal of any sizing system is to choose these size groups in such a way that a limited number of sizes will provide clothing that fits most individuals in the population. Although sizing systems developed by different countries vary in the body dimensions chosen to divide the population, the basic structure of most sizing systems is very similar. To create a sizing system, the population is first divided into different body types based on dimensions such as height or ratios between body measurements. A set of size categories is developed, each containing a range of sizes from small to large. The size ranges within a size category are based on one key body dimension. The sizes are generally evenly distributed from the smallest size to the largest[7]. Once sizes are identified, the remaining body dimensions necessary to design the pattern for the garment must be determined. Dimensions proportional to the key body dimension are chosen so that the garment patterns will be proportional to one another. Tables I and II show two sizing systems, a system from the USA in which size categories are based on proportional differences with no progression
Size categories
Definition of category
Sizes in category
Size breaks
Junior
Youthful body type, high bust, small waist 5'4'' to 5'6'' in height Proportions same as Misses, height 5' to 5'4''
Odd numbers, 3-15
One break, at size 9
Even numbers, 2-14
Two breaks, at sizes 10 and 18 Two breaks, at sizes 10 and 18 One break, at size 18
Petite
Misses
“Average” body type, longer waist than Junior 5'5'' to 5'8'' in height Women’s Larger proportions, fuller figure than Misses, 5'5'' to 5'9'' in height Plus sizes Larger figure types, corresponding to Misses 16 and larger Note: all body dimensions are in feet and inches Source: plus sizes[8] ; all other sizes[9]
Even numbers, 4-18
Even numbers, 14-24
Even numbers, 12-32
One break, at size 16
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Table I. Sizing system from the USA showing size categories and size breaks
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between categories, and a two-dimensional Korean system in which categories are based on height. History of sizing systems in the USA The structure of sizing systems used for ready-to-wear today has its origins in the proportional drafting systems developed by tailors in the latter part of the eighteenth century[10]. Clothing at this time was custom-fitted. Tailors drafted patterns from an individual’s body measurements to create the custom-fitted garments. As tailors drafted multiple patterns, they recognized certain relationships that seemed to exist between body dimensions of different individuals. Gradually pattern drafting systems were developed that made use of these relationships in order to provide a stock of ready-made clothing that could be sold to any number of similarly sized individuals. A gradual transition occurred from systems that provided custom-made clothing to the mass production of ready-to-wear clothing. By the 1920s most clothing produced nationwide was mass produced using methods which have not changed in any essential way up to the present era. Sizing standards in the USA Individual manufacturers in the USA developed their own sizing systems until the first sizing standard was published in 1958. In this year the United States Department of Commerce published Commercial Standard CS 215-58 as a voluntary sizing standard for the apparel industry[11]. The sizes were based on measurements from a 1941 anthropometric study of 10,042 women[12]. Women were divided into different size categories including Misses, Juniors, Women, and Half-sizes. Sizes in the range were based on bust measurements. In 1970 a new standard, PS 42-70 was published that incorporated additional anthropometric data from an army study[13]. Both the CS 215-58 and the PS 42-70 sizing standards are based on an assumption of proportional body measurements. Once the population is divided into the various sub-groups (i.e. Misses or Juniors) the sizes are identified by bust circumference. All other body dimensions for each size are then generated so that they remain proportional with the bust circumference. This results in a sizing system with a linear relationship between sizes.
Table II. Sizing system from Korea[7]
Size categories defined by height in cm
Sizes in category
Size breaks
Height < 150cm
42, 43, 44, 45
None
150cm < height < 155cm
53, 54, 55, 56, 57
None
155cm < height < 160cm
64, 65, 66, 67
None
160cm < height < 165cm
75, 76, 77, 78
None
165cm < height < 170cm
85, 86, 87, 88, 89
None
The development of ASTM D5585-94 By the 1990s the standard sizes from PS42-70 were no longer appropriate for the population of women in the USA. This standard was based on anthropometric data from a 1941 study and the current population is very different from that of the 1940s. There has also been a shift in the relationship between body measurements and size designations. Size designations, the number that identifies each size, are generally not related to body measurements in the sizing systems for women in the USA. For example, the Misses size designations are even numbers from 2 to 20. Originally these numbers probably referred to chronological age, but this connection was lost long ago. Because the size designations do not refer to any actual body measurement, the sizes can easily change. Currently different manufacturers use the same size designations for clothing that fits different body measurements. Because of the confusion about size designations, women must try on multiple garments to discover which ones will fit their particular body size and proportions[14]. Because of the lack of current standards and confusion about size designations a committee of the American Society of Testing and Materials (ASTM), committee D 13.55 has been formed to develop new voluntary standards for the industry. This committee has published an updated standard for Misses sizes, designation D5585-94. The standard is not derived from new anthropometric data, but is compiled from designer experience and market observations to reflect the sizing most commonly used by manufacturers and retail organizations in the USA today. The results were also cross-checked with anthropometric databases from the US Army and the US Navy[15]. The standard consists of ten sizes, each size identified by 39 body measurements. The sizes range from a size 2 with a bust measurement of 32'' (18.28 cm) to a size 20 with a bust measurement of 44.5'' (113.03 cm). Stature measurements range from 63.5'' (161.29 cm) to 68'' (172.72 cm). ASTM D5585-94 was chosen to compare to the optimized sizing systems as it is the most recently developed sizing system for women in the USA that corresponds to some degree to an available anthropometric database. The optimized sizing systems As the goal is to design optimized sizing systems that can be directly compared to ASTM D5585-94, it is necessary to make choices of comparable anthropometric data between the ASTM standard and the anthropometric data to be used to create the optimized systems(1). It is also necessary to limit the sample to a selection of individuals that can reasonably be regarded as the Misses subpopulation since the Misses sizing standard is not intended to fit the whole population. The choices made to calculate the optimized systems are described below. 1988 anthropometric survey of US army personnel There is no current, professionally conducted anthropometric study of the US civilian population with data that are appropriate for apparel sizing. However,
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an anthropometric study of US army personnel was conducted by Gordon et al. (1988) in which 1,774 men and 2,208 women were measured. This study, known as the ANSUR study, was conducted to collect data for the design and sizing of clothing and equipment[16]. Selection of body dimensions Many of the body measurements for the ANSUR study were taken with an anthropometer or with calipers, resulting in the shortest distance between the two landmarks measured. Measurements from the apparel standard ASTM D5585-94 were always made with a tape measure across the surface of the body. An analysis of all the body measurements from ASTM D5585-94 with the measurements from the ANSUR database was made to select those that were comparable. More of the lower body measurements corresponded than upper body measurements; therefore the decision was made to develop sizing systems that would be appropriate for women’s slacks or jeans. An advantage of the optimization formulation used to create the sizing systems is that it is possible to create a truly multivariate sizing system. It is not necessary to select one or two dimensions and to calculate all others so they are proportional to the key dimensions. Instead, any number of dimensions can be used, so that the particular combination of dimensions for each size is optimized for the individuals in the sample that will fit that size. Four dimensions were chosen to create the optimized systems based on a judgement of which variables would provide the best fit for the garment style. The chosen dimensions are: (1) hip circumference; (2) waist circumference; (3) crotch height; and (4) crotch length. Crotch height corresponds to the inseam measurement of the pants. Crotch length is the measurement from the center front at the waist, through the legs, to the center back at the waist. Defining the Misses sub-sample The database was sorted in order to identify the portion of the sample that would be expected to fit the sizes in ASTM D5585-94. None of the apparel sizing standards actually defines a method for identifying the Misses population. The dimensions commonly used by the apparel industry to identify this population are height and the bust to waist or waist to hip proportion. Therefore the sample was sorted using stature and waist to hip proportions derived from ASTM D5585-94. The stature limitations were calculated by subtracting a half grade interval from the smallest stature value and adding a half grade interval to the largest stature value in ASTM D5585-94, resulting in heights between 63''
(160.02cm) and 68.75'' (174.63cm). Limitations on the waist to hip relationship were set using the following formula: 0.95 * (H – 10.5'') ≤ W ≤ 1.05 * (H – 10.5'') where W = waist circumference in inches and H = hip circumference in inches. All hip sizes in ASTM D5585-94 are exactly 10.5" larger than the corresponding waist size. This formula allows 10 per cent variability around the relationship postulated by the ASTM standard. Sorting the sample of 2,208 women for stature and waist to hip proportion resulted in a sub-sample of 752 women. Optimization methodology The sizing systems were created using the optimization methodology described in Paal [17] and McCulloch et al.[18]. The core of this methodology is a mathematical description of the goodness of fit that an individual experiences when wearing a garment of a particular size. The concept of garment fit is captured by a distance measure, which is calculated from the discrepancies between the body measurements of an individual in the sample and the prototype design values of a size. A larger discrepancy results in a larger distance measure and a worse fit of the final garment, which is designed to fit the prototype body for that size perfectly(2). An advantage of the optimization procedure used is its ability to identify the individuals who will be accommodated simultaneously with selection of the prototype body sizes. Another advantage is that the distance measure, which is the basis of the optimization routine, automatically assigns individuals to their proper sizes. By contrast, heuristic methods of choosing prototypes leave the problem of size assignment unresolved. McCulloch et al.[18] gives full details on how nonlinear optimization techniques can be applied to calculate optimized sizing systems. Aggregate loss as a measure of goodness of fit When all individuals’ distances from their assigned size are averaged over the whole population, an aggregate measure is created that represents how well the sizing system performs in fitting the population. Using this measure, which we call aggregate loss, various existing sizing systems can be compared. An optimal sizing system with a given number of sizes is defined as one that minimizes the value of aggregate loss. In such a sizing system the average distance of individuals from their size is as low as possible. Therefore, on average, the system provides the best fit. The prototype design values for each size in the set of optimized sizes are based on the measurements of the individuals in the sample. Any number of body measurements can be used to create the sizing system, and various formulations of the distance measure are possible. For the purpose of this illustration, the quadratic average of log differences was chosen as the distance measure. The advantage of this simple measure is that the value of the distance can be interpreted as the average (over the four body dimensions considered) of
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percentage differences between an individual’s body measurements and the prototype design values of her assigned size. This use of the quadratic average implies that both positive and negative differences increase the distance. Therefore the fit is worse both when the garment is too big and when the garment is too small along a particular dimension. More complete measures and considerations regarding the choice of a proper distance measure are described in Paal[17]. Imposing structural constraints on the sizing systems A further advantage of the above methodology is that it allows a means of incorporating additional requirements that constrain the sizing system to exhibit desired relationships between sizes. The sizes are still chosen so that they represent the optimal placement among those sizing systems that satisfy the structural constraint. Three different structures were considered: a linear system, a two tiered system, and an unconstrained system. Ten sizes were created for each sizing system so that a direct comparison could be made with the ten sizes of ASTM D5585-94. These sizing systems were based on one half of the sub-sample of 752 women in the Misses size range as defined above. The remaining half of the sub-sample was reserved for testing the systems. The linear system was created by finding the ten optimized sizes in such a way that the increments between each of the sizes in each of the four dimensions remained proportionally constant. This can be thought of as a linear system in which the grade increments between the sizes are calculated as percentages of the measurement (for each of the four measurements). The two tiered system with ten sizes can be thought of as the union of two linear systems with five sizes each. The grade increments are proportionally constant within each tier, but not necessarily the same between the two tiers. Geometrically this results in two lines that are not necessarily parallel. The unconstrained system was created by searching for the optimized ten sizes with no constraints. In this case the system created is not linear in any of the four dimensions and the grade between the sizes is different for each size and for each dimension. Results Tables III-VI show the four dimensions for each of the ten sizes from the three optimized systems and from ASTM 5585-94. The grade rules for each size and the percentage of increase between sizes are also shown. Figures 1-4 show each of the sizing systems plotted in three dimensions, along with the data cloud of the sub-sample of 376 individuals who were reserved for testing the sizing systems. (The fourth dimension, waist circumference, is highly correlated with the hip circumference due to the method used to select the sub-sample, so a plot of waist, crotch height, and crotch length would be much the same.) The cubes plotted on these graphs represent size centers and not the extent of each size. They are placed so that the center of each cube is at the prototype design value for that size. They are
ASTM Hip circumference D5585-94 (inches) size Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
Crotch length (inches) Value Grade %gr.
2 4 6 8 10 12 14 16 18 20
24.00 25.00 26.00 27.00 28.00 29.50 31.00 32.50 34.50 36.50
25.00 25.75 26.50 27.25 28.00 28.75 29.50 30.25 31.00 31.75
34.50 35.50 36.50 37.50 38.50 40.00 41.50 43.00 45.00 47.00
1.00 1.00 1.00 1.00 1.50 1.50 1.50 2.00 2.00
2.9% 2.8% 2.7% 2.7% 3.9% 3.8% 3.6% 4.7% 4.4%
1.00 1.00 1.00 1.00 1.50 1.50 1.50 2.00 2.00
4.2% 4.0% 3.8% 3.7% 5.4% 5.1% 4.8% 6.2% 5.8%
29.50 29.50 29.50 29.50 29.50 29.50 29.50 29.50 29.50 29.50
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75
3.0% 2.9% 2.8% 2.8% 2.7% 2.6% 2.5% 2.5% 2.4%
Notes: “Grade” is the amount that this dimension increases to the next size “% gr.” is per cent grade, or the percentage increase to the next size
Linear size
Hip circumference (inches) Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
Crotch length (inches) Value Grade %gr.
A B C D E F G H I J
35.05 35.97 36.91 37.88 38.86 39.88 40.92 41.99 43.09 44.22
24.63 25.55 26.49 27.48 28.50 29.55 30.65 31.78 32.96 34.18
26.91 27.73 28.56 29.42 30.31 31.22 32.17 33.14 34.14 35.17
0.92 0.94 0.97 0.98 1.02 1.04 1.07 1.10 1.13
2.6% 2.6% 2.6% 2.6% 2.6% 2.6% 2.6% 2.6% 2.6%
0.92 0.94 0.99 1.02 1.05 1.10 1.13 1.18 1.22
3.7% 3.7% 3.7% 3.7% 3.7% 3.7% 3.7% 3.7% 3.7%
30.81 30.84 30.87 30.90 30.93 30.96 31.00 31.03 31.06 31.09
0.03 0.03 0.03 0.03 0.03 0.04 0.03 0.03 0.03
Notes: “Grade” is the amount that this dimension increases to the next size “% gr.” is per cent grade, or the percentage increase to the next size
0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
0.82 0.83 0.86 0.89 0.91 0.95 0.97 1.00 1.03
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Table III. Body dimensions, grades, and proportional grades of ASTM D5585-94. All dimensions are in inches
3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% Table IV. 3.0% Body dimensions, grades, and proportional grades of the linear optimized sizing system. All dimensions are in inches
calculated the same way for each sizing system, and are provided for ease of visualization of the three dimensions. The three dimensional plots show how the sizes in each system relate to the data cloud visually, and give some idea of the relationship between systems. However, as the plot can only show three dimensions and the optimized systems are based on four dimensions, these examples cannot show all visual
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Two tier size
Hip circumference (inches) Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
A1 35.86 0.93 2.6% 25.43 0.94 3.7% 30.88 0.20 0.6% B1 36.79 0.95 2.6% 26.37 0.97 3.7% 31.08 0.20 0.6% 332 C1 37.74 0.97 2.6% 27.34 1.00 3.7% 31.28 0.20 0.6% D1 38.71 1.00 2.6% 28.34 1.04 3.7% 31.48 0.21 0.7% E1 39.71 29.38 31.69 A2 37.13 1.17 3.2% 26.59 1.26 4.7% 29.96 0.30 1.0% B2 38.30 1.19 3.1% 27.85 1.31 4.7% 30.26 0.31 1.0% C2 39.49 1.24 3.1% 29.16 1.37 4.7% 30.57 0.31 1.0% Table V. Body dimensions, grades, D2 40.73 1.27 3.1% 30.53 1.43 4.7% 30.88 0.31 1.0% and proportional grades E2 42.00 31.96 31.19 of two tiered optimized Notes: sizing system. “Grade” is the amount that this dimension increases to the next size All dimensions are in “% gr.” is per cent grade, or the percentage increase to the next size inches
Uncon- Hip circumference strained (inches) size Value Grade %gr.
Waist circumference Crotch height (inches) (inches) Value Grade %gr. Value Grade %gr.
A 35.86 1.09 3.0% 25.52 1.08 4.2% 30.57 1.14 3.7% B 36.95 0.74 2.0% 26.60 0.79 3.0% 31.71 –2.30 –7.3% C 37.69 0.47 1.2% 27.39 0.00 0.0% 29.41 0.95 3.2% D 38.16 0.98 2.6% 27.39 1.63 6.0% 30.36 0.62 2.0% E 39.14 –0.37 –0.9% 29.02 –0.69 –2.4% 30.98 1.88 6.1% F 38.77 0.59 1.5% 28.33 0.48 1.7% 32.86 –1.74 –5.3% G 39.36 0.18 0.5% 28.81 0.39 1.4% 31.12 –0.97 –3.1% H 39.54 1.10 2.8% 29.20 1.39 4.8% 30.15 0.65 2.2% Table VI. Body dimensions, grades, I 40.64 0.81 2.0% 30.59 0.76 2.5% 30.80 0.38 1.2% and proportional grades J 41.45 31.35 31.18 of the unconstrained optimized sizing system. Notes: “Grade” is the amount that this dimension increases to the next size All dimensions are in “% gr.” is per cent grade, or the percentage increase to the next size inches
Crotch length (inches) Value Grade %gr. 27.57 28.19 28.83 29.48 30.14 30.93 31.23 31.53 31.83 32.13
0.62 0.64 0.65 0.66
2.2% 2.3% 2.3% 2.2%
0.30 0.30 0.30 0.30
1.0% 1.0% 1.0% 0.9%
Crotch length (inches) Value Grade %gr. 27.44 29.25 28.94 31.30 28.60 29.91 30.54 32.47 30.71 33.08
1.81 6.6% –0.31 –1.1% 2.36 8.2% –2.70 –8.6% 1.31 4.6% 0.63 2.1% 1.93 6.3% –1.76 –5.4% 2.37 7.7%
information. This is true to an even greater extent for sizing systems based on more than four dimensions. A mathematical calculation of the aggregate loss of each of these systems to the reserved sample of 376 individuals was made to provide a basis of comparison of the systems. The total loss as described earlier is an aggregate measure of the discrepancy between each body measurement of each individual
Investigation of the structure of sizing 35
25
20
Figure 1. Three-dimensional plot of ASTM D5585-94 by hip circumference, crotch length, and crotch height. All dimensions are in inches
45
25 30 Hip C
40 ircu 35 mf ere
ht
ig
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Crotch Length
333 30
30
25
45
20 40
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35 cum fer
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Figure 2. Three-dimensional plot of the linear optimized system by hip circumference, crotch length, and crotch height. All dimensions are in inches
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Crotch Length
334 30
25
Figure 3. Three-dimensional plot of the two tiered optimized system by hip circumference, crotch length, and crotch height. All dimensions are in inches
45
20 40
30
ht
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Hip
35 cum fer
35
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35
Figure 4. Three-dimensional plot of the unconstrained optimized system by hip circumference, crotch length, and crotch height. All dimensions are in inches
30
25
45
20 40
30
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Hip
35 cum fer
35
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and the corresponding measurement of their closest size. This aggregate loss can be interpreted as the average proportional difference between subjects and sizes. The aggregate loss of each system is reported in Table VII. The validity of this measure must be discovered by conducting an actual fit test of an optimized sizing system on a sample of individuals from the population; however, in the absence of validation this measure still provides a means of ranking the systems. According to the aggregate loss, all of the optimized systems are more successful than ASTM D5585-94. If the individuals in our control sample had to choose their best fitting size from the ASTM system, they would find on average that their measurements differed by a total of 4.8 per cent from the measurements that these garments were intended for. On the other hand, if they had to choose their best fitting size from any of the optimized systems then on average their measurements would only differ from the intended measurements by a total of 2.7 per cent to 2.9 per cent. It is clear that all of the optimized systems can outperform the ASTM system. From the 3D plot it is also clear that the ASTM system does not line up with the densest portion of the population distribution. This could be because ASTM D5585-94 is not designed for a military population. However it is not likely that the Misses civilian population is so different from the army sample. A more likely explanation is that ASTM D5585-94 is not based on anthropometric data describing the population, but rather on untested industry assumptions about the population. The linear optimized system is designed to have the same basic structure as ASTM 5585-94, but the sizes in this system are selected directly from the four relevant body dimensions. Within the constraint that keeps the system structure linear, the sizes are placed in the most efficient manner to accommodate the individuals in the sample. This is the least effective of the optimized systems based on aggregate loss but outperforms the ASTM system. The two tiered optimized system shows further improvement when measured by aggregate loss. The constraints used to create this system could provide a new way of identifying size categories, as it divides the sample into the groups that will form the most optimal linear systems within the sample as a whole. The best system based on the measure of aggregate loss is the unconstrained optimized system. This is understandable, as this system has no structural Sizing system
Measure of aggregate loss
ASTM D5585-94
0.04806
Linear optimized system
0.02908
Two tiered optimized system
0.02784
Optimized system with no constraints
0.02719
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Table VII. Comparison of the sizing systems by a measure of the aggregate loss of goodness-of-fit of the 376 individuals in the reserved sub-sample
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constraints on the process of locating sizes so that they provide the best fit possible for all of the individuals in the population. The comparison of the optimized systems reveals an additional explanation of why a system like the ASTM system can be outperformed: the structural rigidity imposed by a linear system. Each of the optimized systems is potentially a solution to a “nested” problem. The sizing system that fulfills the linear constraint could also fulfill the two tiered system constraint. If the smallest size in one tier of the two tiered system had turned out to be exactly one grade interval above the largest size of the other tier then the two tiers would form a linear system with a size break in the middle. By extension it can be seen that all sizing systems that fulfill any of the constraint specifications are also candidates for being the unconstrained optimum. The unconstrained optimum could have turned out to be a linear system with no size breaks. In fact, if the population exhibited very small proportional variability, then the optimal unconstrained system would be approximately linear. Therefore the shape of the population dictates the shape of the optimum sizing system. However, there are other issues that must be addressed in an unconstrained system, including issues related to production and distribution of garments. Pattern grading Garment patterns within a sizing system are developed from a single base pattern. The pattern from this base size is graded up and down to create the other sizes in the range. Grading is accomplished by moving each point on the perimeter of the pattern the amount needed to increase or decrease the pattern the desired amount. The movement of each point can be captured with two numbers from an x,y coordinate system. These numbers are the grade rules, which can be recorded and used to grade any pattern of a similar shape[9]. In the optimized sizing systems presented here the pattern of grade rules is not so simple. The grade increments for the optimized systems are percentage changes, and therefore the absolute differences between sizes are not constant. The grade rule in this case would consist of 27 numbers for each pattern point. Comparisons of different grade rules can be seen in Table VIII. Once a pattern is graded it is common practice to stack all of the patterns together into a graded nest in order to check the relationships between sizes. In a simple linear system, it is possible to see any errors in grading easily by checking this nest, as all of the pattern perimeters will be equidistant from one another. This will not be true of a graded nest of patterns from the optimized systems. For the constrained optimized systems the spaces between the pattern perimeters will increase for each size. For the unconstrained system there will not be a relationship between any of the pattern perimeters, and these lines may even cross one another as the proportions between sizes change. Figure 5 illustrates the differences between the various graded nests. If it were still the case that most pattern grading was done by hand, this complexity of grading would be extremely labor intensive, and therefore not worth any gains in the fit of the sizes generated. However, pattern grading in
Grade rule, inches no size breaks 0 –0.25
Investigation of the structure of sizing
Grade rule, inches multiple size breaks 4
0.13
–0.25
Number of breaks 0.13
8 –0.38
Size no., 1st break 0.13
10 –0.19 12
(a)
X, Y movement Size no., 3rd break
0.13 14
–0.31
337
X, Y movement Size no., 2nd break
0
–0.19
X, Y movement
X, Y movement Size no., 4th break
0.13
X, Y movement
(b)
Table VIII. Grade rules for the upper right corner of the patterns shown in Figure 5
Figure 5. Graded nest of a jacket front pattern: (a) shows an even grade with no breaks: (b) shows a grade with a break at each size
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the apparel industry is currently done almost exclusively on the computer, using computer-aided design systems that automatically generate the patterns from the grade rules. Therefore the creation of graded patterns for any of these sizing systems is technically possible. Once the grade rules are calculated they can be stored in the computer and applied to any pattern of a similar style.
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Size designation and selection An advantage of a standard linear sizing system is that size labeling and the choice of the appropriate size is a simple process, especially if hang tags list body measurements as specified in the international standard[19]. If a consumer tries on a garment and finds it is too large or small, she knows to try the size that is one up or one down from this size. However, in the unconstrained system the proportions change with every size instead of overall changes of largeness or smallness. The result is that the consumer would need to try on many more sizes, and would have a more complex problem choosing which sizes to try. The solution to this problem lies in the computer program that creates the optimized sizing systems. This same program could be modified for size selection. This is an ideal system for assigning sizes in mail-order operations as the sizes could be assigned at the warehouse directly from the customer’s relevant body measurements. New technologies in retail operations could also help with size assignment. Computers could be provided where the clothes are purchased that calculate the recommended size (the closest size according to the distance measures relevant for the particular garment) and any variations on this size (the second, third, etc. closest sizes). Distribution A further issue in retail operations is the selection of the appropriate numbers of each size to be sold at each store location. Currently orders for an appropriate set of garments, known as stock keeping units (SKUs), are based on what has sold in the past. Usually fewer garments are ordered in the smallest and the largest sizes. An advantage of the optimized sizes is that the number of individuals who fit in each size is more evenly distributed across the range of sizes. Table IX shows the number of individuals from the reserved sub-sample in each of the ten sizes for each of the sizing systems. Another advantage of the optimized systems is that if each retailer has an accurate anthropometric database of their customers it would be possible to calculate the number of the different sizes necessary to accommodate the population in their area. As the sizes are directly based on data from the anthropometric database, such predictions should be very accurate. Discussion and conclusions A set of three sizing systems were created based on anthropometric data using an optimization method for comparison to one another and to an existing system ASTM D5585-94. In order to make the comparison to the existing system as reasonable as possible and for clarity of illustration, some simplifications and concessions were made in creating these systems. Different
ASTM Linear Size Frequency Size
Two tier Frequency Size
Unconstrained Frequency Size Frequency
2
1
A
11
A1
28
A
45
4
10
B
27
B1
55
B
59
6
23
C
65
C1
60
C
43
8
64
D
82
D1
54
D
26
10
110
E
82
E1
36
E
33
12
89
F
50
A2
17
F
37
14
43
G
28
B2
35
G
40
16
25
H
18
C2
28
H
23
18
10
I
9
D2
33
I
30
20
1
J
4
E2
30
J
40
376
Total
376
Total
376
Total
376
Total
choices would be made to design sizing systems for use in the industry. However, from this example it is clear that these systems can be designed using different variables and different constraints to create sizing systems with different structures. The sizing systems demonstrated in this paper were created for comparison, and are not based on enough data to be reliable. Anthropometric studies of the population are needed to form the basis of this type of sizing system with enough data to calculate robust sizing systems. Such studies will be much easier than traditional anthropometry using new 3D scanning technologies being developed[20]. Only body measurements are discussed in this paper. The issues of ease and design of patterns must be addressed to complete the process and generate appropriate garment measurements. Other issues beyond the scope of this paper relate to individual preferences of fit and the impact on size selection and distribution of garments. Questions of grading methods, size designation and selection, and distribution will ultimately be important in selecting the structure that is most successful. If the development, distribution, and selection of a garment are not effective in terms of time or money, the improved fit may not provide enough of a benefit to justify the expense. However, in a situation where close fit is desired and there is a lot of variation in the population the optimized systems with no constraints provide a clear advantage in their ability to provide sizes that fit a range of body proportions. In this case, with the aid of computerized systems for grading, size selection, and distribution, the best choice may well be the least structured system. The most useful outcome of the method for creating optimized sizes is the creation and comparison of these different sizing structures as a means to
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Table IX. Frequencies of individuals in each of the sizing systems
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investigate the endogenous structures existing within the population. An example of this is the discovery of appropriate tiers within the population as seen in the simplified two tier optimized system. If the sample of 2,208 women representing the population as a whole were used, and a five tiered sizing system was created with a linear structure within each tier, the optimized equivalent of the Junior, Petite, Misses, Women’s and Plus Size sizing system shown in Table I could be created. In this case the size categories would be set based on the true variation in the sample, instead of the ad hoc proportional observations that form the basis of the current sizing. The optimized system would be based entirely on body measurements; however the system described in Table I also provides divisions that accommodate style variations. For example, a junior size is generally thought of as a size for young women with less full figures. Styles designed for this category are therefore generally less classic and trendier, as this type of style is more acceptable for the younger woman. Dividing the population on the basis of age, and then designing systems for each subset would provide a basis of comparison of different segments of the population. Given enough anthropometric data, the potential for designing an optimized sizing system for each situation is endless. Notes 1. Beatrix Paal, currently a doctoral candidate in the Department of Economics at Cornell University, performed the calculations and prepared the graphs and tables for this paper. The optimization method was developed by Beatrix Paal as a thesis project for her graduate work in the Department of Textiles and Apparel, “Creating efficient apparel sizing systems: an optimization approach”, Cornell University, 1997. 2. More precisely, let the distance function between the body measurements of the nth individual, xn , and sth prototype body size, ys be denoted by d(xn, ys ). For individual n, the only distance that is relevant to fit is the distance to the best (i.e. smallest distance) size in the sizing system, which is given by min d(x n, y s ). Our sizing systems only attempt s to accommodate those individuals who can be reasonably fitted by some size. Let cα be the cut off for the distance measure, beyond which an individual is judged not to be accommodated and define the loss function for individual n in a sizing system with sizes y1, . . . ys as
For a given number of sizes, S, and a given loss cutoff, cα , the optimal system is found by selecting the prototypes y 1, y 2, ..., y S so as to minimize the aggregate loss across all individuals, i.e.
Once the prototypes are derived, the individuals who cannot be accommodated can be found by calculating each individual’s distance to the closest prototype. Those closer to a prototype than cα are accommodated and those farther away are not. By varying cα we can achieve the desired accommodation rate.
References 1. LaBat, K.L. and Delong, M.R., “Body cathexis and satisfaction with fit of apparel”, Clothing and Textile Research Journal, 1990, Vol. 8 No. 2 (Winter), pp. 42-8. 2. Goldsberry, E., Shim, S. and Reich, N., “Women 55 years and older: overall satisfaction and dissatisfaction with the fit of ready-to-wear, part II”, Clothing and Textile Research Journal, Vol. 14 No. 2, 1996, pp. 121-31. 3. Workman, J.E., “Body measurement specifications for fit models as a factor in clothing size variation”, Clothing and Textile Research Journal, Vol. 10 No. 1, 1991, pp. 31-6. 4. Tamburrino, N., “Apparel sizing issues, part 1”, Bobbin, April 1992, pp. 44-7; “Apparel sizing issues, part 2”, Bobbin, May 1992, pp. 52-60; “Sized to sell”, Bobbin, June 1992, pp. 68-74. 5. Salusso-Deonier, C.J., DeLong, M.R., Martin, F.B. and Krohn, K.R., “A multivariate method of classifying body form variation for sizing women’s apparel”, Clothing and Textile Research Journal, Vol. 4 No. 1, pp. 38-45. 6. Gordon, C.C. and Friedl, K.E., “Anthropometry in the US armed forces”, in Ulijaszek, S.J. and Mascie-Taylor, C.G.N. (Eds), Anthropometry: The Individual and the Population, Cambridge University Press, Cambridge, 1990. 7. Jongsuk, C.Y. and Jasper, C.R., “Garment-sizing: an international comparison”, International Journal of Clothing Science and Technology, Vol. 5 No. 5, 1993, pp. 28-37. 8. Zangrillo, F.L., Fashion Design for the Plus-Size, Fairchild Publications, New York, 1990, pp. 173-4. 9. Price, J. and Zamkoff, B., Grading Techniques for Modern Design, 2nd edition, Fairchild Publications, New York, 1996. 10. Kidwell, C.B. and Christman, M.C., Suiting Everyone: The Democratization of America, Publication No. 5176. Smithsonian Institution Press, Washington, DC. 11. United States Department of Commerce, Body Measurements for the Sizing of Women’s Patterns and Apparel, A Recorded Voluntary Standard of the Trade CS 215-58, US Department of Commerce, Washington DC, 1958. 12. O’Brien, R. and Sheldon, W.C., Women’s Measurements for Garment and Pattern Construction, Miscellaneous Publication No. 454, US Department of Agriculture, Washington DC, 1941. 13. United States Department of Commerce, Body Measurements for the Sizing of Women’s Patterns and Apparel, Voluntary Product Standard PS 42-70, US Department of Commerce, Washington DC, 1970. 14. Roach, M., “The numbers game”, Vogue, August 1996, pp. 94-6. 15. American Society for Testing and Materials, Standard Table of Body Measurements for Adult Female Misses Figure Type, Sizes 2-20. D5585-94, ASTM, Philadelphia, PA, 1994. 16. Gordon, C.C., Bradtmiller, B., Clausner, C.E., McConville, J.T., Tebetts, I. and Walker, R.A., 1988 Anthropometric Survey of US Army Personnel, Technical Report NATICK/TR89/044, US Army Natick Research, Development, and Engineering Center, Natick, MA, 1989. 17. Paal, B., “Creating efficient apparel sizing systems: an optimization approach”, unpublished Master’s thesis, Cornell University, 1997. 18. McCulloch, C.E., Paal, B. and Ashdown, S.A., “An optimization approach to apparel sizing”, Journal of the Operational Research Society, Vol. 49, 1998, pp. 492-9. 19. Palaganas, D., “Uni-sizing Europe”, Apparel Industry Magazine, September 1991, pp. 82-4. 20. Parroty Interactive, “Cyberscope: scanners”, Newsweek, June 9, 1997, p. 12.
Investigation of the structure of sizing 341
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342 Received March 1998 Revised August 1998 Accepted August 1998
No-interval coherently phased product development model for apparel Traci May-Plumlee Department of Textile Products Design and Marketing, University of North Carolina at Greensboro, Greensboro, North Carolina, USA, and
Trevor J. Little Department of Textile and Apparel Management, College of Textiles, North Carolina State University, Raleigh, North Carolina, USA
International Journal of Clothing Science and Technology, Vol. 10 No. 5, 1998, pp. 342-364, © MCB University Press, 0955-6222
Introduction Apparel manufacturing firms have focused much effort in recent years on improved market responsiveness in a demand activated, customer driven and retail competitive business environment. One major thrust has been on quick response initiatives and the accompanying technologies needed to support the QR business strategy. As barriers to the adoption of quick response have been overcome, and the use of a QR business strategy has become more widespread, attention has turned to the need for improvement in other aspects of the apparel manufacturing business cycle. The literature clearly establishes that development of new products is a critical activity in a successful manufacturing firm (Bruce and Biemans, 1995; Craig and Hart, 1992). It is not surprising, therefore, that one of the business functions currently under scrutiny is the product development process for apparel. Emphasis on improving the product development process in apparel manufacturing firms is concentrating in two areas; improving the cost effectiveness of the process by streamlining and shortening the product development cycle, and improving the market receptiveness of new products through the addition of custom fit products and developing needed products close to the selling season. However, these efforts have been undertaken without benefit of in-depth, comprehensive documentation of the development process. This lack of documentation presents challenges in focusing improvement efforts and gauging their effectiveness. Examples of simple, generic models of the product development process used in the apparel industry can be found in the academic literature (Gaskill, 1992; Regan et al., 1998), trade literature (Fashion Apparel Manufacturing, 1982; Garfield, 1985; Marketing Committee, 1989; Sadd, 1996) and reference books (Burns and Bryant, 1997). Although these models are useful in achieving a fundamental understanding of the process, they provide an inadequate foundation for research efforts to redesign an improved process. This paper
presents a comprehensive model of the process of developing a line of apparel products based on the current practices found in the US apparel industry. Product development models Product development is defined as the design and engineering of products which are serviceable for the target consumer, marketable, manufacturable and profitable (Kunz, 1993). Clearly, integrated efforts of multiple functional units within a business firm must be employed to develop such a product and achieve these outcomes. Review of existing models for the new product development process illustrates the diversity of processes used in manufacturing industries, and provides a framework for understanding the apparel product development process. The product development literature identifies several forms that new products may take. Simply stated, new products may be: • “new to the world” inventions which create a new market; • modifications of existing products; and • existing products introduced to new markets. The new product development process models reviewed in this paper focus primarily on development of products which are new innovations or modifications of existing products. Strategies for introducing existing products to new markets focus on marketing innovation not product innovation and use a truncated model rather than an idea to implementation approach. Many authors have developed normative models of the new product development process. Traditionally, the process was represented by models that were sequential in nature, while more recent ones tend to reflect concurrent product development processes and integrate multiple business units. Sequential models for product development Figure 1 provides examples of sequential models of the new product development process. As is clear from the diagrams, this type of model suggests that a product moves sequentially through a series of defined stages. The processes occurring at each stage, often conceived as taking place in a single department, must be complete before passing the developing product forward to the next stage. Though these models may accurately depict the traditional product development process, they have several limitations in that they omit critical parts of the model needed to address a continuously changing marketplace. First, the product development literature acknowledges various initiating factors for new product development. Gruenwald found that customers, marketing, and research and development were the three most common sources of new product ideas (Gruenwald, 1992). Other authors emphasize the need for input from the consumer and for correlating that input with design objectives in the product development process (Erhorn and Stark, 1994; Himmelfarb, 1992).
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Go DESIGN Consumer Measurement Perceptual Mapping Product Positioning Forecasting Sales Potential Product Engineering and Marketing Mix No Go TESTING Advertising and Product Testing Pretest Market Forecasting Test Marketing No
Figure 1. Sequential models by Urban and Hauser (1980), Gruenwald (1992) and Himmelfarb (1992)
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However, in sequential models, input from marketing is only incorporated in later stages of the process as a means of testing new product concepts and developing marketing plans. Second, sequential models provide no means for efficient communication and movement backward as well as forward in the design and development process. In each model, gates between stages must be cleared to move forward in the process. If a gate is not cleared, the product is moved into an undefined state where it remains pending re-evaluation and possible recycling. This may result in dropping potentially successful products, in long development delays, or in a poor yield of successful new products. Third, the models describe a segmented, and consequently a slow approach, to product development minimizing interaction between departments. Because critical departments have no input until later stages of development, important considerations may be omitted from the process resulting in missed market opportunities, an extended development cycle, and difficult to manufacture products that are too costly and require major revision late in the development process. By the time a developing product has been reviewed by all departments, and perhaps recycled several times, the market opportunity identified at the beginning of the process may no longer exist. An alternative approach to modeling a sequential product development process is to describe it as simply ten to 13 activities in an ordered list. Cooper and Kleinschmidt (1986) used a model, shown in Figure 2, of 13 sequential activities. As shown in Figure 3, the authors found that only 1.9 per cent of the
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Figure 2. Cooper and Kleinschmidt’s new product process activities (1986)
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Figure 3. Number of activities used in new product process (Cooper and Kleinschmidt, 1986) 0
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manufacturing firms studied used all 13 activities in the product development process, and most used just eight or nine activities. Rochford and Rudelius (1992) found similar results using a 12 activity model. Mahajan and Wind (1992) found that even a model limited to ten activities did not accurately reflect the process followed in industry. Although these sequential activity models have
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proved useful in some instances, they are too restrictive to accurately depict traditional industry practices. The more general sequential models that focus on broadly defined process stages are more realistic representations. Parallel and concurrent product development models Due in part to the cited limitations, many authors view sequential processes as obsolete and see industrial product development shifting toward a parallel or concurrent product development process model (Himmelfarb, 1992; Nijssen et al., 1995; Zahra and Ellor, 1993). This process is often represented as sequential steps, but with each step occurring simultaneously in a number of departments. Operating under a concurrent model, communication is enhanced and the expertise of all departments is employed throughout the process. A shorter product development cycle, better products and improved communication between departments are identified as advantages of this approach. Erhorn and Stark (1994) modeled an integrated approach, shown in Figure 4, where product development occurs simultaneously in multiple departments and product improvements are accomplished without hindering the process. According to the authors, use of this model facilitates product innovation, cost management, meeting quality requirements and a shortened product development cycle. Barclay et al. (1995) also emphasized the importance of an integrated approach in discussing their wedge shaped concurrent product development model. It incorporates multiple new product options which are narrowed into a single new product concept through a series of decision points. Concurrent models offer advantages over sequential models of product development. First, the integrated approach supports a faster and more efficient product development cycle. Rosenthal (1992) illustrated this difference clearly in the diagram shown in Figure 5. Concurrent approaches allow for continuous exchange of information and responsibility among many departments involved
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Figure 4. Erhorn and Stark’s (1994) integrated process model
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in development. Second, marketing and manufacturing play a larger role in the entire product development cycle helping to assure that a wide variety of external and internal design requirements are considered in the process. It is notable, however, that these models do not provide for firms which outsource production, thus replacing the manufacturing function with sourcing as in private label apparel development. Multiple convergent product development models Hart and Baker (1994) attempted to limit the functional divisions suggested by “parallel” processes with a multiple convergent processing model. Like Barclay et al. (1995) Hart and Baker recognized that all tasks ideally converge ultimately into a single product launch. In their model, however, multiple convergences occur during the development phases that follow concept generation, development and screening. This is in contrast to Bruce and Biemans (1995) multiple convergent model diagramming the early stages of development. Bruce and Biemans (1995) model, shown in Figure 6, formally recognizes the numerous inputs to the product development process from varying sources. The role those same sources play in the continuing development of the product is also documented. This multiple convergent approach bears great similarity to the product development process used in the apparel industry. Other product development models and methods Saren’s (1994) model also builds on the concept of multiple evaluations, but also formally recognizes with shaded blocks those product development activities that may occur outside of the firm which will market the product. Saren’s “blocks” model, shown in Figure 7, provides for multidirectional movement through the process, allows for simultaneous activities and emphasizes the early stages of the new product development process.
Figure 5. Sequential versus simultaneous process (Rosenthal, 1992)
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Figure 6. Bruce and Biemans’ multiple convergent processing model (1995) Suppliers
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Cooper (1994) discussed limitations of stage-gate models that break the product development process into discrete stages each culminating in a gate where a decision to continue, abandon, hold or recycle the product idea is made. Products are prevented from moving into the next stage of development until the current stage is completed. Cooper’s solution is the Third Generation Process modeled in Figure 8. This process model represents a flexible process with overlapping stages and fuzzy gates that allow a product to advance to the next stage of development conditional on future completion of the activities in the current stage. Implementation of this model could shorten the development cycle by facilitating interaction between stages and eliminating delays at decision gates. Although the need to interface with marketing is emphasized, one of the limitations of the previous authors’ models is in recognizing the role of marketing and marketing research throughout the process. The multiple convergent model developed by Bruce and Biemans (1995) is a notable
Stage 1
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exception to this generalization. Quality Function Deployment (QFD), shown in Figure 9, is a method implemented to aid in developing marketable products with desirable attributes (Erhorn and Stark, 1994; Himmelfarb, 1992). QFD has been used by Japanese manufacturers of a variety of consumer goods including apparel. During QFD, customer expectations are identified and then translated into production process parameters through a series of matrices and tables. Companies credit the QFD process with reducing startup costs, pre-production costs and product changes late in the development cycle (Kinni, 1993). Griffin identified less concrete benefits, but acknowledged that the product development teams she studied had used QFD for a relatively short time, so long term benefits were unclear (Griffin, 1992). Craig and Hart (1992) concluded that study of the new product development process lacks the depth needed for critical evaluation and improvement. This general observation is equally true for the product development process used in the apparel industry as evidenced by the simplicity of examples found in the literature (Sadd, 1996) and reference books (Burns and Bryant, 1997). Yet, Sadd (1996) provided examples of how in-depth study of the product development process served to identify opportunities for increasing margins and sales volume, reducing lead and cycle time, and reducing product development costs. Comprehensive study of the apparel product development process has the potential for fostering competitive improvements in managing the process. Such improvements may result in greater return on product development investments, more consumer responsive products and a quicker development cycle.
Design Attributes Customer Needs Customer Perceptions “Engineering” Measures
Features Design Attributes
Operating Matrix Process Steps
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Operational Conditions Measures Process Steps
Figure 9. QFD’s interaction matrices (Griffin, 1992)
Measures
Apparel development models Published product development models for apparel are of a sequential type; some defining the process in general stages and others using lists of activities. Models by Burns and Bryant (1997), Regan et al. (1998), and Sadd (1996) document apparel development as a series of stages in a linear progression, following the form of the traditional sequential model. In some cases, however, the authors state the process is not as sequential as it appears. Gaskill (1992) incorporates some concurrent activity in a few model stages, but provides no indication of whether that activity occurs in one or many functional units. Other authors represent the apparel development process as a more extensive list of activities completed sequentially (Garfield, 1985; Marketing Committee, 1989). Sometimes a timeline is added to the list of activities to create a merchandising calendar (Fashion Apparel Manufacturing, 1982). In addition to the previously identified limitations of sequential models – limited involvement of critical functional units, no capacity for backward movement or “line optimization” in the process, and inefficiency – these models lack the depth needed for critical analysis of the apparel development process. Current models do not permit process activities or phases to occur concurrently as in the apparel product development process. Cooper’s model allows for such activity, but is not specific to apparel. A model is needed that provides detail in terms of the activities undertaken to complete the development process, provides for concurrent activity and for the involvement of a variety of functional units in each stage of the process. The apparel product development process In addition to the above concerns, the apparel development process varies substantially from the processes used for developing other products and therefore requires a unique model to guide in-depth study. First, in the apparel industry products are developed in seasonal lines rather than as individual products. An apparel line may consist of many groups of products which must be managed simultaneously through the process. The normative models presented earlier in this paper depict the development process for individual rather than groups of related products. This observation suggests that the process for developing an apparel line could be represented by combining multiple smaller processes, each representing a single product. On examination, the process proves more complex; some decisions made during the product development process have implications for all products in the line, while others apply only to a limited number. For example, a line plan establishes parameters for all products to be included in a season’s line, but a color standard may apply to only a few. Recognizing that the outcome of apparel development is multiple related products rather than a single product emphasizes the complexity of the development process. In any given stage of the process, some products in the line may move forward in the process, others may recycle through previous phases and still others may be simply dropped from the line and archived for future reference.
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Second, several lines of new product must be produced annually. Consequently, while one line of product is being developed, the previous line may be in production and a third line may be selling at retail sales (Burns and Bryant, 1997, p. 132). If a company produces more lines each year, stages of the development process may overlap. This fact not only has implications for modeling the process, it highlights the importance of efforts to strategically manage and optimize it. In practice, the merchandising calendar is used to schedule the process. Currently, sales data for the previous season’s line is incomplete, so decisions must be made on new product without benefit of data regarding consumer acceptance of the previous products. In-depth study provides the opportunity to improve, streamline and thus better manage the process. Third, the strategy for developing any one product in the apparel line may differ from the strategy used in developing other products. A sequential model does not differentiate between strategies which may include original design, modification of existing products, knock-offs or take-offs, joint product development and combinations of these methods (Glock and Kunz, 1995). Of these original design, knock-offs or take-offs, and modification of existing products each follow a slightly different path through the product development process. As the last strategy suggests, development of a single line of apparel products may incorporate all of these strategies, different products following different paths. To further complicate matters, a firm may optimize a line which is currently being marketed by adding additional colors or fabrics. A product may be added to a line requiring accelerated development while the remainder of the line is far advanced in the process. Joint product development, a description not so much of how the development process is completed, but rather of who is completing particular phases, can follow any of the above identified paths. Current models are limited by a sequential nature that, though it may provide some sense of order, does not clarify the concurrent nature of some development activities nor the involvement of functional areas of the manufacturing firm. An in depth model of the apparel product development process must accomplish these things and be adaptable for: • developing both product lines and individual products; • development of seasonal lines and multiple seasons annually; • developing new products, take-offs, and modifying existing products. No-interval coherently phased product development model for apparel Typically, the development of new products is viewed as a design and development task. However, the responsibility for new products in apparel firms is coordinated and shared by four functional areas: (1) marketing; (2) merchandising;
(3) design and development; and (4) production. By defining the process in terms of functional divisions rather than departments, internal as well as joint product development strategies are provided for in the model. Figure 10 provides a comprehensive overview of the six phase apparel product development process model indicating the involvement of each of the four functional areas in each phase. The model integrates information from the literature, professional presentations, documentary videos and discussions with industry professionals (Burns and Bryant, 1997; Glock and Kunz, 1995; Kunz, 1993; Littrell, 1997; Magg, 1997). Like Cooper’s, this model incorporates overlapping stages and fuzzy gates which allow for various items within an apparel line to be selectively advanced or recycled through previous development phases. These coherently phased divisions are represented by broken lines between each phase of the process. The location of each gate represents the functional area having possession of the product line at that convergent point. System constraints may vary among product lines and firms, but include limitations such as vendor reliability, raw material availability and target retail price point. The product development process is also subject to constraints imposed by the consumer such as personal consumption expenditures, consumer wants, and consumer feedback which is influenced by a firm’s proximity to the consumer based on the channel of distribution. Figures 12-17 provide an in-depth examination of each phase of the development process. As is detailed in the following paragraphs, the model incorporates parallel processing and multiple convergent points identified as strengths of previous models. Figure 11 provides a legend to aid in model interpretation. Each shape and shading pattern used conveys something about the process depicted. Although each of the six phases is discussed in isolation, the discussion must be interpreted in the context of the complete model shown in Figure 10. The first phase, line planning and research, is shown in detail in Figure 12. Though the impetus for initiating development of apparel lines tends to be a seasonal merchandising calendar, many sources are used in arriving at the concept that will guide the process. Thus, this first phase incorporates the research and parameter establishment that will guide the development process. Marketing, merchandising and design all contribute to this research phase. Marketing provides the most general parameters for the process based upon a variety of research strategies. The marketing plan and sales forecasts developed at this stage provide financial and sales goals, and the financial information needed for planning the product line. Target customer research through focus groups and customer feedback via returns are connected to other areas of the model with dotted lines; an indication that not all firms conduct this research. Merchandising primarily uses this Phase 1 research in development of the line plan. Product development may provide input to the line plan, but also
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Figure 10. Apparel industry product development process overview
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Figure 11. Model legend
utilizes the information gathered in Phase 1 in formulating the creative direction of the line. So, for the current apparel product development process, research conducted by product development in Phase 1 initiates Phase 2, the design/concept development phase. Phase 1 converges into the line plan which establishes the specific parameters for the line under development, and may include some or all of the parameters identified in the model. Phase 2, shown in Figure 13, is the process of initiating development of specific products. The general line concept identified by the line plan is translated to specific color stories and concepts for the multiple product groupings which will compose the line. Often, product development initiates development of more product groupings than will be included in the final line. This allows for selective paring down of the line throughout the process. Following the color and concept meeting to review initial plans, some firms
Figure 12. Phase 1 Marketing
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Sales Data From Previous Lines
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Trade Literature
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take their concept selections to the consumer for review. The mall intercept interview is a common strategy for conducting these concept tests. Approved concepts are then translated into design specifications and sketches. At the conclusion of Phase 2, a preliminary line represented by sketches and specifications is completed. Phase 3 translates the line from sketches and specifications to actual samples of the product line, as seen in Figure 14. Materials are evaluated and ordered to
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construct the prototype of each design to be included in the line. Patterns are developed and fit standards finalized. Constructed prototypes are evaluated for fit using a fit model and in some cases provided to a consumer panel for wear testing. The prototypes are then reviewed by merchandising, marketing and product development culminating in final adoption of the line. Following Phase 3, the line is marketed to retail channels through markets and calls by sales representatives during Phase 4. This process, as shown in Figure 13. Phase 2
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Figure 15, requires duplicating the prototype garments to provide samples for the sales representatives and detail costing to refine preliminary cost estimates. Based on response of buyers and retail accounts, the line may be modified. Phase 5, pre-production, involves translating the prototypes and first patterns in sample sizes into the complete size range required for sales to the consumer. As noted in Figure 16, this is accomplished through the grading process. Additionally, quality, production and process standards must be
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Back to previous phases
Archive for future reference Drop Styles/ Colors Sizes
Adequate
Sales Forecasts
Add Styles/ Colors/ Sizes
Back to previous phases
Merchandising and Marketing
Production Specification
Production, Planning and Control
Merchandising and Marketing
Promotional Materials for Sales Representatives
Merchandising
Fixturing for Market Space
Market Dispays
Sales Reps call on Sales Reps show Retail Accounts Line at Market(s)
Marketing
Review Retail Orders
Inadequate
Replace
Don’t Replace
Ahead to Phase 5
Merchandising
* Adopted Line Minus Dropped Pieces Plus Added Styles/Colors/Sizes
Modified Line
Phase 4: Marketing the Line
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finalized in preparation for manufacture. Sourcing and scheduling production according to sales forecasts generated by sales to retail is also completed. Figure 17 shows Phase 6, line optimization. In this phase, improvements are made to the line as orders continue and sales forecasts are modified. Modifications may be made to the line to enhance sales or to better balance a line which is having erratic sales. Phase 6 may be cycled through indefinitely as
Figure 15. Phase 4
Product Development
Production Patterns
Production Specifications from Phase 4
Figure 16. Phase 5 Final Garment Specifications
Merchandising
Size Specification Sheets Graded Patterns
Final Engineering Specification Verify Grading & Fit
Production Marker
Source Production
External Production Contract
Production, Planning and Control
Internal Production Schedule
Production Fabric, Trim and Findings Orders
Final Line * Quality Specifications * Material Specifications * Engineering/Production Specifications Merchandising and Production, Planning and Control
Ahead to Production
Ahead to Production
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* Adopted Line Minus Dropped Pieces Plus Added Styles/Colors/Sizes
Modified Line
Phase 5: Pre-Production
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production continues, although the ideal is to have as little change as possible at that stage of development.
Conclusion Most previous work in conceptualizing and modeling the new product development process has been of a generic nature contributing much to the
Production
Back to Phase 5 Size Specifications
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Add Styles/ Colors/ Sizes Adequate Orders
Drop Styles/ Colors/ Sizes Inadequate Orders Review Final Line Against Retail Orders
Merchandising
Don’t Replace
Replace
Add Style Archive for future reference
Merchandising and Production, Planning and Control
* Quality Specifications * Material Specifications * Engineering/Production Specifications
Final Line
Back to Phase 2 Design Development
Add Color
Add Size
Back to Phase 5 Production Fabric, Trim and Findings Orders
Back to Phase 2 Color Standard Selection
Quality and Volume Monitoring
Back to Phase 6 Line Optimization
Phase 6: Line Optimization
Product development model
Figure 17. Phase 6
structured understanding of the process, but little depth. These published models have been beneficial in conceptualizing major phases in the process, but do not provide the depth needed for optimizing the process used in a particular industry. This limitation also exists in works of those authors who have developed theoretical frameworks for the apparel design process applying existing design theory (Regan et al., 1998). The no-interval coherently phased product development (NICPPD) model for apparel integrates with previous research. It has been shown that the current practices and process of apparel product development map to the parallel
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processing, multiple convergent points and fuzzy gates identified as strengths of previous models. The main advantage of having a model of the apparel development process is to understand the critical convergent points, concurrent processes and porous phase boundaries. The NICPPD model provides an effective tool for intra-company to inter-business analysis of the apparel product development process. The NICPPD model allows the researcher to: • visualize the impact of changing business environment, processes, suppliers and customer requirements; • provide a context for integrating research projects; • identify opportunities and establish priorities for research; • clarify information flow providing a basis for establishing effective information technology. The NICPPD model allows the practitioner to: • benchmark and modify apparel development processes, such as those documented by Hardaker and Fozzard (1997) and Koh et al. (1997); • build the organizational structure required to effectively execute the apparel development process; • develop effective strategies for the rapid product development required for line optimization and market responsiveness; • strategically plan organizational and procedural changes to facilitate apparel development. As strategies such as automation and QR become standard means of doing business in the apparel industry, new opportunities must be sought to increase the competitiveness of firms in the industry. Optimization of the little studied and extremely important apparel product development process presents such an opportunity. Critical evaluation and improvement of the process can only be undertaken after a thorough understanding of current practices has been attained. This model provides the framework for such understanding, and a solid foundation for research to improve the process. References Barclay, I., Holroyd P. and Poolton, J. (1995), “The new product development process: a sphenomorphic management model”, International Journal of Vehicle Design: The Journal of the International Association for Vehicle Design, Vol. 16 No. 4/5, pp. 365-74. Bruce, M. and Biemans, W. (1995), Product Development: Meeting the Challenge of the DesignMarketing Interface, John Wiley & Sons Ltd, Chichester. Burns, L. and Bryant, N. (1997), The Business of Fashion: Designing, Manufacturing and Marketing, Fairchild Publications, New York, NY. Cooper, R. (1994), “Perspective: third generation new product processes”, Journal of Product Innovation Management, Vol. 11 No. 1, pp. 3-14. Cooper, R. and Kleinschmidt, E. (1986), “An investigation into the new product process: steps, deficiencies, and impact”, Journal of Product Innovation Management, Vol. 3 No. 2, pp. 71-85.
Craig, A. and Hart, S. (1992), “Where to now in new product development research?”, European Journal of Marketing, Vol. 26 No. 11, pp. 1-49. Erhorn, C. and Stark, J. (1994), Competing by Design: Creating Value and Market Advantage in New Product Development, Omneo, Essex Junction, VT. Fashion Apparel Manufacturing (1982), Report of the Technical Advisory Committee, American Apparel Manufacturers Association, Arlington, VA. Garfield, M. (1985, January), “What product? When? How many? Compressing cycle times”, Bobbin, Vol. 27 No. 5, pp. 99-103. Gaskill, L. (1992), “Toward a model of retail product development: a case study analysis”, Clothing and Textile Research Journal, Vol. 10 No. 4, pp. 17-24. Glock, R. and Kunz, G. (1995), Apparel Manufacturing: Sewn Product Analysis, Prentice-Hall, Englewood Cliffs, NJ. Griffin, A. (1992), “Evaluating QFD’s use in US firms as a process for developing products”, Journal of Product Innovation Management, Vol. 9 No. 3, pp. 171-87. Gruenwald, G. (1992), New Product Development, (2nd ed.), NTC Business Books, Lincolnwood, IL. Hardaker, C. and Fozzard, G. (1997), “The bra design process – a study of professional practice”, International Journal of Clothing Science and Technology, Vol. 9 No. 4, pp. 311-25. Hart, S. and Baker, M. (1994), “The multiple convergent processing model of new product development”, International Marketing Review, Vol. 11 No. 1, pp. 77-92. Himmelfarb, P. (1992), Survival of the Fittest: New Product Development in the ‘90s, Prentice-Hall, Englewood Cliffs, NJ. Kinni, T. (1993), “What’s QFD? Quality function deployment quietly celebrates its first decade in the US”, Industry Week, Vol. 242 No. 21, pp. 31-2. Koh, T., Lee, E. and Lee, Y. (1997), “An object-oriented model of apparel pattern making”, International Journal of Clothing Science and Technology, Vol. 9 No. 7, pp. 367-79. Kunz, G. (1993), Phases of Product Development: Preadoption and Postadoption[Video], Iowa State University Media Resources Center, Ames, IA. Littrell, J. (1997), “Design from merchandising to manufacturing”, Paper presented at the 47th Annual Conference of the American Society for Quality Control Textile and Needle Trades Division, Spring. Magg, T. (1997), Product Development Manager, Cross Creek Apparel, Inc., Mt. Airy, North Carolina, Personal interview conducted on site at Cross Creek Apparel, July. Mahajan, V. and Wind, J. (1992), “New product models: practice, shortcomings and desired improvements”, Journal of Product Innovation Management, Vol. 9 No. 2, pp. 128-39. Marketing Committee of the American Apparel Manufacturers’ Association (1989), “Merchandising calendar”, Apparel Manufacturer, Vol. 1 No. 2, p. 26. Nijssen, E., Arbouw, A. and Commandeur, H. (1995), “Accelerating new product development: a preliminary empirical test of a hierarchy of implementation”, The Journal of Product Innovation Management, Vol. 12 No. 2, pp. 99-109. Regan, C., Kincade, D. and Sheldon, G. (1998), “Applicability of the engineering design process theory in the apparel design process”, Clothing and Textiles Research Journal, Vol. 16 No. 1, pp. 36-46. Rochford, L. and Rudelius, W. (1992), “How involving more functional areas within a firm affects the new product process”, Journal of Product Innovation Management, Vol. 9 No. 4, pp. 287-99. Rosenthal, S. (1992), Effective Product Design and Development: How to Cut Lead Time and Increase Customer Satisfaction, Business One Irwin, Homewood, IL.
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Sadd, D. (1996), “Structuring product development for higher profits”, Bobbin, Vol. 38 No. 2, pp. 68-73. Saren, M. (1994). “Reframing the process of new product development: from a ‘stages’ model to a ‘blocks’ framework”, Journal of Marketing Management, Vol. 10 No. 7, pp. 633-43. Urban, G. and Hauser, J. (1980), Design and Marketing of New Products, Prentice-Hall, Inc., Englewood Cliffs, NJ. Zahra, S. and Ellor, D. (1993), “Accelerating new product development and successful market introduction”, S.A.M. Advanced Management Journal, Vol. 58 No. 1, pp. 9-15.
Locating defects on shirt collars using image processing Mustafa Al-Eidarous Department of Electronic Engineering, University of Hull, Hull, UK 1. Faults and inspection system 1.1 Shirt collar description The primary purpose of inspection of shirt collars is to avoid collars with faults, which might affect the saleability of the product. One of the advantages of using a modern Charge Coupled Device (CCD) image sensing device is coverage of a large area (Bradshaw, 1995), using certain edge detection or edge enhancement match filter techniques (Hill et al., 1983; Parui and Hashim, 1986). However most of these techniques cannot detect small defects and are affected by noise. Inspection is usually carried out on the cut pieces of cloth, before stitching, to avoid unnecessary assembly of faulty collars, and because of difficulty of relocating defects in rolls following inspection due to fabric stretch. Faults in shirt collars may be due to faults in the original cloth, or might arise during manufacturing. The shirt collar pieces are cut to the shape shown in Figure 1(a). Each fabric panel is divided lengthwise into two areas, the collar band region and collar region, along the folding line. First panel collar regions (seen during wearing of the garment): (1) Collar Point Left (CPL). (2) Collar Point Right (CPR). (3) Collar Back Centre (CBC). Second panel collar band regions (most exposed in presentation windowing): (1) Right Collar Band (RCB). (2) Left Collar Band (LCB). (3) Central Collar Band (CCB). The two fabric panels (plies) are sewn together on three sides as shown in Figure 1(b). The collar plies are sewn such that their inner sides are situated on the outside. Inverting or “turning” the collar right side out is done by folding the stitch line inside the collar pocket as shown in Figure 1(c) (Norton-Wayne, 1995; Paul et al., 1990; Taylor et al., 1990). The shirt collar is formed by sandwiching lining and stiffening layers between the top and bottom cloths and folding the final product along the folding line.
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CPL
CBC
CPR
Collar Regions
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RCB
Collar Band Regions CCB
LCB
(a) Shirt collar panel Stitch line
Collar point
(b) Top view Collar pocket
Figure 1. Shirt collar panels (c) Oblique view
The two panel areas subdivide into regions of quality importance. The collar areas CPL, CPR and CBC are most exposed in presentation or windowing, whereas the areas RCB, LCB, and CCB are seen during the wearing of the garment. Faults in these areas must be avoided, and collar panels with faults in these areas are not suitable for use as the top ply. A high quality visual appearance is of importance in two stages: (1) the presentation of the product in the merchandising pack (known as windowing ); and (2) during the consumer use of the product. 1.2 Faults and their sizes The faults studied in this work are those found in a group of 22 rejected shirt collar panels of various mono-colours and from various production lines of one manufacturer. The fabrics of the panels were all of the same textural weave, i.e. the yarns woven as a rectangular interlaced grid. Since almost 75 per cent of the manufacturer’s total production is white and other mono-colours, the investigation was based on plain, mono-colour fabrics. The general classification of faults follows that of Taylor et al. (1988), which can be taken as typical of generally occurring faults for mono-coloured materials.
The fault types are: Locating defects on shirt collars • Thick weave: an extra piece of loose yarn trapped in the weave. • Slub flaw: a short thick place in the yarn where the fibres are not spun properly. • Knot: appears like a prominent knot or spot on the surface of the fabric. 367 • Mis-weave: caused by incorrect interlacing between weavers and wrappers, which leaves loose threads over the surface of the cloth. • Ladder: faults due to threads missing from the weft or warp. • Holes: gaps in the cloth larger than the usual spacing between the yarns. • Colour flaw: a short thick piece of a yarn of dissimilar colour trapped in the wrapper or weaver. • Foreign fibre: a foreign fibre, i.e. a strand or piece of fibre of dissimilar colour (usually black), dragged into the yarn during the spinning process. • Black mark: parts of the yarn, either wrapper or weaver. • Stains: marks caused by heavy contamination of fabric by oil or grease or other substances. Typical fault types were used in this research and two (mis-weave and ladder) were not available. The details of the fault types investigated are indicated in Table I, together with the sample coding used for the database information, and programming purposes. 1.3 Image capturing environment The CCD camera and back-light were used in this research. The captured image was digitised to 256 × 256 pixels with 256 grey level (zero representing black and 255 maximum brightness or white). Fault type
Width
Length
Number of samples
Sample coding
Thick weave
0.4mm
6mm+
9
ca1 . . . ca9
Slub flaw
0.40mm
5-50mm
2
cd2, cd3
Knot
1-2mm
any
1
cb1
Mis-weave
0.40mm
2-25mm
none
-
Ladder
several cm
several cm
none
-
Hole
any
any
1
ck2
Coloured flaw
any
0.40mm+
1
ce1
Foreign fibre
0.5mm
2
ch2, ch3
Black mark
any
any
6
cf1 . . . cf6
Stain
any
any
1
cj1
Table I. Fault types, size, number of samples, and sample coding
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Figure 2. Original image (ca) with low resolution
Figure 3. Original image (ca) with higher resolution
The camera focus was set at 5mm and aperture set at 5.6 for an optimum depth of field and suitable exposure. The area of the image was chosen to be 2.3in. by 3.7in. (58.42mm ( 93.98mm ) The field of view captured could be varied by altering the height of the camera. The height of the camera (lens to object distance) was 10in., giving an effective pixel area of (height × width) = 0.11mm × 0.18mm. Illumination is a key parameter affecting the image which directly affects the quality of the output data. It is necessary to customise the illumination for each application. The methods for industrial application can be back lighting , front lighting, structure lighting and strobe lighting. The specific source of lighting energy affects the amount of processing and the result achieved. In this application, back light was used where by the shirt collar was located between the light source and the camera. A frosted glass is normally placed over the light to produce a diffuse area emitter (Louis and Galbiati, 1990). Back lighting has the advantage that it produces high contrast images of the defect on the shirt collar. The high contrast minimises the image processing task and reduces sensitivity of the system to variations in the illumination source. An example of the original image with no defect, shown in Figure 2, is labelled ca. This is used as a reference image for other defects. Every defect type has a subgroup of actual defect images. The labels of the images within each subgroup are also shown in Table I. As an example, Figure 3 shows a thick
weave defect (ca1). Figure 4 shows the area of the image 1.0in. (0.5in. with Locating defects higher resolution to a level where the thread and the thick weave are very on shirt collars obvious. Figure 5 shows an example of an image with three lines, used in the signature counting method, section 2.3.2. 1.4 Inspection system A description of an integrated system for on-line quality inspection of a shirt collar is shown in Figure 6. In order to detect shirt collar defects, the system acquires static images of a moving object on a carrier using CCD cameras. Movements of the carrier may cause vibrations that will degrade the captured image which will affect the result. Several images of different portions of the object would enable parallel fault detection of the parts of the object which would speed the inspection, but several cameras connected to the system would be needed. For the time being only one camera is implemented. The PC system controls the image processing and finds the defect. When a fault is detected, the host computer interrupts the motor driver. The defective
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Figure 4. Thick-weave (ca1) with higher resolution
Figure 5. Image with three lines fault
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moving object carrier
Figure 6. Structure of automated shirt collar inspection system
Rejected shirt collar
shirt collar is then removed from the carrier. This could be done with a robot or any pick and place device. 2. Detection methods 2.1 Statistical techniques Koshimizu and Aoi (1978) developed a statistical method for automatic fabric inspection, which attempts to reduce the amount of computation and data handling associated with statistical techniques by using grey level signatures for horizontal and vertical rows. Horizontal and vertical signatures work because most defects tend to be elongated longitudinally and laterally. The signature for a vertical column or horizontal row of an image is simply the sum of the grey levels in the pixels of the column or the row. Thus, the horizontal signature of row i is: (1) where i = 1, 2, ..., N, vertical signature of column j; (2) where j = 1, 2, ..., N.
The mean and standard deviations of these signatures are found in the usual Locating defects way; on shirt collars (3)
(4) with similar expressions for vertical mean and standard deviation. N is the sample size of the column or row in pixels, which in this application is 256. It is now assumed that the presence of a fault causes a significant deviation in the signatures of the columns or rows in which the fault occurs. 2.2 Control and sensitivity Control limit is very important in the grouping moving averaging method. The appropriate limits depend on the application. The designer must consider the sensitivity; k is the constant of sensitivity. This is usually different in plain fabric than wool fabric. Trial and error is the only way to judge the sensitivity. The constant of sensitivity on this shirt collar application was 2, so the operator used the software with the same control limit. Previous researchers (Bradshaw, 1995; Norton-Wayne, 1995) have suggested that a sensitivity constant of 1-2 is appropriate for similar types of application. The following control limits are defined: Vertical upper control limit VUCL = µv + kσ v Vertical lower control limit VLCL = µv – kσv Horizontal upper control limit HUCL = µh + kσh Horizontal lower control limit HLCL = µh – kσh where k is an adjustable control parameter. The faults are then detected if the signature for a column or row falls outside the respective control limits: v S j ≥ VUCL Light Value Fault in column j. v
S j ≤ VLCL Dark Value Fault in column j. h
S i ≥ HUCL Light Value Fault in row i. h
S i ≥ HUCL Dark Value Fault in row i. Using mean and standard deviation of the column or row signature led to the development and design of two novel approaches: moving group average, and moving divided group average.
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2.2.1 Moving group average A program was designed using mean and standard deviation of the column or row signature to allow the user to capture or load the image, and to select different k values and different group numbers of columns or rows. Calculating the moving average of groups, in each image there are 256 columns (rows). Each signature is the sum of 256 pixels. Grouping every n, n = 1, 2, ..., 256, columns (rows) and taking their average, gives 256/n groups. Each group can be moved by any number of steps m, m = 1, 2, ..., 256, then the next group average calculated, and so on. The result of this grouping and averaging will produce a graph. If the graph lies above or below or touching the two levels, an alarm will sound to void the defective fabric. Figure 7 shows the non-fault image (ca) with a graph not exceeding the upper or lower levels which means that there is no defect and no alarm will sound. Figure 8 shows the defect image (ca1), with a graph exceeding the upper or lower levels which means there is a defect and an alarm will give notice of it. 2.2.2 Moving divided group average The column or row is divided into a number of groups as a power of 2, then the moving average method explained previously is applied. The number of columns or rows times the number of steps should equal 256. The result is shown in Figures 9 and 10. Figure 9 shows the result of the nonfault image (ca), which does not exceed the upper or lower level, meaning there is no defect and no alarm. The graph is more precise than the result in Figure 7, because the graph in Figure 9 is elongated to 512 points since every column is divided into two groups. Figure 10 shows the result of the (ca1) image which exceeds the upper or lower level. This means there is a defect and an alarm will give notice of it. This graph gives a more precise result than Figure 8.
Figure 7. Moving group average of a non-defect (ca)
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Figure 8. Moving group average of a defect image (ca1)
Figure 9. Moving divided group average of a non-defect (ca)
Figure 10. Moving divided group average of a defect image (ca1)
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The mean of each group is: (5)
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where N = 256, m is the step, and Xij is the pixel value at column j and row i. Figure 11 shows the software structure used during inspection of shirt collars. There are many methods to separate the defect from the background, e.g. filtering and edge detection, but most of these methods are not suitable for this application with the experiments done on the images. Hence, the following techniques were used: (1) Highlighting method. (2) Variance method. These are explained below. 2.2.3 Highlighting method The object is separated from the background by choosing a suitable threshold (T ) value where T = µ or T = µ + σ or the mode (the maximum or the minimum frequency). The best automated threshold value for this application is calculated from the grey level T hi = µhi + kσ hi (Bradshaw, 1995). The highlighting function is defined as follows: If Test > Threshold → Result = 256 If Sample < Threshold → Result = 1 Applying this method separated only 60 per cent of the defects from the background, so the variance filtering was used (Phillips, 1994). 2.2.4 Variance method This method is done by sliding a 3 × 3 window along whole the image. Any other window size can be used, but a 3 × 3 window is appropriate for this application because it gives the best result. The variance function replaces the Read the image, process and display Enter space and division or spread and step Loop starting position from 1 to 256 spread
Figure 11. Software structure used during inspection
Get mean standard deviation display for horizontal or for vertical
pixel in the centre of a 3 × 3 area, with the sum of squares of the differences Locating defects between the centre pixel and its eight neighbours. on shirt collars (6) where Xij is the pixel value at the ith row and jth column, Xc is the value of the centre pixel of the window. If variance value ≥ R (reference deviation) the pixel is set to white; otherwise, the pixel is set to black. Applying the variance method over the complete image separated 90 per cent of the defects from the background. Then the moving group average and signature counting method were applied (see sections 2.3.1 and 2.3.2).
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2.3 Detection methods with filtering The variance method was used on the defect image (ca1), to separate the defect from the background. Then, the moving group average method was used to show the shape of the defect. The vertical and horizontal signatures were counted to represent the defect number. The moving group average and signature counting methods will be described, then application to shirt collars discussed and the results presented in sections 2.3.1 and 2.3.2. 2.3.1 The moving group average The moving group average is applied on the signature S, mean µ and standard deviation σ. Figure 12 shows these three graphs. The upper graph represents µ + σ, the lower graph represents µ – σ, and the middle graph represents the mean under the image. The three graphs show the vertical signature and at
Figure 12. Filtered defected image (ca1) with moving group average on S, µ and σ
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the right of the image, they show the horizontal signature, thus clearly showing the defect and its shape. 2.3.2 Signature counting The signature of each column or row of the image is calculated, then the lines in the signature graph counted and compared with the reference (non-defect) to give an indication of the defect. To compare these numbers automatically, the following equation is used: (7) where Ns is the number of columns in the signature graph of the sample image, and N r is the number of columns in the signature graph of the reference image (non-defect). The similarity signature ratio value, Isr , must be zero. The mismatch must lie between 0 and 1, where 0 indicates a perfect match and any number between 0 and 1 a complete mismatch. The signature method, where it works, produces a significant false region. The variance filter shows the image of the thick weave fault (ca1), as in Figure 13, while Figure 14 is an example of an image (ca31) with three continuous straight lines which also shows significant false regions. These can be compared with the non-defect image shown in Figure 9. When we run the variance filter algorithm on a non-defect ca, no defect will be counted. Noise and discontinuity effects on the image will affect the result of the signature counting algorithm. Clearly, further processing and linking would be needed for a characterization of faults. It may be concluded that the variance filtering and signature counting method are suitable for this application.
Figure 13. Filtered defected image (ca1) with signature line graph
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Figure 14. Filtered three lines defect image with signature line graph
Table II shows the image type and the number of objects used to calculate the similarity signature ratio value, Isr. Figure 13 shows the graph of the defect shape of thick weave after using the variance filter on (ca1) image in Figure 3. Figure 14 shows the graph of the three line fault, after using the variance filter on (ca31) in Figure 5. After using the variance filter method, the size of the fault can be calculated but the noise must be removed to get the approximate size. 3. Conclusion The main reason for inspecting shirt collars instead of the fabric roll is the difficulty in relocating defects following inspection, due to fabric stretch. Further, machine vision roll inspection machines are very expensive. The results show that the statistical methods (moving group average and moving divided group average) can be applied to an automated inspection system to detect faults in shirt collars or in any mono-coloured woven fabric. The moving group average is beneficial (in maximising defect contrast) because defects rarely extend the full length of a row or column.
Image type
Number of objects
ca
0
ca1
1
ca31
3
Table II. Resulting number of defects on each image
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The novel techniques using variance filtering with the moving group average on S, µ and σ, or with the signature counting graph showed the fault and its shape very clearly. In both methods the operator can see the defect on the graph and an alarm sounds. This makes the system fully automated and the operator is in the position of an observer. Current work is concentrating on aspects of this general method for all defects, to separate the defect from the background. A CCD line scan camera, by which a large area can be captured, can be used. Defects from other manufacturers with different fabrics will be similar, but dimensions and rates of occurrence may vary. Additional types of faults will occur with patterned fabric and multicoloured fabric. It would be necessary to generalise the system and for each application to have its own library. Information about faults might be used for both quality control and process control. References Bradshaw, M. (1995), “The application of machine vision to the automated inspection of knitted fabrics”, Mechatronics, Vol. 5 No. 2/3, pp. 233-43. Hill, W.J., Norton-Wayne, L. and Finkelstein L. (1983), “Signal processing for automatic optical surface inspection of steel strip”, Trans. Inst. Meas. Control, Vol. 5 No. 3, pp. 137-54. Koshimizu, H. and Aoi, S. (1978), “Study of fabric inspection by computer image processing”, in Proc. 9th Conf. Image Eng. Japan, pp. 55-9. Louis, J. and Galbiati, J.R. (1990), Machine Vision and Digital Image Processing Fundamentals, Prentice-Hall, Englewood Cliffs, NJ. Norton-Wayne, L. (1995), “Automated garment inspection using machine vision”, IEEE Int. Conf. on Systems Engineering, Pittsburgh, ch.152, pp. 374-77. Parui, S.K. and Hashim, A.A. (1986), “Automatic defect detection in textile manufacturing”, Int. Conf. on Computer-Aided Production Engineering, Mechanical Engineering Publ. Ltd, Edmunds, UK, pp. 443-8. Paul, F.W., Torgerson, E., Avigdor, S., Cultice, D., Gopalswamy, A. and Subbarao, K. (1990), “A hierarchical system for robot-assisted shirt collar processing”, IEEE International Conf. on Systems Engineering, Pittsburgh, ch. 152, pp 378-82. Phillips, D. (1994), Image Processing in C, R&D Publication, Inc., Lawrence, KS. Taylor, G.E., Taylor, P.M., Zedeh, J.E. and Monkman, G. (1988), “Automated inspection of shirt collars”, Proc. Int. Conf. Robot Vision and Sensory Controls, pp. 281-91. Taylor, P.M., Wilkinson, A.J., Gibson, I., Gunner, M.B. and Palmer, G.S. (1990), “An integrated automated garment assembly system”, IEEE International Conference on Systems Engineering, Pittsburgh, ch. 152, pp. 383-6.
Anisotropy of fabrics and fusible interlinings
Anisotropy of fabrics and interlinings
C. Cassidy De Montfort University, Leicester, UK, and
S.V. Lomov St Petersburg State University of Technology and Design, Russia
379 Received April 1998 Revised July 1998 Accepted July 1998
Introduction The modelling of fabric, particularly woven fabric, as an orthotropic sheet is widely used in the 3D garment drape visualisation systems[1] and as a tool for prediction of fabric properties[2]. The following equations are used to predict the anisotropic linear elastic behaviour of fabric for in plane and bending deformation[3]: 1/Eθ = 1/E1cos4θ + 1/E2 sin4θ + (1/G-2/K) sin2θ cos2θ (1) 2 2 1/Gθ = 1/G cos 2θ + (1/E1 + 1/E2 + 2/K) sin 2θ (2) 1/K = µ 1/E1 = µ 2 /E2 Bθ = B1 cos4θ + B2 sin4θ + 2B* sin2θ θ cos2θ (3) Where E, G, B and µ designate Young’s modulus, shear modulus, bending stiffness and Poisson’s ratio respectively. Subscripts “1”, “2” and “θ ” – warp, weft and bias directions (θ is an angle relative to the warp direction) and; B* = 2τ + θ2 B1/2 where τ is twisting rigidity and σ – Poisson’s ratio for bending. The assumption made in equations (1-3) is that the linearity of tension, shear and bending, depends on the value of θ . However, the fabric behaviour is not linear, nor are the experimental anisotropy curves smooth. In one of the earliest studies of woven fabric anisotropy by B.P. Pozdnyakov[4] an unexplained significant decrease of fabric tensile resistance for θ = 15° and 75° was observed. Fabric properties are usually measured on KES-F or FAST systems using recommended parameters; if these data are to be fed into a garment CAD system, then the applicability of these measurements to the orthotropic description of the anisotropy of mechanical properties should be improved. The possible errors in predicting fabric in a deformed state with the orthotropic shell model were also discussed by Amirbayat and Hearle[5]. It is useful to compare the anisotropical mechanical behaviour of woven fabric with the behaviour of other sheet materials. A suitable choice of such The authors would like to thank Coats (UK), Leicester, and Lever Bros (UK), Port Sunlight for their help with this work.
International Journal of Clothing Science and Technology, Vol. 10 No. 5, 1998, pp. 379-390, © MCB University Press, 0955-6222
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comparative material is non-wovens (used in fusible interlinings) and fusible interlinings themselves. This would provide important information for the modelling of garments and an opportunity to study the effect of fabric anisotropy in composites. The mechanical properties of fusible interlinings were studied in several works[6-8]; however, the anisotropy of these properties was not fully investigated. The aim of the present paper is to study the feasibility of application of equations (1-3) to the behaviour of woven fabric and fusible interlinings using KES-F measurements. The accuracy of simple predictive models for the mechanical behaviour of textile composites used in other studies will also be investigated[6,8]. Experimental Samples The woven fabrics used to produce fusible interlinings samples (Table Ia) have two different patterns of anisotropy. Both fabrics are plain weave; however, Fabric A has the same yarn for warp and weft, whereas fabric B has warp and weft threads of different linear density. The fusible interlinings b,c and d (Table Ib) are produced from the same base viscose/polyester non-woven fabric. The interlinings were fused to the fabrics on a Reliant Rolamatic continuous fusing press at a recommended temperature of 140°C for 11 seconds at a pressure of 80lb/sq inch. The angle θ is measured relative to warp direction for woven fabrics and relative to the “machine” direction (primary fibre orientation) for nonwovens.
Fabric Weave Composition
Weight, g/m2
B
plain
plain
50% polyester 50% viscose
65% viscose 25% polyester 10% linen
207
198
Threads per cm
warp 17.6 weft 16.0
warp 32.0 weft 18.0
Yarn linear density (Tex)
warp 58.2 weft 59.4
warp 27.9 weft 51.6
Yarn linear density x sett, (Tex/cm) Table Ia. Woven fabrics table
A
Yarn twist (turns/m)
warp 1,024 weft 950
warp 893 weft 932
warp 520 weft 523
warp 733 weft 451
Adhesive
Method of applying adhesive
Weight
a
None
N/A
40g/sq m
b
*PA/PE
Scatter
51g/sq m
c
PE
Scatter
55g/sq m
d
PA
Dot
52g/sq m
Fusible interlining
Anisotropy of fabrics and interlinings 381
Note: * PA = Polyamide, PE = Polyethylene
Table Ib. Fusible interlinings
Results and discussion: bending Tables II and III show the results of bending properties measured using KES-F. Anisotropy trends are summarised in Figure 1. Hysteresis HB(θ ) is strongly correlated with B(θ ). The woven fabrics do not show any unexpected trends. Fabric A with its uniform structure shows a small anisotropy of bending stiffness; the anisotropy in fabric B is quite pronounced.
θ
a
Non-wovens b c
d
Fabric
Fabric B +b +c
+d
Fabric
Fabric A +b +c
+d
0 (warp)
0.15
0.083
0.075
0.16
0.094
0.70
0.71
0.77
0.092
0.78 0.88 0.73
30
0.23
0.16
0.14
0.24
0.070
0.78
0.90
0.83
0.087
0.92 1.07 0.91
45
0.30
0.22
0.22
0.30
0.057
0.84
0.90
1.00
0.085
1.19 1.27 1.10
60
0.31
0.25
0.27
0.35
0.046
1.02
1.10
1.15
0.095
1.12 1.35 1.20
90 (weft)
0.35
0.35
0.26
0.32
0.044
1.10
1.11
1.20
0.11
1.20 1.37 1.30
d
Fabric
Fabric B +b +c
+d
Fabric
θ
a
Non-wovens b c
Fabric A +b +c
+d
0 (warp)
0.093 0.051
0.058
0.097 0.057
0.59
0.55
0.64
0.060
0.61 0.62 0.60
30
0.17
0.085
0.10
0.16
0.044
0.58
0.72
0.62
0.063
0.68 0.72 0.71
45
0.27
0.15
0.19
0.29
0.036
0.58
0.62
0.75
0.065
0.73 0.65 0.86
60
0.38
0.19
0.27
0.42
0.029
0.65
90 (weft)
0.47
0.32
0.33
0.63
0.028
0.067 0.073
Table II. Bending rigidity B, gf cm
Table III. Bending hysteresis HB, gf cm
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B,gf cm
Fabric A +b,c,d
1
0.9
382
Fabric B+b,c,d
0.5 2 a,b,c,d 0.1 0.1 3
Fabric A
0.08 4
Fabric B
0.04
Figure 1. Anisotropy trends: bending
0 0
15
30
45
60
75
90 θ , grad
The values of bending rigidity of non-wovens with and without adhesive are shown to be close and there is no apparent increase in rigidity as a result of the addition of adhesive. However, there is a significant difference of bending rigidity between the two directions (B2 /B1≈ 3). Composite bending stiffness tests revealed a difference between the face and back of the composite. Figure 2 shows the bending diagram of “Fabric A + D” for θ = 90 (weft) and 45 degrees. Bending rigidities shown in Tables II and III are generated by KES-F system automatically and represent the average for these two directions. The trend of composite bending stiffness is determined by the non-woven stiffness; its value is close to the estimation computed with the theory of Kanayama and Niwa[8]. The anisotropy equation (3) describes the bending behaviour of fabrics, nonwovens and composites quite well. Let k be the coefficient relating B* and B1,B2: B* = k(B1 + B2 )/2. The anisotropy curves for fabrics are best fitted with k = 0.66; for non-wovens – with k = 1.4. Results and discussion: tension Tables IV and V show the results of the tensile measurements. The typical tensile diagram is shown on Figure 3. Linearity LT does not appear to depend on the direction of tension. The anisotropy trends are shown in Figure 4.
Anisotropy of fabrics and interlinings
M,gf cm/cm 90° 0.2 45°
0.1
383
0
–2
K,1/cm
2
–0.1
–0.2
Figure 2. “Fabric A + D”
Composite vs. components Data shown in Tables IV and V reveal some unexpected trends. First, deformation is higher for non-wovens with adhesive in comparison to the same non-woven without adhesive. If an adhesive bonds fibres within nonwoven fabric (the bonded regions can be considered rigid in comparison with “free” fabric), then only “free” regions will elongate under the applied load, and
a
Non-wovens b c
0.89
0.85
0.88
d 0.88
Fabric 0.75
Fabric B +b +c 0.90
0.93
+d
Fabric
+b
0.91
0.69
0.90
Fabric A +c 0.97
+d 0.94
Note: * Angular difference of LT is less then 0.02 for all samples
θ
a
Non-wovens b c
d
Fabric
Fabric B +b +c
+d
Fabric
Fabric A +b +c
Table IV. Linearity of tension diagram LT*
+d
0 (warp)
1.50
1.56
1.54
1.53
4.55
1.47
1.41
1.70
3.34
1.85 1.42 1.73
30
2.35
2.82
2.96
2.65
4.58
2.31
1.02
2.46
3.96
2.49 2.03 2.32
45
3.49
5.07
4.09
5.68
5.22
2.82
1.54
3.33
4.76
3.35 2.73 3.00
60
2.42
2.79
2.23
2.74
5.44
3.37
1.68
3.41
4.76
3.71 3.34 3.47
90 (weft)
1.97
2.04
2.05
2.12
3.20
3.07
3.00
3.05
4.07
3.68 3.71 3.55
Table V. Deformation EM, % at Fmax = 500 gf
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E gf/cm 30°
0° 400
45°
384
300
200
100
Figure 3. Typical tensile diagram
E 0
1
2
3
0 =4G 45 4
ε,%
average deformation of the sample should be lower than the deformation in a “free” fabric. This is not the case. Second, the addition of a fabric to non-woven does not necessarily lead to the increase of resistance to tension (for a weft direction there is a pronounced decrease of this resistance). Third, the shape of anisotropy curve can change as a result of bonding a nonwoven to a fabric. For non-wovens and fabrics alone these curves have a maximum near 45 degree direction; for fabric A with interlining, the maximum is shifted to the weft direction (note that bending stiffness behaves quite the opposite – see Figure 1). The possible explanation of these effects is the influence of bonding process on the mechanical properties of fibres within the non-woven layer. Anisotropy curves Strictly speaking, the orthotropic equation (1) cannot be applied to the data retrieved with standard KES-F measurements with Fmax = 500 gf/cm because of a non-linearity of the tensile diagrams. Nevertheless we shall check the applicability of equation (1) to our data to find whether it is possible to use the orthotropic model as an approximate description of anisotropy of fabrics for considerably large deformations. The orthotropic equation (1) should be fully justified for the description of initial tensile moduli; unfortunately, it is very difficult to evaluate the initial modulus of tension, especially for bias angles; therefore the application of the orthotropic model for them cannot be evaluated. Let us consider the equation (1) in the form valid for the linear behaviour of the material: (4) EMθ = EM1cos4θ + EM2sin4θ + EM* sin2θ cos2θ
where constant EM* can be computed from EM45 value: (5) EM* = 4EM45 – EM1 – EM2 Computing the value of EM* from equation (5), we can compare equation (4) with the experimental data for θ = 30 and 60 degrees – Figure 4. It can be noted that the theory agrees reasonably well with the experiment in the case of composite fabrics and can be considered as a very approximate evaluation for non-wovens and woven fabrics. This is quite natural: the less freedom of fibre
Anisotropy of fabrics and interlinings 385
EM,% 5 Key b c d
4 a 3 a
a
2
a
a 1
0
15
30
45
60
75
90 θ, grad
EM,% Key FabricB
5
Fabric A
4 B
A
3 2 1
0
15
30
45
60
75
90 θ, grad
EM,% Key 5
Fabric B +...
4
Fabric A +...
A
3
B
2 1
0
15
30
45
60
75
90 θ, grad
Figure 4. Anisotropy trends: tension
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386
movement in a material, the more close its behaviour is to the behaviour of a solid anisotropic plate. Results and discussion: shear Tables VI and VII show results of shear properties measurements. Anisotropy trends are summarised in Figure 5. There should be a symmetry of shear properties for angles θ and τ/2 – θ , so the data for θ = 30 and 60, 0 and 90 degrees is averaged on this figure. Note that equation (2) can be rewritten in a form (6) 1/Gθ = 1/G – ( 1/G – 1/G*) sin22θ where constant G* = G45. Fabrics There is an apparent difference between the observed shear behaviour of woven fabrics (Figure 5) and the behaviour predicted by Equation 6. There is an
θ
Table VI. Shear modulus G, gf/(cm grad)
Non-wovens b c
d
Fabric
Fabric B +b +c
+d
Fabric
Fabric A +b +c
+d
0 (warp)
7.49
5.75
6.91
8.52
0.68
10.8
11.9
11.7
0.76
12.5
11.6 11.8
30
5.59
5.64
6.25
6.48
3.45
11.5
12.1
11.9
5.56
11.6
11.1 12.5
45
6.52
6.15
6.78
7.44
4.35
10.7
11.1
11.0
3.42
11.3
9.53 11.2
60
6.64
6.19
6.69
6.56
6.56
12.3
12.0
11.3
6.36
11.2
11.6 11.6
90 (weft)
7.70
7.40
7.95
8.36
0.54
12.0
10.9
12.4
0.74
11.9
11.4 12.1
d
Fabric
Fabric B +b +c
+d
θ
Table VII. Shear hysteresis HG/ HG5, gf/cm
a
a
Non-wovens b c
Fabric
Fabric A +b +c
+d
0 (warp)
24.0 17.4
19.7 13.8
15.4 14.9
16.6 16.5
0.68 1.40
28.9 25.2
27.0 25.7
27.0 24.1
1.06 2.91
26.4 28.1
42.7 28.9 35.8 27.3
30
32.8 21.2
24.9 16.2
24.9 17.4
28.5 19.0
7.34 3.66
27.1 23.6
30.2 25.6
22.8 21.2
5.92 4.66
27.3 25.7
39.7 28.4 32.9 25.4
45
29.3 19.2
20.5 15.4
20.2 15.6
21.2 16.5
3.29 3.89
29.1 24.6
30.7 25.1
24.5 21.5
6.83 4.02
32.1 27.0
51.4 34.8 34.3 24.8
60
28.3 19.1
19.7 14.2
14.1 13.9
23.8 16.7
3.71 3.64
25.4 25.5
36.2 30.2
31.6 25.7
5.17 4.67
38.7 30.3
43.1 31.8 32.4 26.8
90 (weft)
28.6 20.4
23.6 16.5
23.1 16.8
24.4 17.8
0.33 1.11
26.6 28.1
30.2 27.3
24.7 26.6
0.96 2.86
25.6 27.7
38.9 27.4 32.6 27.8
Anisotropy of fabrics and interlinings
G, gf cm grad Fabric b,c,d 10
387 8
a
a
c b
a
a
6
a
Key 4
Fabric A Fabric B
2
0
15
30
45
60
75
Figure 5. Anisotropy trends: shear
90 θ, grad
unexpected local maximum of shear resistance for θ = 30 and 60 degrees. The possible explanation for this possibly lies in the bilinear shear behaviour of fabrics. The effective shear force in warp and weft directions is T12 = Tcos2θ, where T is shear force in direction θ . For the directions θ close to 45 degrees T12 is small, so the fabric resists shear in the initial region of the shear diagram, where this resistance is highest. To develop a qualitative description let us consider the “rigid-linear” shear behaviour of a fabric and linear tension behaviour. In this case tension deformation ε and tension force P in the warp and weft directions, shear angle and shear force T will be linked with the following equations : ε1 = P1 /E1 – µ P2 /E2 (7) ε2 = P2 /E2 – µ P1/E1 0, T < HG/ 2 12 Γ (T12 ) γ12 = (T – HG/ 2) / GT > HG/2 = 12 where E is Young modulus and HG is the shear hysteresis.
(8)
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388
For shear in the direction θ with a shear angle γ and shear force T the transformation formulae for components of deformation and stress tensors [9] are : P1 = T sin2θ, P 2 = –T sin2θ, T12 = T cos2θ (9) γ = γ12 cos2θ + (ε1 – ε2 )sin2θ Combining equations (7), (8) and (9), the equation for the dependence of γ on T is obtained: γ = Γ (T cos2θ ) cos2θ + T/k sin22θ (10) where k = 1/E1 + 1/E2 + 2/K = G45 For a given G, HG and G45 equation (10) gives a qualitative description of a fabric behaviour in shear in the direction θ . Table VIII shows initial slopes of γ (T) curves computed from equation (10) for Fabric A and Fabric B with the parameters from Tables VI and VII. The ratio of initial slopes for θ = 30 and 45 degrees is about 1.3 for both fabrics and reasonably agrees with the observed values of G30 /G45 –1.64 and 1.15 respectively. For non-woven and composite fabrics this effect is shaded by the larger value of k = G45 in equation (10). Non-wovens and composite fabrics The addition of glue to non-wovens alters the shear rigidity only slightly (see Tables VI, VII, VIII and Figure 5). Anisotropy curves for non-wovens and composite fabrics correspond to equation (6) (Figure 5). The ratio G0 /G45>1 for composite fabrics. However, the angular variation of shear is feeble (less than 20 per cent for non-wovens and less than 10 per cent for composites). Relationship between tensile and shear parameters The last test for the anisotropy model – the compatibility of shear and tensile behaviour predicted by equations (1) and (2) – cannot be conducted directly with the KES-F data because the relationship between tensile and shear moduli suggested by equations (1) and (2) is valid only for their initial values. The initial value of E45 can be evaluated from equations (1) and (2) as; 0 E 45 ≈ 4G (11)
Fabric Table VIII. Estimated shear rigidity for θ = 30°
A B
G30, gf/(cm grad) estimated
G45, gf/(cm grad)
4.54 5.88
3.42 4.35
(with G<<E1 and E2 ). The values computed with equation (11) correspond reasonably with the tensile diagrams for the bias direction as it can be seen on Figure 3, but the agreement can be considered only qualitative because of uncertainty of E 0 evaluation from diagrams. Conclusion The experimental investigation of an anisotropy of fusible interlinings, conducted on KES-F equipment, shows that the orthotropic model of anisotropy can be applied for the qualitative modelling of the studied materials and that simple mechanical models for the composite resistance to bending can be used. However, the limitations for the quantitative predictions with the orthotropic model have been revealed. These limitations come from: •
different bending properties for the different orientation of a fabric face relative to the direction of bending;
•
huge non-linearity of tensile diagrams, especially in the initial region of deformation where the transition from yarns unbending and slippage in intersections to yarn extension takes place;
•
bi-linear nature of the shear resistance which results in the deflections of shear anisotropy curve from the orthotropic prediction near the bias angle 45°.
As long as the deformation under consideration does not deal with possible deflection of threads (in woven fabrics) or fibres (in non-wovens) from their initially established positions in the material structure, the “solid plate” orthotropic equations describe the material behaviour fairly well. This is a case for bending of all the investigated materials, for tension outside the initial region and for shear of composite fabrics. If only the structural components of a material tend to deflect from their positions, the relative freedom of their movement and frictional forces associated with this deflection shift the mechanical behaviour of the material from the “solid plate” model. References 1. Stylios, G., Wan, T.R. and Powell, N.J., “Modelling the dynamic drape of fabrics on synthetic humans”, International Journal of Clothing Science and Technology, Vol. 7 No. 5, 1995, pp. 10-25. 2. Amirbayat, J. and Alagha, M.J., “A new approach to fabric assessment”, International Journal of Clothing Science and Technology, Vol. 7 No. 1, 1995, pp. 46-54. 3. Shanahan, W.J., Lloyd, D.W. and Hearle, J.W.S., “Characterising the elastic behaviour of textile fabrics in complex deformation”, Textile Research Journal, Vol. 48 No. 9, 1978, pp. 495-505. 4. Pozdnyakov, B.P., The Resistance of Woven Fabric to Tension in Different Directions, Moscow, 1932. 5. Amirbayat, J. and Hearle, J.W.S., “The complex buckling of flexible seat materials”, International Journal of Mechanical Sciences, Vol. 28 No. 6, 1986, pp. 339-70.
Anisotropy of fabrics and interlinings 389
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6.
7.
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8.
9.
Shishoo, R., Klevmar, P.H., Cednas, M. and Olofsson, B., “Multilayer textile structures: relationship between the properties of a textile composite and its components”, Textile Research Journal, Vol. 41 No. 8, 1971, pp. 669-79. Kanayama, M. and Niwa, M., “Mechanical behaviour of the composite fabrics reinforced by fusible interlinings”, in Kawabata, S. et al. (Eds), Proc. of Japan-Australia Joint Symposium on Objective Specification of Fabric Quality, Mechanical Properties and Performance, 1982, pp. 347-70. Kanayama, M. and Niwa, M., “The bending properties of fused fabric composites”, in Postle, R. et al. (Eds) Proc. of Second Australia-Japan Bilateral Science & Technology Symposium on Objective Evaluation of Apparel Fabrics, 1983, pp. 443-51. Hearmon, R.F.S., An Introduction to Applied Anisotropic Elasticity, Oxford University Press, Oxford, 1961.
