No title

IJCST 11,1 10 Received February 1998 Revised September 1998 The concept of virtual measurement 3D fabric drapeability ...

Author: Emerald

55 downloads 419 Views 5MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!

Report copyright / DMCA form

DOWNLOAD PDF

IJCST 11,1

10 Received February 1998 Revised September 1998

The concept of virtual measurement 3D fabric drapeability G.K. Stylios and T.R. Wan University of Bradford, Bradford, UK Keywords 3D, Fabric, Measurement, Textile industry Abstract This paper discusses the concept of virtual measurement in textiles and describes the development of a virtual 3D fabric drape measurement system. In this system, a physical based model is used to predict the draping performance, static and dynamic drape of a given fabric sample. Fabric mechanical properties are used for simulating the virtual 3D shape of the fabric sample, which produce a time-variable deformation of the virtual fabric drape. The 3D fabric drape can be observed under any view angle. An algorithm is developed, applied and integrated into the system for carrying out virtual fabric drape measurements in order to evaluate the drapeability of a given fabric. Important fabric aesthetic attributes such as number of fabric folds, fold variation and depth of fold are presented and implemented together with the drape coefficient.

International Journal of Clothing Science and Technology, Vol. 11 No. 1, 1999, pp. 10-18. © MCB University Press, 0955-6222

The concept of virtual measurement In the last 20 years or so the necessity and means for measurement of textile materials beyond the conventional weight, yarn count and density were put forward. The measurement of the so called mechanical properties has become standard practice, with effective uses in new material developments, in production efficiency, in quality and in better communications between customer and supplier. New measurement technologies in mechanical, aesthetic and performance attributes of textiles have also been developed and implemented. Stylios and Fan (1991) have shown through earlier work how you can predict garment quality from fabric properties and have demonstrated how fabric mechanical properties can control dynamic sewing machine mechanisms so that fabrics can be stitched in the most optimum manner (Stylios and Sotomi, 1994). In recent years it was also shown how a simulation model can be built to predict the 3D behaviour of a garment during wear. Recent new research tries to put forward a new concept in which textile materials can be created in the virtual world by specifying fundamental properties. Virtual materials can be created and viewed in a 3D sequence, from which their behaviour and important attributes are determined in accordance with consumer understanding. Consequently powerful tools can be developed under the generalised concept of virtual measurement systems (VMS) which can aid in the design and development of new flexible materials. This paper describes such a system which enables the designer/ technologist not only to visualise the 3D drape behaviour of a new fabric in the virtual world, but also to assess its aesthetic drape attributes which have been described in a recent paper (Stylios and Zhu, 1997). Figure 1 shows the sub-systems initiated with material properties to produce 3D visualisation and virtual measurment attributes. Reverse engineering to optimise

these aesthetic attributes so that materials are closer to customer demands by altering their properties is then possible, but outside the scope of this paper. Fabric drapeability Fabric drape is one of the most important properties of flexible materials. It is directly related to textile aesthetics which is important for the development and selection of textile materials in apparel industries, and especially for the design of clothes such as dresses and skirts. Drapeability of textiles is judged subjectively and is dependent on people’s skill and experience, which renders difficulties when making drape comparisons, especially when judged by different people. Over the last 50 years or so, research efforts have been directed at the study of fabric drape behaviour, which deals mainly with testing and evaluation of drape, the study of drape behaviour of different textile materials and the development of fabric drape prediction models. Our own work concerned with fabric drape modelling shows that a complete simulation and visualization of 3D drape of various fabrics can be carried out using fabric mechanical properties. This can provide a basis for drape evaluation aesthetically and/or mechanically. Consequently a virtual drape measurement system has been developed based on our physical fabric model (Stylios et al., 1996, 1997), and appears to be an effective and realistic tool for 3D drape prediction and evaluation. This verifies the new concept that we have put forward on VMS.

The concept of virtual measurement 11

Literature survey related to drape measurement and evaluation There are two basic approaches for drape testing and evaluation in textile and apparel industries; the cantilever bending tester (Pierce, 1930) and the draper meter (Chu et al., 1950, 1960; Cusick, 1996). Cantilever bending has been used indirectly for drape assessment, and extensively for simulating fabric behaviour (Lloyd et al., 1996). The first approach is using the elastic cantilever model which was first introduced by Pierce (1930). The behaviour of the fabric sample is evaluated by a quantity called “bending length”, which is in fact a measure of the bending angle. A thin rectangular specimen of fabric is sandwiched between two rod surfaces; the top is pushing the specimen over the stationary one and the specimen is allowed to collapse by its own weight. The measure of length is taken when a certain length of specimen is bent under the action of gravity. A number of modifications to the original cantilever bending have been made (Kalyanaraman and Sivermakrishnan, 1984; Stylios and Fan, 1991). Cantilever method is widely Reverse Engineering

Material Properties and/or Other Parameters for Virtual Fabric Specification

A

Input

3D Simulation for fabric behaviour and Visualisation

B

Output

Virtual Measurement Attributes; Aesthetic, Performance

C

Figure 1. The concept of virtual measurement systems (VMS)

IJCST 11,1

12

used either in fabric testing laboratories for industry or for theoretical drape studies, as a simple, reproducible and reasonably accurate method. Cantilever bending can be simulated relatively easily by theoretic models, so researchers can therefore use the actual bending tester to verify their drape simulation. One of the flaws with cantilever methods is that they are only capable of measuring two dimensional behaviour of fabrics, and because the drape phenomenon in the real world is often three-dimensional, more sophisticated methods are needed to be introduced in order to assess the 3D drape of textile fabrics. The second approach for drape simulation is based on 3D prediction. Fabric drapeability may be defined as a degree of the deformation of fabric which is produced by its own gravity when only supported partly by other objects. The drapeability can be judged subjectively or objectively, and aesthetically and/or mechanically. An instrument called the Drapemeter was first introduced by Chu et al. (1950), which is intended to measure the drape of fabrics objectively. In principle, this instrument contains a centrally located disk as the horizontal fabric support. A larger circular specimen of fabric is placed centrally on it. By the action of gravity, the unsupported outer portion of the fabric specimen is deformed by gravity. A quantity called drape coefficient is used to describe the degree of fabric deformation, which is defined as a percentage of the draped fabric area over its initial flat state, and can be achieved by vertical projection of the fabric. Therefore, a high drape coefficient indicates a low drapeability of a fabric. In recent years, a number of improvements and modifications have been made to improve the accuracy of the measurement using image processing and other computer techniques (Stylios and Zhu, 1997). Although these approaches have introduced real aesthetic attributes in order to increase the accuracy of fabric 3D drape behaviour, they still rely on real fabric and on effective instrumentation for actual physical measurement. Review related to modelling of fabric drape The area of theoretical analysis of the deformation behaviour of textile materials has preoccupied many workers who have concentrated their efforts on textile material modelling, especially, the development of a fabric model (Gan et al., 1995; Lloyd et al., 1996; Shanahan et al., 1978). Recently, with rapid development of computer technology, it has been realized that the use of a practical approach to modelling the flexible nature of textile fabrics is essential for the development of the next generation of CAD/CAE and Global Retailing Systems, which will improve the competitiveness of the textile and apparel industries in world markets. However, the lack of a reliable fabric model is still to be established. Many studies on suitable fabric models using different approaches have been cited in the literature (Cusick, 1996; Eischen et al., 1996; Gan et al., 1995; Lloyd et al., 1996; Shanahan et al., 1978; Terzopoulos et al., 1987). One of the difficulties for the effectiveness of those models is the complex mechanics of textile materials, which appear non-linear, visco-elastic, history dependent and possess large deformations. In the computer animation field, developers are more interested in visual appearance than in the precise

simulation of textile materials, and hence compromises with heuristic methods may be acceptable. These solutions to fabric drape are, however, non-generic. Conventional continuum theory has been applied to the cantilever fabric bending model; however, it is too difficult to be applied to 3D cases. Finite element approaches have also been extensively studied; however, so far with limiting success. Other related approaches like the lumped-parameter model (Stylios and Wan, 1998; Stylios et al., 1996) and the particle model (Breen et al., 1994; Eberhardt et al., 1996) are also reported, which appears more effective and interesting.

The concept of virtual measurement 13

Description of the virtual measurement system (VMS) Figure 2 shows a flow chart diagram of the 3D VMS proposed in this paper. It consists of three basic subsystems: blocks A, B, C. Block A deals with fabric properties and other fabric specification requirements, block B deals with the drape prediction and visualisation and block C with implementing the virtual aesthetic measurement of fabric drape. The fabric properties are input through a window menu system and the parameters for numeric calculation and animation can be set up by the user. The initial state of the fabric is defined as a flat sheet in the horizontal position. The dynamic deformation of the given fabric can be viewed in any angle and the virtual measurements are implemented after the drape calculation. The software is written in C++ using X window and SGI Open Inventor graphics libraries, which renders this system a stand alone fabric development tool without the need of supporting visualisation commercial packages. The fabric drape model The drape model used in the VMS is based on a physical analogue model to deep shell system (Stylios et al., 1996). The fabric is treated as a continuum shell Input: Parameters for Numeric Calculation & Animation Time Step Time length Total Key Frame Number ...

Input: Fabric Properties: Tensile Properties Bending Properties Shear Properties ...

A Start

Output: Virtual Measurement Results

Virtual Measurement: Drape Coefficient Fold Number Fold Variation Maximum Deviation

C Rotation Deformed Fabric Surface Configuration Using Polygons

Render Area Zoom

3D Drape Visualisation

Translation

Drape Model

Animation Control

Key Frame Selection

B

Figure 2. Virtual drape system flow chart

IJCST 11,1

14

system initially, and then is discretized by lumping its distributive mass 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. In order to apply this model to any flexible material, a local surface coordinate system is required. Let us select two orthogonal vectors tangent to the surface as the first two surface coordinates α1, α2 and erect a vector normal to the surface as the third coordinate α3. The deformation of a fabric element can be described by u and v displacements, along α1, α2 within the tangent plane, and w displacement along α3. The material properties of the continuum in all elements can be lumped at these deformable nodes by integrating all the energies within those elements. A suitable conversion between local system and global system must be established and the governing differential equations of the deformation of fabric are then derived from the discretization of the system energies over all fabric material elements. The final global drape governing equations can be written as a general form ~ ~ ~ M ä + C a· + Ka = F ~ ~ ~ where M , C ~and K are mass matrix, damping matrix and stiffness matrix respectively. F is the distributed external force vector applied to each node. Since a large number of nodes are used for the geometrical configuration of the loop structure, it is convenient to express the equations or matrices in an implicit form rather than in an explicit one. Since each node has been coded with node coordinates, a computer program can be used to arrange the items of those matrices of the above equations. The solution of those equations can be found numerically. As time space is split into a series of finite time intervals ∆t, if we know the solution at time tn, we can find the solution at time tn+1= tn+ ∆t by using a singlestep algorithm called a Newmark algorithm (Zienkiewicz and Taylor, 1991). This approach which is found to be realistic, effective and accurate has been applied for the prediction of 3D fabric drape. 3D virtual drape measurement The computer generated 3D drape of a given fabric can actually describe the true 3D shape of the deformation of that fabric which is represented by its mechanical properties. It can therefore be used for fabric drape measurement and evaluation. Since this process is carried out in a virtual environment, it is called virtual drape measurement. In the current work, an algorithm is presented for carrying out a number of virtual drape measurements, which are found to define fabric aesthetics more accurately and realistically. The system is the virtual sister of our true drape measurement system the M3 (Stylios and Zhu, 1997), with which the comparisons and the verification between real and virtual drape measurement have been made. We have tried to define aesthetic attributes using natural psychology of consumers as well as know-how of engineering principles related to fabric drape (Stylios and Zhu, 1997). It has been found that the drape coefficient, although an important property for the assessment of fabric drape, is not an accurate and complete measure of drape since two fabrics can have the same drape coefficient but different drape behaviour. Consequently a number of aesthetic attributes were added to the drape coefficient such as the number of

folds, the variation of the folds and the depth of fold which represent how humans interpret drape aesthetically. Hence four virtual measurements have been used to define the drapeability of a given textile material as follows. Virtual drape coefficient: before carrying out virtual drape coefficient measurement, image processing called image segmentation has to be implemented to identify the projected shadow area of the fabric. Figure 3a shows the initial fabric configuration and Figure 3b shows the draped configuration. As shown in Figure 3, drape coefficient DC which describes the deformation of the fabric sample can be defined by Ashadow DC = , πR02 – πr 2

The concept of virtual measurement 15

Drape fold number: The number of drape folds can be identified and assessed directly by the system after detecting and approximating the boundary curve of the outer edge of the shadow area of the sample as shown in Figure 3. Fold variation: Let the approximated boundary curve be R(a), the fold variation can be determined by  1  1 Var = ∑ ( R (a i ) −  ∑ R (a k ) . n − 1 i =1   n k =1  Fold depth index: Let the maximum value of R(a) be Rmax and minimum value of R(a) be Rmin, the fold depth index of a draped virtual fabric can be determined by ( R max − R min) De = . ( R0 − r ) A more complete treatment of those attributes is provided elsewhere (Stylios and Zhu, 1997). Virtual measurement system: implementation and verification To implement and evaluate the drape measurement system developed in the current work, an experiment was carried out in which two different materials were used. In this experiment the accuracy between real and virtual drape

A

R(a) R0

a r

r

Centre

Centre

Figure 3. Vertical projections from tested fabric (a)

(b)

IJCST 11,1

16

measurement and 3D prediction is assessed, as well as the effectiveness of the implementation of more detailed aesthetic attributes other than the drape coefficient and the number of folds. The actual and mechanically measured fabric properties of those materials are shown in Table I. Actual fabric samples were used to measure drape properties using the M3 system. Table II shows these real drape results. The actual fabric photographs of drape of these two samples are shown in Figure 4a (soft material in top view), Figure 5a (soft material in side view) and Figure 6a (stiff material in side view) respectively. The properties of these fabrics were then fed into our VMS which produced 3D drape simulations and virtual measurements. The drape simulations of the given two materials are shown in Figure 4b (soft material in top view), Figure 5b (soft material in side view), and Figure 6b (stiff material in side view) respectively. The results of the virtual 3D drape measurements are shown in Table III. These results show a good agreement between actual and virtual drape measurement of both test samples. It can also be seen from the figures that the virtual measurement is able to provide detailed information of aesthetic attributes for more accurate drape evaluation. Discussion, conclusion and further work The concept of virtual measurement has been introduced. It is trying to leap forward in providing aesthetic and/or performance virtual measurements for the assessment of the suitability of newly designed and developed materials. This concept is implemented in 3D virtual drape measurement of fabrics. The 3D virtual fabric drapeability measurement and visualisation system described in this work is based on a physical fabric model. It is our intention to develop this system both for research work concerning the engineering of fabric aesthetics and performance and for use in fashion design and apparel manufacture as a tool to evaluate fabric drapeability. With the proposed system, it is possible to study the drapeability of flexible materials without having to actually make

Tensile

Table I. Fabric properties

Weight

Sample A 21.31 0.33 0.0218 186.48 Sample B 16.16 0.88 0.1605 238.92 Note: Tensile energy: gf.cm/cm2; Shear stiffness: gf/cm; Bending rigidity: g.cm2/cm; Weight: g/m2

Drape C

Table II. Real drape measurements

Fabric properties Shear Bending

Sample A 0.279 Sample B 0.54 Note: Drape C is drape coefficient

Fabric properties Fold number 6 4

Category soft stiff

The concept of virtual measurement 17

(a)

Figure 4. Soft material in top view: (a) photograph, (b) simulation

(b)

(a)

Figure 5. Soft material in side view; (a) photograph, (b) simulation

(b)

(a)

Figure 6. Stiff material in side view; (a) photograph, (b) simulation

(b)

Virtual drape measurements

Sample A Sample B

Drape C

Fold number

Fold depth

Fold variation

Category

0.28 0.542

6 4

0.743 0.675

0.226 0.21

soft stiff

Table III. Virtual drape measurements

IJCST 11,1

them, as is common practice, which can reduce lead times, wastage of raw materials and energy, and target closer than ever customer demands. Present work is under way for using a virtual dummy instead of a virtual plate for complete garment drape evaluation and to enable the user to carry out reverse engineering of fabric drape and properties.

18

References 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. Chu, C.C., Cummings, C.L. and Teixeira, N.A. (1950), “The development of a drape meter”, Textile Research Journal, pp. 539-48. Chu, C.C., Milton, M. and Hamburger, W.J. (1960), “Investigation of the factors affecting the drapeability of fabrics”, Textile Research Journal, pp. 66-7. Cusick, G.E. (1965), “The dependence of fabric drape on bending and shear stiffness”, Journal of the Textile Institute, Vol. 56, pp. T596-606. 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. Eischen, W.J., Deng, S. and Clapp, T.G. (1996), “Finite-element modeling and control of flexible fabric parts”, IEEE Computer Graphics and Applications, September, pp. 71-80. Gan, L., Ly, N.G. and Steven, G.P. (1995), “A study of fabric deformation using nonlinear finite elements”, Textile Research Journal, Vol. 65 No. 11, November, pp. 660-8. Kalyanaraman, A.R. and Sivermakrishnan, A. (1984), “Electronic fabrics stiffness meter, performance evaluation with the known instruments”, Textile Research Journal, Vol. 54 No. 7, pp. 479-84. Lloyd, D.W., Mete, F. and Hussain, K. (1996), “An approach to the theoretical mechanics of static drape”, International Journal of Clothing Science and Technology, Vol. 8 No. 3, pp. 43-58. Pierce, F.T. (1930), “The handle of cloth as a measurable quantity”, Journal of the Textile Institute, Vol. 21, pp. T377-417. Shanahan, W.J., Lloyd, D.W. and Hearle, J.W.S. (1978), Characterizing the Elastic Behaviors of Textile Fabrics in Complex Deformation, Textile Research Institute. Stylios, G.K. and Fan, J. (1991), “An expert system for the prediction of fabric sewability and optimisation of sewing and fabric conditions”, Stylios, G. (Ed.), in Textile Objective Measurement and Automation in Garment Manufacture, Ellis Horwood, Chichester, pp. 139-44. Stylios, G.K. and Sotomi, J.O. (1994), “A neuro-fuzzy control system for intelligent sewing machines”, Intelligent Systems Engineering, IEE Publication No. 395, pp. 241-6. Stylios, G.K. and Wan, T.R. (1998), “A new collision detection algorithm for garment animation”, International Journal of Clothing Science and Technology, Vol. 10 No. 1. Stylios, G.K. and Zhu, R. (1997), “The characterisation of the static and dynamic drape of fabrics”, Journal of the Textile Institute, Vol. 88 No. 4, pp. 465-75. Stylios, G.K., Sotomi, J.O. and Wan, T.R. (1997), “The concept of global retailing; the integration of new technologies in the fashion, textile and apparel industries”, The 78th World Conference of The Textile Institute in Association with The 5th Textile Symposium of SEVE and SEPVE, Vol. 1, pp. 45-54. Stylios, G.K., Wan, T.R. and Powell, N.J. (1996), “Modelling 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. Zienkiewicz, D.C. and Taylor, R.L. (1991), The Finite Element Method: Solid and Fluid Mechanics, Dynamics and Non-linearity, 4th edition, McGraw-Hill.

Finite element analysis of sewing process

Finite element analysis of sewing process

Eric Mallet and Ruxu Du Department of Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, Ontario, Canada Keywords Fabric, Sewing, Textile industry

19 Received November 1997 Revised November 1998

Abstract Sewing is one of the most commonly used manufacturing processes in the world. Millions of parts are sewn every day ranging from cloths, shoes, furniture, to automobile seat covers. However, it is also one of the least understood processes. In fact, according to literature survey, few know how to calculate the sewing force or the fabric deformation during the sewing. This paper presents our research on using finite element model (FEM) to study the sewing process. The model is developed using ANSYS software system. In the model, the fabric is approximated by a number of perpendicular beam elements with elastic and plastic capabilities. On the other hand, the needle is modeled by a simple elastic beam. The contact between the two parts is modeled by contact elements. The variations of the needle geometry and the fabric material properties as well as the sewing conditions are also included in the model. The model can simulate the needle piercing through a material, and calculates the sewing forces as well as the fabric deformation forming a hole. It has been verified experimentally and can be used to study the effects of the key sewing parameters such as the fabric material properties and the needle geometry.

1. Introduction Sewing is one of the most commonly used manufacturing processes in the world. Millions of parts are sewn every day ranging from cloths, shoes, furniture, to automobile seat covers. Consequently, small improvements would result in significant economic gain. However, the sewing process is also one of the least understood processes. In fact, according to literature survey, few know how to calculate the sewing force and the fabric deformation during the sewing. It is interesting to know that most studies on the sewing process are based on experimental testing. For example Matthews and Little measured the sewing forces (Matthews and Little, 1988) using two transducers mounted directly on the shaft of the needle bar and on the presser bar. The signal acquired from the former includes the sewing force and the inertia force of the needle, while the signal from the latter is related to the inertia force of the needle. The latter can be measured by sewing without fabric material. Subtracting the former from the latter then gives the sewing force. Their experiments involved various materials (paper, rubber, cotton) and different thicknesses of the materials (one to four piles). Khan et al. (1970) studied the needle-fabric interactions by using an Instron. The needle is mounted in a holder clamped in the top jaw of the Instron, and a This research is partially supported by the GM Canada, Delphi USA, Delphi Mexico, Peregrine Engineering Inc. of Canada, and National Science and Engineering Research Council of Canada. The authors would like to thank Dr Evangelos Liasi, Professor Huijun Zhou, Mr Charles Beauchamp, and Ms Jasmina Bujas-Dimitrejevic for their support.

International Journal of Clothing Science and Technology, Vol. 11 No. 1, 1999, pp. 19-36. © MCB University Press, 0955-6222

IJCST 11,1

20

piece of fabric is wrapped around another holder clamped in the bottom jaw. Their studies concluded that: (1) the effect of sewing velocity is minor; (2) the relationship between needle diameter, d, and penetration force, F, follows the power law F = a•dn, where a is a constant and n ranges from 2.64 to 3.89; (3) the needle surface finish has a significant effect on the friction force; (4) the material and the structure of the fabric play a significant role in needle penetration, but it is difficult to relate the needle-fabric interactions to the fabric material properties; (5) the needle point shape plays an insignificant role. They tested three needles identical except in point shape (standard, diamond and twist points) and did not find a significant difference in penetration force; and (6) the penetration force increases linearly with increasing layers of fabric. However, in their study, the forces are measured under linear velocities instead of sinusoidal in the real sewing process. Also, the magnitude of the velocity is significantly lower than the actual sewing velocities. Other experimental studies on sewing forces can be found (Charrier et al., 1986; Galuszynski, 1986; Howell et al., 1959; Hurt and Tyler, 1975; Simmons, 1979; Kamata et al., 1977; Singer Company, 1981; Stylios, 1987; Stylios and Xu, 1995; Torrington Company, 1981). A few analytical sewing force models have been developed as well. For example, colleagues in our research team are developing a mechanical model (Du et al., 1997). In this model, the needle is modeled by a conic point and a cylindrical shank, and the fabric is characterized by its material properties and thickness. Following the relative motion between the needle and fabric, the sewing force is then formed. It is found that the sewing force is the sum of three forces: (1) The pierce force, Fz. It is the reaction force of the material as the needle tip pierces through the material. (2) The sewing force, Fc. It is the force used to form the hole, which occurs while the needle taper penetrates the material. (3) The friction force, Fr. It represents the friction resistance of the material against the needle motion within the fabric. The predicted sewing forces in this model closely resemble the experimental results provided in Matthews and Little (1988). In order to investigate the sewing force, it is necessary to study the material properties of the fabric. Carr et al. (1988) studied the frictional characteristics of several fabrics. The apparatus used to measure friction is composed of a platform and a sliding sled on the platform. The fabric is glued on both sides. The sled is then loaded with a known normal force moving the thread attached to the

jaw of an Instron. By dividing the measured friction force by the normal force, they found the friction coefficient. It appears that the friction coefficient varies with the normal force. In other words, the fabric does not follow the classical law of friction for solid surfaces in contact. For a similar material, the friction coefficient may vary from to 0.4 (high normal force) to 1 (low normal force). Collins and Chaikin (1969) have experimentally determined the stress-strain curve of wool fibers. As shown in Figure 1, the curve can be approximated by three linear segments under different stresses. From the curve, the following material properties can be calculated: 35 (1) Young’ s modulus : = 1750 MPa E= 0.02 σ y = 35 MPa (2) Yield stress : (3 )

First plastic modulus :

Ep =

Finite element analysis of sewing process 21

50 − 35 ≈ 65 MPa 0.25 − 0.02

Note that the unit conversion: 1 MPa = 1 N/mm2 ≈ 10kg/cm2. According to our literature survey, however, a detailed analytical model of the sewing process has not yet been found. Consequently, many questions, such as how large is the needle friction force and how a hole is formed when the needle pierces through the fabric, remain unanswered. In this study, a finite element model (FEM) is developed to analyze the sewing process. The paper consists of four sections. Section 2 describes the model. Section 3 shows some typical simulation results and uses the model to study the effect of various sewing control parameters to the sewing process. Finally, Section 4 contains the conclusions.

σ(kg/cm2) 1500

500 350

2%

25%

Source: Collins and Chaikin (1969)

60%

ε(%)

Figure 1. Stress-strain curve of a typical wool fiber

IJCST 11,1

22

2. The finite element model Our Finite Element Analysis (FEA) model is developed using ANSYS 5.1 (ANSYS Users’ Manual, 1997). As shown in Figure 2, the model is composed of two major parts: the fabric and the needle. Furthermore, the interactions between the fabric and the needle are modeled by the contact elements and the relative motions between the fabric and the needle are modeled by the boundary conditions. 2.1 The fabric The fabric is modeled by an array of PIPE20 (Plastic Straight Pipe) elements (ANSYS Users’ Manual, 1997). The pipe elements behave similarly to ordinary beam elements but are preferred because of their plastic capabilities. In fact, in the sewing process the fabric undergoes both elastic and plastic deformations. Hence, both deformations must be modeled. As shown in Figure 3, the physical properties of a PIPE20 element include the diameter, do, and the wall thickness, tw. Accordingly, the area, A, and inertia, I, of the element can be computed using the following formula:

Figure 2. Illustration of the FEA model

Y z

do

Figure 3. The section view of a PIPE20 element

tw

x

2  d 2  d   o o A = π    −  − tw    2   2   

(1)

4 4   π   do   do I =    −  − tw   4   2   2  

(2)

Also, the displacement of a beam under a bending force is equal to: y=k

Fl 3 EI

(3)

where k is a constant depending on the boundary conditions; F is the bending force applied; l is the length of the beam; and E is the Young’s modulus of the material. In the model, the features of the fabric are characterized by the following parameters: • th: the thickness (mm), • d: the distance between two parallel threads (mm), • n: the number of threads divided by four, • esp: the number of elements added to increase the length of the threads. Figure 4 shows the mesh of the fabric with the parameters: d = 0.2 mm, n = 2, and esp = 4. Note that the thickness is perpendicular to the paper and hence is invisible. Also, in the global coordinate system, the fabric is located in the plane z = 0, and the beam elements are at the distances (1/2)d, (3/2)d, ..., [(2n – 1)/2]d from the origin. The length of each pair of nodes is equal to the distance between two elements so that the elements have a node located at each intersection with the crossing elements. At the intersections, the node belonging to the vertical element and the node belonging to the horizontal element are constrained to have the same displacement in the Z direction. But they can have different displacement in X-Y plane allowing slide motion on each other. Also, each of the innermost four elements is split into two elements. This allows a node to be added to form the contact node. As the needle pierces through the material, these elements will be pushed away by the needle. Then, they push the surrounding elements following the constraint equations below: uy5 = coef * uy1 (4) uy6 = coef * uy2 (5) ux7 = coef * ux3 (6) ux8 = coef * ux4 (7) where ux i is the displacement of the node i in the X direction, uyi is the displacement of the node i in the Y direction, i = 1, 2, …, 8, and coef is a

Finite element analysis of sewing process 23

IJCST 11,1

24 esp = 4 6 2 7

3

1 5

4

8 d = 0.2mm

Y o

Figure 4. The mesh of the fabric

X

Sewing Force

coefficient chosen between 0 and 1. The coefficient is used to adjust the strength of the pushing effect in the direction perpendicular to their length. If it is chosen as 1, then all the elements will experience the same motion as the one in contact with the needle, which means that the needle punch affects the material on its whole area. On the other hand, if it equals 0, the surrounding elements will not be affected by the needle. As a simplified version of the Collins and Chaikin’s curve (Figure 1), in this study, the fabric material is assumed to follow a bilinear strain-stress curve as shown in Figure 5. Note that the curve is characterized by the following parameters: (1) E1: Young’s modulus (MPa); (2) Ep: Plastic modulus (MPa); and (3) Sigy (σy): Yield stress (limit between elastic and plastic behavior in MPa). From Figure 5, it is seen that if the load is over the yield stress sigy, the elastic deformation εe will disappear after the needle withdraws, but a permanent plastic deformation εp will remain, forming the hole.

σ

Finite element analysis of sewing process

Slope Ep σy

25 Slope E

εp

ε

εe

Figure 5. Stress-strain curve of the fabric

2.2 The needle The needle is modeled by a number of beam elements following its geometry variations. Also, since it is not likely that the needle will experience plastic deformation, the element BEAM4 is adopted (ANSYS Users’ Manual, 1997). In general, industrial sewing needles have complicated geometry. To simplify the study, the geometry of the needle is described by the following parameters: • nlg: length of the needle (mm), • radx: maximum radius in the X direction (mm), • rady: maximum radius in the Y direction (mm), • alphax: angle of the needle cutting point, in the X-Z plane (degrees), • xn, yn and zn: initial coordinates of the needle point (mm), • ns: number of sections, and • needle(a, b): a ns•3 array, with the two radii and the location on the Z-axis of each section (assumed elliptic). In the model, the needle is decomposed into several sections as shown in Table I. Note that the last section may not correspond to the end of the needle, but must be a part of the needle, i.e. z(ns) < z(1) + nlg. Each section is modeled by one beam element. Area and inertia properties at each section are computed from the radius: A = π • radx • rady (8) Section 1

Section 2

…

Section ns

radx(1), rady(1), z(1),

radx(2), rady(2), z(2),

…, …, …,

radx(ns) rady(ns) z(ns)

Table I. The definition of the needle

IJCST 11,1

26

Ix = π radx3 • rady / 4 (9) Iy = π rady3 • radx / 4 (10) If the radii at both ends are different, area and inertia will be averaged. In the global coordinate system, the needle axis is parallel to the Z-axis, and xn and yn are chosen between – d/2 and d/2 (note that it is not necessary to be zero). The material of the needle is steel with the Young’s modulus E2 = 210 GPa, and the Poisson’s ratio ν = 0.3. As an example, Figure 6 shows the mesh of a needle, defined by the following parameters: nlg = 30 mm, radx = rady = 0.8 mm, alphax = 10 degrees. The needle(a, b) array is shown in Table II. 2.3 The contact The contact between the needle and the threads is modeled by CONTAC49 elements (ANSYS Users’ Manual, 1997). These elements are composed of a target area, defined by four nodes, and a contact node. Figure 7 shows the target areas between the fabric and the needle. It can be seen that there are several target areas depending on the sections of the needle. On each section of the contact four new nodes are defined forming a rectangle in which the elliptic Section 1

Section 2

Section 3 nlg

Figure 6. An example of needle mesh

Y z Sewing Force

Table II. An example of needle definition

Section 1

Section 2

Section ns

radx/100 rady/100 zn

radx rady zn + radx/tan(alphax),

radx rady zn + 0.8*nlg

x

Finite element analysis of sewing process

Target area between sections 1 and 2

27

Target area between sections 2 and 3 Sewing Force

Y z

x

section of the needle is enclosed. These nodes are constrained to have the same displacement as the central node (the beam element of the needle). Referring to Figure 7, the green triangles represent these constraints. Each target area is defined by two nodes of the present section and two nodes of the subsequent section. Note that no target area is defined for the last beam element of the needle mesh. This is because it must be a free end to correspond to the sewing force. On the fabric, the target nodes are chosen as the centers of the innermost beam elements. They have the same coordinate xn (the innermost horizontal element) or yn (the innermost vertical element) as the needle. When the contact node goes into a target area, the element status switches from open to close and/or sliding. Consequently, a normal force is generated and the friction force can also be computed through the normal force and the friction coefficient. The contact elements require only one material property: the friction coefficient, mu, and a few other physical properties including the normal stiffness, KN, and the tangential stiffness, KT. These properties are shown in Figure 8. It should be pointed out that the normal stiffness has an important effect on the convergence of the calculation. In fact, it may even lead to convergence difficulties. This problem can be controlled by changing KN. The tangential stiffness KT is less important and takes a default value of KN/100. 2.4 Boundary conditions The boundary conditions are set to emulate the holding of the fabric and the needle. For the fabric, each beam element has 6 degrees-of-freedom (DOF) fixed at both ends, represented by the triangles in Figure 4. For the needle, the last node (opposite to the tip) has the 3 rotation DOF and the 1 translation DOF in Z direction (the translations in X and Y directions are fixed). Also, the translation

Figure 7. Target areas of the contact

IJCST 11,1

Normal force FN

28 Distance between contact node and target area Slope KN

Friction force FS µFN

Distance between contact node and target area Slope KT

Figure 8. Normal and tangential stiffness of the contact elements

-µFN

in Z direction follows a sinusoidal function, in order to simulate the motion of the needle during sewing operations. The parameters that control the needle motions include the maximum displacement of the needle, nuz, and sewing speed, rpm. Note that the needle penetrates the material when its speed is close to its maximum value. To match this condition, the initial location of the needle tip is defined at zn equal to nuz/2. 2.5 The phases of a sewing cycle and load steps Load steps refer to the incremental steps of the simulation. During the simulation, the sewing cycle (one stitch) is divided into six phases and each phase is controlled by a set of load steps control parameters Step. The number of load steps is important because it controls the accuracy of the results. The bigger the load steps, the more accurate the simulation, but the longer the calculation time. The six phases of a sewing cycle are defined as follows:

Phase 1: The Z coordinate of the needle tip is greater than one half of the material thickness th/2. The needle does not touch the material and hence there is no force generated. In this phase, the load step control parameter Step1 can be small (between 2 and 5). Phase 2: The Z coordinate of the needle tip is between th/2 and –th/2. In other words, the needle tip is piercing the material. Hence, a force is generated, and the contact nodes are activated to make the fabric undergo the same displacement as the needle tip multiplied by a coefficient coef 2, which is chosen between 0 and 1. The load step control parameter Step2 should be bigger than Step1 because of the force, but it does not need to be very big, because this phase is relatively short. Phase 3: The Z coordinate of the needle tip is larger than –th/2. The needle tip has gone through the material. The contact constraints defined in Phase 2 are removed. But the conic portion of the needle is still in the material. The load step control parameter Step3 should be close to Step2, because Phases 2 and 3 would last about the same time. Phase 4: The conic portion of the needle has passed the material and the shank of the needle is in the material. Hence, only friction force is generated. In this case, it is not necessary to use large load steps because the force is constant; furthermore high values of Step4 may lead to the convergence problems when the needle starts to withdraw. Phase 5: The needlepoint is withdrawing from the material. Phase 6: The needle is out of the material. The parameter Step6 can be small, such as Step1, because there is no more force. 2.5 Solution The static analysis is carried out using ANSYS software system (ANSYS Users’ Manual, 1997). It is noted that there are three different sources of nonlinearity: the elastic and the plastic deformation of the fabric, the large deflection of the fabric causing geometrical nonlinearity, and the contact nonlinearly. In order to ensure the convergence and accuracy, the Newton-Raphson method is used. It is an iterative method, controlled by two criteria of convergence, one on the forces and one on the displacements. The load step is considered as converged if the forces and displacements do not change by more than 1 percent between the two consecutive iterations. If a load step has not converged after 25 iterations, the calculation is stopped, and it is necessary to change the control parameters. We found that the normal stiffness KN of the contact elements plays a leading role in controlling the convergence. The bigger the expected forces, the larger the normal stiffness will be. A simulation usually takes about three minutes with a Pentium 200 computer.

Finite element analysis of sewing process 29

IJCST 11,1

30

2.6 Post processing Post processing is carried out using ANSYS software system as well (ANSYS Users’ Manual, 1997). Two macro files are developed to control the post processing. The first one, anim, is used to create movie files showing each and every load step. The second one, variable, defines the following viewing variables including: (1) not used (2) reaction on the needle shank in X direction (3) reaction on the needle shank in Y direction (4) reaction on the needle shank in Z direction (5) displacement of contact node 1 in Y direction (6) displacement of contact node 2 in Y direction (7) displacement of contact node 3 in X direction (8) displacement of contact node 4 in X direction (9) displacement of contact node 1 in Z direction (10) displacement of contact node 2 in Z direction (11) displacement of contact node 3 in Z direction (12) displacement of contact node 4 in Z direction (13 to 24) used to sum the normal and friction forces on all contact elements (25) total normal force (26) total friction force (27) sewing force (equals the opposite of variable 4) The simulation results are presented in the following section. 3. Simulation results and experiment verification 3.1 Simulation results Using the FEA model above, a number of simulations have been conducted. In these simulations, the control variables are as follows: (1) material properties: E1 = 1000 MPa, sigy = 50 MPa, Ep = 100 MPa, mu = 0.2. These numbers are similar to those used in Collins and Chaikin’s experiments (Collins and Chaikin, 1969); (2) fabric thickness: th = 2mm; (3) needle geometry: radx = rady = 0.8mm, alphax = 10°, nlg = 30mm; (4) needle motion: nuz = 26mm, xn = yn = 0, zn = 13mm, rpm = 2,000 stitches/min; (5) number of beam elements in each direction: n = 2; (6) distance between the beam elements: d = 0.2mm; (7) number of additional elements for contact: esp = 4;

(8) ratio of displacement between two neighbor beam elements: coef = 0.75; (9) ratio of displacement of the fabric compared to the displacement of the needle during Phase 2: coef 2 = 0.5; (10) number of load steps: Step1 = Step6 = 5, Step2 = Step3 = Step4 = Step5 = 10; (11) normal stiffness of the contact elements: KN = 100 N/mm. Figure 9 shows a typical simulation result displaying the needle cutting actions. From the Figure it is seen that when the needle reaches the fabric, the fabric first bends and then yields, forming a hole. Figure 10 shows a typical sewing force. From the Figure it is seen that the sewing force consists of three components: the needle tip force, the cutting force (generated by the conic portion of the needle), and the friction force. Each component has its own characteristics. For example, the needle force is an impulse of about 5N that disappears after the needle cuts through the material. The cutting force is about 1.5N and has a little tail corresponding to the withdrawal of the needle. The friction force can be further 1

Finite element analysis of sewing process 31

1

Y z

Y z

x

Sewing Force

x

Sewing Force

1

1

Y

Y z Sewing Force

z

x Sewing Force

x

Figure 9. A typical simulation result

IJCST 11,1

6

Key Tip force (N) Point force (N) Friction force (N)

5

Force (N)

4

32

3 2 1 0 0

Figure 10. A typical sewing force consisting of three components

0.005

0.01

0.015

0.02

0.025

0.03

0.035

–1 –2 Time (s)

decomposed into two parts. The first part is about 0.7N, corresponding to the needle piercing through the fabric. The second part is smaller, corresponding to the needle withdrawing because the hole has been formed. Figure 11 shows a typical example of the fabric deformation as a function of time. It is seen that the hole is formed as the needle pierces through but shrinks as the fabric elasticizes back. 3.2 Experiment verification The simulation results are verified experimentally. To measure the sewing force, a special sensor is designed as shown in Figure 12. The sensor is a piezoelectric strain gage mounted on a packet. The detailed design of the sensor

0.8

Key UY node1 (mm) UY node2 (mm)

0.6

Displacement (mm)

0.4 0.2 0

0

0.005

0.01

0.015

0.02

–0.2 –0.4

Figure 11. A typical fabric deformation

–0.6 –0.8 Time (s)

0.025

0.03

0.035

Finite element analysis of sewing process

gage wires

mounting block

33

strain gage

Figure 12. Illustration of the sewing force sensor

needle penetration force (N)

is described in Gao (1998) and is the topic of another paper in preparation. Figure 13 shows a typical sewing force signal. Note that, because of the sensor setup, the signal is reversed in comparison to that in Figure 10; otherwise the results are similar. In summary, Table III presents the simulation and experimental results under various sewing conditions. 4 2 0 –2 4

Figure 13. A typical sewing force sensor

–6 –8 time (seconds)

Sewing speed (rpm) 1,000 2,000 2,000

Material thickness (mm)

Simulated sewing force (N)

Experimental sewing force (N)

Error (percentage)

1mm 1mm 2mm

6.3 6.8 9.1

5.6 6.1 8.2

12.5 11.5 10.9

Table III. A comparison between simulation and experimental results (maximum forces)

IJCST 11,1

34

From Table III, it is seen that the simulation results are close to the simulation results (it should be noted that the sensor signals from the experiments are also subject to errors). Also, it should be pointed out that the simulation results match the experimental results presented by Matthews and Little (1988). 3.3 Discussion Using the presented simulation model, the effects of the fabric materials on the sewing forces and the formation of the holes are investigated. As pointed out earlier, the material properties of a fabric include Young’s modulus, yield stress and plastic modulus. The simulation focuses on the yield stress as it has the largest effect on both sewing forces and hole information. During the simulation, four cases are studied. In addition of the typical value (50 MPa), three other values are tested with yield stresses of 25 MPa (soft fabric), 100 MPa (leather) and 10,000 MPa. The last one can be considered as infinite at which the material will have elastic behavior only. In order to investigate the formation of the holes, an index, hole size ratio, is introduced. It is the ratio of the diameter of the hole and the diameter of the needle as defined below: ratio =

UZ 2 final – UZ 1 final 2 * radx − d

× 100%.

(11)

Table IV shows the simulation results. It is seen that the hole sizes change from 60 percent to 40 percent of the needle size. That is, the strong materials would form small holes. Second, we studied the effect of the needle shape. Under the presented model, the shapes of the needle are mainly determined by two control parameters: the shank radius and the point angle. The simulation has been conducted under three different values of the radius: 0.6mm, 1mm and 1.2mm. It is found that the needle radius does not affect the needle tip force, and the other two force components increase almost linearly with needle radius. Also as shown in Table V, the hole size ratio remains constant. Finally, the effect of the point angle of the needle is studied. Simulations are conducted under three different angles: 10° (typical), 15° and 20° (worn out

Yield stress Table IV. The hole size ratio under Hole size ratio different yield stresses (percentage)

Needle radius (mm) Table V. The hole size ratio under Hole size ratio different needle radii (percentage)

25 MPa

50 MPa

100 MPa

10,000 MPa

60

56

39

0

0.6

0.8

1

1.2

49

56

51

51

needle). It is found that the point angle variations do not change the tip piercing force. However, the sharper the needle, the smaller the needle cutting force because the hole is enlarged more slowly. The friction force is proportional to the point angle because the fabric has experienced a less plastic deformation with sharp needles. This is shown in the change of hole size ratio in Table VI.

Point angle (degrees) Hole size ratio (percentage)

10

15

20

56

68

88

4. Conclusions In conclusion, this paper presents a finite element simulation model for sewing processes. In this model, the fabric is modeled by a number of beam elements which will undergo both elastic and plastic deformation. The needle is also modeled by beam elements. The sewing process is modeled by the contact element between the fabric and the needle. The model simulates the sewing forces and the hole formation under various sewing conditions. Based on the simulation, it is seen that sewing force consists of three components: the needle tip force, the needle cutting force and the friction force. The resultant sewing force matches the experimental results provided by the other researchers well (the average error is about 11 percent). Using the simulation model, it is found that the size of the hole reduces with the increase of the yield stress of the fabric, but increases as the needle wears. However, the ratio of the hole and the needle diameter remains relatively constant, regardless of the diameter of the needle. References ANSYS Users’ Manual (1997). Carr, W.W., Posey, J.E. and Tincher, W.C. (1988), “Frictional characteristics of apparel fabrics”, Textile Research Journal, Vol. 58, pp. 129-36. Charrier, J.M., Maki, S.G. and Gent, A.N. (1986), “Penetration of elastomeric blocks by needles”, ANTEC’86, pp. 761-4. Collins, J.D. and Chaikin, M. (1969), “A theoretical and experimental analysis of the general wool fiber stress-strain behavior with particular reference to structural and dimensional nonuniformities”, Textile Research Journal, Vol. 39, pp. 121-40. Du, R., Liasi, E., Zhou, H.J., Mallet, E., Li, Q.W. and Chen, A.S. (1997), “Report on the sewing process diagnosis and optimization”, report to Delphi USA, Delphi Mexico, and Peregrine Engineering Inc. of Canada. Internal report. Galuszynski, S. (1986), “Effect of fabric structure on fabric resistance to needle piercing”, Textile Research Journal, Vol. 56, pp. 339-40. Gao, X. (1998), “Sensors for industrial sewing processes”, MSc Thesis, The University of Windsor, Ontario.

Finite element analysis of sewing process 35 Table VI. The hole size ratio as a function of point angle of the needle

IJCST 11,1

36

Howell, H.G., Mieszkis, K.W. and Tabor, D. (1959), Friction in Textiles, Butterworth Scientific Publications, London. Hurt, F.N. and Tyler, D.J. (1975), “Seam damage in the sewing of knitted fabrics”, HATRA Research Reports, 35 & 36, 1975. Kamata, Y., Tsunematsu, S., Kinoshita, R. and Naka, S. (1977), “Needle-fabric interaction during the sewing”, Sen-IGakkaishi, Vol. 33, pp. T157-65. Khan, R.A., Hersh, S.P. and Grady, P.L. (1970), “Simulation of needle-fabric interactions in sewing operations”, Textile Research Journal, Vol. 40, pp. 489-98. Matthews, B.A. and Little, T.J. (1988), “Sewing dynamics, Part I: measuring sewing machine forces at high speeds”, Textile Research Journal, Vol. 58, pp. 383-91. Simmons, S. (1979), “An analysis of forces in a fabric-needle sewing system”, Clothing Research Journal, Vol. 1, pp. 51-9. Singer Company (1981), “Improvements in needle design”, Bobbin, 174 and 191, February. Stylios, G. (1987), “A study of problems associated with fabric sewing in commercial garment manufacture”, PhD Thesis, Leeds University. 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. Torrington Company (1981), “Finding solutions to needle problems”, Bobbin, February.

Engineering of clothing systems for improved thermophysiological comfort The effect of openings J.E. Ruckman and R. Murray

Thermophysiological comfort 37 Received July 1998 Revised October 1998

Department of Clothing Design and Technology, Manchester Metropolitan University, UK and

H.S. Choi Department of Clothing and Textiles, Ewha Womans University, South Korea Keywords Clothing industry, Garment, Thermophysiological Abstract To evaluate the effectiveness of ventilation systems in outdoor jackets, two jackets were purchased and modified, one made of PTFE laminated fabric and the other made of polyurethane coated fabric. Six male subjects undertook exercise routines simulating fell walking while wearing these jackets. The skin temperature at four different locations and the amount of perspiration generated during exercise were recorded for analysis. The experimental results were analysed using two-way analysis of variance. From the analysis it was found that during the exercise the design of the pit zip openings, especially with pit zip openings at both sleeve and side seams, in a jacket has an effect on thermal regulation, limiting the rate of temperature increase; however, during rest it is the fabric that plays the more important role. The results for the period of exercise suggest that the provision of ventilation at appropriate positions in the jacket could contribute considerably to heat loss irrespective of the use of breathable fabrics.

Introduction Thermophysiological comfort is a fundamentally important element of outdoor garments since they are intended to be worn in various weather conditions, often while the wearer engages in strenuous activities; the anticipated environmental conditions and the clothing system are accepted to be the other key elements to consider during design and manufacture. The clothing system is especially important since unlike either the environmental conditions or human physiology it can be altered to maximise the comfort of the wearer. Determination of the mechanisms of heat and moisture transfer through a clothing assembly is vital in enabling the development of clothing systems which assist the regulation and maintenance of the thermophysiological balance. Theories and mechanisms have been established on the basis of Fick’s Law and it is now widely believed that both heat transfer and moisture transfer This paper was accomplished with research fund provided by Korea Research Foundation, supported for faculty research abroad.

International Journal of Clothing Science and Technology, Vol. 11 No. 1, 1999, pp. 37-52. © MCB University Press, 0955-6222

IJCST 11,1

38

through a single fabric and layered fabrics are governed by Fick’s Law in an identical manner (Eckert and Drake, 1972; Monteith, 1973; Ruckman, 1997a). The development of modern waterproof breathable fabrics, which claim to provide maximum thermophysiological comfort, has generated considerable interest in the outdoor garment market. These waterproof breathable fabrics, such as those fabrics incorporating PTFE or hydrophilic laminating technology, are generally compared to more traditional fabrics such as polyurethane coated or wax treated fabrics. The comparisons are generally made in terms of performance assessed using testing methods based on Fick’s Law. When waterproof fabrics are used in a clothing system, however, it is to be expected that the performance of the fabric itself is not the only factor which contributes to thermophysiological comfort (Fan and Keighley, 1989; Markee et al., 1991; Ruckman, 1996). The design of the clothing system utilising the mechanisms and theory of heat and moisture transfer through a clothing assembly is also expected to play a part. It is a well-known phenomenon that if some sort of ventilation features are provided within a clothing system excess body heat can pass into the environment via this ventilation thus minimising the discomfort caused by sweating. This ventilation method utilizes forced convection created within the clothing system by body movement. Forced convection accelerates heat loss by convection in excess of that natural minimum due to the rising of gas or liquid heated by a warm surface (Bird, 1960). Thus the movements of the arms and legs, whether the subject is upright or sitting, are considered to generate forced convection. According to Fanger (1970), the velocity of still air relative to the body is between 0.4 and 1.8 m/s while hill walking up a 5-25 per cent gradient. The velocity of the air stream relative to the warm skin or clothing is the additional factor introduced when forced convection is considered over natural convection. An analytical consideration of the basic concepts concerning forced convection generally leads to a statement of the factors on which this heat loss channel quantitatively depends. Fundamentally, the greater the velocity of the air stream and the greater the difference in temperature between the gas and warm surface, the greater the heat loss by convection. Stating this mathematically, it can be said that heat loss by convection is a function of several variables (Hardy, 1968). That is: Hc = f (D, V, µ, ρ, ∆T, K, Cp, t) where, Hc = heat loss by convection D = dimension of the object V = velocity of the gas µ = viscosity of the gas molecule ρ = density ∆T= temperature difference between the warm surface and the air K = thermal conductivity of the gas Cp = specific heat t = time

Büttner has derived the following from analysis of his experimental work for spheres of several diameters: Hc = 0.07/D × (V D ρ/µ)0.52. Winslow et al. (1936) have determined convection constants in much the same way and arrived at the following formula for convection loss: Hc = 2.3√V × ∆T. A number of studies have considered various methods of ventilating a clothing system (Gonzalez and Cena, 1985; Vokac et al., 1976). These papers focused mainly on the “chimney effect” and the phenomenon of natural rising of heat. According to such papers, if an individual begins to feel warm then the head should be partially uncovered first, followed by the neck area and the upper front or back of the torso. This will provide a vent to allow the chimney effect to be induced. Preliminary research shows that several garment manufacturers in the UK have incorporated mass transfer into clothing design. Currently, the three special features which aim to dissipate body heat by ventilation available on the UK market are: (1) “Pit zips”, which appear directly underneath the armpit, are featured on jackets at the higher end of market. Pit zips allow the upper arm to be fully ventilated. They are constructed in much the same way as the pocket openings, in that the degree to which they open is adjustable using a zip. The location of pit zips varied, but all of them fit into three categories: opening using the sleeve seam, opening using the side seam, and opening using both sleeve and side seams. (2) “Venting pockets” are used in a small number of jackets. These are situated diagonally across the chest on some of the jackets sold into the high end of market. Unzipping these pockets allows air movement between the jacket and environment, the pockets having a permeable mesh lining. (3) “Venting back” situated across the back incorporating a yoke feature is used in some jackets for the middle to lower end of the market. The vent usually has a flap covering a mesh lining to conceal the feature and to join the feature to the main part of the jacket. The numbers of jackets exhibiting these three features are roughly in the order of pit zips > venting backs > venting pockets. Since preliminary research has indicated that “pit zips” are not only the latest to appear on the market but also the most common ventilation system, this paper will review the operation of pit zips and the impact on skin temperature of the exact location of the zips. Further reasons for reviewing the effectiveness of pit zip openings are: this area demonstrates the greatest perspiration; forced convection is activated by arm movement; rain cannot be penetrated unless the arm is stretched upward. The paper will also compare the effectiveness of the pit zip and waterproof breathable fabrics for thermophysiological comfort.

Thermophysiological comfort 39

IJCST 11,1

40

Experimental Two waterproof outdoor jackets, one made of PTFE laminated fabrics and the other of polyurethane coated fabrics, were purchased and modified for evaluation. The specifications for these jackets are shown in Table I. In order to assess the effect of ventilation through pit zip openings during an exercise routine and subsequent resting period, the original jackets were modified by inserting two 2-way opening zippers into the under-arm and side seams of both jackets (Figure 1). Ticket No. 80 round needle with No. 120 polyester corespun thread was used. As a result of using a two-way zipper, it was possible to create four pit zip openings for experiments: 10cm sleeve seam and 10cm side seam opening from the armpit to give a combined 20cm opening; 20cm sleeve seam opening from the armpit; 20cm sideseam opening from the armpit; no opening.

GE Jacket

Table I. Specifications of fabrics PE used to construct jackets Jacket

Shell fabric Fibre content Construction

Membrane

Thickness

Weight

100% polyamide

Plain weave (48 × 30/cm2)

PTFE laminated

0.48mm

206g/m2

100% polyamide

Plain weave (40 × 32/cm2)

Polyurethane coated

0.18mm

159g/m2

Sleeve Opening

Both Sleeve and Side Opening Side Opening

Figure 1. Pit zip openings

Six male subjects in excellent physical condition (who regularly engage in physically demanding sports, such as jogging, football, swimming, etc.) undertook exercise routines while wearing these jackets. Their average characteristics are shown in Table II. The exercise routine, which was based on the amount of work done while hill walking, centred on riding an ergometer (Rotronic Hygrometer Monark 814E) for 10 minutes at the speed of 60 rpm with a resistance of 2Kp, which is equivalent to 120 watts working. The whole routine consisted of 30 minutes resting to equilibrate to the ambient atmosphere, 10 minutes exercise and 5 minutes resting after the exercise. Prior to the exercise routine, and towards the end of the 30 minutes resting period, thermistors and blotting papers of area 30.7cm2 were attached to the subjects to measure the skin temperature and the rate of perspiration. All thermistors were securely attached at four different places (chest, upper back, abdomen and upper arm) with micropore tape. Blotting papers were attached to the chest and upper back. The subjects then changed into the experimental garments incorporating a pair of polyester boxer shorts, a turtle-neck cotton interlock T-shirt and the modified experimental jacket. Experiments were conducted in an environmental chamber maintaining 20±2°C, 65±5 per cent r.h. throughout the experiments. During the exercise, skin temperatures at the four different locations were recorded at one minute intervals using a Grant Squirrel meter/logger type SQ8 16U. The amount of perspiration generated during exercise was measured by weighing the blotting papers and T-shirt before and after the exercise. Sweat uptake by the blotting papers was obtained on a balance with a resolution of 0.0001g. Sweat uptake by the T-shirt was obtained on a balance with a resolution of 0.01g. In order to evaluate subjective comfort and thermal and moisture sensation, questionnaires were given to the subjects before and after the exercise routine. Subjective perception of comfort was recorded on a four-point intensity scale. Subjective perception of thermal and moisture sensation was recorded on ninepoint and four point intensity scales respectively. These scales are shown in Figure 2.

Thermophysiological comfort 41

Results and discussion The experimental design was a randomised block with repeated measures (Mason et al., 1989). The experiment results were analysed using two-way analysis of variance (Tables III, IV and V); the calculations used followed the formula described by Klugh (1986). Three treatment factors were used for the Number

Age (years)

Weight (kg)

Height (m)

Surface areaa (m2)

6

21.3 ± 1.89b

78.60 ± 5.26

1.81 ± 0.04

1.995 ± 0.046

Notes: a = DuBois area (Fanger, 1970) b = Standard deviation

Table II. Mean features of the subjects

IJCST 11,1

comfortable

slightly uncomfortable uncomfortable 2

1

42

very hot

hot

warm

1

2

3

Figure 2. Subjective rating scales for comfort, thermal sensation, and moisture sensation

dry

3

slightly neutral warm 4

slightly cool

5

6

slightly wet

1

very uncomfortable

2

4

cool

cold

very cold

7

8

9

wet

very wet

3

4

analysis: the main treatment was the two different types of fabrics, GE (PTFE laminated) and PE (polyurethane coated); the subtreatments were the four locations of zip openings (i.e. both openings (1), sleeve opening (2), side opening (3) and no openings (4)), and the time intervals between the start of the exercise routine and the measurements of skin temperature and perspiration rate.

Table III. Analysis of variance for skin temperature changes during the exercise when wearing GE jackets

Chest Temperature Time Interaction Back Temperature Time Interaction Upper arm Temperature Time Interaction Abdomen Temperature Time Interaction Notes: Df = degrees of freedom S2 = variance F = Snedecor’s F statistic p = probability

Df

S2

3 3 9

1.4505 51.4800 0.3610

1.0915 142.4600 0.2620

ns p < 0.001 ns

3 3 9

0.3411 10.5680 0.1771

0.2590 59.6800 0.5740

ns p < 0.01 ns

3 3 9

2.7200 34.2160 0.2890

1.9340 128.1200 1.0824

ns p < 0.001 p < 0.05

3 3 9

0.9820 22.8800 0.2615

0.2670 87.5000 0.7740

ns p < 0.01 ns

F

p

Chest Temperature Time Interaction Back Temperature Time Interaction Upper arm Temperature Time Interaction Abdomen Temperature Time Interaction

Df

S2

3 3 9

0.8330 65.3200 0.4010

0.6850 162.9000 0.5230

ns p < 0.001 ns

3 3 9

0.2160 25.8400 0.3626

0.2186 71.2500 0.3303

ns p < 0.01 ns

3 3 9

2.5470 49.3000 0.4940

0.9873 312.1200 3.1270

ns p < 0.001 p < 0.05

3 3 9

0.8590 25.5700 0.1605

0.2817 163.9700 1.0300

ns p < 0.001 ns

F

p

Notes: Df = degrees of freedom S2 = variance F = Snedecor’s F statistic p = probability

Source of variation

Df

GE vs. PE Openings (1-4) Interaction

1 3 3

S2 1666.20 177.90 71.66

F

p

47.6700 2.4730 0.9776

p < 0.01 ns p < 0.05

Notes: Df = degrees of freedom S2 = variance F = Snedecor’s F statistic p = probability

Change of skin temperature and thermal sensation The results of the temperature changes during the exercise and resting periods are shown in Figure 3, which is based on the calculation of average skin temperature of chest, upper back, abdomen and upper arm when jackets with various combinations of pit zip openings were worn by the subjects. The first point to note from Figure 3 is the effect of fabrics used on the change in skin temperature. Owing to the GE jackets being constructed from waterproof breathable fabric (PTFE laminated) and the PE jackets being constructed from waterproof non-breathable fabric (polyurethane coated) the

Thermophysiological comfort 43

Table IV. Analysis of variance for skin temperature changes during the exercise when wearing PE jackets

Table V. Analysis of variance for perspiration rate after exercise

IJCST 11,1

36.0

44

Temperature (C)

35.0

34.0

Key GE1 GE2 GE3 GE4 PE1 PE2 PE3 PE4

33.0

32.0 0

Figure 3. Temperature changes for all jackets

1

2

3

4

5

6

7 8 9 Time (mins)

10

11

12

13 14

15

GE1, PE1 = Both Openings GE2, PE2 = Sleeve Opening GE3, PE3 = Side Opening GE4, PE4 = No Openings

GE jacket was expected to provide better thermophysiological comfort. During the exercise, especially at the beginning of the exercise routine, however, there is little effect of fabric type on skin temperature, although after about five minutes of exercise differing average skin temperatures were noted ( p < 0.01). The influence of the fabric on thermal regulation increases significantly ( p < 0.01) immediately after the subjects have finished the exercise routine and begin their resting period. While all GE jackets permit a dramatic fall in average skin temperature, a slight increase in skin temperature was observed for all PE jackets except the jacket with no opening for which there was a fall in temperature. According to the results from tests carried out utilising the methodology described by Ruckman (1997b) on fabrics used to construct the

jackets, this phenomenon is mainly dependent on the fabrics’ ability to transfer water vapour rather than their thermal conductivity properties. As seen in Figures 4 and 5, the PTFE laminated and polyurethane coated fabrics do not differ much in their thermal conductivity, but they do have very different water vapour transfer rates. Heat loss by evaporation would have been greater through PTFE laminated fabrics, resulting in a lower skin temperature when wearing a jacket made of this fabric than a jacket made up from polyurethane coated fabric. The fall in skin temperature observed for the unventilated PE jacket during the period of rest may be explained as follows; during the exercise routine the skin temperature rises rapidly and reaches a high maximum point, especially at the upper arm which recorded 36.2°C at the end of ten minutes’ exercise. At the end of this exercise period there is an excessive amount of moisture on the skin surface which will subsequently cool down on the surface of skin. Since the thermomister is placed on the skin, it is likely that it recorded the temperature of the condensed sweat rather than the skin temperature. The changes of skin temperature before and after exercise, especially the effect of fabrics during the resting period, seemed to affect the subjects’ thermal sensation and comfort. As shown in Table VI, the rating difference between before and after the exercise for PE jacket demonstrated a greater discrepancy than for the GE jacket, suggesting a greater degree of thermophysiological discomfort after the exercise routine.

Thermophysiological comfort 45

37

35

Temperature (C)

33 Key 31

PTFE Laminated Fabric Polyurethane Coated Fabric

29

27

25

23 0

100

200

300

400 500 Time (Minutes)

600

700

800

Figure 4. Temperature change

IJCST 11,1

Time (Minutes) 0

100

200

300

400

500

600

700

800

0 –0.5

46 Water Vapour Change (g)

–1 –1.5 Key PTFE Laminated Fabric Polyurethane Coated Fabric

–2 –2.5 –3 –3.5 –4

Figure 5. Water vapour loss

–4.5

Thermal sensation Before After

Table VI. Subjective ratings for thermal sensation, moisture sensation and comfort (mean ± standard deviation)

GE1 GE2 GE3 GE4 PE1 PE2 PE3 PE4

5.00 ± 1.26 4.67 ± 1.03 4.83 ± 0.41 5.00 ± 0.89 5.00 ± 0.63 5.00 ± 1.10 5.17 ± 0.75 4.83 ± 1.17

2.17 ± 0.75 2.00 ± 0.89 2.33 ± 0.52 2.00 ± 0.63 1.83 ±0.75 2.00 ± 0.63 1.83 ± 0.41 1.67 ± 0.82

Moisture sensation Before After 1.17 ± 0.41 1.17 ± 0.41 1.00 ± 0.00 1.17 ± 0.41 1.00 ± 0.00 1.17 ± 0.41 1.00 ± 0.00 1.17 ± 0.41

3.00 ± 0.63 3.17 ± 0.75 3.00 ± 0.63 3.00 ± 0.63 3.17 ± 0.75 3.33 ± 0.82 3.17 ± 0.75 3.50 ± 0.84

Comfort Before After 1.17 ± 0.41 1.00 ± 0.00 1.00 ± 0.00 1.00 ± 0.00 1.17 ± 0.41 1.33 ± 0.52 1.00 ± 0.00 1.33 ± 0.52

1.83 ± 0.41 1.83 ± 0.41 2.17 ± 0.41 2.50 ± 0.55 2.33 ± 0.52 2.17 ± 0.41 2.50 ± 0.55 2.17 ± 0.41

The second point of note from Figure 3 is the effect of the position of the pit zip on the change in skin temperature. For both GE and PE jackets, pit zip openings at both sleeve and side seam has the greatest effect on regulating skin temperature followed by sleeve opening, side opening and no opening. This effect is not apparent at the beginning of the exercise routine, but differing average skin temperatures were noted for the various jackets after about five minutes into the exercise routine (p < 0.01). In particular, PE jackets with sleeve

and side openings and sleeve openings resulted in lower skin temperatures than GE jackets with no openings after about five minutes into the exercise routine. Furthermore, some of these PE jackets resulted in lower skin temperatures than GE jackets with side openings. This trend, i.e. exhibiting a lower temperature when wearing a PE jacket with sleeve and side openings or with sleeve openings than wearing a GE jacket with no opening, can be seen from Figure 3 to be the case for the remainder of the ten minute exercise period. Although this phenomenon was unexpected it can be explained by the theory of heat loss by convection, especially as formulated by Winslow et al. (1936), described previously. According to their formula, the major factor affecting convective heat loss in a clothing system is the temperature difference between the skin and the environment. It was noticed that the skin temperature during exercise while wearing an unventilated PE jacket is considerably higher than that experienced while wearing an unventilated GE jacket, and therefore there is a potential to generate greater convective heat loss under identical environmental conditions. As discussed before, this became most apparent after five to ten minutes into the exercise period with the ventilated PE jacket demonstrating a lower skin temperature than the unventilated GE jacket. This would have been a result of skin temperature under the PE jacket being higher up to five minutes into the exercise, thereby giving rise to a greater temperature difference between skin surface and air, which in turn increases heat loss by convection over the next five minute period. When the exercise routine stops, however, heat loss by forced convection stops, and therefore the skin temperature beneath the ventilated PE jacket rises to a greater temperature than that beneath the GE jacket which subsequently shows a more rapid cooling effect. In addition to the above, it was also noted that for all configurations of fabric type and jacket design the back exhibited the greatest temperature before the subject commenced exercise, but the chest gave the highest temperature readings during exercise while the upper arm demonstrated the most rapid rise in temperature (Figures 6 and 7). This is probably due to the fact that the upper arm is located on the extremities rather than the body core and hence it is more susceptible to the temperature changes. Perspiration rate and moisture sensation The results obtained for the amount of perspiration produced during the exercise routine (including the resting period) are shown in Figure 8. The total perspiration measured is the total amount of sweat absorbed by the turtle-neck cotton interlock T-shirt and the front and back blotting papers. The analysis of variance is listed in Table V. It is clear from Figure 8 that regardless of the position of the zip opening the PE jacket gave rise to considerably more sweat than the GE jacket. In the case of both opening and no opening, PE shows a considerably higher sweat rate (p < 0.01). This was expected following the fabric test results detailed in Figure 5 and the findings of previous research conducted into the effect of fabric properties on sweat generation (Andreen et al., 1953; Shapiro et al., 1982; Bakkevig and Nielsen, 1995).

Thermophysiological comfort 47

Temperature (C)

32.0

33.0

34.0

35.0

36.0

0

0

32.0

33.0

34.0

35.0

1

1

2

2

3

3

4

4

5

5

Key CHEST

6 7 8 9 10 11 12 13 14 15 Time (mins)

c. Side Opening

6 7 8 9 10 11 12 13 14 15 Time (mins)

a. Both Openings

BACK

Temperature (C) Temperature (C)

Figure 6. Temperature changes at various parts of the body when wearing a GE jacket

36.0

0

1

1

2

2

3

3

UPPER ARM

32.0 0

33.0

34.0

35.0

36.0

32.0

33.0

34.0

35.0

36.0

4

4

5

6 7 8 9 10 11 12 13 14 15 Time (mins)

d. No Openings

6 7 8 9 10 11 12 13 14 15 Time (mins)

ABDOMEN

5

b. Sleeve Opening

48

Temperature (C)

IJCST 11,1

32.0

33.0

34.0

35.0

0

0

32.0

33.0

34.0

35.0

36.0

Temperature (C)

Temperature (C)

1

1

2

2

3

3

4

4

5

5

6 7 8 9 10 11 12 13 14 15 Time (mins) Key CHEST

c. Side Opening

6 7 8 9 10 11 12 13 14 15 Time (mins)

a. Both Openings

BACK

Temperature (C) Temperature (C)

36.0

0

1

1

2

2

UPPER ARM

32.0 0

33.0

34.0

35.0

36.0

32.0

33.0

34.0

35.0

36.0

3

3

4

4

6 7 8 9 10 11 12 13 14 15 Time (mins)

d. No Openings

6 7 8 9 10 11 12 13 14 15 Time (mins)

ABDOMEN

5

5

b. Sleeve Opening

Thermophysiological comfort 49

Figure 7. Temperature changes at various parts of the body when wearing a PE jacket

IJCST 11,1

50 45 40

50 Perspiration (g)

35 30 25 20 15 10 5 0 Both Openings

Sleeve Opening

Side Opening

No Openings

Key

Figure 8. Perspiration rate

GE Jacket PE Jacket

It is interesting to note that the position of the zip opening has no effect on reducing the perspiration rate, although the existence of an opening does result in a reduction in sweat rate for both jackets. This is perhaps due to the fact that data are based on the amount of perspiration measured after a whole exercise routine (i.e. ten minute exercise and five minute resting period). If the amount of sweat was recorded just after the exercise, rather than after the resting period, the effect of the pit zip opening on perspiration rate would have been more noticeable. Conclusions From this research it is shown that both clothing system engineering and waterproof breathable fabrics can contribute to thermophysiological comfort in outdoor jackets when the jacket is worn for mixed activities such as fell walking and resting. During the exercise the design of the openings in a jacket has an effect on thermal regulation, limiting the rate of temperature increase. In the case of both GE and PE jackets, a pit zip with both sleeve and side seam opening has the

greatest effect on lowering skin temperature followed by sleeve opening, side opening and no opening. During the rest period, however, it is the fabric that plays the more important role. A more rapid cooling effect is observed for GE jackets made of PTFE laminated fabrics than PE jackets made of polyurethane coated fabrics. The results for the period of exercise, however, do suggest that the provision of ventilation at appropriate positions in the jacket could contribute considerably to heat loss irrespective of the use of hi-tech fabrics. Skin temperature at the back was greater than at other parts of the body where measurements were taken at the start of the exercise, but the skin temperature at the chest gave the highest reading during exercise. This suggests that an opening near the chest may be the most effective way of creating ventilation. Although several outdoor jacket manufacturers in the UK incorporate a ventilation system around the chest, it is not the most popular position to open during exercise, since it directly exposes the chest to the outer environment. It is recommended, however, to consider engineering a design using a chest opening while minimising direct exposure. In terms of moisture retention the PE jacket was seen to create more discomfort than the GE jacket. Measurements showed a greater amount of sweat being absorbed by the cotton T-shirt and blotting papers for PE jackets than GE jackets. References Andreen, J.H., Gibson, J.W. and Wetmore, O.C. (1953), “Fabric evaluation based on physiological measurements of comfort”, Textile Research Journal, Vol. 23, pp. 11-22. Bakkevig, M.K. and Nielsen, R. (1995), “The impact of activity level on sweat accumulation and thermal comfort using different underwear”, Ergonomics, Vol. 38 No. 5, pp. 926-39. Bird, R.B. (1960), Transport Phenomena, Wiley, New York, NY. Eckert, E.R.G. and Drake, R.M. Jr (1972), Analysis of Heat and Mass Transfer, McGraw-Hill, New York, NY. Fan, J. and Keighley, J.H. (1989), “A theoretical and experimental study of the thermal insulation of clothing in windy conditions”, International Journal of Clothing Science and Technology, Vol. 1 No. 1, pp. 21-9. Fanger, P.O. (1970), Thermal Comfort: Analysis and Applications in Environmental Engineering, McGraw-Hill, New York, NY. Gonzalez, R.R. and Cena, K. (1985), “Evaluation of vapor permeation through garments during exercise”, Journal of Applied Physiology, Vol. 58 Nos 1-3, pp. 928-35. Hardy, J.D. (1968), “Heat transfer” in Newburgh, L.H. (Ed.), Physiology of Heat Regulation and the Science of Clothing, Hafner, New York, NY. Klugh, H.E. (1986), Statistics: The Essentials for Research, Lawrence Erlbaum Associates, London. Markee, N.L., Hatch K.L., French, S.N., Maibach, H.I. and Wester, R. (1991), “Effect of exercise garment fabric and environment on cutaneous conditions of human subjects”, Clothing and Textiles Research Journal, Vol. 9 No. 4, pp. 47-54. Mason, R.L., Gunst, R.F. and Hess, J.L. (1989), Statistical Design & Analysis of Experiments, Wiley, New York, NY. Monteith, J.L. (1973), Principles of Environmental Physics, Edward Arnold, London.

Thermophysiological comfort 51

IJCST 11,1

52

Ruckman, J.E. (1996), “The performance of seams used in waterproof foulweather garments”, The Textile Institute World Conference Proceedings: Niches in the World of Textiles. Ruckman, J.E. (1997a), “Water vapour transfer in waterproof breathable fabrics, Part 2: under windy conditions”, International Journal of Clothing Science and Technology, Vol. 9 No. 1, pp. 23-33. Ruckman, J.E. (1997b), “An analysis of simultaneous heat and water vapour transfer through waterproof breathable fabrics”, Journal of Coated Fabrics, Vol. 26 No. 4, pp. 293-307. Shapiro, Y., Pandolf, K.B. and Goldman, R.F. (1982), “Predicting sweat loss response to exercise, environment and clothing”, European Journal of Applied Physiology, Vol. 48, pp. 83-96. Vokac, Z., Kopke, V. and Keul, P. (1976), “Physiological responses and thermal, humidity and comfort sensation in wear trials with cotton and polypropylene vest”, Textile Research Journal, Vol. 46, pp. 30-8. Winslow, C.E.A., Herrington, L.P. and Gagge, A.P. (1936), “The determination of radiation and convection exchanges by partitimal calorimetry”, American Journal of Physiology, Vol. 116 No. 3, pp. 669-84.

Vibrations of a crank-shaft in a sewing machine induced by a zigzag mechanism Jerzy Zajaczkowski Lodz Technical University, Textile Faculty, Lodz, Poland

Vibrations of a crank-shaft in a sewing machine 53 Received July 1998 Revised September 1998

Keywords Dynamic, Mechanics, Sewing machine, Vibration Abstract Torsional vibrations of a shaft induced by a zigzag mechanism in a sewing machine with an oscillatory hook are studied in this paper. The dynamic equations of the motion are derived using the principle of virtual work. The discrete forms of the functions relating the motion of machine elements are found using Newton-Brent method. They are replaced locally by the continuous ones using Lagrange formula. The limit cycle trajectory is found by direct numerical integration and it is illustrated graphically. The presented mathematical model of the zigzag sewing machine makes it possible to choose the proper parameters of the machine at the preliminary state of design.

Introduction Sewing machines belong to a group of machines where vibrations are excited by the reciprocating motion of working elements (Mayer Zur Capellen, 1967; Sadler et al., 1980). This kind of vibration is essentially non-linear and still not fully understood. It has been found that if the rotating shaft has a relatively small stiffness in comparison to the oscillating shaft then torsional buckling can occur (Zajaczkowski, 1987, 1994a). At the resonance, for a small slope of the motor characteristic, two periodic attractors and two catastrophic bifurcations may exist (Zajaczkowski, 1994b, 1996a). In the sewing machine the needle mechanism and the hook mechanism tend to induce different speed fluctuation of the crank-shaft which results in its torsional vibration (Zajaczkowski, 1996b, 1996c). There are two different oscillations during one shaft revolution. Driving the hook with an extensible timing belt results in a difference between the actual and expected relative position of a needle and a hook (Zajaczkowski, 1997a). The dynamics of the zigzag mechanism have been studied for rigid shafts (Zajaczkowski, 1997b). The main object of the present study is to formulate the mathematical description of a zigzag sewing machine, taking account of the torsional vibration of shafts. Equations of motion The system to be studied, shown in Figure 1, consists of the motor with the belt drive (Φm, Φ1), the crank-shaft (Φ1, Φ2), the slider-crank mechanism (Φ2,Y) driving the needle bar, the crank-rocker-yoke mechanism (Φ1, Ψ1) driving the hook Ψ and the zigzag mechanism giving the horizontal motion of the needle ξ and the hook X. Here, Φm is the rotation angle of the motor; Φ1, Φ2 are the

International Journal of Clothing Science and Technology, Vol. 11 No. 1, 1999, pp. 53-59. © MCB University Press, 0955-6222

IJCST 11,1

54

Figure 1. Mechanisms in a sewing machine

rotation angles of the crank-shaft; Ψ1,Ψ are the rotation angles of the hook; X is the horizontal displacement of the hook; Y is the vertical displacement of the needle; ξ is the rotation angle of the needle bar assembly. The triangle zigzag cam is driven by the crank-shaft through the crossed helical gears (Φ1,γ1). The cam follower ξ1 drives the yoke-crank-yoke mechanism (ξ2, ξ3). The motion of the rocker ξ3 is transformed through the four-bar-linkage to the zigzag motion ξ of the needle and through the slider-crank mechanism to the zigzag motion X of the hook. In Figure 1, mN is the mass of the needle bar; mH is the mass of the hook; A, B are the mass moments of inertia; ra and rb are the radii of the belt pulleys; sb is the belt stiffness; s, k are the stiffnesses of the shafts; D b , D, H are the coefficients of viscous damping; L, R, a, b, h, r, ρ are the dimensions of the

mechanism. The distance h1 can be varied with the screw to enable adjustment of the width of zigzag stitch. The needle mechanism, the hook mechanism and the zigzag mechanisms are treated as the main sources of vibrations while the take-up mechanism, the feed mechanism and the presser foot are not taken into account. The working forces and the friction forces are neglected. The equations governing the motion of the motor are:

(1) (2) where dΦm/dt is the motor angular speed, T is the motor time constant, Cm is the slope of the motor characteristic and Ωm is the angular velocity for which the motor torque is equal to zero. Making use of the principle of virtual work, one has: (3) (4) Here:

(5)

Vibrations of a crank-shaft in a sewing machine 55

IJCST 11,1

Substitution of expressions (5) into equations (3,4) and using the transformation: (6) gives:

56

(7)

(8) The equation governing the vibration of the hook has the form: (9) Replacing the mechanisms by closed vector polygons and projecting the vectors in vertical and horizontal directions gives the set of equations from which the dependence of variable distances and angles on the angle of revolution of the shaft can be determined. The dependence of the rotation angle Ψ1=Ψ1(Φ1) of the rocker r on the rotation angle of the crank Φ1 Lc, given by the function, can be found from:

Vibrations of a crank-shaft in a sewing machine 57 (10) The dependence of the vertical translation of the needle bar on the rotation angle of the crank Rk is described by the function Y=Y(Φ2) (11) The helical gears transform the motion of the crank Φ1 to the motion of the cam γ1 = Φ1/2. The dependence between the angle of revolution of the cam follower ξ1 on the angle of revolution γ1 of the cam can be found from (Zajaczkowski, 1997b):

(12) where the dimensions of the triangle cam are given by (13) The angles ξ2 and ξ3 are found to be (14)

(15)

IJCST 11,1

The function ξ = ξ (Φ1), describing the zigzag motion of the needle, can be found from the set of equations:

(16)

58

The function X = X(Φ 1), describing the zigzag motion of the hook, can be determined from:

(17) On applying Brent’s method (More and Cosnard, 1979, 1980) to equations (10, 12, 16, 17) the discrete representations of the functions Ψ1=Ψ1(Φ1), ξ = ξ(Φ1), X = X(Φ1) can be found. The discrete functions can be replaced globally by the continuous functions using the fast Fourier transform or locally using the Lagrange formula. The set of equations (1, 2, 7, 8, 9) can be numerically integrated until the difference between each period becomes negligible and the solution is closed to the attracting orbit. Numerical results The computations were carried out for the mass moments of inertia (in kg/m2) Am = 0.0001(rb/ra)2, A1 = 0.0005, A2 = 0.00005, B1 = 0.00001, BH = 0.00004, BN = 0.00025, the mass of the needle bar assembly mN = 0.03kg, the mass of the hook assembly mH = 0.5kg, the belt stiffness sb = 25/ra2(Nm/rad), the crankshaft stiffness s = 100(Nm/rad), the hook shaft stiffness k = 100(Nm/rad), the coefficients of viscous damping Db = 0.03/ra2(Nms/rad), D = 0.05(Nms/rad), H = 0.06(Nms/rad), the motor constants C = Cm(ra/rb)2 = 1(Nms/rad), Ω = Ωm(rb/ra), T = 1/(10Ω), the radii of the belt pulleys rb = 0.01m, ra = 0.04m, the dimensions of the slider-crank mechanism Rk = 0.017m, Lk = 0.035m, the dimensions of the crank-rocker-yoke mechanism L = Ld = 0.184m, Lr = 0.021m, Lc = 0.017m, r/h = 1, α = arcsin(h/Ld), the dimensions of the cam mechanism ρb = 0.014m, ρa = 0.025m, αk = π/2, a = 0.005/(2–2sin(αk/2)) and the dimensions of the zigzag mechanisms (in metres) r1 = 0.019, h1 = 0.015, h2 = 0.019, LN = 0.195, LNc = 0.024, LNr = 0.016, LN1 = 0.008, LN2 = 0.195, LH1 = 0.016, LH2 = 0.075, LH = 0.12. Integrating equations (1, 2, 7-9) the limit cycle trajectory was found. It represents the relative speed of the twist V = (dΦ1/dt–dΦ2/dt)/Ω versus the twist of the crank-shaft U = Φ1–Φ2 for Ω = 800rad/s and is shown in Figure 2. Concluding remarks The vibrations are periodical and one cycle of vibrations takes two shaft revolutions. The difference between the parts of trajectory that correspond to two consecutive rotations is small.

Vibrations of a crank-shaft in a sewing machine

0.10

U

0.05 0.00

59

–0.05

Figure 2. The relative speed of the twist V=(dΦ1/dt–dΦ2/dt)/Ω versus the twist of the crank shaft U=Φ1–Φ2 for Ω = 800rad/s

–0.10 –0.15 –7.0

–3.5

0.0

3.5

x10–2

U

The derived equations make the computer simulation of a zigzag sewing machine possible. At the preliminary state of design, one can predict the behaviour of the machine and choose suitable dimensions. References Mayer Zur Capellen, W. (1967), “Torsional vibrations in the shafts of linkage mechanism: Journal of Engineering for Industry”, Transactions of the American Society of Mechanical Engineers, Vol. 89, pp. 126-36. More, J.J. and Cosnard, M.Y. (1979), “Numerical solution of non-linear equations”, ACM Transaction on Mathematical Software, Vol. 5, pp. 64-85. More, J.J. and Cosnard, M.Y. (1980), “BRENTM, a Fortran subroutine for the numerical solution of systems of non-linear equations [C5]”, ACM Transactions on Mathematical Software, Vol. 6, pp. 240-51. Sadler, J.P., Mayne, R.W. and Fan, K.C. (1980), “Generalised study of crank-rocker mechanism driven by a d.c. motor”, Mechanism and Machine Theory, Vol. 15, pp. 435-45. Zajaczkowski, J. (1987), “Torsional vibrations of shafts coupled by mechanisms”, Journal of Sound and Vibration, Vol. 116 No. 2, pp. 221-37. Zajaczkowski, J. (1994a), “Torsional buckling of shafts coupled by mechanisms”, Journal of Sound and Vibration, Vol. 173 No. 4, pp. 449-55. Zajaczkowski, J. (1994b), “Vibrations of shafts coupled by mechanisms”, Journal of Sound and Vibration, Vol. 177 No. 5, pp. 709-13. Zajaczkowski, J. (1996a), “Resonances of symmetric modes in shafts coupled by mechanisms”, Journal of Sound and Vibration, Vol. 197 No. 5, pp. 519-25. Zajaczkowski, J. (1996b), “Dynamics of mechanisms driving a needle and a hook in a sewing machine”, Scientific Bulletin of Lodz Technical University, ZN Nr 760, Wlokiennictwo z. 54, pp. 5-12. Zajaczkowski, J. (1996c), “Torsional vibrations of shafts in a sewing machine”, Fibres & Textiles in Eastern Europe, Vol. 4 No. 3-4 (14-15), pp. 49-50. Zajaczkowski, J. (1997a), “Dynamics of belt drive of a hook in a sewing machine”, Scientific Bulletin of Lodz Technical University, ZN Nr 778, Wlokiennictwo z. 55, pp. 99-105. Zajaczkowski, J. (1997b), “Dynamics of a zigzag sewing machine with an oscillatory hook”, Fibres & Textiles in Eastern Europe, Vol. 5 No. 3 (18), pp. 60-1.

IJCST 11,2/3

76

A mathematical model for predicting fabric loss during spreading Sau Fun Frency Ng and Chi Leung Patrick Hui

Institute of Textiles & Clothing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong and

G.A.V. Leaf

Department of Textile Industries, University of Leeds, Leeds, UK Keywords Fabric, Mathematical model Abstract Fabric loss is a major contribution to material utilization. Minimizing fabric loss during spreading can reduce the total production costs for garment manufacturing. A traditional method of predicting the fabric loss during spreading is mainly based on the experience of domain experts or the historical data of production orders. Such method is subjective, not systematic and non-repeatable. In this paper, assuming the spreading process is handled in a controlled manner and there are no flaws on the fabrics being spread, a mathematical model is constructed for predicting the total fabric loss in the spreading process. The total fabric loss includes the internal wastage (i.e. marker fallout), and the external wastage (including end loss, width loss, and splice loss). Other parameters such as marker length, number of splice lines, remnant, and roll length of fabric are also included in the model.

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 76-82. # MCB University Press, 0955-6222

1. Introduction In the apparel manufacturing, the material cost is a major component in the total cost of production. The cost of material can constitute almost half of its product cost (Glock and Kunz, 1990). An efficient material utilization (MU) system could save the production cost 4-6 per cent (Glock and Kunz, 1990). Continuing efforts to improve material utilization are necessary to maintain a competitive position. A number of ways to optimize the fabric loss have been discussed in past studies. The elements affecting fabric loss such as width loss, marker fall out, end loss, flaws, and remnants have been studied by Magowan (1982). He has proposed guidelines for altering wasteful procedures but his work fails in predicting fabric loss during laying-up. A linear regression model has been developed by Milokhina et al. (1986) to compute fabric loss along the length of the overlay, but it is not an appropriate approach because the nature of spreading problems is non-linear. The study of Glock and Kunz (1990) indicated that splicing is essential for flaw removal. Fabric loss is high if either splicing overlaps; too much or fabric roll ends are handled carelessly. Their studies showed that the occurrence of flaw removal and splicing, and excessive end loss during spreading are the major causes of fabric loss; thus if the spreading process is not well planned, the production cost will be increased.

Spreading or laying-up is the process of superimposing predetermined lengths of fabric on a spreading table for the cutting process. During the fabric laying-up, the overlapping area between the fabric rolls is called a splice loss. Splice line are points on the marker where fabric can be cut to remove flaws or overlapped to begin a new roll of fabric while maintaining complete garment pieces, as illustrated in Figure 1. To minimize the material cost, one major issue is to design a proper cut order plan before the spreading and cutting operations. To provide an effective cut order plan, the production planner should understand the key elements that affect the fabric loss during spreading. The aim of this paper is to present a theoretical model for calculation of fabric loss during spreading in a systematical manner.

A mathematical model

77

2. Theoretical analysis 2.1 The marker Suppose the marker is of length m and width w. It is divided into splicing intervals, which may be different depending on whether the fabric is being laid from left to right or from right to left. When the fabric is being laid from left to right, suppose there are n intervals, numbered 1 to n from left to right, as shown in Figure 2(a). The length of the jth interval is Sj (j = 1,2,. . . ,n) so that: n X Sj m: 1 j1

We shall find it convenient to define the partial sum S Tj

j X

Sk ;

j 1; 2; . . . ; n:

2

k1

Tj is the length of the marker from the extreme left hand end to the right hand end of the jth interval. When the fabric is being laid from right to left suppose there are n0 intervals of lengths Sj0 (j = 1, 2, . . ., n0 '), numbered from right to left as in Figure 2(b). Length to be deducted from the new fabric roll

Number of splice lines covered by remnant New fabric roll

Marker

Splice loss Remnant

Accumulated plies

Figure 1. Basic set-up of splice loss

IJCST 11,2/3

marker

w

78 Figure 2a. The marker intervals when fabric is being laid from left to right

S1

S2

S3

Sj

Sn

m Tj direction of lay

marker

w

Figure 2b. The marker intervals when fabric is being laid from right to left

S'n'

S'j

S'3

S'2

S'1

m T'j direction of lay

We have:

n0 X j1

Sj0 m

3

and we may define Tj0

j X k1

Sk0 ;

j 1; 2; . . . ; n0

4

which is the length of the marker from its right hand end to the left hand end of the jth interval. 2.2 Laying up the rolls The individual rolls of fabric are laid in plies, starting at the end of one of the splicing intervals. Figure 3 shows the ith roll, of length Li and width W( w) when it has been laid down. It starts at the end of one of the splicing intervals, though since it is difficult to align the roll exactly, an overlap of mean length h is/was allowed which will eventually be wasted. After the first (possibly

incomplete) ply has been laid, Pi complete plies are laid leaving a remnant of length Ri ( m). This remnant is cut at the end of one of the splicing intervals, at a distance Vi from the end of the marker from which the fabric is being laid. The fabric cut off is of length xi and is wasted. Hence: Ri Vi xi : 5

A mathematical model

At the end of each ply, an allowance of mean length g must be made for the fabric in the turn between one ply and the next. The length of fabric in each ply is thus effectively (m + g). Consequently, the length of fabric in the first (incomplete) ply is:

79

h m g ÿ Viÿ1 and the length remaining in the roll after the first ply has been laid is therefore: Li ÿ fh m g ÿ Viÿ1 g: This length is laid into pi complete plies, where: pi integer part of

Li ÿ h ÿ m g Viÿ1 mg

6

and any excess length is the remnant. That is, Ri Li ÿ h ÿ m g Viÿ1 ÿ pi m g Li ÿ h ÿ m g1 pi Viÿ1 : Using equation (5) we obtain: xi Li ÿ h ÿ m g1 pi Viÿ1 ÿ Vi :

7 8

The length Vi is equal to one of the partial sums Tj or Tj0 and is determined by the fact that the end of the remnant lies within the (j+1)th splicing interval. If the remnant is being laid from left to right, this condition is: 9a Tj Ri Tj1 while if the remnant is being laid from right to left, it is 0 Tj0 Ri Tj1

9b Ri

xi

Vi-1

Vi

Figure 3. The ith roll laid down

IJCST 11,2/3

80

Whether the ith remnant is being laid from left to right or from right to left depends on the total number of turns that have been made in laying up the i rolls. Assuming, without loss of generality, that the start of the first roll is at the left with remnant allowance Viÿ1 ; the total number of turns is Ni

i X 1 pk

10

k1

and if this is an even number the ith remnant is being laid from left to right; if it is an odd number it is being laid from right to left. Let J = j(i) be the value of j for which the appropriate condition, 9(a) or 9(b), is satisfied. Then: Ri TJ i if Ni is even TJ0 i if Ni is odd

:

11

2.3 Calculation of the total fabric loss We are now able to compute the area of fabric loss in the ith piece. This fabric loss may be divided into two parts. The ``external'' loss partly arises from the fact that the fabric width W is greater than the marker width w. A strip of length Li and width (W ± w) will therefore be removed and wasted. Once this strip has been removed, the effective width of the remaining fabric is w. Another source of external loss is at the turns. These are of length g and width w and there are (1 + pi) of them. There is an overlap of length h and width w and, finally, a piece of length xi is cut from the remnant. Gathering all these sources together we see that the area of ``external'' loss is: Li W ÿ w 1 pi gw hw xi w: The ``internal'' loss arises within each splicing interval, and depends on the way the garment pieces are arranged within the splicing interval. Let k be the proportion of fabric loss in the kth splicing interval. The internal loss in the first ply of ith roll is equal to that in the intervals Jiÿ1 1 to n; i.e. n X k Sk w: kÿJ iÿ11

Similarly, the interval loss in the final ply is equal to that in the first J(i) intervals, or J i X k1

k Sk w:

A mathematical model

Finally, the loss in each of the pi complete plies is: n X

k Sk w:

k1

Hence the total area of internal loss is: 8 9 ji n n < X = X X w k Sk pi k Sk k Sk wBi ; say. :kJ 1 ; k1 k1

81

iÿ1

The final expression for the area of fabric loss in the ith roll is therefore: Ai Li W ÿ w 1 pi gw hw xi w Bi w

12

If there are P rolls altogether, the total area of fabric loss over all pieces is: Aw

P X

Ai

i1

W ÿ w

P X

Li gw

i1

P P P X X X 1 pi Phw w xi w Bi : i1

i1

13

i1

Now, since V0 = 0 where Lt is the total length of fabric laid, from (8) P X i1

xi

P X

Li ÿ Ph ÿ m g

P X

P X 1 pi Viÿ1 ÿ Vi

i1

i1

i1

Lt ÿ Ph ÿ m g

P X 1 pi ÿ Vp :

14

i1

Combining (13) and (14) leads to: Aw WLt ÿ mw

P P X X 1 pi ÿ wVp w Bi : i1

15

i1

The total area of fabric is A Lt W ;so the proportion of area of fabric loss is: ( ) P P X X Aw w 16 m 1 pi vp ÿ Bi : 1ÿ at A WLt i1 i1

IJCST 11,2/3

82

3. Conclusion A mathematical equation is proposed based on the consideration of the process of the fabric spreading in garment manufacturing. The equation could be applied to the prediction of fabric loss, subject to the spreading process being handled in a controlled manner and there being no flaws in the fabrics being spread. References Glock, R.E. and Kunz, G.I. (1990), Apparel Manufacturing: Sewn Product Analysis, Macmillan, New York, NY, pp. 395-404. Magowan, J. (1982), ``Optimising material utilisation'', Readywear, Vol. 11, pp. 28-31. Milokhina, V.A., Burlakin, A.I. and Skututa, M.A. (1986), ``Use of a regression model to calculate fabric loss along the length of the overlays'', Tekhnol. Legkoi Prom., Vol. 29 No. 1 (169), pp. 90-1.

Effect of the air on the motion of light fabric (the second paper) The added air mass of a membrane vibrating with fundamental mode

Effect of the air on light fabric

83

Hirokazu Minami

Center for Space Structures Research, Taiyo Kogyo Corp., Osaka, Japan Keywords Fabric, Membranes, Vibration Abstract The property of the added air mass of a plane membrane suspended between supports and vibrating in x-y plane with a half-sine fundamental mode is obtained again by analysis with a normal method which differs from that presented in the last proceedings. The method used in this paper is that based on a source distribution modeling approach (the last method was that based on a vortex distribution modeling approach). The calculated result of a, the ratio of the added air mass to the mass of the membrane itself, is 0.94/(m/(r)), where m is the mass per unit area of the membrane, r the density of the air flow and l the length of the membrane. This result is roughly equal to that obtained from the method in the last paper.

Introduction A membrane vibrating in the air moves the air and produces some flow of the air. Therefore the membrane oscillates as a membrane with virtual mass which is mass of membrane itself plus added air mass. One of the objectives of this paper is to investigate the fundamental property of such added mass by analysis using the method of a normal approach. Another objective is to verify the result of added air mass obtained in the paper in the last proceedings (Minami, 1997). A simple vibration model, which is an initially plane membrane supported horizontally that oscillates in a x-y plane with a half-sine fundamental mode, is used as object for analysis. In order to derive the equation of the velocity potential at any point in the space on and outside of the membrane, a source distribution modeling approach (Lamb, 1921; Sygulski, 1994), that is a method of application of Green's formula, is applied. Finally an equation of the added mass, mf, per unit area of the membrane, corresponding to the kinetic energy of the air flow is derived. A ratio of mf to the mass of the membrane itself is defined as , and this ratio is investigated and calculated in this paper. Comparing the results of , the method presented in the paper in the last proceedings, which is of vortex distribution modeling approach, is verified. The author is very grateful to the highly useful significant discussion with Dr S. Kawamura (Professor Emeritus of Osaka City University), Dr J. Katsura (Professor of Kyoto University), Dr Y. Okuda (Research Associate, Kyoto University), and the author's colleague Dr C. Yamamoto and Dr K.K. Choong.

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 83-89. # MCB University Press, 0955-6222

IJCST 11,2/3

84

In this paper, this paper is called the second paper, and the previous paper (Minami, 1997) in the last proceedings is called the first paper. The method of analysis In the first paper the analytical method for the two-dimensional problem of the added mass of a membrane vibrating in open air has been presented. The analytical method makes use of a vortex distribution model. The method is verified in this paper by comparing the results shown in the first paper and those obtained from a source distribution Green's function approach which is a normal analytical method. It is assumed that the air flow is incompressible and invisible. And it appears that the velocity potential exists in the air flow outside of the membrane. The source distribution modeling and the definition of orthogonal coordinates system are shown in Figure 1. As shown in this figure, a membrane which is initially flat in x-z plane and vibrates with half-sine modes in both xand z-direction with an amplitude, a, is considered. This membrane model has the length, l, in x-direction which is the same as that used in the twodimensional model analyzed in the first paper, and has the length, l, in zdirection which is considered to be sufficiently long. Therefore, it can be considered that the flow condition around the membrane in x-y plane at z = 0.5 l is approximately equivalent to that around the two-dimensional model in the first paper. In this paper, the following expression for the displacement of vibrating membrane is used in place of expression (1) in the first paper: hx; z; t a sinkx sin z cos!t 0 x; l; 0 z l: 1 l A half-space y 0 denoted by V and the boundaries are defined as shown in Figure 1. The boundaries are those at the infinity and on the x-z plane. The boundary on the x-z plane is denoted by S and is divided into five domains which are denoted by Sa1 ÿ1 < x < 0; 0 z l; Sa2 l < x < 1; 0 z l; Sb1 ÿ1 < z < 0; Sb2 l < z < 1 and Sm 0 x l; 0 z l:

Figure 1. Definition of coordinates system, and source distribution modeling with a half-space and boundaries

0

The velocity potential, 0 , at any point, P (x,y,z), in V can be expressed by the well-known equation which is derived from Green's formula: Z Z 1 0 1 0 P ÿ ds 2 2 s @n rP; Q

Effect of the air on light fabric

where dn is an infinitesimal element of the normal, which is drawn always inwards of the half-space, to the surface, S Sa1 ; Sa2 ; Sb1 ; Sb2 ; Sm ; and r is the distance between P and a point, Q, on the boundary, S. For the sake of the simplification of treatment, it is assumed that the upper surface of the vibrating membrane is equal to Sm and that Sb1 and Sb2 are the surfaces of the rigid buffles. Accordingly, the velocity potential in equation (2) can be written as follows: Z l Z 0 Z 1 0 1 @0 1 @ 1 0 P ÿ d d @y @y 2 0 rP; Q rP; Q ÿ1 l

85

Z

l 0

@h 1 d d @t rP; Q

3

where and are integral variables corresponding to x and z respectively. Referring to the function for the change of the velocity which decreases inversely with the distance from the center of a vortex filament, the function for the distribution of the velocity component normal to Sa1 and Sa2 are assumed respectively as follows: 8 1 > 0 < sin z ÿ"1 l x ÿ"2 l @ x l 4a @y sa1 > :0 ÿ1 < x < ÿ"1 l; ÿ"2 l < x < 0 and

8 > 2 0 < sin z l "2 l x "1 l @ x ÿ l l @y sa2 > :0 l < x < l "2 l; l "1 l < x < 1

4b

where "1 and "2 are constants given for the limitation of the domain where the distribution of the normal velocity exists. By this limitation of the domain, 1 and 2 can be determined by equating the mass of the flow passing through Sa1 and Sa2 per unit time to that displaced by the membrane vibration. It will be appropriate to take "1 to be several multiples of l, and "2 to be very small, that is "2 << 1. Thus the determined 1 and 2 could be expressed as 1

!l sin!t ÿ ÿ2 : log"2 ="1

5

IJCST 11,2/3

86

Consequently, by substituting equation (4) and (1) into equation (3) which is the equation for the velocity potential, a flow pattern that is approximately the same as that obtained from the previous vortex distribution model approach in the first paper can be obtained. Because of the symmetry of the flow pattern, the kinetic energy of the air in the total space around the membrane is twice that in the half-space, V. And the total kinetic energy is equal to the kinetic energy of the vibrating membrane with the mass mf , that is the added air mass. Referring to equation (19) in the first paper, this relation can be expressed as follows: Z Z 0 0 @ ds 2 ÿ @n 2 s Z l Z ÿ"2 l Z l Z 11 l 0 @0 0 @ 2 ÿ 0 dd dd @y @y 2 0 ÿ"1 l 0 l"2 l Z

l

Z

0

Z

0

l

0

l

Z

l 0

0

@h ddg @t

2 1 @h dxdz mf 2 @t

6

Define here the added air mass ratio as mf =m

7

as defined in the first paper, where m is the mass per unit area of the membrane and mf is the added air mass per unit area of the membrane. Consequently, by substituting equation (3) and (1) into equation (6), the expression of added mass ratio is obtained as follows: 4 l 3 m=l

ÿ2l log"2 ="1

l

0

Z "2 l Z l 1 sin sin z l l ÿ"1 l x 0

ÿl I1 ÿ I2 Im d dxdz log"2 ="1

Z

Z

l 0

Z 1 Z l sin sinkx sin z l l 0 0

ÿl I ÿ I2 Im d dxdz log2 =1 1

where

Z I1

1 q d x ÿ 2 z ÿ 2

ÿ"1 l

Z I2

ÿ"2 l

l"1 l

1"2 l

and

l 0

sink q d: x ÿ 2 z ÿ 2

This equation shows that depend on m=1; which is called mass ratio, and does not depend on l and the period of vibration. And, since this method is established approximately based on the assumption that the membrane surface is considered to be on Sm in x-z plane, the amplitude does not appear in this equation. Result of numerical calculation In a different way, the amount of mf can be expressed as a height, H, of the air mounted uniformly on the whole membrane. The mass of the entire layer of this air is l H: Dividing H by l, the following nondimensionalized equation involving height of air H could then be obtained: H m : 9 l l Hence, the added mass ratio is expressed as

H =l : m=l

Effect of the air on light fabric

87

1 q d ÿ 1 x ÿ 2 z ÿ 2

Z Im

8

10

The method presented in the first paper can be verified by the comparison between the values of H/l. Figure 2 shows the results of H/l calculated from equation (9) using obtained from equation (8) taking "2 = 0.001. Two relationships, which are between and H/l and between "1 and H/l, are shown in this figure. According to these relationships, it can be seen that 3 is an adequate value for the objective of this paper. And furthermore, although H/l does not show the tendency of convergence with "1 , it will be possible here to adopt the

IJCST 11,2/3

0

5

10

0

25

50

1.0

88 Figure 2. Variation of H/l calculated with varying û or "1

0

value of "1 3 since the actual y-directional velocity of the point at a distance of three times of l from the ends of the membrane will be considered to be sufficiently small. As a consequence it can be said that the result of H/l in the case of the membrane having a sufficient length in z-direction is approximately 0.94. The result of H/l being obtained in the first paper, that is 0.68 (see Appendix. Partial correction to the method in the first paper), can be compared with the above result, 0.94. Although the difference between these results is not small, it can be confirmed, taking into consideration the existence of some approximations taken into the method in this paper, that the method presented in the first paper is appropriate. Conclusions The added air mass of a horizontally supported initially plane membrane vibrating in half-sine fundamental mode is investigated. The method of source distribution modeling approach which is a normal approach, is applied. From studies on the equation of added mass ratio, , and on the results of numerical calculation, the following conclusions are obtained: . .

The added mass ratio uniquely depends on mass ratio m=1. The height of an equivalent air layer corresponding to the added mass distributed uniformly over the membrane is equal to 94 per cent of the membrane length. Accordingly, is given by the expression 0:94=m=1; from equation (10).

The method presented in the first paper is verified by the comparison with the results obtained approximately from the normal approach presented in this paper. References Lamb, H. (1921), ``On the vibration of an elastic plate in contact with water'', Proceedings of the Royal Society (London), Series A, Vol. 98, pp. 205-14.

Minami, H. (1997), ``Effect of the air on the motion of light fabric ± the virtual mass of a membrane vibrating with fundamental mode in air'', Proceedings of 26th Textile Research Symposium at Mt Fuji, August, pp. 80-7. Sygulski, R. (1994), ``Dynamic analysis of open membrane structures interacting with air'', International Journal for Numerical Methods in Engineering, Vol. 37, pp. 1807-23. Appendix. Partial correction to the method in the first paper The author communicates that the description relating to equations (17)-(20) and the calculated result of H/l in equation (12) and Figure 3 in the first paper should be corrected. The corrected description and corrected Figure 3 with revised value of H/l, 0.68, are as follows: From the energy conservation law, it can be stated that the negative change of kinetic energy of the air, ÿdTf ; in an instant dt is equal to the work done on the whole membrane by the force corresponding to p during dt. This relation could be expressed as Z 1 @h pds dt; ÿdTf 18 @t 0 where

Effect of the air on light fabric

89

s 2 @h ds 1 dx at time t: @x

Denote the added mass per unit area of the membrane as mf : That is to say, the total mass of the membrane is m mf . Since the kinetic energy of the membrane vibrating having the mass equivalent to mf can be considered to be equal to Tf , the following equation could be derived: ( Z l 2 ) Z l @ 1 @h @h @ 2 h dTf dx dt mf dxdt: 19 mf 2 @t 2 @t 0 0 @t @t Define the added mass ratio as mf =m:

20

Equating the right-hand sides of equations (18) and (19), and using equation (1), the expression for is obtained.

20

10

1.0 0.5

0

0

0.5

1.0

1.5

0

Figure 3. Relationships obtained by numerical calculations between mass ratio, m=l, and added mass ratio, , and relationships between mass ratio and H =l

IJCST 11,2/3

90

The objective evaluation of blanket hand and durability A preliminary investigation Masako Niwa

Nara Women's University, Nara, Japan

Mari Inoue

Kobe University, Hyogo, Japan

Sueo Kawabata

The University of Shiga Prefecture, Shiga, Japan Keywords Blankets, Fabric Abstract The purpose of this study is to investigate an objective method of evaluating the tactile comfort of blankets by a method of connecting the mechanical parameters of blankets to subjective evaluation. The two methods are preliminarily investigated as follows: (1) Transformation equations for the fabric hand of suiting, KN-101-W for primary hand values and KN-301-W for THV, are applied, with the mean and standard deviation applied to these equations replaced with new values for the blanket population. (2) A new prediction equation is constructed for deriving THV directly from the mechanical parameters and thermal properties. The prediction accuracy of method (2) is a little higher than that of (1). The durability of blankets during repeated use and repeated dry cleaning determined by the initial performance of blankets is also investigated.

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 90-104. # MCB University Press, 0955-6222

1. Introduction Heat insulation function and other utility functions of blanket are, of course, important performance factors of blankets. Blankets are used throughout four seasons and always in contact with human hands and skin. Tactile performance is, therefore, an important component deciding the quality of blankets. In 1972, Kawabata developed an objective hand evaluation system for fabric using the KES-F system (Kawabata, 1980; Kawabata and Niwa, 1989) and the objective evaluation of many kinds of fabrics were studied; however, the hand of blankets has not been investigated yet. Even for subjective evaluation of blankets, there are no hand expressions that can be commonly used for blankets. In this study, the equations KN-101-W and KN-301-W which have been derived for evaluating hand values (HV) and total hand value (THV) respectively for suiting are applied to the THV evaluation of blanket as the first step in our study. This method is based on the assumption that there is similarity in the good tactile feeling between suiting and blanket. Second, a new prediction equation is constructed for deriving THV directly from the mechanical parameters and thermal properties. Also, the durability of blankets during repeated use and repeated dry cleaning is an important performance factor for consumers. The durability from the initial performance of blankets was investigated.

2. Experiment (1) Subjective assessment The structural characteristics of 78 commercial blankets were used in this study and listed in Table I. The blankets are composed of various kinds of fibers and have various structures designed for either home use or business use. All samples (N = 78) were assessed in regard to the hand touch feeling of blankets by 39 judges from blanket manufacturers and consumers. The judges were asked to rank these blankets on a scale of 1 to 5 of THV (total hand value, i.e. hand quality), representing a range from unsatisfactory to excellent hand. This score is called the subjective THV in this paper. Subjectively assessed THV and the objectively derived THV were correlated and a high quality zone of the characteristics value was derived. The mean values of the correlation coefficients R, both between the subjective THV of each subject and the mean THV values of each group (manufacturer and consumer group), and the mean THV of all subjects for the 78 blanket samples, are shown in Table II(i). This table also shows the mean values of the standard deviation for 78 blanket samples, on subjective THV of Fiber and structure Wool Cotton Acrylics Others** Weave Mayer New mayer Tufted Sum

Number of sample (Ratio %) 19* 16 39* 5 30 26 1 11 78

(24.4) (20.5) (50.0) (5.1) (38.5) (33.3) (14.1) (14.1) (100.0)

Mean (mm)

Thickness

9.9 7.4 15.0 8.9 8.0 16.6 13.9 9.4 11.9

(i) Correlation within a group R** 0.756 SD*** 0.86 (ii) Correlation between groups Manufacturer/consumer

91

Weight Mean CV (mm) (%)

CV (%) 50.6 28.5 28.1 18.9 29.6 30.2 18.6 20.7 43.0

60.1 48.0 77.4 49.4 57.7 80.4 66.3 52.3 65.7

17.9 24.2 22.9 20.9 22.5 28.9 9.3 8.2 29.2

Notes: * 3 samples for business use are included; ** Linen, silk and promix; CV coefficient of variation

Manufacturer (n* = 14)

Objective evaluation of blanket hand

Consumer (n = 25)

All (n = 39)

0.773 1.02

0.747 0.99

Table I. Details of commercial blanket samples (19931995)

R

0.891

= average of correlation between average and each Notes: * n = number of subjects; ** R = average of hand value on subjective hand value of subjects for 78 samples; ***SD standard deviation on subjective hand value of subjects for 78 samples

Table II. Correlation within or between groups of subjective hand value

IJCST 11,2/3

92

each subject to clarify the dispersion of evaluation among the judges for the hand assessment of the touch feeling of blankets. The correlation coefficients are indicating a high level of correlation among the groups. The correlation between the mean THV value of judgments by the group of manufacturers and those by the group of consumers is higher than the correlation within each group as shown in Table II (ii). We used the average value of the scores of all 39 judges in the analysis. (2) Measurement of physical parameters Because of the blanket property, a limited number of mechanical parameters was applied to the characterization. The testers used here are KESF-3 and KESF-4; however, a modified KESF-3 of the compression tester (Kawabata, 1988) and KESF-4 of the surface tester (Kawabata, 1988b) were used. The maximum pressure was 50gf/cm2 and the compression speed was 0.2mm/sec and compression plate 25cm2 was used, because it was assumed that the compression properties of thick blankets are affected by the periphery of the compression plate. The contact area of friction sensor was changed from original 55mm to 1010mm. The characteristic property values and the measurement conditions for these parameters are listed in Table III. In addition, the effect of the thermal/water/air transport properties on THV was investigated. The ambient conditions for all measurements were 20 C, 65 per cent RH. 3. Objective evaluation assessment (1) Application of transformation equations for the fabric hand of suiting It has been found for some materials that the equations deriving hand values (HV) and total hand value (THV) for men's winter suiting are applicable to these materials for estimating the THV. In the application of the equation, HVs (KOSHI, NUMERI, FUKURAMI) of blanket were derived by the equation KN101-W developed for men's suiting. Mechanical parameter is normalized by the and standard deviation j of the new population of blankets, mean Xj HVi C0i

8 X j1

Cij

j Xj ÿ X j

1

where: HVi is the value of the primary hand factor (i = 1,2,3). There are three equations, one each for KOSHI, NUMERI, and FUKURAMI. C0i ; Cij ; constant coefficients, which are different for each primary hand factor, as shown in Table IV (eq. KN-101-W). (i = 1,2,3. j = 1 to 8, meaning the number of mechanical parameters, that is, surface and compression parameters, thickness and weight.) Xj ; jth physical parameter (j = 1 to 8). j ; mean value of the mechanical parameters for 78 blanket samples (j = 1 to 8). X j ; standard deviation of the mechanical parameters for 78 blanket samples (j = 1 to 8).

Blocked properties Compression

Symbol LC WC RC

Surface

Thermal/ water /air transport

Linearity of compressionthickness curve Energy in compression fabric under 50gf/cm2 Compression resilience

Unit ± g.cm/cm2 (= N/m)

Coefficient of friction

±

MMD

Mean deviation of MIU Geometrical roughness

±

T W

Fabric thickness at pressure 0.5gf/cm2 Mass per unit area

Measurement condition Maximum pressure; Pmax = 50g/cm2 Plate area 25cm2

Objective evaluation of blanket hand 93

%

MIU

SMD Thickness and weight

Properties

m

20 parallel steel pianowires 0.5mm diameter and 10mm length. Contact force; 50g Contact force; 10g

mm mg/cm2 (= 10-1N/m2)

Qd

Heat dissipation (dry method)

W/(m2.K)

Area; 100cm2, T; 10 C, Air velocity; 30cm/sec

Qw

Heat dissipation (wet method)

W/(m2.K)

m

Evaporation rate of contact method

C0

Transient heat flow

g/(m2.sec) (= 10-2N/ (m2.sec)) 1/sec

Area; 100cm2, T; 10 C, Air velocity; 30cm/sec m Qw ÿ Qd =b; b; 2,422J/g at 30 C

K

Thermal conductance

W/(m)2.K)

TR AR Pf

Thermal resistance Air resistance Packing factor

m2.K/W kPa.sec/m %

A

Apparent density

g/cm3 (= 104N/m3)

Contact force; 5g/cm2 Contact speed; 1cm/ sec Contact force; 5g/cm2, Area; 50.2cm2, T; 10 C TR 1=K Pf W =T:; ; Fiber density A = (W/T)/100

In case of men's suiting, the 16 mechanical parameters are applied; however, we applied only compression property parameters, surface property parameters and construction parameters. The equation KN-101-W for suiting was constructed by applying a stepwise method and this enabled us the partial use of the parameters.

Table III. Measurement condition and physical parameters

IJCST 11,2/3

After the calculation of HV of the three primary hand values, then THV was calculated by applying KN-301-W for these samples: THV C00

3 X

Zi

2

i1

94 Zi C1i

HVi2 ÿ M2i HVi ÿ M1i C2i : 1i 2i

3

where: HVi ; is the value of the primary hand factor (i = 1,2,3). C0i ; C1i ; C2i ; constant coefficients, which are different for each primary hand factor as shown in Table V (eq. KN-301-W) (i = 1,2,3). M1i ; mean value of primary hand for 78 blanket samples (i = 1,2,3). 1i ; standard deviation of primary hand for 78 blanket samples (i =1,2,3). M2i ; mean value of the square of primary hand for 78 blanket samples (i =1,2,3). 2i ; standard deviation of the square of primary hand for 78 blanket samples (i =1,2,3).

Table IV. Constant parameters of the equation for converting mechanical parameters into primary hand values HVs for men's winter suiting (KN-101-W, n = 214) Table V. Constant parameters of the equation for converting from the fabric characteristic values HVs to the men's winter suiting fabrics THV (eq. KN301-W, n = 214)

Comp. Surf. Const.

j

Xi

1 2 3 4 5 6 7 8

LC logWC RC MIU logMMD logSMD logT logW

Blankets (N = 78) i i X 0.5383 0.8605 48.100 0.3952 ±2.0490 0.3562 1.0343 1.8049

0.0644 0.2557 5.3329 0.0797 0.1183 0.1946 0.1945 0.1198

KOSHI i=1 C01 5:7093 C1i 0.0073 ±0.0646 ±0.0041 ±0.0254 0.0307 0.0009 ±0.1714 0.2232

NUMERI UKURAMI i=2 i=3 C02 4:7533 C03 4:9799 eq. KN 101-W C2i C3j ±0.1703 0.5278 0.0972 ±0.1539 ±0.9270 ±0.3031 ±0.1358 ±0.0122

-0.2042 0.8845 0.1879 ±0.0569 ±0.5964 ±0.1702 0.0837 ±0.1810

Source: Kawabata (1980)

i

HVi

1 2 3

KOSHI NUMERI FUKURAMI

eq. KN-301-W C1i C2i 0.6750 ±0.1887 0.9312 C00 = 3.1466

Source: Kawabata (1980)

±0.5341 0.8041 ±0.7703

M1i 5.7093 4.7533 4.9799

Blanket (n = 78) M2i 1i 32.6327 24.4984 26.4127

0.1926 1.3890 1.2784

2i 2.1660 11.7438 12.0376

In the case of blankets, the mean value Mi and standard deviation i are estimated from the value for each parameter from all 78 blanket samples. The mean value and standard deviation of all 78 blanket samples and the constant coefficient of eq. KN-101-W are shown in Table IV, and the mean value and standard deviation of the primary hand calculated by eq. KN-101-W for 78 samples and the constant coefficient of eq. KN-301-W are shown in Table V. The evaluation ability of the KN-301-W equations for blanket material is shown in Table VI. There are fairly high correlation and standard deviation of the predicted THV error are smaller than standard deviation of the individual subjective THV. These results prove the applicability of the evaluation of the hand touch feeling of blankets calculated by eq. KN-301-W. Almost all samples are within the scatter zone of the evaluated dispersion of the 39 judges. Figure 1 shows the desirable range for the primary hand HV and THV for high quality men's winter suiting. This high quality zone is shown by the shaded area. The THV and corresponding HV of the blanket samples that have good subjective THV (> 4) and lower THV(< 2) are shown on this chart. All of the high THV blanket samples fall in the good zone defined for suiting. These All samples (N = 78)

Acrylics (N = 39)

Wool (N = 19)

Cotton (N = 16)

Correlation coefficient R between individual judges and group mean (39 judges) of THV

0.75

0.85

0.71

0.80

Standard deviation of subjective THV

0.99

0.84

0.97

0.84

Correlation coefficient R between the objective THV and subjective group mean of THV

0.65

0.80

0.64

0.31

Standard deviation of the predicted error

0.74

0.55

0.87

0.94

Objective evaluation of blanket hand 95

Table VI. Correlation coefficient of subjective hand value and objective hand value

Figure 1. The hand chart for men's winter suiting fabrics with high THV. The sample of the higher THV (> 4) (Shown by * symbol) and lower THV (< 2) (shown by * symbol) are clearly separated by the primary hand. The shaded zone shows high quality zone

IJCST 11,2/3

96

results prove the applicability of the evaluation of the hand touch feeling of blankets calculated by eq. KN-301-W. It is found that the hand values of the blankets that were evaluated as having good touch feeling fall commonly in the zone of good hand values for men's winter suiting. (2) Derivation of a new prediction equation using physical parameters Figure 2 shows the physical parameters of the samples with higher THV, lower THV, and the zone of the higher THV (> 4). The scale of the horizontal axis is normalized by the standard deviation of each corresponding hand value of the blanket population. Higher and lower samples are separated by hand values, such as by the surface properties MMD, SMD, compression energy WC and thermal properties. This shows that the physical parameters can also separate the good and poor hand of blanket. The equation for THV which was derived from these physical parameters were investigated. In order to investigate the relative influence of objective properties on the subjective values, step-wise block regression analysis was used. This technique has been developed and successfully used to convert physical data into hand values (Kawabata, 1980). The objective evaluation equation for the hand touch feeling of blankets was derived by using the compression and surface properties, construction properties, and thermal/water transport properties of 66 blanket samples and the rest, 12 samples, were used to investigate the THV prediction ability. The parameters were grouped in the following four blocks: (1) compression; (2) surface; (3) construction; and (4) thermal/water transport properties. We used heat dissipation during water transport Qw , and thermal conductance K in developing the new equation, as thermal/water transport properties because air resistance, packing factor and apparent density are closely related to thickness and weight, which are important factors related to blanket construction. We determined the constant coefficient values for the subjective data on blanket quality assessment by applying a stepwise block regression analysis as follows: THV C00

10 X

Yi

4

i1

Yi C1i :

X 2 ÿ m2i Xi ÿ m1i C2i : i s1i s2i

5

Objective evaluation of blanket hand 97

Figure 2. The physical properties of the higher THV (> 4) (shown by * symbol) and lower THV (< 2) (shown by * symbol) blanket samples. The shaded zone shows high quality

IJCST 11,2/3

98

where: C00 ; C1i ; C2i ; constant coefficients, which are different for each physical parameter (i = 1 to k = 8 or 10). the value of the ith physical parameter. Xi ; mean value of physical parameter Xi for 66 blanket samples. m1i ; standard deviation of the physical parameters for 66 blanket samples. s1i ; mean value of the square of the physical parameters for 66 blanket m2i ; samples. standard deviation of the square of the physical parameters for 66 s2i ; blanket samples. In this equation, the contribution of each physical parameter to THV is given by quadratic equation Yi . Table VII shows constant parameters of the equation. The surface properties are entered as the first block in the regression equation and the correlation with the subjective THV is 0.810, showing a high correlation. The accuracy of the regression increases with an increasing number of blocks, and the regression error is 0.460 in addition to construction and compression properties, that is, it shows high regression accuracy. Furthermore, in addition to thermal/water transport properties, the regression error decreases to 0.450 and the regression accuracy increases only slightly. The smaller values of MMD and SMD give better touch feeling. The values of MIU represent the optimum range that contributes to better touch feeling. With blankets, the results show that too much smoothness is not good. Higher values of thickness T and WC of compression properties give better touch

i

Xi

R* (RMS**)

C1i

C2i

0.5979 ±5.5725 ±0.7383

±0.1514 ±4.5772 1.0765

0.810 (0.541)

0.3928 ±2.0510 0.3711

0.1608 4.2210 0.1751

0.0811 0.1207 0.1949

0.0691 0.4811 0.1835

Construction (Step 2) 7 log T 0.7515 8 log W ±7.0497

±0.8452 7.1707

0.852 (0.482)

1.0515 1.8082

1.1421 3.2843

0.1922 0.1223

0.3929 0.4422

Surface (Step 1) 4 MIU 5 log MMD 6 log SMD

Compression (Step 1 LC 2 log WC 3 RC

3) 0.3020 0.7041 ±1.6167

m1i

m2i

s1i

s2i

±0.4324 0.866 0.5432 0.2995 0.0673 0.0721 ±0.6117 (0.460) 0.8874 0.8489 0.2498 0.4312 Table VII. 1.5879 48.171 2,344.7 4.9555 471.07 Constant parameters of the equation for Thermal property (Step 4) converting physical 0.873 1.0913 1.2021 0.1064 0.2357 9 log Qw ±0.0036 ±0.1795 values into THV for 10 log K ±0.6568 0.8692 (0.45) 0.8162 0.7118 0.2152 0.3834 blankets, and the 3.0802 C00 contribution of physical Notes: * Correlation coefficient between regressed and experimental values; ** Root mean properties to THV square of regression error (n = 66)

feeling. Smaller values of thermal conductance K and heat dissipation Qw give better touch feeling. The results show that higher thermal insulation gives good touch feeling in the case of blankets; however, the values of these thermal/ water transport parameters represent the optimum range and higher values for these parameters give good touch feeling. It might be considered that cotton and linen blankets which are used in summer have an influence on these results. The prediction ability of the objective evaluation for the hand touch feeling of blankets is shown in Figure 3. Figure 3 shows the relationship between subjective THV and THV calculated by physical parameters of three mechanical property blocks (a) and (b) four physical property blocks and (c) eq. KN301-W as reference for the 12 samples. This shows that the touch feeling of blankets can be adequately predicted by physical parameters, for which the calculated error is a little higher than the regression error of the 66 samples. It might also be considered that the hand touch feeling of blankets can be adequately evaluated objectively by using only the measured parameters for the surface and compression properties, thickness and weight directly comparing Figure 3 (a) and (b), or by using eq. KN-301-W, as shown in Figure 3 (c). The prediction accuracy of the new equation was almost the same as the accuracy of the prediction by KN-101 and KN-301 equations. 4. The durability of blanket quality The primary performance requirement for blankets is essentially good thermal insurance, but because modern environment controls now make it possible to maintain a comfortable room temperature, it is important for blankets to also have good tactile performance, good touch sensation and good thermal comfort properties, such as the warmth or coolness of a fabric surface. In a study of blankets, Mizunashi et al. (1962) indicated that the important requirements in the end-use performance of blankets were good thermal insulation in winter, good air permeability in summer and good touch feeling overall. This information is based on the results of a 1962 questionnaire completed by consumers and blanket mills experts (Mizunashi et al., 1962). We examined the fatigue phenomena in blankets caused by repeated use or repeated dry cleaning and how it affects blanket dimensions, compression and surface properties and thermal/water/air transfer properties. (1) Experimental The 12 samples in Table VIII were used for the durability test. The selected samples are composed of various fibers for home use and business use with comparatively high THV and low THV. Repeated loading of compression by repeated use. General tendencies for changes in the physical properties of blankets that occur with repeated use were examined, that is, repeated loading of compression on sheet blankets under what we supposed were the most severe condition under which blankets would be

Objective evaluation of blanket hand 99

IJCST 11,2/3

100

(a)

Figure 3. The relationship between subjective THV and calculated THV

(b)

(c)

used. The arrangement of samples was decided statistically. A 52kg subject lay down on the samples of blankets, and repeated compression was applied during 50 days at an average of seven hours per night. After subjecting the samples to the loading of compression, we measured the compression and surface properties and the construction of the samples, and, after the repeated compression for 50 days, we measured the thermal/water/air transport properties. Repeated dry cleaning and washing. Cleaning conditions used for commercial dry-cleaning and commercial washing. The blankets composed of acrylic or wool were subjected to only dry-cleaning, and those composed of cotton were subjected to dry-cleaning or washing. After each number of cyclic cleanings, the samples were kept in ambient room conditions of 20 C, 65 per cent RH for 24 hours, and the dimension, compression and surface properties of samples were measured. After the ten cyclic cleanings, the thermal/water/air transport properties of the samples were also measured.

Sample number

Fiber

Structure

For home S1 S2 S3 S4 S5 S6 S7 S8

Acrylic Acrylic Alpaca Cashmere Wool Cotton Cotton Cotton

New Mayer Mayer Weave Weave Weave Tufted Weave Weave

For business G1 G2 G3 G4

Acrylic Acrylic Wool Acrylic 70*/Wool 30*

Weave Weave Weave Weave

Thickness (mm) eq. KN301-W THV 10.80 21.00 6.43 6.66 16.80 9.00 5.20 4.81

4.83 3.76 3.87 3.48 2.93 3.09 2.20 2.99

8.67 8.61 8.19 8.33

2.43 1.94 2.62 2.00

Note: * Blend ratio (%)

(2) Durability of blanket hand Figures 4 and 5 show the change of the objective THV after repeated use and repeated cleanings. The THV calculated with eq. KN-301-W of almost all samples increased in relation to the increased NUMERI. However, the durability of acrylic blankets (S1) that have high THV in the initial state is low because MMD and SMD increase and THV decreases. The initial performance of all samples goes down because KOSHI increases, NUMERI decreases, and the THV decreases after repeated cleanings. The change in the width of acrylic blankets for business use which have low THV in the initial state that occurs after repeated cleanings is smaller than the change that occurs with acrylic blankets for home use. Because the change in the acrylic blankets for business use which have woven structure is small, the initial performance of the acrylic blankets which have mayer and new mayer structures with deep piles decreases easily due to the change in the shape of the piles, which become dense and form a type of arc. It is assumed that the initial performance of the cotton blankets decreases because repeated cleanings make cotton fibers shrink. The change in cotton blankets after repeated washings is less than that after the repeated dry cleaning, and there are only small changes in the compression and surface properties after repeated washings. The THV of wool and cotton blankets increases slightly in connection with the decreased thickness, the decreased thermal insurance, the decreased surface properties and the increased compression resilience. However, the total hand value THV of acrylic blankets which have high initial THV decreased in relation to the increased surface properties, nevertheless, the thickness decreased slightly. The THV after repeated dry cleanings decreased in connection with the change of surface properties and surface roughness. We found that the durability of the blankets for business use that have low initial THV is superior to the durability of blankets for home use.

Objective evaluation of blanket hand 101

Table VIII. Sample details for durability test

IJCST 11,2/3

102

Figure 4. The change in objective THV after repeated loading

Objective evaluation of blanket hand 103

Figure 5. The change in objective THV after repeated dry cleaning

IJCST 11,2/3

104

5. Conclusions An objective method of evaluating the tactile comfort of blankets was investigated, a method of connecting the mechanical parameters of blankets to subjective evaluation. The two methods were investigated as follows: (1) Transformation equations for the fabric hand of suiting, KN-101-W for primary hand values and KN-301-W for THV, are applied, with the mean and standard deviation applied to these equations replaced with new values for the blanket population. (2) A new prediction equation is constructed for deriving THV directly from the mechanical parameters and thermal properties. The mechanical parameters applied to both methods (1) and (2) are limited in number because of the blanket property. They are the parameters of the surface properties and compression property. The testers used in this measurement were a modified and simplified KESF-3 compression tester and a modified KESF-4 surface tester. The result shows that the prediction accuracy of method (2) is a little higher than that of (1). The prediction ability of these two methods was fairly good considering the scattered evaluation by the judges in their subjective evaluations. The correlation between the evaluations by (1) and (2) were high. The contribution of the thermal properties of blankets to the tactile comfort was small. This investigation is a preliminary investigation and we are still seeking the simpler equation for predicting the quality of blanket. The durability of the hand property of blankets for business use and home use was investigated and the durability of business use blankets was higher than that of home use blankets. References Kawabata, S. (1980), The Standardization and Analysis of Hand Evaluation, 2nd ed., The Hand Evaluation and Standardization Committee and The Textile Machinery Society of Japan. Kawabata, S. (1988a), Development of a Fabric Property Measurement ± Part I: A New Compression Tester, a report of research project (Grant-in-aide for Co-operative research (A), ``Application of the objective measurement of fabric property to the design of fabric quality'', head investigator Niwa, Masako, Project No. 61300012, supported by Japanese Scientific Fund), pp. 156-161. Kawabata, S. (1988b), Development of a Fabric Property Measurement ± Part II: A Surface Analyzer, a report of research project (Grant-in-aide for Co-operative research (A), ``Application of the objective measurement of fabric property to the design of fabric quality'', head investigator Niwa, Masako, Project No. 61300012, supported by Japanese Scientific Fund), pp. 162-165. Kawabata, S. and Niwa, M. (1989), ``Fabrication performance in clothing and clothing manufacture'', J. Text. Inst., Vol. 80, pp. 19-50. Mizunashi, S., Tsujii, Y. and Niwa, M. (1962), ``Studies on the end-uses requirements for blankets part 1 and part 2'', J. of the Jpn. Res. Assn. Text. End-Uses, Vol. 3, pp. 33, 38, 205,212.

Optimisation of shirt fabrics' composition from the point of view of their appearance and thermal comfort

Optimisation of shirt fabrics' composition 105

Lubos Hes

Liberec, Czech Republic Keywords Cotton, Fabric, Optimisation Abstract In this paper, the thermal contact comfort of a suddenly wetted shirt and some selected mechanical parameters of ten various woven shirt fabrics were measured with the aim of determining the effect of their composition on their complex quality level. In order to explain the thermal contact comfort of superficially wetted shirts, a new parameter called moisture absorptivity was introduced and a simple equation of the moisture transfer between the fabric and skin was derived. Since the direct measurement of the moisture absorptivity is complicated, an indirect method for its experimental determination was described and used for evaluation of thermal comfort. As regards the final complex evaluation of the measured shirt fabrics, it was found that shirts containing 25-40 per cent of classical PES fibres blended with cotton, compared with non-treated pure cotton shirts, have shown similar or even better water vapour permeability, fairly warmer feeling in dry state, better shear, fairly better ability to keep the form and a bit lower moisture absorptivity (worse thermal contact comfort feeling in the case of superficial wetting). Moreover, thermal comfort properties may be still improved by the application of special modified PES fibres.

1. Introduction Many people prefer to wear 100 per cent cotton shirts, because they consider their thermal and sensorial comfort better, especially on hot days, in spite of the common experience, that shirts containing even a small portion of PES fibres exhibit fewer wrinkles, show a smooth surface and can be easily ironed. Wearers generally believe that the higher thermal comfort of pure cotton shirts is due to their higher water vapour permeability compared with shirts made of PES/cotton blends. In order to explain the effect of this parameter, water-vapour permeability of both kinds of shirts was measured in this study. From the measurements made on the Permetest (Sensora) instrument it was found that water-vapour permeability of the measured shirts depends more on their mass per area than on their composition, and that in all cases the relative vapour permeability was very good and exceeded 15 per cent. The next parameter in question is the moisture sorption capacity (absorbency) of shirt fabrics. There are plenty of methods to measure this parameter (Chatterjee, 1985). Nevertheless, the moisture absorbency characterises just the specific moisture retention corresponding to the state of full saturation of the fabric volume by water or sweat, and is directly proportional to the fabric mass. No transient aspects are considered here, and

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 105-115. # MCB University Press, 0955-6222

IJCST 11,2/3

106

no different boundary conditions of moisture transmission between the skin and a fabric are set. Therefore, Cheurell et al. (1985) highlighted the importance of studying the dynamic surface wetness of fabrics, and developed a new method for its determination, which is based on humidity dependent colour changes of a special chemical agent deposited on the fabric surface. In their study, a cotton fabric freely exposed to saturated water-vapour increased its surface humidity two to three times faster then a PES fabric of similar parameters. The authors concluded that the dynamic surface wetness is a very important factor influencing the clothing comfort of garments. A survey of other techniques to measure transplanar liquid transport into fabrics was published (Kissa, 1996). Nevertheless, all the measuring methods are not suitable for simple standard measurement of transient fabric wetting, due to quite complicated preparation of the measurements, poor dynamic properties of some methods or to inexact results evaluation in some cases. Moreover, the reduced comfort caused by wearing the PES/cotton shirts on hot days is felt mainly at the moment when the suddenly wetted fabric touches the skin. Consequently, the cool feeling occurs, which is considered as unpleasant. Within the contact time, heat is transferred by conduction through a thin intermediate layer, created by humid outstanding fibres. Thus, the boundary condition approximates to the heat transfer of first order, which should be respected within a measuring method in question. Therefore, the first objective of the research work was to develop a method of an indirect experimental determination of the so-called surface moisture absorptivity B (Hes, 1998), whose higher level apparently increases the contact comfort of wet fabrics and the opposite. A new measuring method described in the paper is easy and reproducible, and reflects the real moisture and heat transfer conditions between the fabric and the skin. The second objective of the study was to compare the selected mechanical properties of ten shirt fabrics of different composition, ranging from 100 per cent treated or non-treated cotton to blend containing 70 per cent of PES staple fibres. The measured properties should reflect the ability of the fabrics to resist creasing, to display a smooth surface, and to offer good handle. Therefore, the following parameters were measured and statistically treated: angle of crease recovery ', bending rigidity B and bending hysteresis 2HB, shear resistance G and shear hysteresis 2HG and 2HG5. Bending and shear parameters were measured by means of the Kawabata evaluation system. 2. Theoretical part 2.1. Introduction of moisture absorptivity The amount of liquid inside any porous structure or textile fabric can be expressed in terms of the fabric free volume saturation s (Chatterjee, 1985). Thus, for s = 0 the fabric is dry, and for s = 1 all the pores are full of a liquid.

In this case, the saturation propagation within a fabric, either along its surface, but also perpendicularly to its surface, can be characterised by the classical partial differential equation of diffusion processes: @s=@ A@ 2 s=@x2

1

where A [m2/sec] is so called moisture diffusivity. This parameter is for textile fabrics sometimes moisture dependent due to swelling. The solution of equation of this kind for A = const is generally known. If we consider just short time moisture conduction, then we can convert a textile fabric to a semi-infinite body, where the first order boundary condition is applied. In this case, the moisture saturation propagation in the x direction is given by the equation s erfcx=2A1=2 1=2 :

2

The experimental determination of the moisture diffusivity from the moisture propagation along the measured fabric is possible. Unfortunately, the moisture diffusivity in this form does not characterise the volumetric capacity V of the fabric expressed in this case in m3/m2s to conduct the moisture (sweat) from the contacted skin away towards a fabric interior. To cope with this task, a Darcy law modified for the saturation gradient should be introduced as follows: V ÿ s @s=@x

3

where s m2 =s is the volumetric moisture flow conductivity, which is proportional to the fabric permeability. In the next step, we should remind, that in the first Fick's diffusion law, which is used to express the mass flow in the form formally identical with Equation (3), the same diffusion coefficient D occurs, as in the second Fick's law for transient mass transfer by diffusion. By simplifying the problem solved to a simple diffusion, we can express the moisture flow conductivity in Equation (3) s by means of the moisture diffusivity A. From applying this relation in equation (2) follows: V A1=2 s=1=2 1=2 :

4

The first term in this equation fully characterises the fabric's ability to absorb the moisture from any moist surface which contacts the fabric. Then this so called moisture absorptivity B [m3 s1/2] is defined by the next relation: B A1=2 :

5

As shown in Kissa (1996), many researchers have already measured the timedependent longitudinal wicking of fabrics. From these results, the moisture diffusivity A could be determined and its square root used for the calculation of the spontaneous moisture uptake according to equation (4). Some research work in this field is going on at the MINHO University (Hes, 1998). Nevertheless, this approach may produce inaccurate results, since longitudinal

Optimisation of shirt fabrics' composition 107

IJCST 11,2/3

108

wicking rates do not always correlate with the corresponding transplanar ones, due to the complexity of the wicking processes, which besides the diffusion processes include capillary penetration of moisture inside fabrics, and also moisture absorption of the fibre surface. Therefore, the goal of this paper is to develop a technique, which would determine not the moisture absorptivity itself, but its real impact on the comfort properties of a surface wetted fabric. To achieve this, an indirect way was chosen, as explained in the next section. 2.2. Indirect method of the moisture absorptivity measurement The suggested method is based on the objective evaluation of warm-cool feeling perceived by a wearer of clothing, which suddenly comes into contact with a wetted skin. In this moment, the cotton fabric absorbs the liquid sweat rapidly, and conducts it away from the fabric surface. If the amount of sweat is not too high, within a short time the moisture concentration close to the internal fabric surface reduces, and the wearer feels again the pleasant contact of nearly dry fabric. In the case of blended fabrics containing too much PES fibres, the sweat keeps adhered on the skin, and provokes an unpleasant cool feeling due to sweat evaporation. The suggested method is based on the objective evaluation of cool feeling effect within an experimental procedure which simulates the real fabric wearing conditions described above. Before the method is explained, the instruments for the objective warm-cool feeling determination are described. 2.2.1. Instruments for the evaluation of thermal contact feeling of textile fabrics. The first instrument, which was able to evaluate the ``warm-cool'' feeling of fabrics objectively, was developed by Yoneda and Kawabata in 1983. They also introduced the maximum level of the contact heat flow qmax [W/m2K] as a measure of this transient thermal characteristic, and Kawabata has published the first objectively determined values describing the transient thermal-contact properties of textile fabrics, which occur, when we put on the undergarment, shirts, gloves or other textile products, both in dry or in wet state. Since this feeling strongly affects the buyer's choice when buying the clothes or garments, the objective assessment of this feeling became very important in the last decade. The instrument called THERMO-LABO used in this research is commercially available and became used in laboratories. Some years later (Hes, 1987) another instrument for the objective evaluation of warm-cool feeling of fabrics, but of a different concept, was developed at the Technical University in Liberec, Czech Republic. This computer controlled instrument called ALAMBETA calculates also all the statistical parameters of the measurement and by means of an autodiagnostic programme checks the measurement precision and avoids any faulty instrument operation. The whole analysis, including the measurement of thermal conductivity , thermal resistance R, qmax, sample thickness and the results evaluation, takes less than three minutes. As the objective measure of warm-cool feeling of fabrics, the so called thermal absorptivity b [Ws1/2/m2K] was introduced (Hes, 1987).

This parameter (formerly used in civil engineering and health protection sciences) was derived similarly to the moisture absorptivity above mentioned (Hes, 1998). Provided that the time of thermal contact between human skin and a fabric is short, textile fabric was again idealised to be a semi-infinite body of finite thermal capacity c [ J/m3] and initial temperature t2. Transient heat flow q between human skin of temperature t1 and a fabric is then given by the following relation: qdyn bt1 ÿ t2 =1=2 :

Optimisation of shirt fabrics' composition 109

6

Thus derived thermal absorptivity b [Ws1/2/m2K] is given by the following relation: b c1=2 :

7

The simplified scheme of the instrument is shown in Figure 1. The principle of this instrument depends on the application of a direct ultra-thin heat flow sensor, which is attached to a metal block with constant temperature which differs from the sample temperature. When the measurement starts, the measuring head containing the mentioned heat flow sensor drops down and touches the measured sample, which is located on the instrument base under the measuring head. At this moment, the surface temperature of the sample suddenly changes (i.e. the boundary condition of first order is worked out), and the instrument computer registers the heat flow course. Simultaneously, the sample thickness is measured. All the data are then processed in a computer according to an original program which involves the mathematical model characterising the transient 1

2

3

8

H

9

6

4

5

7

Key (1) measuring head (2) copper block (3) electric heater (4) heat flow sensor (5) measured sample (6) instrument base (7) head lifting mechanism (8) resistance thermometer (9) wetted textile interface simulating sweat discharge

Figure 1. Functional scheme of the ALAMBETA instrument for measuring thermal insulation and thermalcontact characteristics of flat textiles

IJCST 11,2/3

110

temperature field in a thin slab subjected to different boundary conditions (Hes and Dolezal, 1989). To simulate better the real conditions of warm-cool feeling evaluation, the measuring head is heated to 32 C, which corresponds to the human skin temperature, while the fabric is kept at the room temperature 22 C. The instrument is commercialised by the SENSORA Company in Czech Republic. The validity of the thermal absorptivity as a parameter expressing the warm-cool feeling of fabrics was confirmed by several tests where the results of subjective feeling of nearly 100 persons were compared with the values of thermal absorptivity found by means of the ALAMBETA instrument. During this experiment, the subjective and objective levels of ``warm-cool'' feeling of nine woven samples of similar structure, thickness and weight per area, but made of nine different fibres and polymers, were determined. The results were analysed statistically and evaluated by means of the Spearman's Rank Correlation Coefficient, whose level generally exceeded 0.9 (Hes and Prommerova, 1992). During the research projects conducted at the Technical University in Liberec and later at the MINHO University in GuimaraÄes in Portugal the thermal-insulation and thermal-contact properties of all common textile products were experimentally investigated. It was found that the practical values of thermal absorptivity range from 20 to 300, where the lowest (warmest) values were exhibited by non-woven interlinings made from PES microfibres. The thermal-contact feeling of the tested fabrics is strongly affected by their structure and composition. It was found (see Hes and Prommerova, 1992), that fibres and fibre polymers exhibiting higher equilibrium humidity, provide also a cooler feeling. Therefore, the warmest feelings can be achieved with fabrics made from PVC, PP, PAN, whereas viscose, flax, cotton and PAD 6 fibres show the coolest feeling. Which feeling is better depends on the customer: for hot summer garments cooler (cotton) feeling is preferred, whereas in the north of Europe warmer clothing, based on the PES/wool yarns, is preferred. An important aspect of the ``warm-cool'' feeling evaluation is the change of this feeling when the textile product gets wet. Because the time of the warmcool feeling evaluation of samples in the ALAMBETA instrument is very short, the evaluation of humid samples is reliable (the sample does not turn dry during the measurement). Because the thermal conductivity and thermal capacity of water is much higher than those of the fibre polymer and the air entrapped in the textile structure, the ``warm-cool'' feeling of garments moistened by sweat can exceed 1,000. The resulting thermal contact discomfort is generally known. Since the thermal absorptivity is mainly a surface effect, its level can be changed by any superficial or finishing treatment, like raising, brushing and coating, as shown in Hes et al. (1990). By means of this measuring technique, some finishing processes can be controlled and optimised, as well as the garments' structure and composition (Hes et al., 1996).

2.2.2. Methodology of the indirect measurement of the moisture absorptivity of fabrics. The intention of this research was to characterise the contact comfort felt by a wearer of a shirt during a hot day, a special very thin interface fabric was prepared, which should simulate the effect of a sudden sweat discharge on the skin. It was found, that this sweat simulator should be as thin as possible, in order not to influence (in dry state) the thermal capacity of the measured fabric, but this interface fabric should absorb a certain amount of liquid injected in the centre of this interface fabric and mainly it should distribute the liquid fast and uniformly within a circle of approx. 50mm diameter (in order to cover the area 2525 mm of the heat flux sensors). After some trials, a thin (0.1mm) nonwoven fabric containing PP on one side and viscose fibres on the other side was found to fulfil all demands. In order to reduce the amount of liquid, the interface fabric was uniformly perforated. At the beginning of the measurement, the ALAMBETA instrument is switched on and the measured shirt was placed on the measuring base of the instrument. Then, the volume of 0.2ml of water (containing detergent) was injected on the centre of the interface fabric surface, covered by the viscose fibres. Within one minute, the liquid distributed uniformly within a circle of 4550mm, and stopped. When this occurred, this interface fabric was turned the viscose side down and inserted into the space between the measured sample and the centre of the measuring head of the instrument (see the position number 9 in Figure 1). At the same time, the interface fabric and the measuring head of the instrument dropped down towards the measured shirt fabric. Within a few seconds, the liquid from the interface fabric was more (in case of pure cotton shirt) or less (in other cases) taken away by absorption in the lower fabric. In the case of low absorption into the shirt fabric, the thermal capacity of the interface fabric is kept quite high due to higher relative moisture w and the initial level of thermal absorptivity b is significantly higher. When considering a simple parallel combination of thermal conductivity and w of fabric and water respectively, and the same approach for their thermal capacities, the resulting thermal absorptivity btot of wet interface fabric after some simplifications is given by the expression btot w2 b2w ww b2 = 1 ÿ wb2 1=2

8

In the case of measurement of ``warm-cool'' feeling of the wetted pure cotton fabrics, characterised by higher moisture absorptivity, the moisture is rapidly distributed within the whole volume of the fabric, so that the interface fabric gets nearly dry, and the instrument shows a lower level of the resulting thermal absorptivity. 3. Experimental results and their evaluation 3.1. Thermal-contact comfort after sudden wetting The composition of the investigated plane fabrics varied from 100 per cent cotton to 100 per cent PES fibres; in the first case also PP fibres were applied. Medium values of the results are shown in the following Table I.

Optimisation of shirt fabrics' composition 111

IJCST 11,2/3

112

Table I. Cool feeling of various fabrics measured by the ALAMBETA instrument in conditions simulating their wearing on suddenly wetted skin

Sample composition and structure 50% cotton 50% PP smart knit 100% PES knit Du Pont Coolmax 100 % cotton denim 100% cotton shirt 100% cotton shirt 70% cot. 30 % PES woven shirt 35 % cot. 65 % PES woven shirt 75 % cot. 25 % PES woven shirt 35 % cot. 65 % PES woven shirt 100% cotton shirt resin treated

Sample thickness h [mm] 0.66

Thermal Peak value Thermal of heat flux Temperature absorptivity diffusivity conductivity qmax[mW/ b[Ws1/2/ a[m2s] [mW/mK] m2K] m2K] 100

2.20

0.057

421

0.54 0.71 0.43 0.38

97.2 86.2 83.1 90.1

2.29 2.72 2.32 2.61

0.048 0.028 0.027 0.025

443 452 508 565

0.21

78.7

2.41

0.012

731

2.52

0.026

751

2.99

0.010

875

0.28 0.23

120 88.9

0.26

123

2.77

0.017

935

0.22

149

2.68

0.016

1,178

The following conclusions were drawn from these first experiments: . With an increasing portion of PES fibres in common woven shirt fabrics this increases the unpleasant cool feeling (i.e. increases thermal absorptivity) when worn in conditions of surface wetting, which matches the practical experience of wearing the tested shirts. . Special fabrics with improved thermal comfort properties like double layered knits (Hes et al., 1996a) or T-shirts knitted from Coolmax modified PES fibres reveal more pleasant contact feeling in conditions of superficial wetting. . Exceptionally some cotton/PES blend fabrics made from common fibres may exhibit relatively good thermal contact comfort in the wet state, even with a quite high portion of PES fibres, due to some unknown effect or due to a special fabric structure (confirmed by wearers). . Cotton shirt weaves containing too many chemical agents deposited inside the fabric may show worse contact comfort feeling in the wet state, in spite of the fact that their steady-state water vapour permeability stays very high. Lukas (1998) proved that closing-up the finest capillary channels (for example by resins) should reduce the vertical suction height of water in these fabrics (which should result in worse moisture uptake). 3.2. Selected mechanical parameters of the measured shirt fabrics All the mechanical parameters, except the recovery angle, were measured by means of the KES-F instruments. The results are displayed in Tables II and III.

Sample composition and mass per area of selected woven shirt fabrics

Sample thickness h [mm]

100 % cotton + resin treatment, 100g/m2 100% cotton + resin treatment, 121g/m2 100 % cotton, no treatment, 109g/m2 80% cotton 20% PES, 122.1 g/m2 75 % cotton 25 % PES, 152.4 g/m2 70 % cotton 30 % PES, 112.2 g/m2 60 % cotton 40 % PES, 83.0 g/m2 45 % cotton 55 % PES, 96.7 g/m2 40 % cotton 60 % PES, 95.7 g/m2 30% cotton 70% PES, 85.1 g/m2

0.30 (3.1) 0.33 (3.4) 0.34 (3.3) 0.40 (2.2) 0.59 (0.6) 0.40 (1.4) 0.28 (1.6) 0.33 (1.4) 0.32 (2.1) 0.28 (3.4)

Thermal Peak value Air Thermal of heat flux absorptivity b [Ws1/2/ permeability conductivity qmax m2K] [cm3/cm2s] [mW/mK] [mW/m2K] 55.5 (2.1) 68.2 (2.8) 59.4 (1.9) 60.7 (3.1) 63.7 (1.9) 61.3 (1.5) 50.8 (3.2) 50.6 (4.8) 55.6 (2.8) 46.6 (1.6)

1.73 (1.1) 1.79 (4.5) 1.77 (1.1) 1.68 (2.6) 1.55 (3.2) 1.57 (2.6) 1.56 (1.8) 1.49 (1.9) 1.60 (1.6) 1.5 (3.3)

374 (14.1) 418 (3.1) 349 (12.5) 311 (4.5) 282 (13.7) 300 (9.5) 298 (14.0) 270 (6.8) 334 (10.1) 255 (18.0)

13.65 (3.0) 8.02 (4.74) 7.85 (6.04) 9.00 (3.7) 9.40 (4.9) 17.07 (2.4) 19.32 (3.11) 29.45 (4.8) 13.65 (4.6) 14.15 (3.7)

From these second measurements it follows that resin treated cotton fabrics show highest angle of the recovery, but the pure cotton ones show the lowest one, which may drop to 57 per cent of the former maximum value. This undesired situation often appears after several washings of the anti-crease treated cotton shirts. For blend fabrics with 30 per cent of PES fibres or more, the recovery angle keeps fixed at the level of 77 per cent of the mentioned maximum value, independently of the washing applied. As regards the fabric smoothness, the best results were found for the blends with 20 and 25 per cent of PES fibres. The lowest levels of the shear values (highest ability of deformation in the bias direction) was found for the classical blend containing 55 per cent of PES fibres. Nevertheless, all the differences in mechanical properties did not reveal any significant differences among pure cotton and blend fabrics, except the angle of recovery, where the results for the blend fabrics are better and do not reduce with washing. Regarding the thermal properties of the tested samples in dry state, samples containing more PES fibres showed fairly lower thermal conductivity and substantially warmer feeling (up to 60 per cent), than the pure cotton samples. All these results have preliminary character and some measurements should be repeated. Nevertheless, even in this research state the following observations can be presented:

Optimisation of shirt fabrics' composition 113 Table II. Medium values of thermal insulation and thermal contact properties of various fabrics in dry state measured by means of the ALAMBETA instrument. Variation coefficient CV [%] based on four measurements on every sample is presented in parentheses

IJCST 11,2/3

Sample composition and mass per area of selected woven shirt fabrics (mainly in plain weave)

114

100% cotton + resin treatment, 100 g/m2 100% cotton + resin treatment, 121 g/m2 100% cotton, no treatment, 109 g/m2 Table III. 80% cotton 20% Medium weft/warp PES, 122.1 g/m2 values of selected 75% cotton 25 % mechanical properties PES, 152.4 g/m2 of various fabrics 70% cotton 30 % measured by means of PES, 112.2 g/m2 the KES-F instruments 60% cotton 40% (except the angle of PES, 83.0 g/m2 recovery). Variation 45% cotton 55% coefficient CV[%] PES, 96.7 g/m2 based on four 40% cotton 60% measurements on every PES, 95.7 g/m2 sample is presented in 30% cotton 70% PES, 85.1 g/m2 parentheses

.

.

.

Angle of crease recovery [ ] due to EN 22 313 (1992) 15.3 12.5 8.0 6.2 7.7 12.8 7.4 10.7 11.7 10.8

Bending rigidity B [mgf.cm2/ cm]

Bending moment hysteresis 2HB [gf.cm/cm]

Shear rigidity G [gf/cm]

Shear force hysteresis 2HG[gf/cm]

34.4 (12.2) 60.5 (8.2) 46.9 (11.6) 90.7 (6.3) 66.2 (12.7) 21.4 (17.7) 34.6 (5.8) 32.9 (8.1) 41.5 (11.3) 31.7 (8.6)

18.8 (18.5) 35.4 (19.1) 29.7 (18.8) 21.9 (22.9) 70.3 (3.6) 26.1 (5.4) 23.8 (7.2) 15.1 (18.5) 38.4 (4.1) 17.9 13.9)

0.429 (7.2) 0.817 (7.7) 0.908 (6.4) 0.875 (7.4) 1.050 (4.5) 0.925 (9.5) 0.871 (4.7) 0.621 (8.7) 1.211 (4.4) 0.784 (12.8)

0.384 (10.6) 0.850 (5.9) 1.292 (3.1) 1.733 (3.8) 2.592 (2.1) 1.575 (7.5) 1.408 (4.8) 0.642 (8.9) 2.909 (4.8) 1.450 (5.3)

Shirts containing 25-40 per cent of classical PES fibres blended with cotton, compared with non-treated pure cotton shirts have shown similar or even better water vapour permeability, fairly warmer feeling in dry state, better shear, fairly better ability to keep the form and a bit lower moisture absorptivity (worse thermal contact comfort feeling in the case of superficial wetting). Moreover, thermal-comfort properties may be still improved by the use of modified PES fibres. The cotton anti-crease treated shirts compared with the non-treated ones can be characterised by similar water vapour permeability, relatively cool (less pleasant) feeling in dry state, temporary smooth surface, high but temporary form keeping and by substantially lower (less pleasant) moisture absorptivity. Theoretical shirts containing up to 50-70 per cent of special liquid transporting fibres (e.g. Du Pont COOLMAX) may exhibit, compared with pure cotton non-treated shirts, higher water vapour permeability, warm feeling in dry state, smooth surface, good shear, very high ability of form keeping and excellent (most pleasant) thermal contact comfort feeling in the case of superficial wetting (high moisture absorptivity). The importance of the cotton fibres then would depend on their contribution to the lower bending and shear rigidity of the fabric.

4. Conclusions From the first application of the indirect method of experimental determination of the moisture absorptivity, which was described in this paper, it may be concluded that superficially wetted non-finished 100 per cent cotton fabrics show substantially warmer (more pleasant) feeling than those of cotton/PES blends, which correlate with practical experience. Special products like Coolmax knits made of modified PES fibres or double layered cotton/PP knits exhibit the same or even better ``warm-cool'' feeling as the pure cotton woven fabrics. On the other hand, fabrics containing low percentage of PES fibres, may exhibit higher complex quality, due to their better ability to keep the form and easier maintenance, whereas the reduction of their moisture absorptivity might be relatively low. References Chatterjee, P.K. (1985), Absorbency, Elsevier Science Publ., Amsterdam. Cheurell, D.M., Spivak, S.M. and Hollies, R.S. (1985), ``Dynamic surface wetness of fabrics in relation to clothing comfort'', Textile Res.J., Vol. 55, pp. 394-9. Hes, L. (1987), ``Thermal properties of nonwovens'', Proc. INDEX 1987 Congress, Geneva, 1987. Hes, L. (1998), ``A new indirect method for fast evaluation of the surface moisture absorptivity of engineered garments'', Internat. Conference on Engineered Textiles, UMIST, 20-22 May. Hes, L. and Dolezal, I. (1989), ``New method and equipment for measuring thermal properties of textiles'', J. Text. Mach. Soc. Jpn, Vol. 42, T124-8. Hes, L. and Prommerova, M. (1992), ``The effect of thermal resistance and absorptivity of various fabrics on their thermal contact characteristics'', 21st Textile Res. Symp. at Mt Fuji. Hes, L., ArauÂjo, M. and Djulay, V. (1996), ``Effect of mutual bonding of textile layers on thermal insulation and thermal-contact properties of fabric assemblies'', Textile Res., Vol. 66, pp. 245-50. Hes, L., ArauÂjo, M. and Storova, R. (1996a), ```Thermal-comfort properties of socks containing PP filaments'', World Congress on Polypropylene in Textiles, Huddersfield. Hes, L., Dolezal, I., Hanzl, J. and Miklas, J. (1990), ``Neue Methode und Einrichtung zur objektiven Bewertung der thermokontakten Eigenschaften der textilen FlaÈchengebilde'', Melliand Textilber, Vol. 71, pp. 679-81. Kissa, E. (1996), ``Wetting and wicking'', Textile Res.J., Vol. 66, pp. 660-8. Lukas, D. (1998), ``3d Ising model for the Lucas-Washburn equation'', 3rd. Internat. Conference TEXSCI 98, Tech. Univ. of Liberec. Yoneda, M. and Kawabata, S. (1983), ``Analysis of transient heat conduction in textiles and its applications, Part II'', J. Text. Mach. Soc. Jpn, Vol. 31, pp. 73-81.

Optimisation of shirt fabrics' composition 115

IJCST 11,2/3

116

Air permeability and light transmission of weaves

JirÏÂõ MilitkyÂ, Marie TraÂvnõÂcÏkovaÂ and VladimõÂr BajzõÂk

Technical University of Liberec, Department of Textile Materials, Liberec, Czech Republic Keywords Fabric, Light Abstract The main aim of this contribution is characterization of fabric porosity by the light transmission and comparison of this characteristic with air permeability and idealized geometrical structure of selected weaves. For characterization of air permeability the classical apparatus has been used. The transmission of light through fabrics has been measured on the system LUCIA for image analysis. The porosity of textiles has been evaluated from corresponding construction parameters and idealized models of fabric geometry. The dependencies between the above mentioned characteristics were formalized by using regression analysis.

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 116-124. # MCB University Press, 0955-6222

1. Introduction It is well known that air permeability and light transmission through fabrics depend on the many factors starting with geometrical structure. Both properties are apparently very closely connected and can be explained as so called porosity. Porosity has a decisive influence on utilization of fabric for some technical application (filters, sails, parachutes) and clothing application as well. Fabric porosity depends generally on the fabric and yarn construction. Numerous methods have been proposed for porosity measurement. A classical one is based on the investigation of air permeability. Modern systems of image analysis enable the measurement of porosity as transmission of light through fabric. It has been shown that for tightly woven fabrics there exists good agreement between air permeability and interfiber pore volume (porosity) (Robertson, 1950). For open-woven fabrics the correlation between air permeability and construction parameters of fabrics is not so strong. The main aim of this contribution is measurement of fabric porosity by light transmission and comparison of this characteristic with air permeability and idealized geometrical structure of simple weaves. For characterization of air permeability the classical apparatus is selected. The transmission of light through fabrics is measured on the system LUCIA for image analysis. The apparent porosity of textiles is evaluated from corresponding construction parameters and idealized fabrics models. The dependencies between the above mentioned characteristics are formalized by using regression analysis. This work was supported by the Grant GACR 106/99/11844.

2. Evaluation of fabric porosity Air permeability There are a lot of models characterizing the idealized porosity PI from some and light construction parameters of weaves. Classical parameters are sett (texture) of weft transmission DC [1/m], sett of warp DM [1/m], fineness of weft yarn TC [tex], fineness of warp yarn TM [tex], planar weight of weave WP [kg m-2], density of fibers F [kg m-3] and thickness of fabric tW [m]. The idealized arrangement of yarns in fabric is tI dc dm

1

where dC is diameter of weft yarn and dM is diameter of warp yarn. When tW & tI the yarns in fabric are roughly circular. This type of arrangement is assumed in sequel. The idealized circular yarn with the same packing density is simple to compute diameters from relation p 2 TC dC 6 2 10 C p 2 TM 3 dM 6 10 M Here C and M are unknown densities of weft and warp yarns. These densities are combinations of densities of fibers F and air A = 1.175 [kg m-3] according to packing of fibers in yarns. For known packing density M is M = M F and the same relation is valid for weft yarn. The values C and M are therefore function of twist and method used for yarn creation. For the moderate level of twist it has been empirically found that C =F C 0:525 4 and this correction can be imposed to the relations (2) and (3) for computation of dC or dM. For the noncircular yarns we can simply compute the area of yarn cross section SYC TC =C 106

5

SYM TM =M 106

6

or It is clear that ideal fibrous form (without pores) having the area SY has density equal to fiber density F. The yarn porosity is then defined as PYC c =F

7

PYM M =F

8

or

117

IJCST 11,2/3

In the same way we can evaluate the ``density'' porosity of fabrics from relation W W =F

9

where W is defined by the relation W

118

mV WP V tW

10

where m [kg] is weight of fabrics and V [m3] is corresponding volume of fabrics having a surface of 1m2. From the measured planar weight WP, fabric thickness tW and known density of fibers it is simple to compute the ``density'' porosity PW R

WP : F tW

11

Ideal value PWI can be evaluated from ideal thickness tI (equation (1)) and ideal planar weight WP I 10ÿ6 DC lC TC DM lM TM

12

where lC is length of weft yarn in the 1m portion of fabric and lM is length of warp in the 1m portion of fabric. In some cases the yarn shortening in weft SC [%] and warp SM [%] directions (due to crimping of yarns in fabric) are known. Then lc = (1 + SC /100) and lm 1 SM =100: For practical computations it is better to use PWR value which is not based on the simplified model assumptions. Second possibility of porosity evaluation is based on the definition of hydraulic pore for the filtration purposes (Robertson, 1950). The ``volume'' porosity is defined as PHW 1 ÿ

volume covered by yarns Y Y 1ÿ : 1ÿ V tW whole accessible volume

13

The vY is equal to the sums of volume of weft yarns SUC and warp yarns SUM: Y SUC SUD

14

SUC DC 1C

15

SUD DM 1M :

16

where

Here the 1C and 1M are volumes of weft and warp yarn in the 1m portion of fabrics

1C ÿ lC dC 2 =4 lC

TC 1 SC =100TC : 3 10 C 525 103 C

17

For 1M the indexes C are replaced by the indexes M. Combination of equations (14), (15), (16) and (17) and rearrangement leads to the equation 1 SC =100TC 1 SM =100TM : 18 DM Y DC 525 103 FC 525 103 FM

Air permeability and light transmission 119

For the case of negligible SC and SM and FC FM F can be porosity PHW expressed by the relation PHW 1 ÿ

1:9 10ÿ6 1 DC TC DM TM 1 ÿ DC 1 DM 1M : f tw tW

19

From a pure geometrical point of view surface porosity can be evaluated from cover factor CF of fabric. Classical Pierce definition of CF is based on the idealized projection of fabric (see Figure 1). CF is defined as the area of yarn in the dotted rectangle AY

dc dM ÿ dc dM DM Dc

divided by the area of dotted lines bounded rectangle AC DC DM ÿ1 . The CF has then the form CF DC dc DM dM ÿ dc dM Dc DM : The diameters of yarn can approximately be computed from equations (2), (3) with corrections (4). More realistic are elliptical shapes of yarns (Hoffmann, 1952).

dM 1/DM

dC 1/DC

Figure 1. Idealized fabrics for computation of cover factor CF

IJCST 11,2/3

Porosity based on CF is then PGW 1 ÿ CF

20

This surface porosity is nearly the same as porosity Po used in the evaluation of air porosity (see equation (22)).

120

3. Air permeability and porosity Let the fabrics be modeled as the semi-porous sheet of thickness tW. The overall pressure drop p pahead ÿ pbehind of air flow passing through this semiporous sheet is dependent on its porosity. This loss is suitably indicated by the loss of pressure coefficient LP (Hoerner, 1952). p LP 2 21 0:5 w where is air density (for dry air at standard atmosphere and 258C is = 1.175kg m±3) and w is air velocity ahead of the material. The pressure loss depends upon the Reynolds number Re (ratio of the dynamic to the viscous forces of the flow).The Re can be expressed as (Hoerner,1952): w d Re 22 P0 # Here d is diameter of mean (cylindrical pore), Po is the so called surface porosity (open area of fabric divided by the total area of fabrics) and # is kinematic viscosity of air. In the standard tests the mean value Re 200. The pressure loss can be divided into the dynamic losses and friction losses. Combining these losses the LP can be expressed in the semi-empirical form (Hoerner, 1952): 1 ÿ Po 40 LP 1 ÿ P 23 o Po2 Re0:75 valid between Re 1 (for all porosity ratios) and Re 103 (for porosity ratio lower than 0.5). Gorbach (1968) derived the semi-empirical relation LP k1

k2 1 ÿ P0 p Po2 Po Po

24

where coefficients k1 and k2 are dependent on the Re and fabric structure. If the pressure drop p is small, the airflow through fabrics of surface area SW follows Darcy's law w 1 p 25 SW Ro tW

where Ro is air flow resistance. In the standard test is SW = 20cm2 and p (200 Air permeability Pa) fixed. The air permeability AP is expressed in the form and light w 3 ÿ1 ÿ2 transmission AP 50 wm s m : 26 SW The standard test is the dry air permeability AP connected with coefficient of pressure loss LP through relation 400 LP 8:5 105 =AP 2 : 27 1:175 w2 The relation between air permeability and porosity can be obtained by combining the relation (27) and (23) or (22). In the contribution by Dent (1976) the relation between AP and planar weight of fabric W P has been derived. 4. Experimental part The 40 various weaves from wool and blends of wool with polyester, polyamide and viscose fibers has been selected. The following construction parameters of fabrics are measured: sett (texture) of weft DC [1/m], sett of warp DM [1/m], fineness of weft yarn TC [tex], fineness of warp yarn TM [tex], planar weight of weave WP [kg m±2], density of fibers F [kg m±3] and thickness of fabric tW [m]. Three specimens were measured and the means used for calculations. From these parameters the following porosity characteristics PWR, PHW and PGW were computed. The air permeability AP has been measured at standard conditions p = 200 Pa and Sw = 200cm2 in the standard atmosphere. Ten repeats of measurements were realized and mean value is used for calculations. The light transmission was investigated by the image analysis system. The system consists of microscope, CCD camera and personal computer. The treatment of digital images was made by the software LUCIA-M. This software is designed for analysis of the high color (3 5 bits) images having resolution of 752 524 pixels. The original image of one fabric is shown in Figure 2. The white objects (corresponding to the areas transmissible for light) were extracted from the original image. The threshold value 62 (all gray patterns are converted to black) has been chosen. The relative porosity for light PL was defined as the area of white objects divided by the whole area (see Figure 3). 5. Results and discussion First of all the correlation between various characteristics of porosity (PWR, PHW, PGW) and variables (AP or PL) has been computed by using ADSTAT package (Meloun et al., 1994). . Correlation for variable PL. Paired correlation coefficients: PL, PWR = ±3.1190E-01

121

IJCST 11,2/3

122

Figure 2. Original image for one fabric

Figure 3. Inverted image (white objects are black spots) for one fabric

PL, PHW = 1.8418E-01 PL, PGW = 5.3734E-01 PL, AP = 8.4862E-01 .

Correlation for variable AP. Paired correlation coefficients: AP, PWR = -4.1622E-01 AP, PHW = 1.75152E-01 AP, PGW = 4.47988E-01 AP, PL = 8.48620E-01

It is clear that the highest correlation exists between air permeability AP and light transmission PL. From the point of view of correlation of fabrics the

geometric characteristics of porosity with air permeability and light Air permeability transmission is the best the surface porosity PGW (Table I). and light In the second run the relation between air permeability and porosity PL transmission evaluated from light transmission has been created. Based on the preliminary analysis the linear regression model has been selected. Parameters have been estimated by the least squares criterion by using of ADSTAT package (Meloun 123 et al., 1994). Regression line has the form AP 2:8881E 02 3:6364E 03 PL The regression line and experimental data are shown in Figure 4. From Figure 4 and results of linear regression it is clear that the dependence of air permeability on porosity evaluated from light transmission is without marked nonlinearity. The nonlinear dependence of AP on the geometrical porosity predicted by equations (24) and (28) has been tested in the work (MilitkyÂ and TraÂvnõÂcÏkovaÂ, 1998). The porosity computed from the geometrical characteristics of fabrics was probably far from reality.

Parameter B[0] B[1]

Estimate

Standard deviation

1.8985E+03 3.5960E+04

2.8881E+02 3.6365E+03

Test of H0: B[j] = 0 vs. HA: B[j]<>0 t-criterion H0 hypothesis is Sig. level 6.5736E+00 9.8890E+00

Rejected Rejected

0.000 0.000

Notes: Correlation coefficient, R = 8.4862E-01; Predicted correlation coefficient, Rp^2 = 8.3098E-01

Table I. Results of regression of AP on PL

Linear Regression 1.20 1.00 0.80 Y

xID

4

0.60 0.40 0.20 0.00 0.00

0.10

0.20 X

Figure 4. The regression dependence of AP on the PL and experimental data

IJCST 11,2/3

6. Conclusion The image analysis can be simply used for prediction of air permeability. Porosity computed from fabric geometry is too idealized for close correlation with air permeability. The analysis of porosity estimated from air permeability data is presented in the contribution of MilitkyÂ and TraÂvnõÂcÏkovaÂ (1998).

124

References Dent, R.W. (1976), J. Text. Inst., Vol. 67, p. 220. Gorbach, N.I. (1968), IVUZ Technol. Text. Prom., Vol. 64, p. 8. Hoerner, S.F. (1952), Text. Res. J., Vol. 21, p. 274. Hoffmann, R.M. (1952), Text. Res. J., Vol. 21, p. 170. Meloun, M., MilitkyÂ, J. and Forina, M. (1994), Chemometrics for Analytical Chemistry, Vol. 2, Ellis Horwood. MilitkyÂ, J. and TraÂvnõÂcÏkovaÂ, M. (1998), ``Novelties in weaving research and technology'', Proc. Int. Conf. Liberec, September. Robertson, A.F. (1950), Text. Res. J., Vol. 19, p. 838.

Effect of mechanical properties of fabrics on change of bloused line during cyclic wear

Effect of mechanical properties 125

On front open part made up of fused composites Michiko Kihara

System Aid Co. Ltd, Tokyo, Japan

Machiko Murakami

Gifu City Women's College, Gifu, Japan and

Takako Fujimoto

Hokkaido University of Education, Iwamizawa, Japan Keywords Fabric, Mechanical properties Abstract This study focuses on the objective evaluation of the silhouette of the front bloused line of women's blouses, in which fusible interlinings are used in the construction of the open part. The wearing test was carried out over three seasons and the bloused lines before and after the fatigue were observed and examined in relation to the mechanical properties of fused composites and their constituent fabrics. Since bending properties and KOSHI were found to play an important role in the change of the silhouette formation of bloused lines, those of fused fabrics were studied by applying the simple model. Finally, in the sensory test, applying the blouses fatigued and not fatigued, their open parts made up of fused composite are evaluated by 71 consumers for their preference. The essential concepts involved in the consumer preference were clarified.

1. Introduction Although many studies on the appearance of the tailored suits have been developed (Fan et al., 1997a,b,c) there have been fewer studies on the front open part of women's blouses. Despite the large amount of possessions and of wearing rate among women, the objective evaluation on open parts of casual wears has not developed yet. It is important to study the change of properties of casual wears, taking into account of the deterioration in daily life. For the suits of wool, it was reported that the retention of the initial formation during the wear is most important. We realize the fatigued jeans pant to bring out better feelings to us than the one not fatigued. Such a realization seems to include the nature of performance of casual wears. In this study, taking women's casual blouses with open parts, we approach the answer to the following questions: ``How and why does the front bloused line change during cyclic wear and cleaning in daily life?'', ``Can we estimate the change of silhouette quantitatively?'', and ``What is the consumers' conception of the preferred blouse?''

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 125-133. # MCB University Press, 0955-6222

IJCST 11,2/3

126

2. Method 2.1 Wearing test The wear test was carried out over three seasons using two kinds of blouses and four wearers. Two kinds of blouses were made from cotton and polyester plain weaves nominated (Table I), respectively. Three of the subjects wore cotton blouses and the one remaining put on the polyester one for six-eight hours in their daily lives and the total wearing time amounted to 1,000 hours. A subject wears two blouses in shifts of wearing for 70-80 hours. One of the two blouses was cleaned by home laundry or commercial dry cleaning while the another was worn by the subject. On the other hand, other samples not worn but cleaned were prepared and examined. During the test, dimensions and weight were measured. Before and after the wear test, fabric mechanical properties were measured and hand values were calculated by the KN- equations. 2.2 Sensory test The sensory test for the evaluation of both blouses fatigued and not fatigued was conducted under the condition of touch, using the semantic differential grids by the important polar paired attributes. The respondents who were used to define and measure the front open part of blouses were 71 female consumers (college students); the differences between the factors that affect the total concepts of both blouses were derived by factor analysis. 3. Results and theoretical approach 3.1 Wearing test Figure 1 shows mechanical properties and the calculated hand value of cotton fabrics before and after the wear on the KN202-type normalized chart. A difference is seen in bending and shear properties from the figure. Then, it is involved in the change in KOSHI of the hand value. As for the polyester material as well, the same result was obtained. It shows how wearing and washing affect face fabric and fusible interlinings with aspect to bending properties which change hand values. Figure 2 shows the changes in the bending properties of front open parts of blouses before and after the wear test. Oblique and black lines indicate bending rigidity and hysteresis respectively. Both sides indicate no-treatment face fabric (left side: C) and fusible interlinings (right side: F) respectively, and the centre lines present untreated fused composite (CF). Compared with the results of face fabric properties, the fused composites show the remarkable increase in

Table I. Samples

Symbol

Fiber

P C F

Polyester Cotton Nylon/Acrylic

Structure

Weight mg/cm2

Thickness mm

Plain weave Plain weave Knitted

13.47 11.88 4.45

0.49 0.54 0.59

Density Picks Ends cm±1 42.5 42.5

±

27.1 25.0

Yarn count Warp Weft Tex 21.0 19.5

±

18.0 14.5

Effect of mechanical properties 127

Figure 1. Mechanical properties and hand values of face fabric

0.35 0.3

Figure 2. Bending properties of each sample before and after the wear test

B (gf • cm2/cm)

0.25

128

0.35 c : cotton fabrics

0.2

Key

0.3

B 2HB

f : fusible interlining D : commercial dry cleaning

0.25 0.2

W: home laundry

0.15

0.15

0.1

0.1

0.05

0.05

0

C

CD

CW

CD wear

Cf

CfD

CfW

CfD wear

f

2HB (gf • cm/cm)

IJCST 11,2/3

0

bending rigidity and hysteresis. The effect of cyclic cleaning or washing during the wear test was more remarkable than that of cyclic deformation by body actions only. Furthermore, it is understood that a change in the adhesion cloth is bigger than that of the base cloth. Bloused line can be thought to take the influence of the change in bending properties of the adhesion cloth strongly from these results. Therefore, it could be thought that bloused line is predicted from each bending properties of the base cloth and the adhesion cloth.

Cotton

M, gf, cm/cm

3.2 The prediction of bending rigidity of composite fabric Kanayama and Niwa (1982) published the theory, in which they predicted bending rigidity of men's suit (Kanayama and Niwa, 1982; Niwa, 1979). On the other hand, as for the materials for the blouse, it is a well-known fact that their thickness is light in comparison with those for the men's suit. Therefore, the influence of adhesion wick on the materials for the blouse fabric might be greater than that for men's suit fabric. Figure 3 shows bending deformation histories in the weft direction of two kinds of material. Solid and broken lines show fused 0.6

–3

3 K, /cm

Figure 3. Bending properties of fused composites (solid line) and face fabrics (broken line)

–0.6

composites and face fabrics respectively. As Kanayama and Niwa (1982) mentioned, characteristic deformation is caused by the melting region of fusible interlinings and solid not bend part, occupied by major parts of the fused composites. Moreover, remarkable difference is underlying between surface bending and back bending. But no report has been published as to whether such differences also apply to men's suits or not. So we must apply surface and back bending to Kanayama's formula respectively. Figure 4 shows computed and actual values of bending rigidity. Vertical axis shows computed value using Kanayama's theory and horizontal axis shows actual value using KES-FB system. Solid data points present surface bending, while white clear data points do back bending. This time we selected woven base fabric, so plotting separately. From this figure, we come to the conclusion that we can calculate precisely without modification when using warp bending and back bending.

Effect of mechanical properties 129

3.3 The prediction of projecting length of bloused line Figure 5 shows a schematic outline of bloused line, in which horizontal buckling is observed. Figure 6 shows the Kawabata's solution on buckling of fabric in a gravitational field (Kawabata, 1974), and expressed by the following equations: q 1 Hcm 1:973 Bgf cm=wgf =cm2 : Dcm 1:083

q Bgf cm=wgf =cm2 :

2

By applying the equation on the case, results of the formation of bloused lines were considered. Good agreements were shown between experimental values in 1.0 WARP WEFT WARP WEFT

B (cal) , (gf • cm2/cm)

0.8

Surface Back

0.6 Bp=2.16B(cal)

0.4

0.2

0

0

0.2

0.4 0.6 B (exp) , (gf • cm2/cm)

0.8

1.0

Figure 4. Actual and calculated by Kanayama's equation values of bending rigidities

IJCST 11,2/3

130 * Hb

Figure 5. Schematic outline of bloused line

90°

Figure 6. Kawabata's solution on buckling of fabric in a gravitational field

H D

this study (Hb in Figure 5) and the calculated buckled lengths (H in Figure 6) as shown in Figure 7. Why these agreements came out, but we could show guidelines to predict the projecting length of the bloused line. If the bloused line 4.2 P before wearing P after wearing C before wearing C after wearing

4

H (cal) , (cm)

3.8

3.6

3.4

Figure 7. Comparison of H(cal) calculated from the Kawabata equation for buckled fabric with buckled length Hb(exp).

3.2

3

3

3.2

3.4 3.6 3.8 Hb (exp) , (cm)

4

4.2

could be predicted from the mechanical properties of the fabric by using this technology, we can design blouses which consumers require. 3.4 Sensory test Figure 8 shows bloused lines of cotton and polyester before and after the wear test. Both materials show high position projecting before wearing, but as wearing, the top position goes back and down, no matter what designs or colors may be. Figure 9 shows the results of the sensory test. Solid lines indicate before wearing and dotted lines indicate after wearing. In both cases of cotton and polyester, it can be confirmed that the touch after wearing is preferable more than that before wearing. We chose some important factor by analysis to understand which parameters affected the total preference of the blouse. As a result, the following equation to calculate the total preference value of blouse, PRV, could be derived: X PRV C0 Ci Xi 3

Effect of mechanical properties 131

where PRV ; total preference value C0, Ci ; constant coefficients Xi ; the sensory value of the ith item on semantic differential scales ( i = 1,. . .5) Before wearing

After wearing

P

C

Figure 8. Bloused lines before and after wearing, P: Polyester, C: Cottons

IJCST 11,2/3

very

slightly

neutral

slightly

very

5

4

3

2

1

a. soft b. limp

crisp

c. clammy

132

absorbent

d. thin

thick

e. rough

smooth

f. resilient

spongy

g. elastic

inelastic

h. bulky

sheer

i. pleasant touch

unpleasant touch

Key A B C D

j. light Figure 9. Result of sensory test

hard

k. flowing l. preferable

heavy clingy not preferable

Table II shows C0 and Ci values obtained by the factor analysis. From this formula, not only lightness and easy fitness but also a direct hand feeling of the material are reflected on the consumer's taste. On one side, we did another sensory test to examine whether the outlines of the blouse affect to consumer's taste. We could find the correlation between them. Which of the cotton and the polyester did the research of whether to like it, too? A consumer found out that it was cotton pointing as that result. 4. Summary By the cyclic wear and cleaning, the bending rigidity of the open part of the blouse, which consists of fused composites, decreases and results in the reduction of KOSHI. Relevantly, the fatigued open part is evaluated as better than the one not fatigued by the consumers. Constant coefficients

Table II. Constant coefficients of PRV equation

C0 C1 C2 C3 C4 C5

i= 0.374 ±0.108 0.146 0.525 0.103 0.232

1 2 3 4 5

X, item Rough ± smooth Bulky ± sheer Pleasant touch ± unpleasant touch Light ± heavy Flowing ± clingy

Notes: Results of regression ``preferable ± not preferable''; P < 0.001; R = 0.793

The silhouette of the open part of the bloused line could be predicted by using the simple model for buckling and the equation for bending deformation. Generally speaking, in casual wear consumers require the open parts of the garment to be flexible. References Fan, J., Leeuwner, W. and Hunter, L. (1997a), Textile Res. J., Vol. 67, pp. 137-42. Fan, J., Leeuwner, W. and Hunter, L. (1997b), Textile Res. J., Vol. 67, pp. 194-7. Fan, J., Leeuwner, W. and Hunter, L. (1997c), Textile Res. J., Vol. 67, pp. 258-62. Kanayama, M. (Murakami) and Niwa, M. (1982), ``Mechanical behaviour of the composite fabric reinforced by fusible interlining'', Proceedings of the Japan-Australia Joint Symposium on Objective Specification of Fabric Quality, Mechanical Properties and Performance, p. 347. Kawabata, S. (1974), ``Study on the buckling of fabric in gravitational field'', Abstract of the 3rd Textile Research Symposium, Vol. 37. Niwa, M. (1979), ``Adhesion of fabrics'', Nippon Secchaku Kyokaishi, Vol. 15 No. 53.

Effect of mechanical properties 133

IJCST 11,2/3

A guide line for manufacturing ``ideal fabrics'' Sueo Kawabata

134

The University of Shiga Prefecture, Shiga, Japan

Masako Niwa

Nara Women's University, Nara, Japan and

Yoshihiro Yamashita

The University of Shiga Prefecture, Shiga, Japan Keyword Fabric Abstract This paper reports the recent progress in the ideal fabrics project that started in 1996. The aim of this project is to initiate the engineered manufacturing of ``ideal fabric''. Ideal fabric is the fabric which satisfies the three conditions, good hand, good appearance of suit, and mechanical comfort for wear. The objective evaluation method of these three properties has been developed, and these objective method and fabric mechanics theories are fully applied to this project. Some ideal fabrics have been manufactured as the guideline for manufacturing ideal fabrics in the future. These trial fabrics are now being commercialized to assess the response of consumers.

1. The ideal fabric project In the coming twenty-first century, consumer demand for quality will become increasingly strong. A trend already seen in Japan is for consumers to possess less clothing, but of higher quality. To meet this consumer demand, clothing manufacturing must be changed from experience-oriented manufacturing to a system of engineered manufacturing for higher quality fabrics. Not limited to this recent trend, engineered design of the fabric quality has been a target of textile technologists for many years. In 1970, a research committee, the Hand Evaluation and Standardization Committee (HESC) was organized by the present author in Japan. The committee progressed the standardization of subjective hand evaluation methods, which were used in textile mills, and also the development of an objective evaluation system. This research was connected to the objective evaluation system of fabric hand. The objective evaluation system was almost completed around 1975. Following the development of the objective evaluation system of fabric hand, the criteria for predicting suit appearance and mechanical comfort have been developed by the collaboration of universities and apparel engineers. Although the fabric hand is based on the traditional evaluations, which have been used by many experts in textile mills, there has

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 134-140. # MCB University Press, 0955-6222

This research project was supported by the Research Project supported by Grant-in-Aid for Scientific Research A(1), Monbusho (The Ministry of Education, Science, Sports and Culture). This research was carried out in collaboration with these companies and persons: Wool Research Organization of new Zealand, Mitsuboshi keito Co, especially Mr S. Kurihara, Kanjyu Co., Hashimoto Keori Co., Soto Co., Crown Co., Nippon Keori Co., Toyobo Co., Trenza Co., especially Mr K. Ito, and Oga Hirakata Co. We appreciate this collaboration.

been no such tradition of fabric evaluation from the tailoring engineering side. The apparel engineers have tried to apply the mechanical parameters to tailoring process control and have also attempted to connect these parameters with suit appearance, that is, making-up performance of suiting. The prediction value is the total appearance value (TAV) and the equations for deriving the TAV have been investigated by apparel engineers and university researchers. On the basis of these developments, the ideal fabric project started in 1996 under the support of the Japanese government scientific fund. The definition of the ideal fabric is that the fabric satisfies the following three conditions: (1) Good hand; (2) Possibility of making of good appearance. (3) Mechanical comfort for wear.

A guide line for manufacturing ``ideal fabrics'' 135

The objective evaluation for these three properties has been developed (Kawabata et al., 1997; Kawabata, 1998) in the past 20 years. The evaluation is made based on fabric mechanical properties. Numbers for total hand value (THV) and total appearance value (TAV) express the quality of fabric hand and the appearance prediction respectively. The mechanical comfort is expressed by a limited range of mechanical parameters of fabric tensile and shear deformation properties as shown in Table I; these parameters are closely related to comfort. The conditions for the ideal fabric are shown in Table II. 2. Investigation for the guide line for weave design of suiting In order to make a guide line for manufacturing ideal fabrics, we have organized the ideal fabric project for manufacturing ideal fabrics in 1996 in collaboration with universities and industries (Kawabata et al, 1997; Kawabata, 1998). This project has been supported by the Japanese scientific fund. The system of development is as follows. A fabric is selected as the base fabric; this fabric is not yet the ideal fabric; however, it has a good property which may be improved by a weave design change in the trimming level to bring the fabric to the ideal fabric. The process of the improvement is as follows: . Finding out a base fabric of which property may be improved by a weave design change in the trimming level to bring the fabric to the ideal fabric. Property

Evaluation

Details

Fabric hand

THV(total hand value)

THV = 5 (excellent), 4 (good), 3 (average), 2 (below average), 1 (poor)

Good appearance of suit

TAV (total appearance value)

TAV = 5 (excellent), 4 (good) 3 (average), 2 (below average) 1 (poor)

Mechanical comfort

Seven mechanical parameters of tensile and shear deformation properties

These seven parameters must fall in a good zone

Table I. The three properties for evaluating fabric quality and performance

IJCST 11,2/3

136

Winter-Autumn

Conditions Mid-summer

1 Total hand value (THV)

THV 4.0

THV 3.5

2 Total appearance value (TAV)

TAV 4.0

TAV 4.0

3 Mechanical comfort Table II. The criteria for the ideal fabric

.

.

.

0.60 LT 0.50 0.58 LT 0.50 78 RT 73 78 RT 73 5.1 EM1 4.3 5.1 EM1 4.3 18 EM2 7.5 18 EM2 7.5 3.0 EM2/EM1 1.3 3.0 EM2/EM1 1.3 0.65 G 0.50 0.65 G 0.50 1.5 2HG5 0.8 1.5 2HG5 0.8

Remarks

LT: Average of LT1 and LT2 RT: Average of RT1 and RT2 Suffix 1: warp, and 2: weft direction

Weave design is made for improvement on the basis of the analysis of objective measurement of the fabric. In many cases, we have to seek yarns suitable for the weft or warp, and then, in some cases, seek fibers to get the suitable yarns. We need a cooperation with fiber producers. In this step, textile and fiber mechanics are widely applied. Based on the new design of weave, the THV, TAV and other performances of the fabric are predicted by the objective evaluation system. The design is repeated until the ideal fabric property is predicted. Then weaving and finishing start. It is important to measure and inspect the fabric property in the finishing process once at least.

The development system of ideal fabrics is shown in Figure 1. 3. Example of the ideal fabrics which have been developed A base fabric of worsted fabric, W5, was chosen. As shown in Table III, this fabric is woven by the fine yarns spun from fine wool of 17.3 micron in diameter. A serious problem is, however, in its low TAV. It is only 1.76. From analysis, it was found that this low TAV is caused by the low value of the fabric bending stiffness of W5. It was confirmed that if the low bending stiffness was improved to the higher value, the TAV might be also improved. In order to increase bending stiffness, we have to change yarn from fine count to the thicker count with using coarse wool. The expert group was afraid that the increased stiffness might decrease total hand value. Careful experiments have been repeated. A little change in weave design increased the bending stiffness of W5, and then improved TAV a little without change in THV. An important result obtained in this experiment was that the predicted value based on the objective evaluation system and the application of fabric mechanics

SELECTION OF BASE FABRIC

Fabric mechanics

WEAVE DESIGN

Experts’ experience

FIBER SELECTION

PREDICTION OF THV & TAV

137

YARN DESIGN

Objective evaluation

WEAVING

Fiber Science

Experts’ experience

A guide line for manufacturing ``ideal fabrics''

Yarn mechanics technology and science

INSPECTION BY OBJECTIVE MEASUREMENT

FINISHING

PRODUCT

Figure 1. The development system of the ideal fabrics under the cooperation of university and industry

INSPECTION BY OBJECTIVE MEASUREMENT

PF (Ideal fabric)

Weft

THV

TAV

Total M.Comf quality

Original fabric 2/91S,17.3 m W5 100%

2/91S,17.3 m 100%

3.68

1.76*

non-PF

non-PF

W5-10 new design and prediction

2/60S,20.3 m 100%

2/56S,20.3 m 50%, 21.5 m 50%

4.24

4.14

PF

PF

W5-10NA experimental result

2/60S,20.3 m 100%

2/56S,20.3 m 50%,21.5 m 50%

4.36

4.30

PF

PF

Warp

Note: PF: perfect; * This property must be improved for approaching ideal

could predict well the experimental result. This fact encouraged us towards proceeding to the next stage of the experiment. The ideal fabric was designed in the second trial. The new design for W5 is also shown in Table III. The predicted properties of the new fabric W5-10

Table III. Example of the development of ideal fabric

IJCST 11,2/3

138

satisfy the conditions of ideal fabric. The experimental result is shown in the third line of this table. As predicted, this fabric, W5-10NA, was improved, and became ideal fabric. Table IV shows the improvement in the bending property and its effect on hand values. The whole performance properties are expressed in a chart as shown in Figure 2. This chart shows the plot of the properties of W5-10NA. The shaded zones in the hand chart, suit appearance chart and mechanical comfort chart are the zones that good fabrics fall in. THV, TAV and mechanical comfort zone indicate clearly whether the fabric is perfect or not, and the good zones indicate how the fabric must be improved when the fabric is not perfect. Equation for predicting bending stiffness of the fabric woven by blended-fiber yarn When the bending stiffness of a standard fabric and its structure are known, then the properties of the fabric having different fiber diameter, different yarn count, and different weave densities are estimated by the relative method as follows (Figure 3): D/a = Constant is assumed; B = Bending stiffness of fabric; B0 = Bending stiffness of standard fabric; 0-0 Trial W5-original n1 92 n2 86 W 18.0 EM-1 5.38 EM-2 10.5 B-1 0.0798 B-2 0.0553 2HB-1 0.0243 2HB-2 0.0150 KOSHI 3.64 NUMERI 6.54 FUKURAMI 5.30

Table IV. Improvement in the bending property and its effect on hand values

Number 10NA

10NAK

W5-10 Design and prediction 74 72 22.8 4.5 10.0 0.135 0.112 0.0396 0.0329 5.22 6.90 5.80

W5-10NA Experimental result 74 72 22.8 4.79 10.1 0.143 0.116 0.046 0.035 5.19 7.08 6.34

Notes: KOSHI: stiffness; NUMERI: smoothness; FUKURAMI: fullness Number of weft and warp yarns per inch respectively, 1 indicates warp and n1, n2 2 weft. W: Fabric weight (mg) per 1cm2, 10W is equivalent to g/m2. EM-1, EM-2: Fabric tensile strain (%) in warp and weft directions respectively measured with KESF-standard conditions (SC). B-1,B-2: Fabric bending stiffness per 1cm width (gf cm) in warp bending and weft bending respectively. 2HB-1, 2HB-2: Hysteresis of bending moment (gf cm/cm) in warp bending and weft bending respectively.

A guide line for manufacturing ``ideal fabrics'' 139

Figure 2. The chart overlooking whole performance of a suiting. The ideal fabric,W5-10NA suiting (100 per cent wool) is shown

IJCST 11,2/3

140

d1 = Fiber diameter of fiber 1; d2 = Fiber diameter of fiber 2; d0 = Fiber diameter of standard fabric; V1 = Weight fraction of fiber 1; V2 = Weight fraction of fiber 2; S = Yarn count (worsted count); S0 = Yarn count (worsted count) of standard fabric; n = Number of yarn per unit width; n0 = Number of yarn per unit width, of standard fabric. 2 2 ! B d1 d2 S0 n V1 V2 d0 d0 S n0 B0 s S n n0 S0

2

n 1=a a,Yarn space

Figure 3. Weave model

1

3 Yarn crosssection

D, yarn diameter

4. Conclusion Two ideal fabrics for winter/autumn suiting and one of summer suiting have been developed. This procedure for developing ideal fabric is, however, not needed for all fabrics for manufacturing ideal fabrics. It will be applied only for the development of some core fabrics, which are the guide line fabrics for the ideal fabrics. When we have the guide line design for several types of fabrics, then, we may modify the weave design following the guide line to manufacture many types of ideal fabrics. Some of the trial fabrics are now being commercialized to assess consumers' response. References Kawabata, S. (1998), ``Trial manufacturing of the ideal fabrics, its guide line'', Report Research Project supported by Grant-in-Aid for Scientific Research A(1), Monbusho (The Ministry of Education, Science, Sports and Culture) Report No. 08555237. Kawabata, S., Niwa, M., Kurihara, S., Yamashita, Y. and Inamura, A. (1997), ``Development of high quality apparel fabrics by means of objective measurement'', Proc. 78th World Conference of the Textile Institute, 26 May, Thessaloniki, Greece.

Surface appearance irregularity of nonwovens

Surface irregularity of nonwovens

JirÏÂõ MilitkyÂ and Jitka RubnerovaÂ

Technical University of Liberec, Department of Textile Materials, Liberec, Czech Republic and

141

VaÂclav KlicÏka

Â stõÂ nad OrlicõÂ, Czech Republic BASATEX, U Keywords Textiles, Structures Abstract Visual and subjective methods for evaluation of surface appearance irregularity of chemically bonded nonwovens are compared. The image analysis system LUCIA is used for estimation of characteristics describing appearance. The analysis of subjective and objective estimates of surface appearance irregularity is realized by the coefficient of variation and by the ANOVA type model.

1. Introduction Surface appearance irregularity is interesting for woven structures and in some cases for nonwovens as well. This characteristic is closely connected to the variation function for transparency, reflectivity, planar mass and to other properties, for example, air permeability. Corresponding to the description of unevenness of linear textile structures by the length variation function, a surface variation function can be constructed for textile fabrics. The surface variation function can be easily used for description of unevenness or uniformity. The unevenness can be categorized according to the investigated characteristics in the following main groups: . mass unevenness (mostly found); . structural unevenness; . visual (optical) unevenness; . mechanical or physical properties unevenness; . appearance unevenness. There are some connections between the above mentioned categories of unevenness. For example, Huang and Bresee (1993) derived a connection between mass unevenness and optical unevenness (characterized in both cases by the coefficient of variation). The main aim of this work is to attempt to describe uniformity of appearance of lightweight nonwoven textile structure. For quantification of appearance uniformity the characteristics of visual unevenness are used. These characteristics are measured by the image analysis and subjectively by the This work was supported by the Czech Ministry of Education Grant No. VS 97084.

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 141-150. # MCB University Press, 0955-6222

IJCST 11,2/3

142

human eye. The evaluation of appearance uniformity is based on the variation coefficient estimation and on the ANOVA (analysis of variance) model. 2. Appearance unevenness characterization Nonuniformity of appearance has been evaluated from selected visual characteristics measured in cells of defined size (see Figure 1). These rectangular cells divide the microscopic image of sample and create a rectangular net. As the visual characteristics of appearance unevenness the following were selected: . number of white spots evaluated by the human eye NE; . number of white objects NW evaluated by the image analysis (see white areas in Figure 2); . relative surface porosity (portion of white area) AF defined in Figure 2.

Figure 1. Inverted image of tested sample (planar weight 60g/m2).White spots are shown here as black

y y1

Ai

Figure 2. Definition of relative surface porosity (Ai is the area of white objects)

AP = ∑Ai AE = xi .yi AF =

0

x1

x

AP AE

Samples were oriented in the following way (see Figure 1). Direction X is equivalent to the machine direction (cells denoted i). In this direction is N cells. Direction y is equivalent to the cross direction (cells denoted j). In this direction is M cells. Results of evaluation are data arrays NEij, NWij, AFij, i = 1 . . . n, j = 1 . . . m. Appearance uniformity is described by the following methods: . Coefficient of variation (CV) . Analysis of variance (ANOVA) Analysis based on CV Coefficient of variation is traditionally used as the characteristic of unevenness. According to the common definitions we can simply compute the overall mean 1 XX Pij m 1 MN i j variance s2

1 XX Pij ÿ m2 MN i j

and coefficient of variation CV

s m

2

3

Here Pij is the selected visual characteristic of appearance (NEij or NWij or AFij). The quantity CV is external variation coefficient CB(F) between cell areas F (Wegener, 1986). The total variance s2 can be divided into two terms by the use of means in the machine direction and cross direction: 1 X 1X Pij moj Pij : mio M j N j Symbol ``o'' denotes the index used for summation, i.e. mio is the mean value for the ith position in the machine direction. For the machine direction (expansion of equation (2) by the use of the mio) the following relation results (Cherkassky, 1998): s2 s2L s2HL where the variance in the machine direction is 1X mio ÿ m2 s2L N i

4

5

Surface irregularity of nonwovens 143

IJCST 11,2/3

144

and the variance in the transversal direction is 1 XX Pij ÿ mio 2 : s2HL MN i j

6

For the cross direction it is s2 s2H s2LH where the variance in the cross direction is 1 X moj ÿ m2 s2H M j and the variance in the longitudinal direction is 1 XX Pij ÿ moj 2 : s2LH MN i j

7

8

9

Dividing the corresponding standard deviations by the mean m the coefficients of variation CVL , CVHL, CVH and CVLH result. Analysis by the ANOVA The Pij can be interpreted as a discrete presentation of the random field on the discrete two-dimensional integer valued rectangular mesh (Cherkassky, 1998). Let Pij be described by the following model Pij ij "ij

10

where ij is the true value in the ij cell and "ij is random error. The term ij can be decomposed to the terms ij i j c:i : j

11

where i are effects in the cross direction, j are effects in the machine direction and c is the constant of Tukey one degree of freedom non-additivity. Uniformity in the machine direction is equal to validity of the hypothesis H0 : j 0; j 1 . . . M and uniformity in the cross direction is equal to the validity of the hypothesis H0 : i 0; i 1 . . . N : Testing of these hypotheses can be realized by the ANOVA (model with a single observation per cell). For the ANOVA model the following constraints are imposed

X i

i 0;

X

j 0;

X

j1

i j 0;

i

X

Surface irregularity of nonwovens

i j 0:

j

For the pure additive effects the interactions ij c:i : j 0 and then 1 Xÿ 1 Xÿ ^i Pij ÿ m ^j Pij ÿ m M j N i

145

where m is the total mean defined by equation (1). ^i ^j the parameter c can be simply From residuals ^eij Pij ÿ m ÿ estimated PP ^eij : ^i : ^j i

j

c PP i

j

^2i : ^j2

:

12

For ANOVA testing the sum of squares due to machine direction (effects ^j ), to ^i ) and to interaction is computed and compared with cross direction (effects the total sum of squares s * M * .N. Statistical tests based on the F-criterion may be performed (Meloun et al., 1992). According to the results of testing of the null hypothesis H0 j 0 or i 0 the statistical uniformity in the machine and cross direction can be accepted or not. 3. Experimental part The chemically bonded (by the acrylate binder) nonwoven from viscose fibers (VS) was prepared. Starting lap of planar weight 60gm±2 was created on the pneumatic web former. The lap consists of two types of viscose fibers mixed in the weight ratio 67/33 (VS 3,1 dtex/60mm and 1,6 dtex/40mm). Binding acrylate (relative amount 20 per cent) was applied by padding. The qualitative visual appearance unevenness of the final structure is clearly visible in Figure 3. The rectangular samples of dimensions 100 100mm (area Aj ( 100mm2 and weight 6mg)) were cut for further analysis (KlicÏka, 1998). Subjective visual appearance Subjective visual estimation of appearance is based on the evaluation of the number of local maximal illumination Lm in individual mesh of defined rectangular net by the human eye. The maximal illumination corresponds to the spots without material. The human eye is able to distinguish the spots of dimension to a greater degree than approximately m = 0.5mm. By using the microscope MEOFLEX (21 times magnification) the lower bound of visible spots is found to be approximately equal to 0.05mm. This subjective visual evaluation was used for the above mentioned nonwoven structure. The microscope image of the sample was divided into the net consisting of the 25 rectangular mesh (dimension 2 2mm). The number of

IJCST 11,2/3

146

Figure 3. Tested nonwoven structure

spots NEij in the ijth mesh having maximum illumination was evaluated by direct visual inspection (KlicÏka, 1998). The basic statistical characteristics of NE are: sample mean = 12.38 spots and coefficient of variation CVn = 15.78 per cent. From the combination of six microscope images the areas of the same level of local numbers of white spots are shown graphically in Figure 4. The bivariate spline smoothed surface of NE for one image is shown in Figure 5. Application of the image analysis Subjective visual evaluation of the number of white spots is very tedious and subject to errors. The image analysis system is suitable for objective visual estimation. The system consist of microscope, CCD camera and personal computer has been used. The treatment of digital images was made using the software LUCIA-M. This software is designed for analysis of the high color (3 5 bits) images having a resolution of 752 524 pixels. The threshold value 62 (all gray patterns are converted to black) has been chosen. The rectangular net dividing the image into equal cells has been defined by the same means as at subjective visual evaluation. The following characteristics of appearance uniformity have been evaluated in each cell: . number of white spots NW; . relative porosity AF.

Surface irregularity of nonwovens

S10 S9 S8 S7 S6

147

A1 - A6

S5 S4 S3 S2 1

2

3

4

5

6

7

8

S1 9 10 11 12 13 14 15

20-25 15-20 Figure 4. Areas of the same levels of numbers of white spots

10-15 5-10 0-5

15.

00

10.

00

5.00

4.50

4.00

3.50

3.00

2.50

2.00

1.50

0 1.00 1.0

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Figure 5. Bivariate spline smoothed surface of NE

IJCST 11,2/3

Bivariate spline smoothed surface of NW is shown in Figure 6 and for AF in Figure 7.

148

4. Results and discussion From the surfaces of NE, NW and AF it is possible directly to identify the local variation of these characteristics. Quantification of appearance uniformity has been realized by the analysis of coefficient of variation CV and analysis of variance ANOVA. Analysis based on the CV The values of CV, CVL and CVHL are given in Table I. It is clear that the objective visual characteristics are closer than the subjective number of holes NE and objective number of white areas NW. This difference is probably due to higher resolution of the image analysis system in comparison with the human eye. Analysis based on the ANOVA Detailed results of ANOVA analysis computed by the ADSTAT package are presented for the porosity AF only. For the NE and NW only the results of testing (Tables II and III) are summarized.

20.0

0

10.0

1

.0 0 1 .0 0

1

1 .5

.5

0

0

2

2

.0

.0

0

0

2

.5

2 .5

0

0

3

3 .0

.0 0

0

3

3

.5 0

.5 0

4

4 .0

.0

0

0

4

.5

0

5

.0

0

5

.5

0

6

.0

0

0

6.00 5.50 5.00 4.50 4.00 3.50 3.00 2.50

Figure 6. Bivariate spline smoothed surface of NW

2.00 1.50 1.00 1.00

1.50

2.00

2.50

3.00

3.50

4.00

Surface irregularity of nonwovens

0.1

0

0.0

5

0 6.0

0

5.5

0

5.0

0

4.5

0

0

4.0

0

3.5

0 3

0

3.0

.0 0 3

0

2.5

0

2.0

0

1.5

2.5 .00

0 2

0 00 1.0 1.

1.5

6.00 5.50 5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 1.00

1.50

149

4.0 .50

2.00

2.50

3.00

3.50

4.00

Characteristics

CV (total)

CVL (machine direction)

CVHL (transversal)

NE NW AF

0.1494245 0.3944114 0.5806934

0.0609795 0.2029876 0.2569274

0.1364155 0.3381662 0.5207620

Variables NE and NW (number of white spots) are H0 accepted on the significance level 0.95 as well. Therefore the ANOVA analysis leads to the conclusion that the variability of NE, NW and AF in the cells is not statistically significant. 5. Conclusion The proposed methods for appearance evaluation can be used for lightweight nonwovens without any problem. Objective evaluation by the image analysis allows identification of a lot of other characteristics such as the mean area of pores, objects with some gray levels, etc. In further investigation these characteristics will be also used.

Figure 7. Bivariate spline smoothed surface of AF

Table I. Coefficients of variation

IJCST 11,2/3

150 Table II. Porosity AF means and level effects

Level 1 2 3 4 5 6

9.0583E-02 4.8533E-02 6.5917E-02 4.8650E-02 7.3675E-02 7.9725E-02

Effect

Level

2.7163E-02 ±1.4888E-02 2.4958E-03 ±1.4771E-02 1.0254E-02 1.6304E-02

1 2 3 4

Factor B Mean 5.8900E-02 4.5650E-02 4.0350E-02 8.2225E-02

Effect ±4.5208E-03 ±1.7771E-02 ±2.3071E-02 1.8804E-02

Notes: Tukey's one degree of non-additivity C = ±8.3761; Total mean = 6.3421E-02; Residual variance = 1.3626E-03

Source of variance Table III. ANOVA table for model with Tukey's interaction

Factor A Mean

Level A Level B Interactions Residuals Total

Degree of freedom

Sum of squares

Mean square

Testing criterion

3 5 1 14 23

7.1031E-03 6.3723E-03 1.3232E-04 1.9076E-02 3.2551E-02

2.3677E-03 1.2745E-03 1.3232E-04 1.3626E-03 1.4153E-03

1.738 0.935 0.097

Conclusion H0 is Accepted Accepted Accepted

Computed significant level 0.205 0.488 0.760

For evaluation of results both CV and ANOVA are suitable. The behavior of effects in the machine and cross directions computed by the ANOVA can be analyzed by regression methods (trends, nonlinearities, etc.). References Cherkassky, A. (1998), Text. Res. J., Vol. 68 No. 242. Huang, X. and Bresee, R.R. (1993), INDA JNR, Vol. 5 No. 28. KlicÏka, V. (1998), Doctoral Thesis, TU Liberec. Meloun, M., Militky, J. and Forina M. (1992), Chemometrics for Analytical Chemistry, Ellis Horwood. Wegener, W. (1986), J. Text. Inst. Vol. 77 No. 69.

Towards the objective evaluation of garment appearance J. Fan, C.L.P. Hui and D. Lu

Evaluation of garment appearance 151

Institute of Textiles & Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong and

J.M.K. MacAlpine

Department of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Keywords Garments, Seam pucker Abstract As a first step towards objective evaluation of garment appearance, the present work considered seams on three-dimensional surfaces which simulate actual garment surfaces. The geometric profiles of the 3-D seams were scanned using a laser scanner. 1-D and 2-D digital filters were applied to obtain pucker signals from the geometric profiles by removing ``high frequency'' components due to fabric surface texture and ``low frequency'' components due to garment silhouette and drape. The advantages and disadvantages of the 1-D and 2-D digital filters are compared. Four physical parameters are examined to see their relevance to the subjective pucker grade. It was found that log(2 ), i.e. the logarithm of the variance of the heights of pucker signals, is the best set of physical parameters for the objective evaluation of seam pucker. In addition, latest attempts at capturing and analyzing 3D garment image using a Cyberware laser scanner and Surfacer software are reported.

1. Introduction Garment appearance or aesthetic quality is one of the most important aspects of garment quality. Aesthetics is a very complicated subject because what one person would define as appealing may not necessarily be another person's view. It is therefore almost impossible to universally define garment aesthetic appeal. Nevertheless, people do have a reasonably common notion or concept of what is good or bad appearance. With the exception of some deliberate use of ``puckered'' or ``wrinkled'' surface, nicely smooth and curved garment surface is regarded as desirable. The aim of this study is not to try to define garment appearance, but to try to objectively evaluate garment appearance in terms of the smoothness of surface contours. 2. Literature review Subjective assessment AATCC test methods 88B, 88C and 124 (1993) are still the most commonly used test methods in the industry for the assessment of garment appearance for wash and wear garments even though the actual tests are conducted on fabrics The authors would like to thank The Hong Kong University Grant Committee and Hong Kong Polytechnic University for funding the project.

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 151-159. # MCB University Press, 0955-6222

IJCST 11,2/3

152

or seams in fabrics rather than on finished garments. The results of the three AATCC methods can only provide an indication of the appearance of garments made of the same fabric and threads after repeated home laundering. For constructed wool garments, IWS Japan (1978) branch proposed a test method for assessing the appearance retention of men's suits after final pressing and prior to sale. In this test, garments' appearance are graded according to photographic standards after exposure to high temperature and humidity conditions. Objective evaluation Previous related work is limited to the objective evaluation of puckering in flat seams. From the 1950s to the 1970s, several instruments (Belser et al., 1993; Hebeler and Kolb, 1950; Shiloh, 1971) were developed to measure the surface contours of seams using photo or displacement sensors. However, these instruments were not widely accepted due to problems in terms of accuracy and reproducibility. In recent years a number of new attempts were made in which CCD cameras (Stylios and Sotomi, 1993a; 1993b) or laser scanners (Inui and Shibuya, 1992; Kawabata and Niwa, 1996; Park and Kang, 1997; Stylios et al., 1992) were used to capture the image of a seam, and artificial intelligence was applied to establish the relationship between the objectively measured parameters and subjective grades. CCD cameras were later found less appropriate due to the difficulties with patterned fabrics. The application of the Weber-Fechner Law by Kawabata and Niwa (1996) in discovering an almost linear relationship between subjective pucker grade and a physical quantity is a very important contribution, as it improves the accuracy of objective pucker evaluation dramatically. Although much work has been carried out on the objective evaluation of puckering of flat seams, as few garment seams are flat, such work cannot directly be applied to evaluate garment seams. 2. Experimental Two cotton fabrics, one white and one red-and-white check, of similar weight and density were used to prepare the samples. (Very dark fabrics were not used as laser scanners cannot measure the profiles of dark surfaces.) From each fabric, five 160 160mm samples were made up by sewing two pieces together with superimposed seams, adjusting the thread tension to give varying degrees of pucker. The seam pucker grades of these samples were evaluated by a panel of ten observers according to the AATCC method 88B (1993), in each case omitting the most extreme grade of the ten obtained and then determining the average grade from the remaining eight. The standard deviations of the eight estimates obtained for each of the ten samples ranged from 0.37 to 0.65, the mean value being 0.50. This means p that the 95 percent confidence limits on the grades is close to 1.96 0.50/ 8 or 0.35. A commercial laser scanning system (Figure 1) was used to obtain geometrical profiles of the seam samples. The ten samples were casually (sometimes twisted) placed on the three supporting surfaces under the laser

Evaluation of garment appearance 153

Figure 1. The laser scanner measurement system

The height of pucker(mm)

The height of pucker(mm)

scanner: one horizontal surface; one double curvature surface bowed downwards and one double curvature surface bowed upwards. Examples of the profiles of a seam on the three surfaces are shown in Figure 2 (a)-(c). The measurement data for the system are believed accurate to 0.001mm. Data were obtained at 0.5mm intervals along a straight 120mm line, parallel, or

20 10 0 –10 –20 40

10 0 –10 –20 40

100

20 Across the seam(mm)

20

0 0

Along the seam(mm)

Across the seam(mm)

(a) On the plane surface The height of pucker(mm)

100

20

50

50 0 0

Along the seam(mm)

(b) On the bowed downwards surface

20 10 0 –10 –20 40 100

20 Across the seam(mm)

50 0 0

Along the seam(mm)

(c) From the bowed upwards surface

Figure 2. The typical geometrical profiles of a seam sample on three different surfaces

IJCST 11,2/3

nearly parallel, with the seam. This scanning was repeated for 240 parallel lines, each separated by 0.5mm from its neighbor, 120 on each side of the seam. A square array of profile data was thus obtained for each of the ten seam samples placed on each of the three supporting surfaces, which were then stored in the associated computer as data files, one for each data array.

154

3. Results analysis Processing the profile measurements To remove high ``frequency'' components in the scanned profiles owing to individual threads or surface texture and low ``frequency'' components owing to the curvature of the supporting surfaces (simulating 3-D garment surface), a 1-D and 2-D (Park and Burrus, 1987) band-pass digital filter was tried. The cut-off ``frequencies'' were set at 1/60mm and 1/1.5mm (or 16.7 cycles/m and 667 cycles/ m). Figures 3 (a)-(c) show the pucker profiles obtained by the application of the 1D FIR band-pass filter described in our previous publication (Fan et al., 1998b). Figures 4 (a)-(c) show the pucker profiles obtained by the application of the 2-D FIR band-pass filter (Fan et al., 1998a). The improvement of the 2-D filter over the previous 1-D filter is apparent from the similarity of the three pucker profiles filtered from the original ones obtained from the same seam on different surfaces.

15

Height of pucker(mm)

Height of pucker(mm)

Measures of pucker Based on the Weber-Fechner law on the relationship between human sensation and physical quantity, Kawabata and Niwa (1996) suggested the use of the 10 5 0 –5 40 30 20 10 Across the seam(mm)

10 5 0 –5 40 30

100 120 60 80 20 40 Along the seam(mm)

Height of pucker(mm)

(a) From the plane surface

Figure 3. Pucker profiles obtained from 1-D filtering the geometrical profiles of a sample on three different surfaces

15

20 10 Across the seam(mm)

100 120 60 80 40 20 Along the seam(mm)

(b) From the bowed downwards surface

15 10 5 0 –5 40 30 20 10

Across the seam(mm)

60

80

100

120

40 20 Along the seam(mm)

(c) From the bowed upwards surface

The height of pucker(mm)

The height of pucker(mm)

10 5 0 –5 40

5 0 –5 40

100

20 Across the seam(mm)

Evaluation of garment appearance

10

0 0

Across the seam(mm)

Along the seam(mm)

(a) From the plane surface The height of pucker(mm)

100

20

50

155

50 0 0

Along the seam(mm)

(b) From the bowed downwards surface

10 5 0 –5 40 100

20 Across the seam(mm)

50 0 0

Along the seam(mm)

(c) From the bowed upwards surface

logarithm of the average displacement from the mean magnitude as a measure of the severity of a seam's pucker, log (Ra), where N 1 X Ra z i ÿ z i : N i 1 Here z(i) is the height of the ith measurement, and N is the number of measurement points. We have also considered three other parameters, the variance, skewness and pointedness of the height distribution (Thomas, 1982), in case they gave further identification of the degree of pucker. The variance is given by N 2 1 X 2 z i ÿ zi : N i1 The skewness of the distribution of the heights of the pucker profile is given by , N 3 1 X 3 z i ÿ z i S N i1 and the pointedness (Kurtosis) of the distribution of the heights of the pucker profile is given by

Figure 4. Pucker profiles obtained from 2-D filtering the geometrical profiles of a sample on three different surfaces

N 4 1 X K z i ÿ z i N i1

IJCST 11,2/3

156

, 4 :

Figures 5 and 6 show correlation between the values of log(Ra) and log(2 ) and subjective pucker grade. As can be seen, strong linear correlation exists between the pucker grade and log(Ra) or log(2 ). The correlation coefficients between the pucker grade and log(Ra) or log(2 ) computed from the profiles after 2-D filtering are significantly higher than those after 1-D filtering, indicating the superiority of the 2-D filter. By comparing Figures 5(b) and 6(b), it can be seen that the correlation between the subjective pucker grade and log(2 ), obtained after 2-D filtering, is slightly better than that between the pucker grade and log(Ra). This is interesting as it conforms with the findings in mechanical engineering that 2 is the better measure of surface roughness (Thomas, 1982). We have also tried to correlate subjective pucker grades with the logarithm of the absolute value of skewness (log(abs(S))) and logarithm of pointedness 6

y = –5.9131x + 2.8227 R2 = 0.7952

Pucker grade

5 4

Key surface 1 surface 2 surface 3

3 2 1 0 –0.6

–0.4

–0.2

0 0.2 Log(Ra)

0.4

0.6

(a) Obtained by passing through 1-D FIR filter 6

y = –7.4421x – 1.6495 R2 = 0.9443

Pucker grade

5 4

Key surface 1 surface 2 surface 3

3 2 1

Figure 5. Correlation between subjective pucker grade and Log (Ra)

0 –1

–0.8

–0.6 Log(Ra)

–0.4

(b) Obtained by passing through 2-D filter

–0.2

6 5 Pucker grade

Evaluation of garment appearance

y = –2.9524x +3.3266 R2 = 0.7889

4 Key surface 1 surface 2 surface 3

3

157

2 1 0 –1

–0.6

–0.2 0.2 Log(σ2)

0.6

1

(a) Obtained by passing through 1-D FIR filter 6

y = –4.4724x – 1.6495 R2 = 0.9551

Pucker grade

5 4

Key surface 1 surface 2 surface 3

3 2 1 0 –1.6

–1.2

–0.8

–0.4

Log(σ2) (b) Obtained by passing through 2-D FIR filter

(log(K)) of the pucker height distribution, but found little correlation and the inclusion of S and K in a multiple regression also cannot improve the correlation. 4. Objective evaluation of overall garment appearance Attempts are being made to capture the 3-D garment surface profiles using a 3D laser scanner such as Cyberware and 3-D Model Maker, and analyse the profiles of garment appearance using image-processing techniques. Figure 7 shows an image of a shirt captured by a Cyberware laser scanner. The following procedure was tried to analyse the image: (1) Selection of a particular portion of 3D garment appearance for analysis Using a speciality software Surfacer Version 3.0 run in a UNIX platform, a portion of area is selected.

Figure 6. The correlation between subjective pucker grade and log 2

IJCST 11,2/3

158

Figure 7. Objective evaluation of overall garment appearance

(2) Segmentation of a particular portion Using the segmentation technique, curves that separated in the same distance along the X-Y axis of a particular portion are determined with the same direction as scanning of the garment surface. (3) Digital filtering 2-D digital filtering techniques are applied to remove the high frequency components due to fabric surface texture and the low frequency components due to garment silhouette and drape so as to obtain surface profiles representing the roughness, wrinkle or puckering of the surface. (4) Evaluating surface roughness The degree of the roughness, wrinkling or puckering of the garment surface are measured using the physical parameters as we did with garment seams. 5. Conclusion The work presented represents only the initial developments towards the objective evaluation of overall garment appearance. Considerable further work is needed. References AATCC TEST Method 88B-1992 (1993), AATCC Technical Manual, AATCC. Appearance Retention Test Method (1978), Clothing Service Information No. 10, IWS Japan, August.

Belser, A.B., Kwon, C.T. and Meaders, M.C. (1993), ``Instrument for grading seam pucker'', Text. Res. J., No. 38, pp. 315-18. Fan, J., MacAlpine, J.M.K. and Hui, P. (1998a), ``The use of a 2-D digital filter in the objective evaluation of seam pucker on 3-D surfaces'', Journal of Textile Institute. Fan, J., Lu, D., MacAlpine, M. and Hui, P. (1998b), ``Objective evaluation of pucker in 3dimensional garment seams'', Textile Research Journal. Hebeler, H.H. and Kolb, H.J. (1950), ``The measurement of fabric wrinkling'', Text. Res. J., No. 20, pp. 650-3. 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. Kawabata, S. and Niwa, M. (1996), ``An experiment on human sensory measurement and its objective measurement, case of the measurement of a seam pucker level'', 25th Textile Research Symposium, at Mt Fuji, Japan, pp. 85-8. Park, C.K. and Kang, T.J. (1997), ``Objective rating of seam pucker using neural networks'', Text. Res. J., Vol. 67, pp. 494-502. Park, T.W. and Burrus, C.S. (1987), Digital Filter Design, John Wiley & Sons, Inc., ch. 7, section 7.3.3. Shiloh, M. (1971), ``The evaluation of seam pucker'', J. Text. Inst., Vol. 62, pp. 176-80. Stylios, G. and Sotomi, J.O. (1993a), ``Investigation of seam pucker in lightweight synthetic fabrics as an aesthetic property, Part 1: A cognitive model for the measurement of seam pucker'', J. Text. Inst., Vol. 84, pp. 593-600. Stylios, G. and Sotomi, J.O. (1993b), ``Investigation of seam pucker in lightweight synthetic fabrics as an aesthetic property, Part 2: Model implementation using computer vision'', J. Text. Inst., Vol. 84, pp. 601-10. Stylios, G., Fan, J., Sotomi, J.O. and Deavon, R. (1992), ``A new concept in garment manufacture: the sewability integrated environment incorporating automated objective measurement systems'', International Journal of Clothing Science and Technology, Vol. 4 No. 5, pp. 45-8. Thomas, T.R. (1982), Rough Surface, Longman, New York, NY, chapter 5.

Evaluation of garment appearance 159

IJCST 11,2/3

160

Using the compression properties of pillows to estimate sleeping comfort Hiroko Yokura

Shiga University, Shiga, Japan

Masae Nakanishi

Kobe Women's University, Kobe, Japan and

Masako Niwa

Nara Women's University, Nara, Japan Keywords Compression, Pillows Abstract In order to establish an objective method of evaluating pillow comfort, we investigate the relation between the sensory evaluation of pillows and the compression properties of pillows. The subjective evaluation values of hardness, fitness and total comfort were correlated with the compression energy WC and the displacement T at the maximum load of 50gf/cm2. Pillows which have large values of WC and T were regarded as softer, fitter and more comfortable. Pillows estimated as being soft and fit showed larger contact areas and less contact pressure on the head. For the pillows which have the same flat shape, a good agreement between the individual judges was shown in the assessments of hardness and height. The shape and packing density of polyethylene pipes affected the degree of the hardness of the pillows.

1. Introduction The quality of a pillow is one of the important factors related to comfortable sleep. Recently, pillows of various shapes and made with a variety of padding materials are being marketed. New artificial materials such as polyethylene pipe and sponge are being used as pillow padding, in addition to natural materials. However, only few reports evaluate quantitatively the performances of pillows which are made from new artificial materials. The purpose of this study is to establish an objective evaluation method based on scientific grounds for use in designing comfortable pillows. In addition to the shape and mechanical properties of a pillow, the heat and moisture transport properties between the human head and the pillow are also considered to be related to comfortable sleep. In our previous study, we examined the general relationships between the mechanical and thermal transport properties and the hardness or coolness of pillows, and confirmed the applicability of the objective evaluation method of the performance (Nakanishi et al., 1997). We also examined the thermal transport properties of pillow padding (Nakanishi et al., 1998). In this paper, we address the compression properties of pillows as they relate to comfortable sleep.

International Journal of Clothing Science and Technology, Vol. 11 No. 2/3, 1999, pp. 160-167. # MCB University Press, 0955-6222

2. Experimental 2.1 Test pillows Two groups of the test pillows were selected in this study. The first group of pillows (Sample group I) consisted of 25 commercially available pillows with

various kinds of shapes, sizes, and padding materials (Nakanishi et al., 1997). They were used for the investigation of the general relationship between the compression properties and the sensory evaluation of pillows. The second group of pillows (Sample group II) consisted of 15 pillows, as shown in Figure 1. All of them have the same flat shape, 63cm in width and 43cm in depth, in order to exclude the effect of shape on the subjective evaluation. The size of commercial pillow No. 10M, having a high value for total comfort at the sensory test of sample group I, was used as a model of the trial pillows. Its padding is pipe, and the height is 9.5cm. We made five pillows with the same size as pillow No. 10M and varied the padding materials. For the pipe pillows, we made six pillows with different heights and padding pipe sizes. As shown in Figure 1, we made 11 trial pillows and also selected four commercial pillows from sample group I.

Compression properties of pillows 161

2.2 Subjective evaluation of pillows A sensory test of pillows was conducted to investigate the important factors by which consumers chose their favorite pillows. The judge lay on a cotton futon and a pillow sample was inserted under her head. The judge occasionally changed her posture and evaluated the hardness, fitness (adjustability to the back head and neck by the change of shape), height, contact touch, coolness and total comfort of the pillows, using a scale range from ±2 to +2 according to the semantic differential method. The time required for the assessment was about two minutes, in order to examine the sleeping comfort at the first stage of sleep. This feeling, such as ``comfortable'', is considered to be one of the important factors in good sleep. Also the choice of the pillow might be strongly influenced by the consumer's preference. The pillows in sample group I were judged by 31 females 18 to 38 years old. The pillows in sample group II were Trial Pillows [phi]

Height (cm)

t

11.5

PE Pipe (10H) t=0.3, [phi]=5.0, L=5.5 mm

PE Pipe (30H) PE Pipe (31H) t=0.5, [phi]=7.8, t=0.3, [phi]=4.9, L=10.1 mm L=5.0 mm

9.5

PE Pipe (10M)

PE Pipe (30M)

PE Pipe (31M)

7.5

PE Pipe(10L)

PE Pipe(30L)

PE Pipe (31L)

Japanese cypress chip/PET knops (4)

PET knops (6)

Feather (7)

PE pipe/PET knops (17)

12.51

16.45

14.64

16.72

L

Buckwheat chaff (32)

Styrol form beads(33)

Commercial Pillows

Height(cm)

( ): Sample No.

Figure 1. Pillows in sample group II: flat type (63cm in width and 43cm in depth). The size of sample No.10M was used as a model for the trial pillows

IJCST 11,2/3

judged by 30 female students 20 to 24 years old. The sensory tests were conducted in a room at 28 to 318C and 47 to 70 percent RH.

162

2.3 Contact pressure and contact area between the head and pillow The pressure on the human head Pc was measured for the six female subjects with an air pack type analyzer for measuring the pressure of clothing (Ino et al., 1998) when the subject lay on the futon using the pillow. We selected the maximum load of the compression test according to the Pc value. The contact area of head and pillow was measured for the nine female subjects. The contact area was traced on a sheet of paper when the judge put her head on the pillow, and then the area was calculated. 2.4 Compression properties of pillows The compression properties of pillows were measured at 208C and 65 percent RH with a KES-G5 handy compression tester (Kawabata, 1988). The tester's support stand was improved to adjust to the complex shape of pillows. A test pillow was put on the cotton futon, and was compressed by a 25cm2 tester disk at a constant speed of 1mm/sec. until the compression force reached the maximum load of 50gf/cm2. A 15cm diameter ball indenter for compression was also tested as the model for the human head. The linearity LC, compression energy WC, resilience RC, and the displacement at the maximum load T were calculated from the compression-thickness curve. Repeated compression tests were carried out three times. The compression properties of pillow padding were also measured. A sample was placed in a cell with a cross-sectional area of 60cm2. Compression-release cycle tests were carried out at a constant speed of 1mm/sec and a maximum load of 50gf/cm2. 3. Results and discussion 3.1 Subjective evaluation of commercially produced pillows (sample group I) The correlation between the individual scores and the mean scores of the subjective assessments of pillows was examined. In this study, the mean score of all judges was used as the subjective evaluation values in each item. As shown in Table I, the correlation coefficients among the subjective evaluation

Q1 Hardness

Table I. Correlation matrix for the sensory evaluation test

Q1 Q2 Q3 Q30 Q4 Q5 Q6

1.000 ±0.846* 0.326 0.474 0.875* 0.836* ±0.711*

Q2 Fitness

Q3 Q30 Height Absolute height

1.000 ±0.459 1.000 ±0.426 0.416 ±0.743* 0.236 ±0.544* 0.147 0.880* ±0.354

1.000 0.475 0.353 ±0.544*

Q4 Touch

1.000 0.730* ±0.726*

Q5 Warm/cool Q6 feeling Comfort

1.000 ±0.374

Notes: Number of samples: n = 25; *: Significant at the 0.01 level (r > 0.505)

1.000

items was calculated. The values for total comfort showed good correlation with the fitness, hardness, touch and the absolute value of height. As a result, it became clear that the pillows which were evaluated as being soft, fit for the head shape, smooth in touch and having good height were preferred by the female judges. From these results, we considered the evaluation of fitness and hardness to be very important subjective evaluation values. 3.2 Subjective evaluation, contact pressure and contact area Figure 2 shows the relationships between the contact area and the subjective evaluation of hardness (a) and fitness (b). The pillows with large values for the contact area showed soft and fit feelings. Figure 3 shows the relationships between the contact pressure Pc and the subjective evaluation of hardness (a) and fitness (b). The mean values of the six subjects are indicated by the open circles. In general, the pillows with small values for Pc showed soft and fit 2.0

1.5

1.5

1.0

1.0

0.5

0.5

0.0 –0.5

0.0 –0.5

–1.0

–1.0

–1.5

–1.5

–2.0 100 150 200 250 300 350 400 Contact Area, cm2

–2.0 100 150 200 250 300 350 400 Contact Area, cm2 (b)

2.0

2.0

1.5

1.5

1.0

1.0

0.5

0.5

Fitness

Hardness

(a)

0.0 –0.5

0.0

–1.0

–1.5

–1.5

–2.0

–2.0

20 40 60 80 100 120 Contact Pressure Pc, g/cm2 (a)

Key Mean Value

Figure 2. Relationship between the contact area and the subjective evaluation of hardness (a) and fitness (b)

–0.5

–1.0

0

163

r= 0.886

2.0

Fitness

Hardness

r= –0.889

Compression properties of pillows

0

20 40 60 80 100 120 Contact Pressure Pc, g/cm2 (b)

Figure 3. Relationship between the contact pressure on the head and the subjective evaluation of hardness (a) and fitness (b)

IJCST 11,2/3

164

Table II. Correlation coefficients (r) for the correlation between the sensory test value and compression properties of pillows

feelings. We select the maximum load of the compression test, 50gf/cm2, according to the Pc value. 3.3 Subjective evaluation and compression properties of commercial pillows Table II shows the correlation coefficients between the subjective evaluation values and compression properties of pillows. Table II(a) shows the results from the first curve of the compression test, and Table II(b) shows the results from the second curve. The subjective evaluation values of hardness, fitness and total comfort were correlated with WC and T. That is, the WC and T of the pillows which were soft, fit and comfortable, were large. The subjective evaluation value of height was correlated with thickness T under a 0.5gf/cm2 load. The results from the 15cm diameter ball compression indenter were almost the same as those from the 25cm2 disk. Therefore, we used the disk indenter for compression in this study. In the comparison of the result of the first curve to the second curve, the correlation coefficient between the fitness and WC1 from the first curve is higher than with WC2 from the second curve. We think the value of WC1 is influenced by the slippage between padding pieces; therefore, good correlation was obtained. Figure 4 shows the relationships between WC1 and the subjective evaluation of hardness (a) and fitness (b). The pillows with large values of WC 1 showed soft and fit feelings. The ranges of the compression properties, contact area and contact pressure for the pillows were calculated as shown in Table III, using the pillows having the five highest values for total comfort (50.50) and the five lowest (4±0.65). These properties should be used for designing high-quality pillows. We also examined the correlation between the subjective evaluation values and the compression properties of pillow padding. Figure 5 shows the relationships between the subjective evaluation of hardness and the values of Hardness

Fitness

Height

Total comfort

(a) First curve LC1 WC1 RC1 TO1 T1 TO1-T1

±0.109 ±0.909 ±0.297 ±0.420 ±0.862 ±0.122

0.134 0.735 0.018 0.241 0.694 ±0.006

±0.065 ±0.305 ±0.022 0.516 ±0.326 0.680

0.142 0.562 0.094 0.286 0.525 0.107

(b) Second curve LC2 WC2 RC2 TO2 T2 TO2-T2

±0.098 ±0.886 ±0.214 ±0.361 ±0.880 ±0.101

0.083 0.632 ±0.060 0.163 0.633 ±0.035

±0.189 ±0.218 ±0.049 0.542 ±0.219 0.673

0.031 0.514 0.020 0.247 0.532 0.093

Notes: Italics: significant at the 0.01 level (r > 0.505)

2.0

1.5

1.0

1.0

0.5

0.5

Fitness

Hardness

1.5

0.0 –0.5

–0.5 –1.0

–1.5

–1.5 0

20

40 60 80 WCI, gf/cm/cm2

–2.0

100

165

0.0

–1.0 –2.0

Compression properties of pillows

2.0

0

20

40 60 80 WCI, gf/cm/cm2

(a)

100

(b)

Total comfort Contact area (cm2) Pc (gf/cm2) WC1 (gf.cm/cm2) T1cm

Good (n = 5)

Poor (n = 5)

0.57 + 0.07 306.9 + 53.5 50.3 + 4.0 62.4 + 30.2 2.8 + 1.6

±0.95 + 0.33 198.7 + 31.0 73.9 + 4.4 26.1 + 17.5 1.2 + 0.7

Figure 4. Relationship between the compression energy WC1 and the subjective evaluation of hardness (a) and fitness (b)

Table III. The ranges of the properties for the pillows having the five highest and five lowest values for total comfort

2.0 Japanese cypress

1.5

Pipe

Hardness

1.0

Buckwheat chaff

0.5 Pipe

0.0 Beads

–0.5 Wool

–1.0

Styrol foam beads

–1.5 –2.0

PET

0

25

Key pillows pillow paddings

50

75 100 WCI gf/cm/cm2

125

150

Figure 5. Relationship between the subjective evaluation of hardness and the value of WC1 of the pillows (*) and pillow paddings (*)

IJCST 11,2/3

166

WC 1 for the pillows and pillow padding. The values of WC 1 for the pillow padding show a rough tendency for correspondence to the evaluation of the hardness of the pillow. However, for pipes and beads, the values of WC 1 for pillow padding were too small. The necessity for considering the influence of slippage between padding pieces was suggested. 3.4 Subjective evaluation and compression properties of flat type pillows In the case of the pillows in sample group II, which have the same flat shape, a good agreement between the individual judges was shown in the assessments of hardness and height. On the other hand, good agreement was not shown on the assessments of fitness and total comfort. It seems that the assessments of fitness and total comfort are affected by the shape of the pillow. The subjective evaluation values of hardness were correlated with WC and T, in the same way as the results for the commercial pillows in sample group I. The pillows estimated as being soft showed large values for WC and T. Figure 6 shows the relationships between WC1 and the subjective evaluation of hardness. As shown in this Figure, the pillows with large values for WC1 showed soft touch. In the case of polyethylene pipe pillows, the pillows with larger height (larger packing density) were evaluated as being harder than those with lower height (smaller packing density). Also, the pillows containing large polyethylene pipes were evaluated as being harder than those containing small pipes. It was clear that the shape and packing density of the padding affected the evaluation of the hardness of the pillows.

2

Hardness

1

0

–1

–2

Figure 6. Relationship between the subjective evaluation of hardness and the value of WC 1 of the pillows in Sample group II

0

20

40 60 WCI, gf/cm/cm2

80

Height Pipe (10) Pipe (30) Pipe (31) Buckwheat (cm) large chaff 11.5 9.5 7.5 Commercial Pillows:

100

Styrol form beads

4. Conclusions The subjective evaluation value of the total comfort (preference) of pillows showed good correlation with the fitness, touch, hardness and the absolute value of height. The pillows which were evaluated were judged as being soft, fit, smooth to the touch and having a height preferred by the female judges. A test pillow was put on the cotton futon, and was compressed by a 25cm2 tester disk at a constant speed of 1mm/sec, until the compression force reached the maximum load of 50gf/cm2. The subjective evaluation values of hardness, fitness and total comfort were correlated with the compression energy WC and the displacement at the maximum load T. The pillows which have large values for WC and T were regarded as being softer, fitter and more comfortable. The pillows estimated as being soft and fit showed large values for contact area and small values for contact pressure on the head. The WC values of the pillow padding show a tendency for rough correspondence to the evaluation of the hardness of the pillow, suggesting the necessity for considering the influence of slippage between padding pieces. In the case of the pillows which have the same flat shape, a good agreement between the individual judges was shown in the assessments of hardness and height. The shape and packing density of polyethylene pipes affected the degree of the hardness of the pillows. References Ino, H., Shimizu, T. and Kominami, Y. (1998), Jpn. J. Neurosurg, Vol. 7, pp. 415-20. Kawabata, S. (1988), Proc. of the 17th Textile Research Symposium at Mt Fuji, pp. 58-62. Nakanishi, M., Yokura, H. and Niwa, M. (1997), Proc. of the 26th Textile Research Symposium at Mt Fuji, pp. 51-62. Nakanishi, M., Yokura, H. and Niwa, M. (1998), Japan Journal of Thermophysical Properties, Vol. 12 No. 4, pp. 198-204.

Compression properties of pillows 167

The current issue and full text archive of this journal is available at http://www.emerald-library.com

A study of the effect of time variations for assembly line balancing in the clothing industry Chi Leung Patrick Hui and Sau Fun Frency Ng

Institute of Textile and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

A study of the effect of time variations 181 Received March 1997 Revised March 1999 Accepted March 1999

Keywords Assembly lines, Clothing industry Abstract The problem of assembly line balancing is to assign different tasks to individual workstations for ensuring the sum of task times at any station not exceeding the station time. Standard minute time is generally used in the clothing industry as a predictor of sewing speed and production efficiency. In the clothing industry, the standard minute time derived from the work study methods is generally assumed as a constant for line balancing. However, a lot of factors cause variations on operational time of the same task such as the fabrics and sub-materials, performance of the machinery, working environment and quality level of the product. With the aid of an illustrating example selected from a men's shirt manufacturing factory, the effect of time variations for assembly line balancing has been studied in this paper.

Introduction Garment manufacturing in nature is complicated, it involves a number of machines, hundreds of employees and thousands of bundles of sub-assemblies producing different styles simultaneously. In the apparel industry, assembly lines are widely adopted for mass production. Garment components are sub-assembled and eventually completed by final assembly. The design of the bundle assembly line is one important issue for efficient production. It consists of assigning and balancing tasks between workstations of an assembly line in order to minimise balance delay, labour force and ultimately minimising the total production cost. In assembly line balancing, an apparel manufacturer is interested in whether assembly work will be finished on time for delivery, how machines and employees are being utilised, whether any station in the assembly line is lagging behind the schedule and how the assembly line is doing overall. The role of supervisor is to ensure the tasks are allocated to each workstation as evenly as possible and to assign appropriate operatives to each station of an assembly line. The determination of the production time for each task is critical in the line balance of an assembly line. Ideally each workstation on the assembly line should receive an equal amount of work in time units; otherwise a bottleneck may occur on an assembly line. In most apparel enterprises, the estimation of production time for each task is by reference to Standard Minute Value, SMV. The characteristic of SMV is deterministic in nature, derived from

International Journal of Clothing Science and Technology, Vol. 11 No. 4, 1999, pp. 181-188. # MCB University Press, 0955-6222

IJCST 11,4

182

the method of work study. However, it cannot reflect the real production environment because a lot of factors such as the properties of fabric and submaterials, performance of machinery, working environment and quality level of the product may cause variations on the task time. Such variations on task time cause the assembly line balancing problem in the clothing industry to become more complicated. This paper aims to review the time measuring method used for the assembly line balancing in the clothing industry, to discuss their pitfalls and to recommend the possible solution in order to improve the effectiveness of assembly line balancing. Definition of assembly line and classification of assembly line balancing An assembly line is defined (Baybars, 1986, p. 909) as a set of distinct tasks which is assigned to a set of workstations linked together by a transport mechanism under detailed assembling sequences specifying how the assembling process flows from one station to another. A task is a smallest indivisible work element, and the order in which the tasks can be performed is restricted by a set of precedence relationships. The time required for the completion of a task is known as the task time (process time). The cycle time (station time) is the amount of time available at each station as well as the time between successive units coming off the line. Generally, the cycle time is predetermined based on the demand for the product in the given period (and/or the given operating time for the manufacturing system in that period), in other words, by what rate of production is desired. A manufacturing item is fed to the first station of the line at a predetermined constant feed rate. The production rate which associates with the feed rate is one unit of the finished product which emerges from the last station along the production line every T time units. Line balancing is classified into the following categories according to the work of T.K. Bhattacharjee (Bhattacharjee and Sahu, 1987, pp. 32-43): (1) Single model line: deterministic task time. It is assumed that the task time of work element in each station is constant and a unique product is produced in each line. (2) Single model line: stochastic task time. Same as (1) but the station times are assumed to be independently normally distributed with known means and variance. (3) Multi/mixed model line: deterministic task time. It is assumed that the task time of work element in each station is constant and an assembly line produces more than one style of the same product. (4) Multi/mixed model line: stochastic task time. Same as (3) but the station times are assumed to be independently normally distributed with known means and variance.

Time measuring method used in assembly line balancing in the clothing industry Several techniques have been used for determining time standards in the clothing industry (Friend, 1981, pp. 43-5; Luk, 1982, pp. 65-7; Manufacturing Clothier, 1988, pp. 49-51). It includes: (1) time study; (2) predetermined motion time systems; (3) work sampling; and (4) standard data development. The predetermined motion time system (PMTS) has become increasingly important in setting time standards for various work tasks. PMTS are systems that synthesise operation times by combining pre-determined times of basic human movements. Over the years, various PMTS have been developed for the clothing industry. These have included Singer Data, Stamp and GMD (Garment Making Data) using MTM (Methods Time Measurement), GSD (General Sewing Data) using MTM Core Data and CME (Clothing Methods Engineering). The MTM is commonly used for evaluating the task time for each operation in the clothing industry. According to the report of the Clothing Technology Committee of the Clothing Institute (The Clothing Technology Committee, 1980, pp. 145-8), the definition of MTM is a procedure of analysing any manual or the method into the basic motions required. Each motion is also assigned in a predetermined time standard that is determined by the nature of the motion and the conditions under which it is made. Garment making is by nature more complicated than many other industries. Many factors such as the properties of fabrics and human emotions will affect the performance of the operatives that ultimately will cause variance on the task time in an assembly line. In the study by Betts and Mahmoud (1989, pp. 427-45; 1992, pp. 23-33), the varying skill of operatives and stochastic task time were considered for assembly line balancing in the clothing industry. In practice, the variation of task time is affected not only by operatives' skill but also other factors such as working environment, performance of machinery and also the quality level of the product. As a result, the actual time for the completion of each task varies between different operatives and such variations also exist at the same task repeatedly performed by the same operative. Time variation between each task becomes important for the assembly line balancing. The skill level of each operative for each task of the assembly line has to be known if the varying skills of operatives were adopted for assembly line balancing. In real production environment, one workstation normally involves more than one task. When there are several tasks to be carried out by an operative in a workstation, it is difficult to determine the skill level of an

A study of the effect of time variations 183

IJCST 11,4

184

operative who works for a combination of various tasks. Therefore, it would be more practical to consider the time variance of each workstation than the variance of each single task for assembly line balancing. Because of the time variations, the task time is not deterministic in nature. As more than one style of garment would be produced in an assembly line, assembly line balancing in the clothing industry belongs to the class of ``multi/ mixed model line ± stochastic task time'' according to Bhattacharjee's classification scheme (Bhattacharjee and Sahu, 1987, pp. 32-43) as described in the previous section. To measure the effectiveness of balance for this class of assembly line, the smoothness index (SI) developed by Moodie and Young (1965, pp. 23-9) was used in this study. Criteria for measuring the effectiveness of balance in the assembly line Moodie and Young (1965, pp. 23-9) proposed the SI for measuring the effectiveness of balance in assembly lines. SI is defined as: SI k1 Smax ÿ Sk 2 1=2

k 1; 2; ::::; Nstns

1

where Smax is the maximum station time, Nstns is the number of workstations and Sk is the individual station time. The station time (Sk) of each station is calculated using the relation Sk Smean Svar 1=2

2

where is the confidence coefficient for normally distributed work element times and Smean and Svar are the sum of the means and the sum of the variances, respectively, of all the tasks allotted to that particular workstation. The value of can be varied according to the wish of the decision maker. For the illustrating example in the next section, the value of chosen is 1. In equation (1), time variance is a crucial element in measuring the effectiveness of line balancing. The smaller the SI, the higher the balancing effectiveness. An example to illustrate the importance of time variations in assembly line balancing An illustrating example of the line balancing problem in making men's shirt is shown in Figure 1. The data were collected from a men's shirt manufacturing factory in Hong Kong. In this example, the total numbers of workstations and tasks in the assembly line are 87 and 40 respectively. Each task is represented by a circle, labelled by an integer number inside the circle as task number. These circles are connected by arrows indicating the precedence relationship between the tasks. From the data illustrated in Table I, as computed by equation (1) and (2), the SI without variance and the SI with variance are 540 and 485 respectively.

7

1

12

26

28

30

35

2

13

27

29

31

36

3

14

32

37

4

15

33

38

5

16

34

39

6

17

8

18

9

19

10

20

11

21

A study of the effect of time variations 185

40

22 23 24 25

The above computational results are supported by the following mathematical proof. In equation (1), SI k1 Smax ÿ Sk 2 1=2 ; In equation (2), Sk Smean Svar 1=2 ; and If variance ? 0, it implies that Sk = Smean. ;Smax ÿ Smean ÿ Svar 1=2 Smax ÿ Smean ) SIwith time variance SIwithout time variance Conclusion and recommendations The nature of assembly line balance in the clothing industry is stochastic because of the existence of task time variations. The smoothness index (SI) developed by Moodie and Young (1965, pp. 23-9) is an appropriate tool for measuring the effectiveness of assembly line balance in the apparel industry. Based on the real production data collected from a men's shirt manufacturing factory, the example illustrated in this study clearly showed that the SI with time variance is smaller than the SI without time variance. The smaller the SI, the higher the effectiveness the line balancing. This implies that the variance of

Figure 1. Precedence relationship of 40 tasks for men's shirt manufacturing

IJCST 11,4

186

Table I. Actual line balance of making men's shirt in terms of workstation number, task number to be performed, station time and station variance

Station number

Task number

Station mean

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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

24 24 38 08, 38 06 38 03, 06, 07, 35 38 03, 07, 35 05 27 25 25 25 25 09 25 18, 21 25 05 14 06 23 22 24 18 16 12 28 24 15 29, 32 23 29 29 17 26 22, 24 21 13 28 30 23 18 18 20 19 21 29, 32

30.67 19.55 6.22 16.58 6.58 12.09 14.67 13.17 6.91 3.29 3.67 9.80 28.64 22.78 28.73 1.86 22.88 7.56 24.73 2.38 4.60 1.07 13.47 10.16 20.72 9.28 10.26 5.15 8.68 20.12 7.72 21.73 13.83 12.96 6.64 11.91 3.83 35.1 10.82 5.97 7.89 10.36 5.89 7.95 7.06 10.69 10.11 12.32 25.48

Station variance 0 9.27 4.35 7.41 3.42 3.15 5.4 2.45 8.64 2.16 1.72 3.77 7.11 11.08 9.91 0.87 12.44 3.55 9.73 1.11 2.20 0.50 6.36 5.22 9.25 3.17 4.81 2.55 3.59 9.71 3.65 11.54 6.22 6.07 2.96 5.11 1.80 13.67 5.68 2.81 4.14 4.85 0 3.58 3.54 4.99 4.74 5.17 10.32 (continued)

Station number

Task number

Station mean

Station variance

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87

19 26 31 23 17 20 34 06 33 20 06, 39 11, 22, 40 07, 08, 19, 38 38 06 07 39 09 06 04 08 38 01, 06 01, 06 04 04 37, 38 11, 19, 22, 40 01, 03, 09 01, 36 05 04 09 36 06, 10, 39 08, 38 02, 06, 19, 38 07

11.28 3.84 6.03 12.74 8.29 11.02 2.81 2.74 4.28 11.85 7.25 13.95 30.5 9.36 1.73 3.68 3.14 3.75 7.05 16.13 9.18 9.73 10.5 12.37 13.37 19.23 13.51 15.48 16.1 63.14 4.81 11.13 2.13 20.64 12.15 17.29 23.94 3.72

4.90 1.80 2.73 6.36 6.45 5.53 1.32 1.19 1.97 4.75 3.21 6.11 17.7 5.00 0.79 1.70 1.69 2.67 3.08 8.73 5.33 5.23 9.51 10.27 7.52 4.64 1.4 7.69 13.36 35.68 3.10 6.81 0.99 20.18 4.29 9.47 17.45 1.81

task time in clothing production is significant in assembly line balancing. The time variance should be taken into consideration for improving effectiveness of line balancing. The study also found that using time variance of each workstation rather than the time variance of each task would be beneficial for assembly line balancing in the clothing industry. References Baybars, I. (1986), ``A survey of exact algorithms for the simple assembly line balancing problem'', Management Science, Vol. 32 No. 8, p. 909.

A study of the effect of time variations 187

Table I.

IJCST 11,4

188

Betts, J. and Mahmoud, K.I. (1989), ``Identifying multiple solutions for assembly line balancing having stochastic task times'', Computers Industrial Engineering, Vol. 16 No. 3, pp. 427-45. Betts, J. and Mahmoud, K.I. (1992), ``Assembly line balancing in the clothing industry allowing for varying skills of operatives'', International Journal of Clothing Science and Technology, Vol. 4 No. 4, pp. 28-33. Bhattacharjee, T.K. and Sahu, S. (1987), ``A critique of some current assembly line balancing techniques'', International Journal of Operations & Production Management, Vol. 7 No. 6, pp. 32-43. (The) Clothing Technology Committee (1980), ``Summary of PMTS'', The Clothing Technology Committee Report of the Clothing Institute, pp. 145-8. Friend R.L. (1981), ``Predetermined motion time systems: 2- general sewing data (GSD)'', Knitting Ind. Tech. Rev., Vol. 1, August, pp. 43-5. Luk, C.M. (1982), ``Operation standard time'', Textile Asia, Vol. 8, September, pp. 65-7. Manufacturing Clothier (1988),``PMTS: a clothing industry update'', Vol. 71 No. 4, April, pp. 49-51. Moodie C.L. and Young H.H. (1965), ``A heuristic method of assembly line balancing for assumptions of constant or variable work element times'', Journal of Industrial Engineering, No. XVI, pp. 23-9.

The current issue and full text archive of this journal is available at http://www.emerald-library.com

Thermophysiological comfort of silicone softeners-treated woven textile materials Tzanro Tzanov

Textile Engineering Department, University of Minho, Guimaraes, Portugal and

Rossitza Betcheva and Ivan Hardalov

Thermophysiological comfort 189 Received July 1997 Revised February 1999 Accepted February 1999

Textile Department, University of Chemical Technology and Metallurgy, Sofia, Bulgaria Keywords Thermophysiological, Silicone, Woven fabrics Abstract In this study the effect of the aminofunctional silicone softeners on fabrics' heat and moisture transport properties has been investigated by means of Alambeta and Permetest instruments. The silicone treated PES blended fabrics are warmer to the touch, but less comfortable as regards their reduced water-vapour permeability. The finishing stage of the fabrics has considerable influence on their thermal touch sensation and water-vapour permeability.

The thermophysiological comfort experienced through wearing clothing is determined by the sensation of warmth or coolness at contact with the fabric as well as by the loss of water vapour through the clothing, which regulates the heat balance of the body. The so-called ``warm-cool feeling'' is included in the overall assessment of the handle of the textile materials, together with the lowstress physicomechanical properties of textiles. Traditionally, most of the measurements in that area have been conducted in equilibrium state, analysing such easily measured properties as thermal conductivity, resistance and moisture permeability. Although the steady-state methods provide good data, they cannot explain heat and moisture related subjective sensations that determine human comfort. This approach in studying the thermophysiological component of the clothing comfort does not reflect the real wearing situation, when the human body is rarely in a steady thermal state and interacts with clothing continuously and dynamically. The fabric provides a barrier to the passage of water vapour to the environment. Water vapour diffusion in fabric could be realised through the interfibre and yarn spaces, through the fibre substance itself and through the free air spaces. There is no single simple diffusion process of moisture passage through a fabric[7]. Vapour diffusion through the clothing involves phase changes at the surface of the fabrics. There exist two states of water phase ± gaseous and liquid ± and consequently several different interfaces between moisture and clothing medium. Not only does the fibre content and fabric structure, but also the specific finish of the fabric have strongly influencing effects on heat and moisture transportation[1]. In the final stage of fabric production different softening

International Journal of Clothing Science and Technology, Vol. 11 No. 4, 1999, pp. 189-197. # MCB University Press, 0955-6222

IJCST 11,4

190

finishes are applied to overcome the stiffness of the fabrics and to improve their handle. During this treatment, not only the mechanical, but also the thermal insulation, thermal contact and water-vapour transfer properties of the fabrics change. The silicone softeners are amongst the most largely applied, with good efficiency, chemicals for these finishes. The present study deals with aminofunctional polysiloxanes as the most prospective class of silicone softeners with universal application to all textile substrates, independently of fibre type. Our objective was to evaluate the effect of these silicone softeners, applied in different concentrations, on the heat and moisture transport properties of textiles concerning their thermophysiological comfort. Experimental Apparatus Thermal resistance, thermal conductivity and thermal absorptivity of the silicone treated fabrics have been measured by means of the new computer controlled Alambeta device[2,5] which enables fast measurement of both steady state and transient state thermal properties of any plain compressible non-metallic materials like textile fabrics, plastic or rubber foils, paper products, liquids, pastes and fine powders. The heat flow passing between the textile sample and measuring head during thermal contact is measured by a special thin sensor, whose thermal inertia is similar to that of human skin. A new parameter to describe the warm-cool feeling of fabrics, namely the thermal absorptivity has been introduced. Thermal absorptivity is defined as: p b :p:c The thermal absorptivity is the parameter, characterizing the level of heat flow q, which passes between the human skin of infinite thermal capacity and temperature t1 and the contacting textile fabric, idealized to a semi-infinite body of finite thermal capacity and temperature t2 according to the equation: qdyn bt1 ÿ t2 =1=2 This equation is valid just for the short initial time of thermal contact between the skin and the fabric and this initial sensation is most important for the warm-cool feeling. For longer time, exceeding a few seconds, the heat flow q loses the dynamic (transient) character and reaches steady state level. The thermal absorptivity reflects the surface properties of fabrics and does not depend on experimental conditions, except for fabric type. This parameter substantially changes with the surface finishing of the fabric (coating, raising, grinding, pressing)[3]. Permetest[4,8] instrument has been used to determine the relative watervapour permeability. The outer surface of the tested sample is exposed to a parallel air flow and the other sample side faces a porous humid layer (0.2 ml of water has been injected into the layer), which simulates any underwear filled with liquid sweat. A space of 1mm between the sample and this layer separates

the liquid and vapour phase of the water. The working principle of the instrument consists in measuring the dynamic heat flow caused by the evaporation of water passing through the tested specimen. Relative water vapour permeability is defined as the ratio of the heat loss measured with sample and the heat loss measured without sample. Similar to the first described instrument thermal sensor, it simulates the sensitivity of human skin. Materials The following woven textile materials have been used: viscose/polyester 33/67 fabrics, twill weave 2.2, warp setting 284, 2062 tex, weft setting 210, 40 tex, mass per unit area 206g/m2 ± undyed, non-thermoset, and respectively dyed, thermoset. Sample dimensions are 80 6 80mm. Fabrics have been treated with water emulsions of three commercially available aminofunctional polysiloxane softeners ± Sandoperm MEJ, Sandoperm MEW, and Sandoperm FE, applied in three concentrations ± 10, 20 and 30g/l. After padding (wet pick-up 80 per cent) fabrics have been dried at 1208C for one minute and then cured without steam at 1808C for 30sec. Treated samples have been stored under standard conditions (218C and 65 per cent RH) until performance properties were evaluated. The discussion of the results is based on the average values of five measurements of the thermal properties and relative water-vapour permeability, taken at five different places on every sample. The results have been automatically processed and the coefficients of variance (CV) are presented as well.

Thermophysiological comfort 191

Thermal properties and water-vapour permeability of the untreated samples Comparison of the results for the untreated samples (Table I) of fabrics of same fibre type and construction, undyed, non-thermoset and dyed, thermoset showed differences in all measured parameters. The dyed sample has higher thermal conductivity () and absorptivity (b), and the amount of heat flow (q) transferred between the instrument's measuring head and the sample is greater, while the thermal resistance and sample thickness are lower. Higher thermal absorptivity means that the dyed fabric is cooler to touch. Relaxational processes during thermosetting and high temperature dyeing alter the thermal properties of fabrics. The non-thermoset samples have loose structure with more air involved within. Therefore, their thermal resistance should be higher

Sample

Water-vapour CV 103 b rCV 103 hCV qCV permeability pCV 2 W =m:K W : s=m :K K:m2 =W mm W =m2 (per cent)

Undyed, unthermoset

65.1(1.3)

333(2.9)

6(1.8)

0.39(1.9) 1.49(2.5)

22.81

Dyed, thermoset

70.4(1.4)

400(3.1)

5.3(1.5)

0.37(1.7) 1.7(2.4)

18.58

Table I. Thermal properties of the untreated fabrics

IJCST 11,4

192

and their thermal conductivity and absorptivity lower, before silicone application. Dyed, thermoset fabric has higher thermal absorptivity and lower water-vapour permeability. This could be explained by the reduction of the free air spaces within the fabric caused by thermosetting and high temperature dyeing. Dyeing and thermosetting relax the internal stresses in fabric structure and render it tighter. The size of the air voids entrapped in the initial undyed and non-thermoset sample decreases. Thus, the barrier for heat transfer gets lower. On the other hand, most of the moisture passes directly through the air spaces as vapour because of the fabric air volume. Reduction of this air volume impedes the passage of the water-vapour flux. The rest of the moisture interacts with the fibres depending on their hydrophility. The studied fabrics represent relatively hydrophobic blends (67 per cent PES, 33 per cent viscose). Possibility of moisture diffusion in dense fabrics is related to absorption and redistribution of liquid water through the fabrics and consecutive reevaporation. The hydrophilic component in the studied blends does not play the main influence in the moisture transfer, unlike the free air volume of the fabric. Hydrophility of viscose fibres cannot cause significant change in heat and moisture transport properties of the blended fabric because there is only a small proportion of their volume within the fabric. It is more likely that these properties depend mainly on fabric thickness, construction and bulk density. Thermal properties and water-vapour permeability of silicone softeners-treated fabrics The thermal conductivity (Figure 1) of both thermoset and non-thermoset fabrics slightly changed after the softeners' application. 74

Thermal conductivity

72

Dyed

70 68 66

Undyed

64 62 60 0

5

Key

Figure 1. Thermal conductivity, [W/m.K 6 103]

MEJ MEW FE

10 15 20 concentration, g/l

25

30

The two parameters describing the warm-cool sensation as a component of fabric handle are: according to Hes[2,5], the thermal absorptivity (b) and according to Yoneda and Kawabata[12], the amount of heat flow (q) transferred between the fabric and the hand. Both of them decreased after silicone softeners' treatment (Figures 2 and 3). The reduction of the thermal absorptivity means that the fabrics are warmer to touch. However, this decrease has a different magnitude, depending on the applied silicone and the finishing stage of the fabrics. Dyed and thermoset fabric undergoes approximately twice lower reduction of b (7 per cent) than the undyed, non-thermoset one (14 per cent). As might be expected, thermal properties of the relaxed, thermoset fabric structure were not affected so markedly by the silicone treatment. The silicone softeners' application affected most considerably the thermal resistance and the thickness of the non-thermoset fabric (Figures 4 and 5). The values of r increased for the undyed, non-thermoset sample up to 50 per cent and for the dyed, thermoset ± about 10 per cent, while the increase in fabric thickness for the above fabrics is respectively 46 per cent and 15 per cent. An increase in fabric thickness usually induces an increase in the thermal insulation. This large increase in thickness of the non-thermoset samples is mainly caused by the relaxational shrinkage, occurring at the high temperature silicone curing (1808C). Relaxational process, due to the polyester component, resulted in a visible tightening of the structure and reduction of fabrics' initial dimensions. Tightening drives away the air included in fabric structure and increases the bulk density. Therefore, the thermal conductivity and absorptivity should increase. However, the thickening of the non-thermoset

Thermophysiological comfort 193

420 Dyed Thermal absorptivity

400 380 360 340

Undyed

320 300 280 260 0

5

10 15 20 concentration, g/l

25

30

Key MEJ MEW FE

Figure 2. Thermal absorptivity, p W : s=m2 :K

IJCST 11,4

1.7

Dyed

194

Heat flow

1.6 1.5

Undyed

1.4 1.3 1.2 0

5

10 15 20 concentration, g/l

25

30

5

10 15 20 concentration, g/l

25

30

Key MEJ

Figure 3. Heat flow, [W/m2]

MEW FE

Thermal resistance

10 9

Undyed Dyed

8 7

Undyed

6 5

Dyed 0 Key

Figure 4. Thermal resistance, [K.m2/W 103]

MEJ MEW FE

fabric is so significant that the inverse phenomenon is presented ± thermal conductivity and absorptivity decreased, while resistance increased considerably. This interpretation of the results for the non-thermoset samples seems to be reasonable. As far as the dyed, thermoset samples are concerned, where structural changes are not crucial, the same explanation does not fit properly.

Thermophysiological comfort

0.65

Thickness

0.60 0.55

195

0.50 0.45

Undyed

0.40 Dyed 0.35 0

5

10 15 20 concentration, g/l

25

30

Key MEJ MEW FE

Some authors[11] assumed the fabric as a three-layer structure consisting of a core and an outer layer at each face. Fabric structure and particularly surface properties have great influence on the warm-cool feeling. The core is dense and has higher thermal inertia than the outer layer, which consists mainly of air with projecting fibres. The resistance of the outer layer depends on its thickness. Thus the thinner the outer layer, the cooler the fabric will feel to the touch. As the silicone softening finishes do not penetrate the fibre structure, their effect is presented on fabric surface (the outer layer) through formation of a polymer film; thereby the total thickness of the silicone treated samples increases. It is the bulk density of the fabric surface that is important to the warm-cool feeling rather than the overall bulk density[6]. Aminofunctional silicone softeners are known to provoke certain hydrophobity of the treated fabrics. Such a hydrophobic treatment promoted lower water-vapour flux than for the untreated samples (Figure 6). Water vapour has to diffuse through the voids between fibres and yarns, since liquid water cannot be absorbed by the fibres. Silicone application reduced the wicking (spreading) ability of the fabric. Water-vapour permeability decreased with the increase of softeners' concentration. The applied aminofunctional silicones interact with the textile material by formation of a polymer film on the fabric surface[9, 10]. This film has low surface tension, inherent to silicone polymers and does not allow the moisture from the surface to penetrate fabric bulk easily. Conclusions The applied aminofunctional silicone softeners imparted to the PES containing fabrics a warmer handle. Silicone treated fabrics are warmer to touch, but less

Figure 5. Fabric thickness, [mm]

196

24 Water-vapour permeability

IJCST 11,4

Undyed

22 20

Dyed

18 16 14 12 10 0

5

10 15 20 concentration, g/l

25

30

Key

Figure 6. Water-vapour permeability, per cent

MEJ MEW FE

comfortable as regards their reduced water-vapour permeability. Changes in the warm-cool feeling of the thermoset fabrics are mainly due to changes in their surface characteristics, caused by the formation of a silicone polymer film on their surface. For the non-thermoset fabrics the structural changes, which occurred during the softeners' curing, are determinant. The silicone softeners' application does not alter markedly the thermal properties of the dyed, thermoset fabrics. The finishing stage of the fabrics has considerable influence on their thermal touch sensation and water-vapour permeability. Clear dependence between the discussed thermal and moisture transport properties and softeners' concentration in the frames of concentrations prescribed in the recipes for application of the products is hardly to be established. Water-vapour permeability, however, decreases depending on the increase of softeners' concentration. Small differences in the effect of various aminofunctional polysiloxanes on fabric thermal and moisture transport properties are detectable by means of Alambeta and Permetest instruments. Thus, the apparatus could be a useful tool for control of fabrics' thermophysiological comfort at finishing operations. References 1. Brownless, N.J., Anand, S.C., Holmes, D.A. and Rowe, T., ``The dynamics of moisture transportation, part I: the effect of `wicking' on the thermal resistance of single and multilayer fabric systems'', J. Textile Inst., Vol. 87 Part 1, No. 1, 1996, pp. 173-82. 2. Hes, L., Hanzl, J., Dolezal, I. and Miklas, Z., ``New method and instrument for the objective evaluation of thermal-contact properties of flat textile fabrics'', Melliand Textilberichte, Vol. 71, 1990, pp. 679-81.

3. Hes, L., Hybl, V. and Bandyopadhyay, B.B., ``The effect of fibre polymer on warm-cool feeling'', Ind. J. Fib. Textile Res., Vol. 16, 1991, pp. 195-8. 4. Hes, L. and Carvalho, M., Indian J. of Fibre and Textile Res., Vol. 19, 1994, pp. 147-50. 5. Hes, L. and Dolezal, I., ``New method and equipment for measuring thermal properties of textiles'', J. Textile Mash. Soc. Jpn., Vol. 42 No. 8, 1989, pp. T124-128. 6. Holcombe, B.V., Brooks, J.H., Schneider and Watt, I.C., Pre-print of Conference Proceedings: Annual World Conference of the Textile Institute, The Textile Institute, Manchester, 1988, p. 436. 7. Hollie, N.R.S., ``The comfort characteristics of next-to-skin garments, including shirts'', Shirley Int. Sem. Textile Comfort, Manchester, 1971. 8. Hollie, N.R.S. and Hall, P., ``Comfort acceptance in knit structures'', AATCC Symp., No. 17, 1975, pp. 88-93. 9. Jang, K.O. and Yeh, K., ``Effects of silicone softeners and silane coupling agents on the performance properties of cotton fabrics'', Textile Res. J., Vol. 63 No. 10, 1993, pp. 557-65. 10. Lautenschlager, H.-J., Bindl, J. and Huhn, G., ``Structural correlations for aminofunctional silicone softeners, alkylation and acylation for changing the property profile'', Textil Praxis International, May, 1993. 11. Schneider, A.M. and Holcombe, B.V., ``Properties influencing coolness to the touch of fabrics'', Textile Res. J., Vol. 61 No. 8, 1991, pp. 488-94. 12. Yoneda, M. and Kawabata, S., ``Analysis of transient heat conduction and its applications, part II'', J. Textile Mash. Soc. Jpn., Vol. 31, 1985, pp. 73-80. Further reading Hong, K., Hollies, N.R.S. and Spivak, S.M., ``Dynamic moisture vapor transfer through textiles, part I: clothing hygrometry and the influence of fiber type'', Textile Res. J., Vol. 12, 1988, pp. 697-706. Instruction Manuals of Alambeta and Permetest Instruments, SENSORA, Liberec, Czech Rep.

Thermophysiological comfort 197

The current issue and full text archive of this journal is available at http://www.emerald-library.com

IJCST 11,4

Water transfer properties of two-layer weft knitted fabric Hai-Ru Long

198 Received July 1998 Revised March 1999 Accepted March 1999

College of Textiles, China Textile University, Shanghai, P.R. China Keyword Knitwear Abstract The aim of this study is to investigate the water (moisture vapor and liquid) transfer properties of two-layer weft knitted fabric and some related factors. Some experimental fabrics with specific yarns and stitch densities were prepared. Water vapor permeability rate through the fabrics and liquid water transfer from inner to outer layers were measured. The results show that the permeability rate is closely related to the porosity within the fabric while the transfer depends mainly upon the water absorption properties of the fibers on the two layers and degree of their difference.

1. Introduction When garments are worn, heat and moisture diffused from the skin will exchange, through garments, with the external environment. Garment fabrics play an adjusting role and their properties of heat and moisture transmission have very important effects on wearing comfort. A lot of research work has focused on this field. In order to make use of the structural features of knitted fabric and the difference of water absorbability between fibers, and develop knitted fabric for sportswear or hygienic underwear, some scholars have investigated the heat and moisture transfer through two-layer knitted fabrics whose two sides were made of loops of different fiber materials (Piller, 1985a; 1986b; Umbach, 1986, 1986). These works are valuable for improving comfort of knitwear.

International Journal of Clothing Science and Technology, Vol. 11 No. 4, 1999, pp. 198-205. # MCB University Press, 0955-6222

2. Water transfer mechanism There are two forms of water loss from dressed body: water vapor and liquid water. Any knitted fabric has a resistance to water vapor diffusion from the skin to the environment. The difference of the resistance, which can be determined by the water vapor permeability rate, will influence wearing comfort directly. As for knitted fabric worn in a hot, humid environment or under strenuous exercise conditions, liquid sweat may transfer within fabrics. According to different water absorbability of used fiber materials, two-layered knitted fabrics can be divided into the following four kinds: (1) As shown in Figure 1(a), water has diffculty in being absorbed by the fabric, and so sweat on the skin will diffuse mainly as water vapor through the pores within the fabric and evaporate slowly, which causes the wearer a thermal and wetness discomfort. (2) As shown in Figure 1(b), although sweat is absorbed by the inner layer nearest to the body, it can not be transferred to the outer layer due to hydrophobicity of the fibers. After the water fills the pores in the inner

layer, static air cannot be kept in the pores, so that thermal insulation capacity of the fabric comes down and the fabric feels wet and cool. (3) As shown in Figure 1(c), sweat absorbed by the inner layer is transferred first to the outer layer, and then evaporates from the outer wet area into the environment. Under the same climate conditions, the rate of evaporation is in direct proportion to the area. As much water remains in the inner layer and the outer wet area is smaller, the rate of evaporation will be lower. In addition, the fabric feels wet and cool. (4) As shown in Figure 1(d), sweat is hardly absorbed directly by the inner layer, but it can be transferred to the outer layer with the aid of the wicking action of capillary among fibers of inner layer. Outer layer has good water adsorbability and produces a larger wet area that can promote quick water evaporation while the inner layer only plays a conduction role and keeps dry, so that wearing comfort is achieved.

Water transfer properties

199

3. Experimental Several two-layer weft knitted fabrics were manufactured on an automatic Vbed flat machine. Yarn specifications, knitting notation, knitting specifications and parameters of finished fabric are respectively given in Tables I to IV. There are various methods used to measure the water vapor permeability rate through textiles (American Society of Testing and Materials, 1995; British Standards Institute, 1992; Canadian General Standards Board, 1986; Din, 1972,

Figure 1. Moisture transfer model

Yarn designation A B C D E F G H

Linear density

Fiber content

Production method

2671 tex 1671 tex 1640 tex 2640 tex 4616.7 tex/32f 3616.7 tex/32f 1663 tex 1667 tex

100 percent cotton 100 percent cotton PP PP PET PET PAN Wool-PAN-PA(55:30:15)

Combed Combed Spun Spun Filament Filament Spun Spun

Table I. Yarn specifications

IJCST 11,4

200 Table II. Knitting notation

Notation no.

Schematic diagram

IV

Tuck on even needles front needle bed and knit on all needles real needle bed

III

Tuck on odd needles front needle bed and knit on all needles real needle bed

II

Knit all needles real needle bed only

I

Knit all needles front needle bed only

1991). In this study, the rate (g/m2 24h) was determined as weight loss of water in a 24-hour period through the finished fabric over a water reservoir. Samples and measuring devices were placed inside a controlled chamber at air temperature of 20 18, the relative humidity was 65 2 percent, and the air current was 1 0.05 m/s. Liquid water transfer from the inner to the outer layer was measured by a simulation method. With the help of a pipette, a definite quantity (0.2ml) of colored water was dropped on the inner layer of finished fabric and then the colored wet area on the two layers was measured by integraph. Water transfer could be evaluated by the ratio of the outer-layer to the inner-layer wet area. The ratio = 0 means that no water was transferred from the inner to the outer layer. The ratio » 1 indicates that, on the one hand, most of the water was transferred to the outer layer and, on the other, a larger wet area was favorable for quick water evaporation into the environment. Therefore, the higher the ratio of a fabric, the better its wearing comfort. Five separate samples were cut from finished fabrics to measure the water vapor permeability rate and wet area ratio, and then mean values were calculated for each experimental fabric. 4. Results and discussion 4.1. Water vapor permeability rate (WVPR) 4.1.1. General trend. Table V gives the WVPR of each fabric. According to the differences of the yarns used on the two layers (outer/inner layer), all experimental fabrics can be divided into six groups: (1) Cotton/Cotton; (2) Cotton/PP; (3) Cotton/PET; (4) Cotton/PAN; (5) Wool-PAN-PA/PP; and (6) Wool-PAN-PA/PET.

Sample No. 1

2

3

4

5

6

7

8

9

10

11

Course No.

Yarn designation

Knitting notation

Loop length (mm)

1 2 3 4

A B A B

I III I IV

5.7 4.2 5.7 4.2

1 2 3 4

A C A C

I III I IV

5.7 4.2 5.7 4.2

1 2 3 4

A C A C

I III I IV

6.2 4.2 6.2 4.2

1 2 3 4

A D A D

I III I IV

6.2 4.2 6.2 4.2

1 2 3 4

A D A D

I III I IV

5.7 4.2 5.7 4.2

1 2 3 4

A E A E

I III I IV

5.7 4.2 5.7 4.2

1 2 3 4

A F A F

I III I IV

5.7 4.2 5.7 4.2

1 2 3 4

A G A G

I III I IV

5.7 4.2 5.7 4.2

1 2 3 4

H D H D

I III I IV

5.2 4.2 5.2 4.2

1 2 3 4

H E H E

I III I IV

5.2 4.2 5.2 4.2

1, 3, 5, 7, 9, 11 2, 4, 8, 10 6 12

A B B B

I II III IV

5.2 4.2 4.2 4.2 (continued)

Water transfer properties

201

Table III. Knitting specifications

IJCST 11,4

Sample No.

12

202

13

Table III.

Course No. 1, 3, 5, 7, 2, 4, 8, 6 12 1, 3, 5, 7, 2, 4, 8, 6 12

Yarn designation

Knitting notation

Loop length (mm)

A D D D A E E E

I II III IV I II III IV

5.2 4.2 4.2 4.2 5.2 4.2 4.2 4.2

9, 11 10 9, 11 10

From the data in Table V, it cannot be observed that some groups of fabrics exhibit generally higher or lower WVPR. The results indicate that, whether hydrophobic or hydrophilic fiber material is used, the fiber nature has no apparent effect on WVPR. 4.1.2. Effect of bulk density. Figure 2 shows the relation between bulk density B and WVPR for three groups of fabrics (Cotton/PP, Cotton/PET, Cotton/Cotton). The fabric with the highest B in the same group gives the lowest WVPR, which can be explained by the following formula: P F ÿ B=F 100%; Sample No.

Table IV. Fabric parameters

Table V. WVPR of fabrics

Stitch density (stitch/cm2)

Tightness factor K

Mass (g/m2)

Thickness (mm)

Bulk density (g/m3)

29.07 29.04 21.71 22.58 27.01 29.07 29.12 27.70 31.34 35.51 38.85 38.62 39.42

16.9 9.5 9.5 19 19 15.9 11.9 15 19 15.9 16.9 19 15.9

572.74 467.90 411.18 524.28 574.14 588.10 531.06 532.60 587.80 616.34 656.90 684.14 677.80

2.703 2.453 2.568 2.986 2.949 3.087 2.907 3.042 3.362 3.547 2.774 3.056 2.960

0.2119 0.1907 0.1601 0.1755 0.1946 0.1905 0.1826 0.1751 0.1748 0.1738 0.2368 0.2238 0.2289

1 2 3 4 5 6 7 8 9 10 11 12 13 Sample no. outer/inter WVPR (gm2 24h)

1 C/C 1042.86

2 C/PP 1058.76

3 C/PP 1149.39

4 C/PP 1131.51

5 C/PP 1055.97

6 C/PET 1010.24

Sample no. outer/inter

8 C/PAN

12 C/PP

13 C/PET

1029.81

10 W-PANPA/PET 1004.29

11 C/C

WVPR (g/m2 24h)

9 W-PANPA/PP 996.77

1025.79

989.71

972.76

7 C/PET 1066.85

Water transfer properties

WVPR (g/m224h) 1200 1100 1000

203

900

B (g/m3)

800 1.6

1.76 1.91 1.94 2.24 1.83 1.91 2.29 2.12 2.37

Key C/PP

C/PET

C/C

Figure 2. The relation between B and WVPR

where P is porosity of fabric and F is volume density of fiber. It is obvious that P is inversely proportional to B. Higher B leads to lower P and reduces water vapor diffusion through the pore within the fabric. 4.1.3. Effect of stitch density and yarn count. As can be seen from Tables IV and V for two pairs of Cotton/PP fabrics (sample No. 2 with 3; 5 with 4), a fabric of higher stitch density has lower WVPR under otherwise equal conditions. Figure 3 shows the effect of the difference of the inner-layer yarn tex Ti on WVPR for three pairs of fabrics (sample No. 2 with 5; 3 with 4; 7 with 6). Although fabrics in the same pair were kept unchanged in loop length l and outer-layer yarn tex, their tightness factors Ki (Ki2 = Ti/l) of the inner layers are different. The fabrics with higher Ti produce tighter structures on the inner layer and lower WVPR. 4.2. Liquid water transfer 4.2.1. Effect of the fiber materials. Table VI gives outer-layer wet area Ao and inner-layer wet area Ai as well as the wet area ratio WAR for each fabric. Cotton/PET and Cotton/PP fabrics show apparently higher WAR, because WVPR (g/m224h) 1200 1100 1000

Ti (tex)

900 40 Key 2 with 5

80

40 3 with 4

80

50

7 with 6

67

Figure 3. The relation between Ti and WVPR

IJCST 11,4

204

Table VI. Liquid water transferability from the inner to the outer layer

their outer layers and inner layers were made respectively with hydrophilic cotton yarns and hydrophobic PET or PP yarns. This result indicates that most of the water is transferred from the inner to the outer layer for the two kinds of fabrics. When comparing outer-layer and inner-layer wet area of these two fabrics, it can be seen that Cotton/PET fabrics have much a smaller inner-layer wet area than Cotton/PP fabrics while the differences of the outer-layer wet area between the two kinds are not so large as that of the inner-layer wet area. As for PET and PP fiber itself, the water absorption of the former is better, so Cotton/ PET fabrics would have had a larger inner-layer wet area. In fact, the yarn structure also influences water transfer due to capillary interstices in the yarn. Polyester filament and polypropylene spun yarn were used to knit inner layers of fabrics. The monofil arrangement in PET yarn is parallel on the whole and most capillaries among monofil are also parallel to the yarn axis, while fibers in PP yarn appear to have a spiral distribution and some capillaries are at an angle to the yarn axis, which causes water to spread during the wicking transport. For Cotton/Cotton, Cotton/PAN, Wool-PAN-PA/PET fabrics, there is no difference of water absorption between the inner and the outer layer, or the mixed yarn is not so good as the cotton yarn in water absorption, or the hydrophobicity of PAN is not so strong as that of both PP and PET, such that these four fabrics reveal much smaller WAR. 4.2.2. Effect of stitch density and yarn count. By comparing the data in connection with the two pairs of Cotton/PP fabrics (sample No. 2 with 3; 5 with 4), it is found that the fabrics with lower stitch density exhibit higher WAR under otherwise equal conditions. The difference of stitch density between a pair of fabrics influences not only Ai but also Ao. From the data relevant to three pairs of fabrics (sample No. 2 with 5; 3 with 4; 7 with 6), it can be seen that Cotton/PP fabrics with lower Ti present higher WAR under otherwise equal conditions, while Cotton/PET fabrics present lower. The capillary action depends on the capillary diameter, the quantity and direction that bear a relation to fiber nature and count, the yarn composition and structure, fabric construction and density. The relations between these are complex. Sample no. Ao (mm2) Ai (mm2) WAR (Ao/Ai)

1 298.2 471.5 0.63

2 610.4 57.9 10.53

3 606.5 21.1 28.83

4 576.9 16.2 35.7

5 482.6 24.1 20.01

6 543.8 1.7 319.86

Sample no. Ao (mm2) Ai (mm2) WAR (Ao/Ai)

8 434 111.4 3.9

9 214 94.6 2.26

10 59.6 136.7 0.44

11 260.6 297.2 0.88

12 497.8 39.6 12.56

13 486.5 1.7 293.08

7 556 1.4 402.88

5. Conclusion The water vapor permeability rate through a two-layer of weft knitted fabric is mainly related to the porosity within the fabric, while the water absorption of the used fiber materials on the inner and outer layer has hardly any effect on it. Variations of bulk density and tightness factor will change porosity and apparently influence WVPR. Liquid water transfer from the inner to the outer layer depends upon the water absorption of the fiber materials on the two layers and to a great extent their difference. The better the water absorption of the outer-layer yarn and the poorer that of the inner-layer yarn, the more water can be transferred from the inner to the outer layer by capillary action. In addition, it can increase the water transfer to a certain degree if the inner layer is made with hydrophobic filament and the stitch density is reduced properly. References and further reading American Society of Testing and Materials (1995), ASTM E96-95, Annual Book of ASTM Standards, Philadelphia, PA. British Standards Institute (1992), BS 7209-90, BS Handbook, London. Canadian General Standards Board (1986), GGSB 4-GP-2, National Standards of Canada. Deutsches Institut fuÈr Normung e. V. (DIN), (1972), DIN 53122, Beuth Verlag GmbH, Berlin. Deutsches Institut fuÈr Normung e. V. (DIN), (1991), DIN 54101 Teil 1, Beuth Verlag GmbH, Berlin. Piller, B. (1985), International Textile Bulletin ± Fabric Forming, Vol. 31 No. 72. Piller, B. (1986a), Melliand Textilberichte, Vol. 67 No. 412. Piller, B. (1986b), Melliand Textilberichte, Vol. 67 No. 489. Umbach, K.H. (1986), Chemiefasern/Textilindustrie, Vol. 36 No. 57.

Water transfer properties

205

The current issue and full text archive of this journal is available at http://www.emerald-library.com

IJCST 11,4

Application of cluster analysis to fabric classification Y. Chen and B.J. Collier

206 Received October 1997 Accepted March 1999

Louisiana Agricultural Experiment Station, School of Human Ecology, Louisiana State University, Baton Rouge, Louisiana, USA, and

J.R. Collier

Department of Chemical Engineering, Louisiana State University, Baton Rouge, Louisiana, USA Keywords Fabric, Analysis, Clothing Abstract This paper introduces a new way of classifying clothing fabrics objectively. Representative apparel fabrics were collected and measured by the Kawabata Evaluation System for Fabrics (KES-FB). The disjoint clustering method was used to divide fabrics into four clusters, each representing particular fabric performance and end-use characteristics. These classified clusters were further analyzed applying the method of principal-component analysis to acquire factor patterns that indicate the most important fabric properties for characterizing different fabric end-use. Extracted information from the instrumentally obtained data in terms of fabric physical properties is useful to fabric and garment producers, apparel designers, and consumers in specifying and categorizing fabric products, in insuring proper fabric use, and in controlling fabric purchase.

1. Introduction Evaluation of fabric quality by expertise and experience has been the practice in fabric and garment manufacture for many years. With the increasing use of various kinds of synthetic fibers, and the scarcity of experienced tailors and industry experts, this traditional approach is now not practical. As industrial and commercial life becomes more diverse and rushed, and fabric classification becomes more difficult, a reliable method for categorizing fabrics is needed. This depends on two technical aspects. One is the development of fabric objective measurement technology to instrumentally determine fabric properties. The other is applied mathematical methods that can be used practically for fabric classification and quality prediction. Use of the Kawabata KES-FB instruments (Kawabata, 1980) and the Fabric Assurance by Simple Test (FAST) instruments (CSIRO, 1989) has achieved industrial impact because these two measuring systems are commercially available and industrially applicable (Kawabata et al., 1982; 1986; Postle et al., 1983; Chen, 1995). Using these instruments, a set of digital data that determines fabric physical properties can be acquired, and objective classification of fabrics then becomes possible. Meanwhile, some mathematical methods have also been employed in International Journal of Clothing Science and Technology, Vol. 11 No. 4, 1999, pp. 206-215. # MCB University Press, 0955-6222

Approved for publication by the Director of the Louisiana Agricultural Experiment Station as manuscript No. 96-25-0161. The authors are indebted to Mrs P. Rabalais and Mrs Y. Marquette for their help in sample collection.

attempting to reveal the complexities of fabric hand and fabric classification (Baten, 1950; Howorth and Oliver, 1958; Stearn et al., 1990). For example, quadratic discriminant function was used to establish the statistical model for predicting end-use of apparel fabrics (Chen and Collier, 1997). In this paper, another mathematical method, cluster analysis, is introduced to solve the problem of classifying apparel fabrics objectively. Suitable fabric clusters, that can be used as a reference data set to classify any new fabric, are formed from a large data set obtained by measuring a variety of commercial fabrics using the KES-FB instruments. 2. Mathematical principle Cluster analysis is undertaken under a multivariate vector framework. A vector is used to describe fabric physical properties, and can be expressed as x x1 ; x2 ; :::; xp 0

1

where x1, x2, ..., xp are instrumental fabric measurements. Using this pdimensional vector each fabric sample (observation) is represented as a point in a p-dimensional space. A fabric cluster can then be described as a continuous region appearing in this space having a relatively high density of points and separated from other clusters by regions having a relatively low density of points. Cluster analysis involves two fundamentals: measures of similarity within observations and clustering algorithms that are selected to produce a rule of classification. 2.1 Similarity measures The distance between two observations represents the closeness of this pair of observations and can be used as a measure of similarity between observations. A general distance measure is the Minkowski metric (Dillion and Goldstein, 1984), defined by p X 1 jxik ÿ xjk jr g2 dij f 2 k1

where dij denotes the distance between observations i and j. Setting r = 2; the Euclidean distance (Murtagh and Heck, 1987) is obtained p X 1 xik ÿ xjk 2 g2 dij f 3 k1

This equation can be expressed by the following vector form: d2ij x k xi ÿ xj k2 xi ÿ xj xi ÿ xj k xi k2 k xj k2 ÿ2x0i xj

4

where k xi k and k xj k are the norms of vector x indicating the vector length, which are defined as:

Application of cluster analysis

207

IJCST 11,4

208

k xi k

q q x0i ixi x2i1 x2i2 x2ip

k xj k

q q x0j xj x2j1 x2j2 x2jp

The Euclidean distance is the major classification criterion for clustering sample points. 2.2 Cluster algorithms The target of the present work is to classify a considerably large volume of fabrics into a fixed number of fabric clusters according to fabric physical properties, such that each fabric sample belongs to one and only one cluster. For this purpose, a disjoint clustering technique called nearest centroid sorting (Anderberg, 1973) is used. A set of observations is first selected to constitute cluster seeds that are considered as an initial assumption of cluster means. After determination of these initial seeds, each sample point is assigned into the nearest seed to form temporary clusters by calculating the Euclidean distance. These seeds are replaced by the means of the temporary clusters that represent centroids of these temporary clusters. The distance between the centroids of any two temporary clusters CM and CN is defined as (SAS Institute Inc., 1988): N M ÿ x N k2 k x M k2 k x N k2 ÿ2 x0M x DMN k x

5

M and x N are mean vectors for clusters CM and CN. The temporary where x clusters are updated each time an observation is classified into a cluster. The calculating procedure is repeated until no further changes occur in the clusters. Thus, final clusters are formed. Observations that are very close to each other are grouped in the same cluster, while observations that are far from each other are sorted in different clusters. Selection of initial cluster seeds is based on MacQueen's k-mean method (MacQueen, 1967) in which a specified minimum radius (coarsening parameter) is selected. This radius determines the least Euclidean distance and can be used as a threshold for selecting initial cluster seeds. If the number of clusters is fixed at k, the first k observations in the data set will be assessed as initial cluster seeds. The first observation is selected as the first seed. The second observation is taken as the second seed if it is separated by a distance no less than the specified radius. This procedure continues till the kth observation. If there exist some observations that are unqualified as initial seeds, they will be replaced by other sample points from n-k observations. 3. Experimental Ninety commercial fabrics were collected for classification. These fabric samples included a variety of fiber components and weave structures. Fabric

end-uses, suits, jackets, coats, blouses, and shirts, were determined according to experience from garment makers and the school's apparel design laboratory. Fabric physical properties were measured on the KES-FB instruments. As shown in the summary of the obtained data set in Table I, there is a large range of values for each of the properties measured. All computations were executed on the UNIX system using SAS software.

Application of cluster analysis

209

4. Results and discussion 4.1 Fabric objective classification Each fabric sample has 27 instrumentally measured variables. A clustering calculation was carried out under this multivariate framework. Considering the fact that most fabrics in the present data set were used for suits, outer (jackets and coats), shirts, and blouses, the number of fabric clusters was proposed to be four. Table II lists the results of the clustering computation. Examining the Fabric property

KES-FB parametera

Name

N

Mean

Std dev

Min

Max

Tensile

LT1 WT1(gf-cm/cm2) RT1(%) LT2 WT2(gf-cm/cm2) RT2(%)

x1 x2 x3 x4 x5 x6

90 90 90 90 90 90

0.672 7.97 62.09 0.669 13.13 59.17

0.136 4.32 6.13 0.087 5.09 9.05

0.064 3.45 44.22 0.433 5.15 40.25

1.008 26.30 74.58 0.868 25.85 79.05

Shear

G1(gf.cm-degree) 2HG-1(gf/cm) 2HG5-1(gf/cm) G2(gf/cm-degree) 2HG-2(gf/cm) 2HG5-2(gf/cm)

x7 x8 x9 x10 x11 x12

90 90 90 90 90 90

0.800 1.061 2.559 0.766 1.007 2.504

0.550 0.929 2.052 0.512 0.932 2.027

0.123 0.000 0.075 0.188 0.000 0.000

2.500 4.500 9.500 2.375 4.375 9.000

Bending

B1(gf.cm2/cm) 2HB1(gf.cm/cm) B2(gf.cm2/cm) 2HB2(gf.cm/cm)

x13 x14 x15 x16

90 90 90 90

0.0992 0.0775 0.0640 0.0440

0.0767 0.0695 0.0505 0.0389

0.0100 0.0025 0.0038 0.0025

0.3700 0.3200 0.2550 0.2475

Compression

LC WC(gf.cm/cm2) RC(%)

x17 x18 x19

90 90 90

0.378 0.196 43.30

0.109 0.211 7.99

0.232 0.023 4.62

0.759 1.366 58.10

Thickness Weight

T0(mm) W(mg/cm2)

x20 x21

90 90

0.623 14.9

0.501 6.5

0.127 5.4

3.200 35.9

Surface

MIU1 MMD1 SMD1 (micron) MIU2 MMD2 SMD2 (micron)

x22 x23 x24 x25 x26 x27

90 90 90 90 90 90

0.0396 0.0308 3.524 0.1438 0.0209 2.775

0.1415 0.0108 0.580 0.1370 0.0097 1.060

0.3270 0.1415 15.860 1.5200 0.1056 13.070

Note: a1 = warp direction, 2 = filling direction

0.2046 0.0422 6.301 0.2360 0.0339 5.064

Table I. Descriptive statistics of the KES-FB data set

IJCST 11,4

210

fabric samples and end-uses included in each cluster can provide useful information on fabric properties related to specific clothing uses (Table III). There are two fabric end-uses represented among the fabrics in Cluster 1: blouses and shirts. Since shirting fabrics account for 78.6 percent of the total number of samples in this cluster, Cluster 1 is designated as a ``shirts'' model. Although Cluster 2 includes blouses, shirts, and jackets, the majority are blouse fabrics, and thus this cluster well exhibits fabric features for blouse use. Cluster 3 is a mixed group composed of fabrics for suits, jackets, and coats, and one blouse fabric. This fabric cluster could be characterized as a group of fabrics used for outer garments. Most suit and jacket fabrics are assigned to Cluster 4, so that this cluster becomes a typical suiting group. 4.2 Interpretation of clustering solution After computation on the present Kawabata data set, the four proposed fabric clusters were partitioned. Each cluster should occupy a room in multidimensional space. To describe the ``neighborhood'' among these four clusters, the Mahalanobis distance (Lindeman et al., 1980) can be used to measure how far these clusters are from each other. The Mahalanobis distance between any two cluster centroids is defined as: D2 ijj j 0 Vÿ1 j xi ÿ x xi ÿ x

6

i; x j indicate mean values of x related to fabric cluster i, j; V-1 is the where x inverse matrix of covariance matrix V. The obtained distance matrix is given in Table IV. A graphical way to explain how well the four fabric clusters differ from each other is to use the canonical discriminant function, which is expressed as (Lindeman et al., 1980): Cluster

Table II. Cluster summary

Table III. Number/percent of fabrics for different end-uses in each cluster

Number of samples

Maximum DAM within cluster

Nearest cluster

DAM between cluster centroids

28 21 11 30

20.4220 18.8128 19.2728 20.8228

4 4 1 2

17.8237 12.9160 18.6224 12.9160

1 2 3 4

Fabric end-use

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Total

Blouses Shirts Suits Jackets Coats Total

6 / 21.4 22 / 78.6 ± ± ± 28 / 31.1

17 / 81.0 2 / 9.5 ± 2 / 9.5 ± 21 / 23.3

1 / 9.1 ± 3 / 27.3 4 / 36.4 3 / 27.3 11 / 12.2

6 / 20.0 6 / 20.0 8 / 26.7 10 / 33.3 ± 30 / 33.3

30 / 33.3 30 / 33.3 11 / 12.2 16 / 17.8 3 / 3.3 90 / 100

Z u1 x1 u2 x2 u27 x27

7

where Z is the discriminant score; u's are canonical coefficients (discriminant weights), and x's are the KES-FB variables. For the four fabric clusters, three canonical discriminant functions were obtained. Table V lists the three sets of u's in terms of eigenvalues li. These coefficient u's are used to calculate canonical discriminant scores Z1, Z2, and Z3, allowing the four classified fabric clusters to be plotted in three-dimensional space, as shown in Figure 1.

Application of cluster analysis

211

4.3 Critical factors for portraying end-use To reveal the importance of the individual mechanical parameters in clustering fabrics and characterizing fabric end-use, principal-component analysis was used to examine relationships among the all KES-FB parameters and to determine factors. The number of KES-FB parameters was reduced to 16 by taking an average of warp and filling values. The component analysis model (Lindeman et al., 1980) can be expressed as: Z CF

From cluster 1 2 3 4

u1 u2 u3 u4 u5 u6 u7 u8 u9 u10 u11 u12 u13 u14

To cluster

1

2

0 24.0817 69.4309 20.8008

24.0817 0 97.8283 18.5483

Z1

Z2

Z3

1.0024 0.0203 0.0088 0.0836 0.2957 0.0561 ±0.8476 ±0.7649 1.2472 1.2091 0.3501 ±0.4382 ±1.0881 ±5.3386

0.7711 0.1110 0.05478 ±9.1490 ±0.0901 0.1967 2.4294 ±0.4153 0.5770 ±0.6886 ±0.1323 ±0.9669 ±4.8654 9.8994

4.6150 ±0.0137 0.0414 1.6000 0.0800 0.0222 ±3.0581 ±0.7614 0.7857 2.1950 1.5455 ±0.8581 ±7.6834 0.0763

8

u15 u16 u17 u18 u19 u20 u21 u22 u23 u24 u25 u26 u27 li*

3

4

69.4309 97.8283 0 57.2179

20.8008 18.5483 57.2179 0

Z1

Z2

Z3

10.5421 ±26.2080 4.7728 ±7.7623 0.0596 7.4841 0.0814 ±16.0827 6.3569 0.0301 ±0.1273 ±30.2708 0.1641 8.3628

±10.5538 12.6603 1.0276 ±2.6277 ±0.0152 1.3969 0.0485 ±10.0988 ±4.6972 0.0678 ±1.2492 ±4.8843 0.0577 3.6943

16.7173 1.5028 4.2184 ±9.7155 ±0.0895 5.7260 ±0.1948 8.0289 ±22.1235 0.1193 2.1224 ±16.5117 ±0.1571 1.6617

Note: * li = eigenvalue (see Chen and Collier (1997) for details)

Table IV. Mahalanobis distances between clusters

Table V. Canonical coefficients u

IJCST 11,4

1

212

2

3

Z3

Z1

4

Figure 1. Classified four fabric clusters

Z2

where 2

z1l 6 z2l 6 Z 6 .. 4 .

znl

z12 z22 .. .

zn2

2 3 z1N c1l 6 c2l z2N 7 6 7 .. 7C 6 .. 4 . 5 .

znN

cnl

c12 c22 .. .

cn2

2 3 c1m f1l 6 f2l c2m 7 6 7 .. 7F 6 .. 4 . 5 .

cnm

fml

f12 f22 .. .

fm2

f1N f2N .. .

3 7 7 7 5

fmN

C is a pattern matrix in which elements are loadings of each of the n KES-FB parameters (n = 16) on each of the m factors (m = n); F is the factor score matrix, and N is number of measured samples (N = 90). Z is a standardized raw data matrix, in which zji is the standardized value of xji, the observation of the ith individual on the jth variable, that is, xji ÿ x j 9 zji Sj where S2j

1X xji ÿ x j 2 i N

i 1; 2; ; N j 1; 2; ; n

According to the above definition, we have 1X 2 z 1 zj i ji N

10

This equation means that for each variable the variance of the standardized value zj equals one. Obtained factors can be considered as indications of the characteristics of fabric clusters, which are objectively described by the KESFB variables having correlated values (as shown in matrix C) to these factors. Factors with eigenvalues greater than one (Kaiser, 1960) were retained. An orthogonal rotation method (Varimax (SAS Institute Inc., 1988)) followed by an oblique rotation (SAS, Institute Inc., 1988) was used to enhance the separation between factors. Factor coefficients having absolute values larger than 0.5 were retained to determine factor patterns (Lindeman et al., 1980). As summarized in Table VI, the factor analysis reveals that Clusters 1 (shirts) and 2 (blouses) have a similar five-factor pattern and Clusters 3 (outerwear) and 4 (suiting) have a similar four-factor pattern. The factors are ordered in F1, F2, ..., F5 according to eigenvalues, in which factor F1 has the largest eigenvalue and factor F5 has the smallest eigenvalue. Therefore, the KES-FB parameters included in F1 are most operative and those included in F5 are least operative in characterizing the clustered fabric groups. Tensile linearity, shear rigidity and shear hysteresis figure prominently in the first factor for Clusters 1, 2, and 4, indicating the critical importance of these parameters for shirt, blouse, and suiting uses. Bending properties, fabric thickness, and weight are most important for the outerwear cluster and second most important for the suiting cluster. Fabric surface and compressive properties have the least influence upon measurement of characteristics of the suiting cluster (LC and RC were not even included in the four factors of the suiting cluster). These factor patterns are useful for determining the most important fabric properties that dominate specific end-uses. The properties indicated should be controlled in the process of fabric and garment manufacturing. A caveat is that factor patterns may vary with different data

Cluster

Factor

1 (shirts)

2 (Blouses)

F1

LT, G, 2 HG, 2HG5, B WC, RC, T, SMD 2HB, LC, MMD WT, RT, W MIU

LT, G, 2HG, 2HG5 LC, WC, T, SMD

F2 F3 F4 F5

B, 2HB, W WT, RT, MMD RC, MIU

3 (Outerwear)

4 (Suiting)

B, 2HB, WC, T, W, MIU WT, G, 2HG, 2HG5, RC LC, MMD, SMD LT, RT ±

LT, RT, G 2HG, 2HG5, RC B, 2HB, T, W WT, WC MIU, MMD, SMD ±

Application of cluster analysis

213

Table VI. Most important factors for end-use clusters

IJCST 11,4

214

sets because individual manufacturers may use their own commercial experience and expertise, and fabric end-use may be changed to keep pace with fashion. 5. Conclusions Twenty-seven instrumental variables were obtained using the KES-FB instruments to measure fabric physical properties. A cluster analysis was undertaken on a framework of multidimensional vectors composed of these variables. Using the nearest centroid sorting technique of clustering, 90 commercial fabrics having a variety of fiber contents and fabric structures were classified into four clusters based on apparel end-use. This result illustrated application of the disjoint clustering method in classifying apparel fabrics objectively, and could offer guidance to fabric and garment manufacturers in fabric selection, fabric proper use, and new fabric categorization. A feature of the cluster analysis for fabric classification is that no prior knowledge of classes is required. Clustering results are direct and simple and can also be used to classify any new fabrics. For industrial implementation, establishment of a database of the Kawabata instrument data is needed. Such a database would incorporate companies' previous commercial experience and expertise in fabric and garment manufacturing. Classified fabric clusters could be used as references in categorizing new fabrics or predicting fabric end-use. Using the method of principal-component analysis, primary factors for characterizing fabric end-use were obtained. These factor patterns can help fabric and garment manufacturers in understanding and identifying the most important and desirable fabric properties in terms of specific end-use, so that particular care and quality manipulation can be made during processing. References Anderberg, M.R. (1973), Cluster Analysis for Applications, Academic Press, Inc., New York, NY. Baten, W.D. (1950), Text. Res. J., Vol. 20, pp. 869-72. Chen, Y. (1995), Thesis, The University of Leeds, Leeds. Chen, Y. and Collier, B.J. (1997), Text. Res. J., Vol. 67 No. 4, pp. 247-52. CSIRO (1989), FAST Instruction Manual, CSIRO, Ryde. Dillon, W.R. and Goldstein, M. (1984), Multivariate Analysis: Methods and Applications, John Wiley & Sons, Inc., New York, NY. Howorth, W.S. and Oliver, P.H. (1958), J. Text. Inst., Vol. 49, pp. T540-T553. Kaiser, H. (1960), Educational and Psychological Measurement, Vol. 20, pp. 141-51. Kawabata, S. (1980), The Standardization and Analysis of Hand Evaluation, 2nd ed., The Textile Machinery Society of Japan, Osaka. Kawabata, S., Postle, R. and Niwa, M. (1982), Objective Specification of Fabric Quality, Mechanical Properties and Performance, The Textile Machinery Society of Japan, Osaka. Kawabata, S., Postle, R. and Niwa, M. (1986), Objective Measurement: Applications to Product Design and Process Control, The Textile Machinery Society of Japan, Osaka.

Lindeman, H.R., Merenda, P.F. and Gold, R.Z. (1980), Introduction to Bivariate and Multivariate Analysis, Scott, Foresman and Company, Glenview, IL. MacQueen, J.B. (1967), Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, Univ. of California Press, Berkeley, CA, pp. I-281-297. Murtagh, F. and Heck, A. (1987), Multivariate Data Analysis, D. Reidel Publishing Company, Dordrecht, Holland. Postle, R., Kawabata, S. and Niwa, M. (1983), Objective Evaluation of Apparel Fabrics, The Textile Machinery Society of Japan, Osaka. SAS Institute Inc. (1988), SAS/STAT User's Guide Release 6.03 edition, SAS Institute Inc., Cary, NC. Stearn, A.E., D'arcy, R.L., Mahar, T.J. and Postle, R. (1990), in Kawabata, S., Postle, R. and Niwa, M. (Eds), Objective Measurement: Applications to Product Design and Process Control, The Textile Machinery Society of Japan, Osaka, pp. 557-66.

Application of cluster analysis

215

The current issue and full text archive of this journal is available at http://www.emerald-library.com

IJCST 11,4

216 Received November 1998 Accepted April 1999

Made-to-measure garments for ladies ± catering for wide ranging stature and length measurements for standard and outsize ladies John Patrick Turner and Terry Bond

The Manchester Metropolitan University, Hollings Faculty, Fallowfield, Manchester, UK Keywords Clothing, Patterns, Measurement Abstract A computer system for made-to-measure pattern production should have the capability of determining default measurements for sets of customer measurements input to the system where one or more of these measurements are missing. This paper recommends the use of default formulae rather than mathematical interpolation of size charts. These default formulae, when applied to a given size chart set, enable measurements to be determined efficiently over wide ranging customer sizes in both stature and girth. The specific default formulae for the German DOB charts are derived for Regular and Outsize charts and also for the full range of Height categories and Bust to Hip relationships, so that all sizes and shapes of customers are catered for. Default formulae have been applied in the MicroFit made-to-measure system from Garment Micro Systems and also implemented on this system for checking the validity of measurements entered into the system for each individual customer.

International Journal of Clothing Science and Technology, Vol. 11 No. 4, 1999, pp. 216-225. # MCB University Press, 0955-6222

Introduction to computer systems for made-to-measure patterns Made-to-measure clothing pattern systems have the capability of receiving sets of an individual person's (customer's) body measurements and from these producing patterns for a chosen garment which will, when made up, fit the customer well. A fully commercial system will also produce pattern lay-outs (markers) for a given cloth width by means of semi-automatic or automatic algorithms which are efficient in the use of material, i.e. giving near to minimum wastage. The hardware is likely to be PC based (currently one of the Pentium Series) and utilise an A0 or A00 size digitiser for the input of original pattern shapes, and a plotter for the output of markers to paper for eventual cutting. The software is based on one of the following methods of MTM pattern production: (1) The patterns are drafted directly according to the body measurements and a set of drafting rules, either built into the software coding or entered into a rule database. (2) The patterns are graded by standard numeric grading and the made-tomeasure patterns subsequently produced by applying further numeric

alterations to the nearest graded size. The alterations are built into an Made-to-measure alterations table for each garment type and are applied to points round garments for the patterns as percentages of the difference between the measurements ladies of the standard size as found in the related size chart and the equivalent measurements of the customer. (3) The base patterns (input by digitisation) are graded by algebraic 217 formulae based on proportions of the body dimensions which together with the standard size chart determine and apply the grading increments. These algebraic grading formulae are utilised directly (without the need for any alterations tables) to produce made-to-measure patterns. The German GRAFIS system uses method (1) pattern drafting ± Gerber Garment Technology (GGT), Investronica, and Assyst commercial grading systems utilise method (2) numeric grading followed by application of alterations. The MICROFIT system of Garment Micro Systems developed by the author (Turner, 1994) for the made-to-measure trade uniquely uses method (3) where grading rules and alterations are combined into algebraic formulae. All the above methods have the common feature of reference to size charts for either standard size grading or calculation of the alterations to be applied. When personal body measurements are input and certain key measurements, required either in the drafting rules or in the alterations, are missing, default values must be applied which are realistic and based on other key measures. Ideally the computer system itself should determine these default values. This paper concerns itself with the derivation of formulae for such computer application. Default measurements The obvious method of obtaining girth defaults by computer is to interpolate within the chosen size chart, according to the control measurement ± usually the bust girth in ladies' charts. Thus for a given bust girth, the default neck girth, for example, could be obtained by looking down the correct vertical columns between which the bust girth lies and interpolating between the values of neck girth in those columns. The method is, however, complicated by the fact that a large number of charts may exist, and the correct one needs to be chosen. In the well developed and up-to-date German DOB (Ladies' Outerwear) system, there are nine separate size charts for the normal bust girth range 76-104cm. An extract from the regular chart for standard height and hips is shown in Table Ia. The nine charts arise because there are three hip categories (slim, standard and broad) and three height categories (short, medium and tall). A further nine charts for outsizes with bust girth range 110-146cm complete the system. In effect, each individual chart requires two-dimensional interpolation but the whole system requires four-dimensional interpolation in order to achieve the best possible defaults. This is of course theoretically possible, but would entail entering all

IJCST 11,4

218

Table Ia. Extract from German DOB size chart ± regular sizes, standard height and hips

Ladies' outwear sizes ± standard range (height + hips: standard) 34 38 42 Codes commonly used 32/34 36/38 40/42 in Germany: 32 XS(DOB) 36 S(DOB) 40 M(DOB) 44 Control dimension (cm): 1 Bust girth 76 80 84 88 2 Hip girth 86 90 94 97 3 Height 168 168 168 168 Secondary dimensions (cm): 4 Waist girth 62 65 68 72 8 Back width 32.5 33.5 34.5 35.5 9 Back waist length 41.4 41.4 41.4 41.6 10 Front waist length 41 41.9 42.8 43.7 15 Waist to hips 21 21 21 21 16 Outside leg length 106 106 106 106 17 Inside leg length 78.3 78.3 78.3 78.1 19 Neck-base girth 34.2 34.8 35.4 36 20 Shoulder length 11.9 12 12.1 12.2 22 Arm length 59.4 59.6 59.8 60 23 Upper arm 25.6 26.2 26.8 28 24 Wrist girth 14.6 15 15.4 15.8 Proposed European size code: 76 80 84 88 Proposed European EDP code: 076421 080421 084421 088421

46 44/46 L(DOB)

92 100 168

96 103 168

100 106 168

104 109 168

76 36.5 41.8 44.6 21 106 77.9 36.6 12.3 60.2 29.2 16.2 92

80 37.5 42 45.5 21 106 77.7 37.2 12.4 60.4 30.4 16.6 96

84 38.5 42.2 46.4 21 106 77.5 37.8 12.5 60.6 31.6 17 100

88 39.5 42.4 47.3 21 106 77.3 38.4 12.6 60.8 32.8 17.4 104

092421 096421 100421 104421

these charts onto the computer system database and working out an interpolation algorithm. There is, however, an alternative. If equations relating dimensions and measurements can be derived, these can be applied directly to calculate default values. It is logical first to separate the girth/width dimensions of a size chart from the length dimensions. Thus, eight common girth and width dimensions are Bust Girth, Hip Girth, Waist Girth, Back Width, Shoulder Length, Upper Arm Girth, Wrist Girth and Neck Girth, where the control dimensions are Bust Girth and to a lesser extent Hip Girth minus Bust Girth difference (Hip Category). The further seven common length measurements are Stature (Height), Nape to Waist, Front Length, Arm Length to Cuff, Outside Leg, Inside Leg and Waist to Hips distance, where the primary control dimension is Stature. Length measurement defaults The control dimension for the length measurements is Stature and, while this is not normally a measurement directly utilised in clothing, it is necessary in order to calculate correctly the other default length measurements according to the person's height. The first examples of default formulae developed here for the purpose of this paper relate to length measurements and are derived from the German DOB

size charts. Length measurements are not well catered for in these charts. Only Made-to-measure three height groups are categorised, namely 160cm stature (short), 168cm garments for (medium height) and 176cm (tall). While the majority fall within these three ladies categories and default lengths can be interpolated, many ladies are shorter than ``short'' and many taller than ``tall'' and default lengths must be extrapolated. Formulae provide a means of covering all statures from extra short to extra tall. 219 The first stage of analysis of the DOB charts length measurements is to set down the measurements for each height category at a fixed bust size and thus determine the increment for each length between stature categories. This is presented in Table Ib for Normal sizes. The Stature and Nape to Waist relationship can be easily seen by inspection as L = S/4 (2/8 = ) for Bust 96cm. However, by further inspection of the size charts it can be seen that the relationship also changes with Bust girth, the Nape to Waist increasing by 0.2cm per 4cm Bust girth size (in the Bust Girth range from 84-104 cm of Normal sizes). This ratio is therefore 0.2/4 = 0.05 or 1/20. The full relationship is therefore expressed by the formula L = S/4 + (B ± 96)/20. The exception to this formula is that Nape to Waist remains the same for the first three Bust sizes (76, 80, 86cm) and for these the formula may be expressed as L = S/4 ± 0.6. These two formulae hold true for all Statures and Hip categories within the nine DOB size charts. In a similar way the increment ratio for Front Length (F) is 1.4/8 = 0.175 and F = S*0.175 + K where K is a constant. For S =160 and F = 44.1, 44.1 = 160*0.175 + K and K = 44.1 ± 28 = 16.1, so that for 96cm Bust Girth, F = S*0.175 + 16.1. Again, the Front Length increases by 0.9cm per Bust Girth increment of 4cm, so that the increment ratio is 0.9/4 = 0.225 and the full relationship is therefore: F = S*0.175 + 16.1 + (B ± 96)*0.225 Such formulae hold true for any interpolation and are probably reasonably accurate for extrapolated statures well outside the groups 160, 168 and 176cm, thus catering for much shorter and much taller women.

Measurement/ dimension Height (stature) Nape to waist Front length Arm length to cuff Outside leg Inside leg Waist to hip

Abbreviation

Short (cm)

Medium (cm)

Tall (cm)

Increment (cm)

S L F A O I D (standard) (slim hips) (broad hips)

160 40 44.1 58 101 73.3 20 19 21

168 42 45.5 60.4 106 77.7 21 20 22

176 44 46.9 62.8 111 82.1 22 21 23

8 2 1.4 2.4 5 4.4 1 1 1

Table Ib. DOB size charts (Germany 1994), length defaults for regular sizes, comparison of measurements for the different statures, for bust size 96

IJCST 11,4

220

The full set of default formulae for Normal Size Bust Girths 76-104 (76-106.9 cm) are shown in Table Ic. Notice that the formulae for Outside Leg (O) and Waist to Hip (D) both include a function of the Hip Girth minus Bust Girth difference (H ± B), i.e. they depend on the relative hip size (Hip Categories: Slim, Standard or Broad). From the Size Charts: . Standard Hip-Bust differences range from 5-10cm and no adjustment needs to be made to the formulae for O and D. . Slim Hip-Bust differences range from 1-4cm and 1cm is subtracted from Standard O and D. . Large Hip-Bust differences range from 11-16cm and 1cm is added to Standard O and D. This addition or subtraction of 1cm or leaving the increment as zero is performed in a formula by the device of using the INT function whereby a number is rounded down to the nearest integer. The function INT ((H ± B + 1)/6) ± 1 ensures that the correct amount 0, ±1 or + 1 is included. INT is found in most computer languages as an arithmetic function. Outsize sizes Table II shows the equivalent formulae for Outsize Bust Sizes 110-146. Notice that the Outside Leg (O) and Waist to Hips distance (D) have the same formulae as for Normal Bust Sizes. The small increments resulting from the Bust girth changes are based around Bust size 110cm although any Bust size could have been chosen as the base. The recalculated increment ratios depend on the outsize Bust increment of 6cm compared to the 4cm increment of the Normal Bust Size Charts. In order to ensure continuity and to include the Bust sizes between the two Size Charts (Normal and Outsize) of 104-110cm, the range of Bust sizes included in the Normal set of formulae would be taken to be 76-106.9cm, whereas the Outsize set of formulae would apply to the range 107-146cm.

Table Ic. DOB size charts (Germany, 1994), default formulae to obtain length measurements for given stature, S cm bust girth range 76-106.9cm

Measurement/dimension

Default formula (cm)

Nape to waist (L) for bust 76-83.9 for bust 84-106.9 Front length (F) Arm length to cuff (A) Outside leg (O) Inside leg (I) Waist to hips (D)

L = S/4 + (B ± 96)/20 L = S/4 ± 0.6 F = S* 0.175 + 16.1 + (B ± 96)*0.225 A = S* 0.3 + 10 + (B ± 96)/20 O = S* 0.625 + INT((H ± B + 1)/6) I = S* 0.55 ± 14.7 + (96 ± B)/20 D = S/8 + INT((H ± B + 1)/6) ± 1

Note: Where B = bust girth cm, H = hip girth cm

Measurement/dimension Nape to waist (L) for bust 107-127.9 for bust 128-146 Front length (F) Arm length to cuff (A) for bust 107-127.9 for bust 128-146 #Outside leg (O) Inside leg (I) for bust 107-127.9 for bust 128-146 #Waist to hips (D)

Default formula L = S/4 + 0.7 + (B ± 110)/20 L = S/4 + 1.6 F = S* 0.175 + 19.3 + (B ± 110)* 0.233 A = S* 0.3 + 10.7 + (B ± 110)/20 A = S* 0.3 + 14 O = S* 0.625 + INT((H ± B + 1)/6) I = S* 0.55 ± 15.3 + (110 ± B)/30 I = S* 0.55 ± 15.9 D = S/8 + INT((H ± B + 1)/6) ± 1

Notes: # These measurements have the same default formulae as for the normal bust girth range (see Table Ic) (Where B = bust girth cm, H = hip girth cm)

The eight head theory This theory divides the body into eight sections along the vertical stature axis. Figure 1 shows diagrammatically the relationship of the length measurements to stature, according to this theory. It is interesting to note that these proportions are immediately apparent in the default formulae: .

Nape to Waist (L) is one quarter of Stature (S).

.

Outside Leg (O) is five eighths of Stature (S).

.

Waist to Hips (D) is one eighth of Stature (S).

The minor variations around these exact proportions are in those extra functions of the Bust size and Hips-Bust difference. Practical application of length default formulae on a computer system Following input of the girth/width measurements, either direct or interpolated, the prompts for the length measurements would appear on the computer screen, starting with Stature (Height) S. Normally the Stature value would be entered in cm and this entry followed by Nape to Waist (L), Front Length (F) etc. in normal size chart sequence. Where a dimension is not known (not measured) the ``Enter'' key would be pressed, and immediately the value would be calculated from the relevant default formula and entered by the computer system's program. In an extreme case, the Stature only could be entered and all other vertical measures entered as defaults in this way. Supposing, however, the Stature is not known, but at least one of the other vertical measures is known. By back calculation the stature could be

Made-to-measure garments for ladies 221 Table II. DOB size charts (Germany 1994), default formulae to obtain length measurements for given stature, S cm. Bust girth range (outsize) 107-146cm

IJCST 11,4

222

Figure 1. Eight head theory

established and all other defaulted values recalculated. An example of such a back calculation for a Normal Bust (between 84 and 106.9cm) would be if the Nape to Waist was known, but the Stature not known, the Nape to Waist formula L = S/4 + (B ± 96)/20 could be transposed to S = (L ± (B ± 96)/20*4 and the Stature calculated. The remaining defaults would then be available using this value of Stature. A practical way of implementing these back calculation formulae on a computer system is suggested as follows: Where the Stature is unknown and all following defaulted dimensions are given a blank value (by pressing the ``Enter'' key only) then, after one measurement (i.e. with a numeric value) has

been input and a back calculation for this has been carried out to obtain the Made-to-measure Stature, all other defaulted (unknown) measurements previous to that known garments for measurement are calculated using the default formulae. ladies The transposed back calculation formulae for Normal Bust Girths (76106.9cm) are shown in Table III. The back calculation formulae for the Outsize sizes may be obtained by transposition in the same way. Girth default measurements Where the control girth measurements of Bust and Hip are known (i.e. have been input for the individual) it is possible to derive the other girth and width default formulae in a similar way to those for length measurements. This has been done for the DOB charts and the results shown in Table IV for Normal bust sizes and Table V for Outsize sizes. It can be seen that for the common measurements shown in the Tables only the waist girth and upper arm girth measurements differ for slim and broad hips by comparison with standard hips. The other four girths are exactly the same irrespective of the Bust-Hip category. Back calculation formulae are not necessary for the girth measurements as it would be a requirement that at least the control measurements of Bust and Hip would be taken on the customer to obtain a reasonable fit.

Measurement/dimension

Formula for back calculation

Nape to waist (L) for bust 76-83.9 for bust 84-106.9 Front length (F) Arm length to cuff (A) Outside leg (O) Inside leg (I) Waist to hips (D)

S S S S S S S

= = = = = = =

(L + 0.6)*4 (L ± (B ± 96)/20)*4 (F ± 16.1 ± (B ± 96)*0.225)/0.175 (A ± 10 ± (B ± 96/20)/0.3 (O ± INT(H ± B + 1)/6)/0.625 (I + 14.7 ± (96 ± B)/20/0.55 (D + 1 ± INT((H ± B + 1)/6))*8

Note: Where B = bust girth cm, H = hip girth cm

Hip-bust category Hip-bust range (H-B) Waist (W) Back width (R) Shoulder length (E) Upper arm girth (U) Wrist girth (G) Neck girth (N)

Slim hip (cm)

Standard hip (cm)

Broad hip (cm)

Increment (cm)

1-4 77 37.5 12.4 29.4 16.6 37.2

5-10 80 37.5 12.4 30.4 16.6 37.2

11-16 83 37.5 12.4 31.4 16.6 37.2

3 0 0 1 0 0

223

Table III. DOB size charts (Germany 1994), back calculation formulae for bust range 76-106.9cm to obtain stature, S cm from other body measurements

Table IVa. DOB size charts (Germany 1994), girth and width defaults for regular sizes. Comparison of measurements for the different hip categories. For bust size 96cm

IJCST 11,4

224

Measurement/dimension

Default formula (cm)

Waist (W) for bust 76-77.9 for bust 78-81.9 for bust 82-106.9 Back width (R) Shoulder length (E) Upper arm girth (U) for bust 76-83.9 for bust 84-106.9 Wrist girth (G) Neck girth (N)

W = B ± 14+3*(INT((H ± B + 1)/6) ± 1) W = B ± 15+3*(INT((H ± B + 1)/6) ± 1) W = B ± 16+3*(INT((H ± B + 1)/6) ± 1) R = B/4 + 13.5 E = B/40 + 10

Table IVb. DOB size charts (Germany 1994), girth and width default formulae. Regular sizes, Notes: Results should be rounded to the bust girth 76-106.9cm

Hip-bust catgeory Table Va. DOB size charts (Germany 1994), girth and width defaults for outsizes. Comparison of measurements for the different hip categories. For bust size 110cm

Hip-bust range (H-W) Waist (W) Back width (R) Shoulder length (E) Upper arm girth (U) Wrist girth (G) Neck girth (N)

U U G N

= = = =

B*0.15 + 14.2 + INT((H ± B + 1)/6) ± 1 B*0.3 + 1.6 + INT((H ± B + 1)/6) ± 1 B/10 + 7 B*0.15 + 22.8

nearest mm (0.1 of a cm)

Slim hip (cm)

Standard hip (cm)

Broad hip (cm)

Increment (cm)

1-4 91.5 41 12.8 33.6 18 39.5

5-10 94.5 41 12.8 34.6 18 39.5

11-16 97.5 41 12.8 35.6 18 39.5

3 0 0 1 0 0

Measurement/dimension

Default formula (cm)

Waist (W) Back width (R) Shoulder length (E) Upper arm girth (U) Wrist girth (G) Neck girth (N)

W = B*1.083 ± 24.6+3*(INT(H ± B + 1)/6 ± 1) R = B/4 + 13.5 E = B/30 + 9.1 U = B*0.3 + 1.6INT((H ± B + 1)/6) ± 1 G = B/10 + 7 N = B*0.183 + 19.37

Table Vb. DOB size charts (Germany 1994), girth and width default formulae. Outsize sizes, Note: Results should be rounded to the nearest mm (0.1 of cm) bust girth 107-146cm

Conclusion Default formulae for obtaining measurements should not be seen as an entirely satisfactory way of replacing the actual measured values, but only as a best guess of the measurements where these have not been measured for the individual customer. The formulae replace what would otherwise be a complex system of interpolation and extrapolation of the size chart data within the computer system's database. The formulae are derived for a given set of size charts and are only applicable to that set. The DOB German System of charts has been chosen as

the example here because it is the most comprehensive national system and is Made-to-measure also the basis for European Standards. For any other logical system of size garments for charts, it should be possible to derive the equivalent default formulae using the ladies same method of analysis. Default formulae and the back calculation formulae in conjunction with the DOB size charts have been implemented satisfactorily as subroutines on the 225 MICROFIT made-to-measure system from Garment Micro Systems. The default formulae are further utilised on the MICROFIT system to determine the range of validity of measurements entered for a female individual customer, whereby any value entered outside this range is rejected. References DOB-GroÈssentabellen Deutschland (1994), DOB-Verband, KoÈln. Turner, J.P. (1994), ``Development of a commercial made-to-measure garment pattern system'', International Journal of Clothing Science and Technology, Vol. 6 No. 4, pp. 28-33.

The current issue and full text archive of this journal is available at http://www.emerald-library.com

IJCST 11,4

Microclimate ventilation of infant bedding E.J. Holland, C.A. Wilson, R.M. Laing

226

Clothing and Textiles Centre, University of Otago, Dunedin, New Zealand and

B.E. Niven

Centre for Application of Statistics and Mathematics, University of Otago, Dunedin, New Zealand Keywords Blankets, Clothing Abstract The rate and volume of air exchanged between the bed microclimate and the ambient environment determines in part how much heat is lost from the human body. This study investigated the ventilatory characteristics of infant over-bedding to determine whether different combinations of bedding items (i.e. sheets, blankets, duvets) and types of tucking (i.e. loosely, firmly and swaddled/firmly tucked) affected microclimate ventilation. Microclimate volumes and air exchange rates were determined and used to calculate the ventilation indices. The presence of a duvet in the bedding combination resulted in lower ventilation indices than when bedding did not include a duvet. The type and combination of blankets did not significantly affect ventilation indices. The type of tucking had a significant effect on ventilation indices only when the assembly did not include a duvet.

Introduction In order to achieve human thermoneutrality, heat production must be balanced by heat loss. Infants have comparatively larger heat loss than adults because of the lesser distances from core to skin, larger body surface area to mass ratio, larger insensible perspiration, and smaller radius of curvature of exchange surfaces (Berg and Celander, 1971; Darnall, 1987). The ventilation properties of the clothing and bedding used on infants are thus of particular interest in understanding heat loss from an infant. Three avenues of heat loss from the clothed body have been proposed (Birnbaum and Crockford, 1978): (1) heat transferred directly through the clothing ensemble; (2) heat transferred from the body to the air of the clothing microclimate, then transferred to the environment through garment openings or through the clothing; and (3) heat transferred from the body by vaporisation of sweat.

International Journal of Clothing Science and Technology, Vol. 11 No. 4, 1999, pp. 226-239. # MCB University Press, 0955-6222

In the case of the human body covered by bedding, Ray (1981) proposed heat loss occurs when heat is transferred through the upper bedding layers, through leakage of microclimate air into the ambient environment (e.g. around the shoulders), and a small amount through the mattress (although Ray did not quantify this).

Research into ventilatory characteristics of clothing has focused on semipermeable and impermeable garments (Birnbaum and Crockford, 1978; Bridgman, 1990; Crockford et al., 1972; Crockford and Rosenblum, 1974; DukesDobos et al., 1992; Lotens and Havenith, 1988; Lotens and Wammes, 1993; Shivers et al., 1977; Sullivan et al., 1987). The ventilatory characteristics of permeable clothing and bedding have been the subject of fewer studies: Crockford et al. (1972) investigated the air exchanged between the microclimate of conventional clothing and raincoats and the ambient environment; Shivers et al. (1977) investigated the effect sleeve design had on air exchange rates of raincoats; Thomas (1983) conducted a pilot study on the ventilation indices of continental quilts; Reischl et al. (1987) investigated the ventilation of various combinations of industrial clothing (e.g. coveralls, apron, work-pants) and Havenith et al. (1990) conducted a study on the ventilation and clothing vapour resistance of work-wear (e.g. work clothing, coverall, rain coverall). Information about clothing, bedding and practices used to cover and care for infants has generally been collected by researchers interested in Sudden Infant Death Syndrome (SIDS), particularly those cases in which a possible explanation was overheating of the infant. The most extensive documentation of the type of bedding and clothing commonly used by infants in New Zealand is that provided by Wilson et al. (1994). The most common bedding and clothing combinations used to form the bed microclimate were identified as either one duvet and two blankets (13.59 per cent, n = 214) or just two blankets (17.27 per cent, n = 272); sheet, underblanket without sheepskin or waterproof layer (27.6 per cent, n = 436); ``stretch and grow'' and singlet (21.29 per cent, n = 421); fluffy overpants (generally acrylic with a highly brushed surface) and fabric nappies (presumably twill brushed cotton) (29.9 per cent, n = 474) (Wilson, 1992). The aim of this investigation was to compare the ventilation indices of various types and combinations of over-bedding and methods of tucking commonly used for infants in New Zealand. Method Simulating a clothed infant in the bed microclimate A manikin was used to simulate an infant in a bed. The length and weight of the manikin corresponded to the approximate size of a two-month old infant (Begg et al., 1975). The manikin was placed on its left side during each test (Plate 1). Clothing and bedding used in this experiment are given in Table Ia, with the selection based on Wilson et al. (1994) and Wilson (1992). The clothing worn by the manikin was identical in all experimental procedures. Common combinations of bedding items and the order of use is given in Table Ib. The mass and bending stiffness of the bedding combinations were determined using BS 2471: 1978 (British Standards Institution, 1978) and BS 3356: 1990

Microclimate ventilation of infant bedding 227

IJCST 11,4

228

Plate 1. Placement of microclimate air exchange sampling and distribution tubes over the infant manikin

(British Standards Institution, 1990). All experimental testing was undertaken in a standard environment of 20 28C and 65 2 per cent RH, with materials conditioned for 24 hours prior to testing (British Standards Institution, 1992). Tucking method Three different types of tucking (loose, firm and swaddled/firm) (Figure 1) were evaluated. Loose tucking involved draping the bedding over the manikin. Bedding which extended beyond the bed edge was folded so it lay on top of the mattress with the blanket edges extending towards the manikin. Firm tucking entailed pulling the bedding taut over the manikin and anchoring bedding under the mattress. Swaddling consisted of wrapping the manikin in a muslin cloth secured at the back of the neck. Over-bedding was then tucked firmly over the manikin using the method described previously. Microclimate volume The procedure developed by Crockford and Rosenblum (1974) was used to determine microclimate volume. The manikin, bedding and mattress, arranged as required in the experiment (Table Ib), were sealed in a plastic enclosure constructed of 0.125mm AgphaneTM plastic sheeting. (As the focus of this investigation was the ventilation of the over-bedding, the mattress was encapsulated in plastic during both volume and air exchange tests to ensure the air within the mattress did not influence the experimental procedure.) Articles of bedding were subject to minimal crushing. Determining microclimate volume involved extraction of air until the water manometer reached 500mm water gauge pressure, after which recording of the microclimate volume commenced and air allowed to flow back into the enclosure at a rate of 3l/min.

Product

Dimension/size

1. Standard clothing Nappy 740 6 785mm Fluffies Small (brushed overpants) Singlet (short sleeved)

00

``Stretch and grow''

0

2. Bedding Muslin wrap

920 6 1,000mm

Sheet Air-cell blanket Twill blanket Duvet (or panel quilt)

Cot mattress

Fibre content*

Structure

Cotton

2 6 2 twill weave stepping 1 napped on back surface sett 27.6 6 26.4 yarns/10mm Acrylic 1 6 1 weft knit rib napped on front surface Wale density 5.2 wales/10mm course density 6.4 courses/10mm Cotton 1 6 1 rib knit wale density 23.6 wales/10mm course density 14.5 courses/10mm Polyester/cotton Plain knit loop pile wale density 17.6 wales/10mm course density 12.5 courses/10mm

Plain weave two layers sett 16.3 6 11.0 yarns/10mm 1,180 6 1,600mm Cotton Plain weave napped on both sides sett 18.8 6 16.6 yarns/10mm 1,150 6 1,490mm Wool Cellular fabric/leno wave variety, weft faced sett 5.2 6 4.0 yarns/10mm 1,160 6 1,485mm Wool Herringbone twill weave napped on both sides sett 8.0 6 9.3 yarns/10mm 850 6 1,120mm Polyester/cotton Plain weave sett upper layer cover DacronTM filling 32.8 6 19.5 yarns/10mm sett under-layers and flaps 45.7 6 30.3 yarns/10mm 1,250 6 580 6 Foam slab 130mm

Microclimate ventilation of infant bedding 229

Cotton

Note: * as labelled

Code

Description

Mass g/m2

Bending length mm

SAA SAT STA STT SAAD SATD STAD STTD

Sheet, Sheet, Sheet, Sheet, Sheet, Sheet, Sheet, Sheet,

835 940 941 1,038 1,422 1,526 1,526 1,623

38 42 43 45 57 58 57 63

air-cell blanket, air-cell blanket air-cell blanket, twill blanket twill blanket, air-cell blanket twill blanket, twill blanket air-cell blanket, air-cell blanket, duvet air-cell blanket, twill blanket, duvet twill blanket, air-cell blanket, duvet twill blanket, twill blanket, duvet

Table Ia. Description and bedding ± type of clothing and bedding

Table Ib. Description of bedding and clothing ± combination of bedding

IJCST 11,4

230

Figure 1. Tucking method: (a) firm; (b) loose; (c) swaddled/firm

When the pressure reached 250mm the micromanometer was turned on and recording continued until the pressure reached 0mm. Three replicates were obtained for each arrangement of bedding. Volume of the microclimate was calculated by multiplying the air flow (3l/min) by the time taken for the water gauge pressure to reach 0mm. Microclimate air exchange The rate of air exchanged between the manikin/bedding microclimate and the ambient environment was measured by modifying the procedure developed by

Crockford et al. (1972). Nitrogen distribution and oxygen sampling tubes were placed over the manikin immediately below over-bedding, and secured to the lower bedding at marked positions. Nitrogen gas was distributed at a rate of 15l/min throughout the bed microclimate by four blind-end PVC tubes (6mm internal diameter, 32mm long) perforated with a double row of holes (1mm diameter every 20mm). An impermeable cover constructed of 0.125mm AgphaneTM plastic sheeting was used to cover the bed and bedding combination in order to stabilise the experimental conditions until the oxygen concentration in the manikin/bedding microclimate decreased to 15 per cent. The flow of nitrogen was halted and the impermeable cover removed. Oxygen concentration in the bed microclimate was sampled at a rate of 0.2l/ min through four PVC tubes (0.86mm internal diameter). The tubes were of equal length and open at one end to ensure that dilution did not occur. Oxygen sampling tubes were connected to a modified Teledyne Portable Oxygen Analyser (Series 32DA) for recording oxygen concentration every second. Each test was terminated when the oxygen concentration in the manikin/bedding microclimate returned to normal (20.9 per cent). Five replicates were obtained for each combination/arrangement. The behaviour of oxygen concentration can be expressed as: O2 (t) = 0.209 ± A *exp±Rt where O2 (t) = proportion of oxygen in the system at time t; 0.209 = asymptote or eventual value of oxygen in the system provided all nitrogen leaves the microclimate; A = bulk constant such that 0.209 ± A is the initial value of O2(t) at time t = 0; R = rate constant and is the exponential decay parameter of the curve; t = time (minutes) (Birnbaum and Crockford, 1978). Only points above 15 per cent oxygen concentration were used for analysis. Data from each trial were fitted to equation 1 using non-linear regression (SPSS Inc., 1995). R-values were used for further statistical analysis. Ventilation index The ventilation index (Q) is given by the formula: Q = Vm * R where Vm = microclimate volume; R = microclimate air exchange (Birnbaum and Crockford, 1978). Mean values were used to calculate the ventilation index, as matching specific values of microclimate air exchange and volume for the same replicate in each bedding combination was not possible. Variables significantly affecting the ventilation index were identified by two-way analysis of variance. Tukey's method as described by Montgomery

Microclimate ventilation of infant bedding 231

IJCST 11,4

232

(1976) was used to determine whether an effect of the bedding combination/ type of tucking interaction was present in the residual (because matching microclimate volume and air exchange values for a particular bedding combination was not possible (i.e. there was only one observation per cell)). This method involves partitioning the residual term into components for error and nonadditivity (interaction). A non-significant F ratio for nonadditivity would indicate no interaction between the type of bedding and the type of tucking used, and the residual mean square can then be used as an error term in testing for main effects. Results and discussion Microclimate volume The volumes of various bedding combinations were significantly different (F7,48 = 38.21, p40.001), with combinations including a duvet being significantly larger (Tables II and III). The type of blanket used (air-cell and/or twill) did not significantly affect the volume (Table III), nor did the type of tucking (loosely, firmly, swaddled/firmly tucked) (F2,48 = 1.49, NS) (Table III). However, results show that the microclimate volume of the sheet, air-cell blanket, air-cell blanket, duvet combination (SAAD) was smaller in firm tucking than for other types of tucking and bedding combinations that included a duvet (F14,48 = 3.66, p40.001) (Table IIIa). The fabric structure of the air-cell blanket (open weave, low bending stiffness and mass) in combination with the characteristics of the tucking method (i.e. blankets lying taut over manikin) may have led to the smaller volume. Microclimate air exchange The rate of air exchanged between the bed microclimate and the ambient environment depended on the bedding combination (F7,96 = 17.14, p40.001). Bedding combinations which included a duvet had slower rates of air exchange

Bedding code

Table II. Microclimate air exchange, microclimate volume and ventilation indices for bedding combinations and type of tucking

R min±1

Loose Vm l

Types of tucking Firm Q R Vm l/min min±1 l

a. Bedding combinations not including a duvet SAA 1.16 41 48 2.18 SAT 1.29 38 49 2.53 STA 1.51 36 54 1.92 STT 1.05 37 39 1.55 b. Bedding combinations including a duvet SAAD 0.62 53 33 0.91 SATD 0.71 47 33 0.82 STAD 1.05 47 49 1.10 STTD 0.69 47 33 0.82

Q l/min

Swaddled/firm R Vm Q min±1 l 1/min

36 38 38 38

78 97 73 59

2.56 2.22 1.87 1.56

41 37 41 33

105 83 76 52

45 55 50 55

41 45 55 45

0.92 0.86 0.97 0.68

58 52 46 51

54 44 45 35

(e.g. 0.62 min±1, loosely tucked: sheet, air-cell blanket, air-cell blanket, duvet (SAAD)) (Tables II and III). The inclusion of an air-cell blanket resulted in faster rates (e.g. 2.56 min±1, swaddled: sheet, air-cell blanket, air-cell blanket (SAA)). (Higher R-values indicate a faster rate of air exchange.) The type of tucking also had a significant effect on the air exchanged between the bed microclimate and the ambient air (F2,96 = 39.03, p40.01). Source

d.f.

SS

MS

F

Significance

a. Microclimate volume 1. Analysis of variance Bedding combination Type of tucking Bedding combination/type of tucking Error

7 2 14 48

3,063.76 34.21 586.70 549.81 X 36 38 38 39 48 51 51 52

437.68 17.10 41.91 11.45

38.21 1.49 3.66

p40.001 NS p40.001

2. Tukey grouping for main effects Bedding combination STT SAT STA SAA STAD STTD SATD SAAD Bedding combination/type of tucking interaction STT Swaddled/firm SAA Firm STA Loose STT Loose SAT Swaddled/firm STA Firm STT Firm SAT Firm SAT Firm STA Swaddled/firm SAA Loose SAA Swaddled/firm SAAD Firm STAD Swaddled/firm STAD Loose SATD Loose STTD Loose STAD Firm STTD Swaddled/firm SATD Swaddled/firm SAAD Loose SATD Firm STTD Firm SAAD Swaddled/firm

33 36 36 37 37 38 38 38 38 41 41 41 45 46 47 47 47 50 51 52 53 55 55 58

]

]

]] ]

]] (continued)

Microclimate ventilation of infant bedding 233

Table III. Effect of bedding combination and type of tucking on microclimate volume and air exchange analysis of variance

IJCST 11,4

234

Source

d.f.

SS

MS

F

Significance

b. Microclimate air exchange 1. Analysis of variance Bedding combination Type of tucking Bedding combination/type of tucking Error

7 2 14 96

8.56 5.57 27.65 6.85

1.22 2.79 1.98 0.07

17.14 39.03 27.68

p40.001 p40.01 p40.001

2. Tukey grouping for main effects Bedding combination STAD STTD STA SATD STT SAAD SAT SAA Type of tucking Loose Swaddled/firm Firm Bedding combination/type of tucking interaction SAAD Loose STTD Swaddled/firm STTD Loose SATD Loose SATD Firm STTD Firm SATD Swaddled/firm SAAD Firm SAAD Swaddled/firm STAD Swaddled/firm STAD Loose STT Loose STAD Firm SAA Loose SAT Loose STA Loose STT Firm STT Swaddled/firm STA Swaddled/firm STA Firm SAA Firm SAT Swaddled/firm SAT Firm SAA Swaddled/firm Table III.

X 1.01 1.11 1.13 1.14 1.33 1.36 1.59 1.85 1.01 1.45 1.48

0.62 0.68 0.69 0.71 0.82 0.82 0.86 0.91 0.92 0.97 1.05 1.05 1.10 1.16 1.29 1.51 1.55 1.56 1.87 1.92 2.18 2.22 2.53 2.56

]

]

]

]

]

]

]] ] ]

]] ]

Note: Means group by ] are not significantly different/interaction between two variables

Bedding combinations that were loosely tucked had significantly slower rates of air exchange than swaddled/firmly tucked or firmly tucked combinations. Additionally, bedding combinations that did not include a duvet but included an air-cell blanket and with firm or swaddled/firm tucking had faster rates than other bedding combinations (F14,96 = 27.68, p40.001) (Table III). The rate of air exchange is affected by the volume of the microclimate; however, in the present study the microclimate volumes of the various tucking arrangements were similar, indicating that differences in air exchange rates were not due to differing microclimate volumes. Previous research on thickness of the same bedding has indicated that loose tucking incorporates larger air spaces among the various layers of bedding, but reduces the air space between the manikin and the upper sheet (Wilson et al., in press). Perhaps, in loosely tucked combinations there is less air exchanged from around the shoulders of the manikin than with other tucking arrangements. Additionally, in loosely tucked combinations, air flow through the bedding may be impeded by the larger air spaces between various bedding layers. This may account for the slower rates in loosely tucked bedding. Ventilation index Because the rate at which microclimate air is exchanged with ambient air depends to some extent on the volume of the particular microclimate (Birnbaum and Crockford, 1978), the ventilation index is considered to be more useful for comparing different assemblies and arrangements. Surface area has not been included in the present study as, although it has been shown to be a relevant variable under some conditions (Harter et al., 1981), the surface areas of the various bedding assemblies in the present study were considered similar. The ventilation indices ranged from 33l/min (loosely tucked: sheet, air-cell blanket, air-cell blanket, duvet (SAAD); sheet, air-cell, twill blanket, duvet (SATD); sheet, twill blanket, twill blanket, duvet (STTD)) to 105l/min (swaddled: sheet, air-cell blanket, air-cell blanket (SAA)) (Table II). Lower ventilation indices occurred with bedding combinations which included a duvet, which is most likely due to the structure of the duvet layer impeding air flow through the bedding assembly. When all bedding combinations (i.e. those with and without a duvet) were included in an analysis of variance, the test for nonadditivity was significant (Table IVa) (indicating a bedding combination/type of tucking interaction) and accordingly no further interpretation of results was made (Table IV). Analysis then focused on bedding either combinations with or without a duvet. In both cases, the test for nonadditivity was non-significant, indicating no interactions. For bedding combinations not including a duvet, the particular combination of blankets had no significant effect on ventilation indices (Table IV). However, the type of tucking appeared to have some influence (F2,5 = 10.95, p40.05). Loosely tucked bedding combinations had significantly lower ventilation indices (Table IV). In these, the air space between the manikin and the upper

Microclimate ventilation of infant bedding 235

IJCST 11,4

236

Source a. All bedding combinations Analysis of variance Bedding combinations Tucking Residual Nonadditivity{ Error b. Bedding combination not including a duvet 1. Analysis of variance Bedding combination Tucking Residual Nonadditivity Error 2. Tukey grouping Type of tucking Loose Firm Swaddled/firm

Table IV. Effect of combination of bedding and type of tucking on microclimate ventilation indices ± analysis of variance

c. Bedding combinations including duvet Analysis of variance Bedding combinations Tucking Residual Nonadditivity Error

d.f.

SS

MS

F

Significance

7 2

5,410.93 2,007.14

772.99 1,003.57

10.59 13.75

p40.001 p40.001

1 13

818.13 948.76

818.13 72.98

11.21

p40.01

3 2

1,435.78 2,451.07

478.59 1,225.54

4.28 10.95

NS p40.05

1 5

306.60 559.54

306.60 111.91

2.74

NS

47 77 79 3 2

238.66 206.01

79.55 103.00

1.70 2.20

NS NS

1 5

17.19 233.62

17.19 46.72

0.37

NS

Notes: Means group by ] are not significantly different As the test for nonadditivity is significant (indicating some interaction among the main effects), post hoc analysis could not be undertaken

{

sheet was smaller that other tucking arrangements as the bedding was loosely draped over the manikin (Figure 1). This may account for the lower ventilation indices in loosely tucked bedding combinations. For bedding combinations including a duvet, neither the combination of bedding nor the type of tucking had a significant effect on ventilation indices (Table IV). However, lower ventilation indices were observed with loosely tucked bedding combinations (Table II). The stiffness and weight of the duvet layer may have masked differences in ventilation indices related to the type of tucking, i.e. by trapping extra air in the loosely tucked bedding combinations between the duvet and upper blanket, or alternatively the weight of the duvet may have reduced the microclimate of bedding combinations that were firmly tucked. Similar results were found by Wilson et al. (in press) in a study of thickness of bedding combinations. Significant differences in thickness were observed among the various bedding combinations which did not include a duvet, but these were no longer apparent when a duvet was added.

The type and combination of blankets had no significant effect on ventilation of bedding combinations either including or without a duvet (Table IV). This result suggests that differences in blanket construction (air-cell, twill weave) appeared to have little if any influence on the ventilation index. The air exchanged between bedding microclimates and the ambient air has been the subject of very few studies. Thomas (1983) investigated the pattern of air exchanged from continental quilt sleeping bags while adults slept. While the volumes of the sleeping bags were similar to the present study (31-59l), air exchange was considerably slower (i.e. 0.33 - 0.72min±1) and hence ventilation indices were lower (15-32l/min). Differences in materials (e.g. amount of filling, fabric structure of outer cover) and the structure of the bed (i.e. sleeping bag vs bed with top covers) may have contributed to differences in the ventilation indices. Ventilation of impermeable garments designed for adults have been shown to vary with garment style, fit, tightness of apertures of garment, and whether or not the test procedure involved exercise (Birnbaum and Crockford, 1978; Lotens and Havenith, 1988; Sullivan et al., 1987). Generally, an increase in exercise intensity or air movement resulted in faster ventilation indices (Lotens and Havenith, 1988). Movement by a human infant in a bed could result in faster ventilation indices than those presented here, although in the case of the swaddled/firm tucked bedding combinations, any body movement would be restricted. Diffusion and convection of a tracer gas to simulate that of water vapour has been demonstrated (Havenith et al., 1990; Lotens and Wammes, 1993), although it is not claimed in the present study. Effects of differences in experimental conditions among the various studies were considered likely to be too great (i.e. non-thermal infant manikin compared with exercising adult humans, < 400mm height of bedding assembly compared with > 1,600mm of clothing assembly as worn, nitrogen compared with argon as the tracer gas, and differences in sensitivity of instrumentation to concentrations of the relevant gas). Conclusions Four main conclusions can be drawn from this work: (1) the method for measuring the ventilation developed by Crockford et al. (1972) and Crockford and Rosenblum (1974), can be applied to infant bedding; (2) air in the bed microclimate is exchanged with ambient air more slowly when a duvet is present; (3) differences in blanket construction (air-cell, twill) have little effect on ventilation indices; and (4) the effect of the type of tucking (loose, swaddled/firm, firm tucking) was significant only when a duvet was not included.

Microclimate ventilation of infant bedding 237

IJCST 11,4

238

References Begg, N.C., Mackay, B.J. and Salmond, G.C. (1975), Growth of New Zealand Pre-School Children, Department of Health Special Report Series, 44, Management Services and Research Unit, Wellington. Berg, K. and Celander, O. (1971), ``Circulatory adaptation in the thermoregulation of full-term and premature newborn infants'', Acta Paediatrica Scandinavica, Vol. 60 No. 3, pp. 278-84. Birnbaum, R.R. and Crockford, G.W. (1978), ``Measurement of the clothing ventilation index'', Applied Ergonomics, Vol. 9 No. 4, pp. 194-200. Bridgman, L.E. (1990), ``The effect of vents on garment microclimate air exchange'', unpublished dissertation. Bachelor of Consumer and Applied Sciences with Honours, University of Otago, Dunedin, New Zealand. British Standards Institution (1978), BS 2471: 1978; ISO 3801-1977. Methods of Test for Textiles ± Woven Fabrics ± Determination of Mass per Unit Length and Mass per Unit Area, British Standards Institution, London. British Standards Institution (1990), BS 3356:1990. British Standard Method for Determination of Bending Length and Flexural Rigidity of Fabrics, British Standards Institution, London. British Standards Institution (1992), BS EN 20139:1992; ISO 139:1973. Textiles ± Standard Atmospheres for Conditioning and Testing, British Standards Institution, London. Crockford, G.W. and Rosenblum, H.A. (1974), ``The measurement of clothing microclimate volumes'', Clothing Research Journal, Vol. 2 No. 3, pp. 109-14. Crockford, G.W., Crowder, M. and Prestidge, S.P. (1972), ``A trace gas technique for measuring clothing microclimate air exchange rates'', British Journal of Industrial Medicine, Vol. 29 No. 4, pp. 378-86. Darnall, R.A. (1987), ``The thermophysiology of the newborn infant'', Medical Instrumentation, Vol. 21 No. 1, pp. 16-22. Dukes-Dobos, F.N., Reischl, U. and Buller, K. (1992), ``Assessment of ventilation of firefighter protective clothing'', in McBriarty, J.P. and Henry, N.W. (Eds), Performance of Protective Clothing. ASTM STP 1133, American Society for Testing and Materials, Philadelphia, pp. 629-33. Harter, K.L., Spivak, S.M. and Yeh, K. (1981), ``Applications of the trace gas technique in clothing comfort'', Textile Research Journal, Vol. 51 No. 5, pp. 345-55. Havenith, G., Heus, R. and Lotens, W.A. (1990), ``Clothing, ventilation, vapour resistance and permeability index: changes due to posture, movement and wind'', Ergonomics, Vol. 33 No. 8, pp. 989-1005. Lotens, W.A. and Havenith, G. (1988), ``Ventilation of rainwear determined by a trace gas method'', in Mekjavic, I.B., Banister, E.W. and Morrison, J.B. (Eds), Environmental Ergonomics: Sustaining Human Performance in Harsh Environments, Taylor and Francis Ltd, London. Lotens, W.A. and Wammes, L.J.A. (1993), ``Vapour transfer in two-layer clothing due to diffusion and ventilation'', Ergonomics, Vol. 36 No. 10, pp. 1223-40. Montgomery, D.C. (1976), Design and Analysis of Experiments, John Wiley & Sons, New York, NY. Ray, R.D. (1981), ``The thermal performance of a continental quilt'', Journal of Consumer Studies and Home Economics, Vol. 5 No. 1, pp. 37-43. Reischl, U., Spaul, W.A., Dukes-Dobos, F.N. and Hall, E.G. (1987), ``Ventilation analysis of industrial protective clothing'', in Asfour, S.S. (Ed.), Trends in Ergonomics/Human Factors IV, Elsevier Science Publishers B V, North Holland.

Shivers, J.L., Yeh, K., Fourt, L. and Spivak, S.M. (1977), ``The effects of design and degree of closure on microclimate air exchange in lightweight cloth coats'', in Hollies, N.R.S. and Goldman, R.F. (Eds), Clothing Comfort: Interaction of Thermal, Ventilation, Construction and Assessment Factors, Ann Arbor Science Publishers Ltd, MI, pp. 167-81. SPSS Inc. (1995), Statistical Package for Social Sciences, Version 6.1.1, SPSS Inc., Chicago. Sullivan, P.J., Mekjavic, I.B. and Kakitsuba, N. (1987), ``Ventilation index of helicopter pilot suits'', Ergonomics, Vol. 30 No. 7, pp. 1053-61. Thomas, N. (1983), ``Protection, comfort and the clothing ventilation index'', in Coombes, K. (Ed.), Proceedings of the Ergonomics Society Conference, Taylor and Francis Ltd, London, pp. 3-7. Wilson, C.A. (1992), ``Commonly used clothing and bedding combinations'', Personal Communication. Wilson, C.A., Niven, B.E. and Laing, R.M. (in press), ``Estimating thermal resistance of the bedding assembly from thickness of materials'', International Journal of Clothing Science and Technology. Wilson, C.A., Taylor, B.J., Laing, R.M., Williams, S. and the New Zealand Cot Death Study Group (1994), ``Clothing and bedding and its relevance to sudden infant death syndrome: further results of the New Zealand Cot Death Study'', Journal of Paediatrics and Child Health, Vol. 30 No. 6, pp. 506-12.

Microclimate ventilation of infant bedding 239

The current issue and full text archive of this journal is available at http://www.emerald-library.com

Effect of hem edges on the interface pressure of pressure garments Sau Fun Frency Ng and Chi Leung Patrick Hui

Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong Keywords Pressure, Garments

Effect of hem edges

251 Received January 1998 Revised April 1999 Accepted April 1999

Abstract Pressure garments are mainly made of elastic Lycra fabrics and tailor-made to individual patients' measurements to provide an appropriate amount of skin-garment interface pressure for burn rehabilitation. However, the fabric tension would be different at various locations from the hem edges of pressure garments, and thus the skin-garment interface pressure cannot be uniformly maintained over the interface surface. Aims to investigate the pattern of interface pressure changes caused by the different types of edge finish used for making pressure garments. The effect of garment sizes on the change of interface pressure was also examined. Experiments were carried out using two selected elastic Lycra fabrics, four types of hem finish and three different garment sizes. The results of the study provide a guideline for designing the edge finish of pressure garments, and a minimum margin from the hem edges of garments to the scar area is also recommended.

1. Introduction Pressure garments constructed using a variety of elastomeric fabric play a very important role in burn rehabilitation by helping to prevent or reduce the formation of hypertrophic scars. The method of manufacturing pressure garments is basically ``cut-and-sewn'', and most pressure garments are constructed in tubular form for the limbs and trunks of body. Many hospitals make their own pressure garments using approximation of percentage reduction of garment dimensions; for example 15 or 20 per cent is taken off the circumferential body measurements. Fabric compression will be induced by the elastomeric fabric when the pressure garment is stretched and worn by the patient. In view of the comments from occupational therapists and medical specialists which were collected in a previous study (Ng, 1990), there is a change of skin-garment interface pressure near the edges of pressure garments. In order to maintain a constant pressure over the scar area, a certain amount of margin is needed from the scar area to the hem edges of the garment. In practice, there are several types of hem edges used for making pressure garments. The cut edges of the pressure garments made by hospitals are neatened by a three thread overlocking stitch (BS 504) or it may simply be left as raw edges without any finish. In some cases, there is a piece of rubber band This article is extracted from an unpublished PhD dissertation. The author acknowledges the supervision, assistance and guidance of staff of De Montfort University, UK, The Hong Kong Polytechnic University, Hong Kong and others for having made this work possible.

International Journal of Clothing Science and Technology, Vol. 11 No. 5, 1999, pp. 251-261. # MCB University Press, 0955-6222

IJCST 11,5

252

sewn at the hem edges of sleeves, pants and waistlines in order to prevent the edges of elastomeric fabric from rolling up. Edges finished with single turn-up hems are also used in making-up of pressure garments. This study aims to investigate the pattern of interface pressure changes caused by the different types of edge finish used for making pressure garments as well as the effects of garment compression on the changes of interface pressure. It attempts to provide a useful guideline on selection of edge finish method as well as determine a minimum margin from hem edges of garments to the scar area. 2. Experimental 2.1 Specimen preparation The tests were performed on 24 sets of pressure garments (in the form of fabric tubes) which were made of a combination of two different types of elastic fabrics, four different types of edge finish methods, and three different sizes of garment specimen made based on a selected cylindrical tube model. 2.1.1 Fabric selection. Two elastic Lycra fabrics (No. 25034 and No. 28432) were used for this study. These fabrics are supplied by a major manufacturer in the UK, and they are commonly adopted by hospitals in the UK and Hong Kong. The particulars of these two fabrics are shown in Table I. Each of fabrics was cut in rectangular shape with the long edges parallel to the wales direction of fabric (i.e. stretch direction of the fabric) and were made in the form of tubes by seaming at their two vertical edges. All the test specimens were seamed by the three-point zig-zag stitch (BS 308) with 1cm seam allowance from the cut edges (as shown in Figure 1). Three horizontal lines were marked on the fabric tubes at distances of 1.5cm, 3cm, 5cm and 7.5cm parallel and above the hem edges of the specimen, and four measuring locations of equal space intervals were marked on each of the circumferential lines. A medium size cylindrical tube (40.4cm circumference) was selected for the study. In order to examine the effect on the change of interface pressure by various amounts of garment compression, three reduced sizes of garment specimens which were 15, 25 and 35 per cent smaller than the cylindrical tube were made for the test. Five samples were made for each size of specimen.

Table I. Particulars of tested fabrics

Fabric no. 25034

Fabric no. 28432

Gauge Fabric weight Composition

Raschel warp knitted on 56 gauge 220g per square metre 67 dtex nylon and 470 dtex elastane

Breaking load

60kg (lengthways) 48kg (widthways) 380 per cent (lengthways) 320 per cent widthways)

Raschel warp knitted on 56 gauge 270g per square metre 56 dtex nylon; 480 dtex elastane and includes 16 per cent of 100Nm cotton 58kg (lengthways) 75kg (widthways) 360 per cent (lengthways) 280 per cent (widthways)

Breaking extension

Effect of hem edges

253 Figure 1. Fabric tube sample seamed by three-point zig-zag stitch with 1cm seam allowance

2.1.2 Methods of edge finish for the garment specimen. Four methods that are commonly used on the hem edges of pressure garments are selected for the preparation of a garment specimen. (Methods A, B, and C are illustrated in Figure 2): . Method A. The cut edges were neatened by a row of overlocking stitches (BS 504 at density 55 stitch per 10cm). . Method B. A rubber band (1.5cm in width) was stitched (by stitch BS 304) at the hemline of the garment. . Method C. The raw edges of the hemline were turned up (1cm) and stitched in position by a row of zig-zag lockstitches (BS 304). . Raw edges. No finish on hem edges. 2.2 Description of measuring pressure device The Oxford pressure monitor MKII (as shown in Figure 3) is selected as a measuring pressure device in this study. It is a microprocessor controlled monitor for measuring the patient orthotic interface. It was primarily designed to monitor the pressures between skin tissue and the support media for chair or bed bound individuals, or indeed any application in which the pressure range could be expected to be 0-240mmHg, such as the pressure between support hosiery and the limb. The sensor cell of the pressure monitor is a small bag made of thin plastic layers (size 2cm 2cm) which could be inserted between the pressure garment and the interface surface. 2.3 Test method The specimens made of raw edges and with 15 per cent reduction were stretched onto the cylindrical tube, and the interface pressure at the four measuring locations (at the circumferential line 7.5cm above the hem edge) were recorded. With the fabric tube remaining on the tube model, the pressure transducer was moved beneath the garment specimen to different sites as

IJCST 11,5

254

Figure 2. Methods of edge neatening for pressure garments

Figure 3. The Oxford pressure monitor MKII

already marked on the fabric tubes for pressure recording. Pressure was never measured when the transducer was moving, but was measured with the transducer stationary at each appropriate location for about five minutes to allow the fabric tube specimens to relax completely before the measurements were taken. In order to avoid unnecessary stress on the garment specimen that subsequently may affect the experimental results, care should be taken to insert the specimen into the cylindrical tubes with the minimum disturbance. It

is difficult to maintain uniform fabric stretch when setting the garment specimen onto the tube model. In order to avoid the large variation in fabric tension on different parts of the model surface, after putting the garment specimen on the tube model, a small round pencil is inserted in between the specimen and the tube. It is moved around the cylindrical tube two to three times in order to make the distribution of the fabric compression more evenly distributed over the cylindrical surface. The procedures of carrying out the test method are illustrated in Plates 1 and 2. With the seam of the garment specimen facing out, the interface pressure was measured twice at each measuring location. The tests were repeated on other sets of garment specimen which were made in other sizes (25 and 35 per cent size reduction) and with the hem edges finished by ``Method A'', ``Method B'' and ``Method C'' as described above.

Effect of hem edges

255

Plate 1.

Plate 2.

IJCST 11,5

256

In order to compare the differences in interface pressure at different distances from the hem edge, the data obtained from the four measuring points (P1, P2, P3 and P4 as shown in Plate 2) at the same circumferential line of each specimen were averaged out. Test results are presented in the following section. 3. Results and discussions Test results are summarized and shown in Table II and Figures 4-9 (Details of test results are listed in Table III). 3.1 Specimens of raw edges (no finish on hem edges) It was noted that the interface pressure measured at the locations 5cm from the hem edges was lower than those measured at 7.5cm from the hem edges. The

Per cent reduction

25034 3cm

Fabric number 25034 25034 28432 28432 Distance from the hem edge 5cm 7.5cm 1.5cm 3cm

28432

5cm

7.5cm

Raw edge Method A Method B Method C

10.2 9.8 10.1 13.4

11.3 12.0 12.0 11.8

12.3 13.0 13.4 13.2

12.4 13.2 13.5 13.2

10.5 10.0 10.6 13.6

11.9 11.6 12.2 12.5

13.2 12.4 13.9 13.7

13.0 12.5 13.6 13.8

25

Raw edge Method A Method B Method C

15.1 14.5 16.0 18.5

16.2 16.8 17.3 17.0

18.8 19.2 19.2 18.5

18.5 19.2 19.3 18.6

15.6 15.6 16.0 20.2

17.2 17.5 18.1 18.2

18.6 18.8 20.1 19.5

18.8 19.0 20.0 19.8

35

Raw edge Method A Method B Method C

21.5 21.4 20.4 25.8

23.8 23.5 24.0 24.0

26.0 26.0 26.4 25.3

26.2 25.8 26.0 25.6

22.0 22.3 21.5 28.5

25.0 25.2 24.5 25.5

27.4 27.4 26.8 28.2

27.4 27.2 26.8 28.0

14 13 12 11 10 9 1.5

3

5

7.5

Distance from the hem edge (in cm)

Figure 4. Size of reduction at 15 per cent (fabric no. 25034)

28432

15

Interface Pressure (mmHg)

Table II. Interface pressure measured at various distance from hem edges

Method of 25034 neatening edges 1.5cm

Key Raw Edge

Method A

Method B

Method C

Interface Pressure (mmHg)

Effect of hem edges

15 14 13 12 11 10

257

9 1.5

3

5

7.5

Distance from the hem edge (in cm)

Figure 5. Size of reduction at 15 per cent (fabric no. 28432)

Key Raw Edge

Method A

Method B

Method C

Interface Pressure (mmHg)

20 19 18 17 16 15 14 1.5

3

5

7.5

Distance from the hem edge (in cm)

Figure 6. Size of reduction at 25 per cent (fabric no. 25034)

Interface Pressure (mmHg)

Key Raw Edge

Method A

Method B

Method C

22 20 18 16 14 1.5

3

5

Distance from the hem edge (in cm) Key Raw Edge

Method A

Method B

Method C

7.5

Figure 7. Size of reduction at 25 per cent (fabric no. 28432)

258

Interface Pressure (mmHg)

IJCST 11,5

27 26 25 24 23 22 21 20 1.5

3

5

7.5

Distance from the hem edge (in cm)

Figure 8. Size of reduction at 35 per cent (fabric no. 25034)

Key Raw Edge

Method A

Method B

Method C

Interface Pressure (mmHg)

30 28 26 24 22 20 1.5

Figure 9. Size of reduction at 35 per cent (fabric no. 28432)

3

5

7.5

Distance from the hem edge (in cm) Key Raw Edge

Method A

Method B

Method C

interface pressure recorded at the locations 3cm and 1.5cm from the edge was much lower than those at the distance 5cm from the edge. The results clearly showed that the interface pressure started to decrease from a distance of 5cm from the hem edges. The amount of pressure loss became higher the closer the location of the pressure measurement was to the hem edge. The three sizes of garment specimen behaved similarly, but in the smaller size of garment (that means higher percentage of reduction), the amount of pressure loss at the hem edges was higher. For example, the garments made at 35 per cent reduction lost about 5mmHg when the pressure measurement was recorded at a position 1.5cm from the hem edge instead of 7.5cm from the hem edge, but the garments made with 15 per cent reduction lost only about 2mmHg. The two fabrics tested have similar behaviour.

Percentage Method of Fabric number 25034 of neatening Distance from hem edge reduction edges Position 1.5cm 3cm 5cm 7.5cm

Fabric number 28432 Distance from hem edge 1.5cm 3cm 5cm 7.5cm

15

Raw edge P1 P2 P3 P4 Mean Method A P1 P2 P3 P4 Mean Method B P1 P2 P3 P4 Mean Method C P1 P2 P3 P4 Mean

10.0 11.0 11.8 8.0 10.2 8.4 6.6 14.0 10.2 9.8 7.0 10.0 10.2 13.2 10.1 14.0 16.2 13.4 10.0 13.4

14.0 11.2 9.0 11.0 11.3 11.4 10.8 12.8 13.0 12.0 10.4 13.6 12.4 11.6 12.0 10.6 14.4 8.4 13.8 11.8

14.2 12.2 10.2 12.6 12.3 12.0 16.0 10.6 13.4 13.0 13.4 9.2 14.0 17.0 13.4 12.8 12.8 13.2 14.0 13.2

13.0 11.8 11.4 13.4 12.4 10.6 15.2 15.0 12.0 13.2 11.8 14.0 14.6 13.6 13.5 8.8 16.0 15.0 13.0 13.2

10.2 12.0 11.8 8.0 10.5 7.6 11.4 8.8 12.2 10.0 10.6 14.0 6.6 11.2 10.6 14.0 12.6 12.4 15.4 13.6

11.0 14.2 10.4 12.0 11.9 10.4 13.0 11.4 11.6 11.6 11.4 10.0 12.8 14.6 12.2 11.2 16.2 12.2 10.4 12.5

11.2 14.4 15.2 12.0 13.2 12.2 11.8 9.8 15.8 12.4 13.0 12.2 13.2 17.2 13.9 12.6 13.4 15.0 13.8 13.7

11.0 15.0 10.0 16.0 13.0 11.6 16.4 12.0 10.0 12.5 13.2 14.4 10.6 16.2 13.6 13.0 14.6 13.0 14.6 13.8

25

Raw edge P1 P2 P3 P4 Mean Method A P1 P2 P3 P4 Mean Method B P1 P2 P3 P4 Mean Method C P1 P2 P3 P4 Mean

14.0 12.4 18.2 15.8 15.1 13.4 14.4 17.4 12.8 14.5 13.2 16.0 16.6 18.2 16.0 18.4 15.6 20.8 19.2 18.5

16.6 16.2 14.0 18.0 16.2 16.8 16.2 14.2 20.0 16.8 15.6 20.8 14.8 18.0 17.3 17.2 14.0 17.0 19.8 17.0

19.0 16.0 18.0 22.2 18.8 18.2 21.0 18.4 19.2 19.2 17.4 19.2 19.8 20.4 19.2 18.4 21.2 15.8 18.6 18.5

18.2 19.8 17.4 18.6 18.5 20.0 18.4 19.8 18.6 19.2 19.6 21.4 17.4 18.8 19.3 17.0 18.6 19.4 19.4 18.6

14.2 17.0 13.0 18.2 15.6 14.4 17.6 15.0 15.4 15.6 15.4 16.4 18.0 14.2 16.0 21.0 21.0 19.2 19.6 20.2

18.0 16.2 14.6 20.0 17.2 17.0 19.0 13.8 20.2 17.5 19.2 17.8 18.2 17.2 18.1 18.0 16.8 21.4 16.6 18.2

18.6 18.4 18.0 19.4 18.6 16.8 20.6 17.8 20.0 18.8 18.8 23.0 17.2 21.4 20.1 18.2 23.6 16.8 19.4 19.5

17.4 16.0 21.0 20.8 18.8 19.2 15.8 21.6 19.4 19.0 19.6 18.2 20.0 22.2 20.0 19.6 17.8 20.0 21.8 19.8

35

Raw edge P1 P2 P3 P4 Mean

21.2 23.4 21.4 20.0 21.5

23.8 26.0 22.6 22.8 23.8

26.6 27.8 25.2 24.4 26.0

25.0 26.2 25.0 28.6 26.2

20.2 21.8 22.4 23.6 22.0

25.4 24.0 22.6 28.0 25.0

27.6 26.8 26.6 29.0 28.0 27.6 27.4 26.2 27.4 27.4 (continued)

Effect of hem edges

259

Table III. The interface pressure measured at various positions from the hem edge

IJCST 11,5

260

Table III.

Percentage Method of Fabric number 25034 of neatening Distance from hem edge reduction edges Position 1.5cm 3cm 5cm 7.5cm Method A P1 P2 P3 P4 Mean Method B P1 P2 P3 P4 Mean Method C P1 P2 P3 P4 Mean

21.4 18.0 22.0 24.2 21.4 17.6 20.0 22.8 21.2 20.4 24.6 25.8 27.6 25.2 25.8

22.8 26.0 23.2 22.0 23.5 24.0 24.4 27.2 20.4 24.0 23.2 22.8 24.0 26.0 24.0

24.2 29.6 22.6 27.6 26.0 25.2 29.8 26.4 24.2 26.4 24.4 26.8 22.8 27.2 25.3

25.4 25.0 25.8 27.0 25.8 24.8 27.4 27.0 24.8 26.0 25.8 28.0 23.2 25.4 25.6

Fabric number 28432 Distance from hem edge 1.5cm 3cm 5cm 7.5cm 21.2 22.6 22.4 23.0 22.3 20.4 20.6 21.4 23.6 21.5 27.0 29.4 29.2 28.4 28.5

25.0 23.6 27.8 24.4 25.2 26.0 27.2 22.0 22.8 24.5 23.4 25.0 27.2 26.4 25.5

29.2 27.2 24.2 29.0 27.4 27.6 28.0 25.8 25.8 26.8 30.0 25.8 28.0 29.0 28.2

26.6 26.2 28.8 27.2 27.2 26.2 27.8 24.0 29.2 26.8 29.4 28.8 28.8 25.0 28.0

3.2 Specimen of hem edges finished by Method A and by Method B The test results of both methods were very similar to those of ``raw edges''. The interface pressure near the specimen edges was lower than those measured at or beyond 5cm from the hem edge. This indicated that the use of overlocking or the addition of a rubber band to the hem edges of the garment could not prevent the pressure loss caused by the edge effect. For the Method B, even though the rubber band could not prevent the pressure decreasing near the edges of the garment, the interface pressure measured underneath the rubber band at the edge of the garment behaved differently. The degree of compression on that area depends completely on the size and the type of rubber band used on the garment. 3.3 Specimen of hem edges finished by Method C The interface pressure measured at a distance of 3cm above the hem edges was slightly lower (about 2mmHg) than those measured at the locations 5cm and 7.5cm from the edges, but the test results obtained at the locations 1.5cm, 5cm and 7.5cm beyond the edges were all similar. This indicated that the use of ``turn-up'' at the hem edges of pressure garment was effective in maintaining the interface pressure at the garment edge similar to the pressure obtained from the area at or more than 5cm from the edge, but there is still some pressure loss in the region between the ``turn-up'' hem and the distance 5cm from the hem edge. In sum, the phenomenon of the study was most likely due to the Poisson effect that occurred at the edges of the specimen and also by the friction with the tube. When the specimen of a pressure garment is fitted to a tube that is of larger circumference than the initial circumference of the specimen, the specimen is subjected to hoop stresses uniformly in latitude direction, and the

slipping of fabric towards the centre in the longitudinal direction will occur. It is likely that the only stresses in longitudinal direction of the specimen are those introduced by the Poisson effect and by friction with the tube. It is also believed that the experimental arrangement would have more effect at the ends of the tube in helping the specimen to overcome friction and reduce the biaxial stresses by slipping longitudinally rather than towards the centre. This would explain why there is a drop in pressure at the edge of the specimens in all experiments except in the case of ``turn-up hem''. As the specimen with a turnup hem has a double thickness of fabric at the hem edges, it implies twice the effective modulus at the point where the end measurements of pressure were taken, and thus may increase the interface pressure at that point. 4. Summary and conclusions The test results revealed that the interface pressure started to decrease at the point 5cm beyond the hem edges and closer to the hem edges the interface pressure became lower. The garment specimens with raw edges and those neatened by Method A and Method B showed similar behaviour. The test results indicated that the method used to neaten the raw edges by overlocking or by adding a rubber band at the hem edge could not prevent the loss of interface pressure due to the edge effect. Only the specimens with the turn-up edges (Method C) behaved differently. The test results showed that there was still some pressure loss in the region around 3cm from the hem edge, but the turn-up edge was functional in providing an interface pressure similar to those obtained at the area at or beyond 5cm from the edges. Examination of the interface pressure at the hem edges of tubular pressure garments indicated that a loss of interface pressure is most likely due to the Poisson effect and friction with the interface surface. Starting at the region about 5cm from the edge, the interface pressure begins to decrease. Thus the minimum margin from the hem edges to the scar area should be 5cm if a constant and uniform interface pressure is needed to be induced by the pressure garment. Reference Ng, S.F.F. (1990), ``The properties and comfort of pressure garments for hypertropic scar treatment'', MPhil thesis, Leicester Polytechnic.

Effect of hem edges

261

The current issue and full text archive of this journal is available at http://www.emerald-library.com

IJCST 11,5

262 Received September 1998 Revised October 1998

Estimating thermal resistance of the bedding assembly from thickness of materials C.A. Wilson, B.E. Niven and R.M. Laing University of Otago, Dunedin, New Zealand

Keywords Thermal, Materials Abstract The purposes of this work were to determine: whether thickness of single layers can be used to accurately predict thickness and thermal resistance of multiple layer assemblies; and to identify variables affecting the total thickness (i.e. textile plus air layers) of bedding during simulated use. Thickness was determined when: materials were flat; and arranged over an infant manikin simulating use. Thermal resistance was measured using a guarded-hotplate similar to that specified in ISO 11092:1993(E). During simulated use, the site of measurement, body position, tucking, and product type significantly affected thickness of bedding. Equations for predicting thickness and thermal resistance (dry) of multiple-layer materials are described. While it was possible to predict thickness and thermal resistance of flat bedding from estimated values, extrapolation to bedding during simulated use was considered inappropriate, with significant differences of over 1,000 per cent.

Introduction The major contribution of still air to thermal resistance of textiles is widely acknowledged. This has led to use of a fabric's thickness to predict its thermal resistance. Thermal resistance (m2K/W) of materials has been expressed as the equivalent thickness of still air in the following ways: 0.03984 specimen thickness (mm) (or 0.3984 togs/mm) (Peirce and Rees, 1946); 0.0276 specimen thickness (mm) ± 0.0108 (or 0.276 specimen thickness ± 0.108 togs) (Holcombe and Hoschke, 1983); and 0.028 specimen thickness (mm) (or 0.28 togs/mm) (Weatherall, 1983). Critical evaluation of this relationship between the behaviour of textiles when measured flat and during use has occurred. Increasingly researchers are attempting to accommodate the effect of a number of variables which may affect the distribution and thickness of air spaces and thermal resistance of a garment assembly (e.g. fabric characteristics, multiplelayers, fit, product design, and surface area and geometry) (Fan and Keighley, 1991; Holcombe and Hoschke, 1983; Lotens, 1989; McCullough and Wyon, 1983; Spencer-Smith, 1977). In the case of bedding, variables such as the surface area and geometry are likely to have a greater effect on thermal resistance than occurs with clothing because the outer surfaces are some distance from the human body, thus increasing the size of the air space and the surface area available for loss of heat. International Journal of Clothing Science and Technology, Vol. 11 No. 5, 1999, pp. 262-276. # MCB University Press, 0955-6222

During part of this study C. Wilson was partially supported by the New Zealand Cot Death Association, a division of the National Child Health Research Foundation. The assistance of J. Anderson, R. Caley and M. Wilson with development of instruments and data collection, and Burston Nuttall and Alliance Textiles for donation of bedding, is also gratefully acknowledged.

As early as 1955 the linearity of the relationship between thermal resistance and thickness of flat materials was questioned and in the case of multiple layers of materials shown to be non-linear (Epps and Song, 1992; Morris, 1955). Despite these findings, adding measurements of fabric thickness taken using flat, single layers has continued to be used to predict thermal resistance in both textile and epidemiological applications, such as investigations of infant overheating and sudden infant death syndrome (SIDS) (Peirce and Rees, 1946; Weatherall, 1983; Bolton et al., 1996; Nelson and Taylor, 1989; Tuohy and Tuohy, 1990). Estimating the proportion of the body surface area covered, insulation score (presumably dry), tog value, and number of layers of bedding covering the infant, have also been used as indicators of insulation (Bacon et al., 1979; Cowan, 1990; Nelson and Taylor, 1989; Tuohy and Tuohy, 1990; Wailoo et al., 1989). Continued extrapolation from results determined using single layers of materials probably reflects the relative ease with which these measurements can be obtained under field conditions. However, the lack of knowledge about the size of error emphasises the need for further work on this topic. A few researchers have attempted to consider the three-dimensional shape when describing characteristics of bedding assemblies during use (Kerslake, 1991; Ray, 1981). The purposes of this work were to determine, for a range of upper-bedding materials used to cover New Zealand babies and infants: .

.

whether thickness of single layers can indeed provide the basis to accurately predict thickness of multiple-layered assemblies measured flat and/or during simulated use, and thermal resistance of single- and multiple-layered fabric assemblies (flat); and variables affecting the total thickness of bedding during simulated use, that is thickness of both the textile and air layers.

Method Instruments and testing conditions Thickness was determined in two ways: materials were measured: flat; and when arranged over an infant manikin (dimensions approximately those of a 02 month old infant (Snyder et al., 1977) dressed in standardised clothing (singlet, stretch and grow, cloth nappy and fluffies (a commonly used nappy cover)). Specimens of bedding materials were measured flat using a counterbalanced thickness tester which consisted of a pulley and weight (4.57g) supporting a 33.2cm2 foot (British Standards Institution, 1987). Thickness of bedding during simulated use was measured using a platform and horizontal staging (60 120cm) which supported a counter-balanced thickness tester with a foot dimension of 0.07cm2 (Plate 1). Both instruments applied the same load of 0.07gf/cm2. The device was transported across the platform on a rail (Plates 2 and 3) and a ``crane'' arm extended the foot 30cm down the platform to approximately the centre of the torso.

Estimating thermal resistance 263

IJCST 11,5

264

Plate 1. Counter-balanced thickness tester

Plate 2. Instrument in use, showing the measurement arm

Dry thermal resistance was determined using a guarded-hot plate (similar to that specified in ISO 11092:1993(E)) (International Organization for Standardization, 1993).

Estimating thermal resistance 265

Plate 3. Rail system transporting the tester across the width of the bed

Materials Bedding items were those commonly used to clothe and wrap infants as identified in the New Zealand three year cot death study (Wilson, 1992; Wilson et al., 1994). Items were: .

cotton muslin wrap, plain weave, double layer, 16.3 11.0 yarns/cm;

.

cotton sheet, plain weave, napped, 18.8 16.6 yarns/cm;

.

wool blanket, cellular or air-cell, weft faced, 5.2 4.0 yarns/cm;

.

wool blanket, herringbone, twill weave, napped, 8.0 9.3 yarns/cm;

.

panel quilt or duvet, bulked polyester filling, polyester/cotton, woven plain weave cover, 32.8 19.5 yarns/cm.

A foam slab cot mattress (13 58 125cm) and non-compressible board (1.3 58 125cm), covered in a cot sheet, were used as base layers. Testing procedure Thickness was measured according to BS 2544:1987 (British Standards Institution, 1987). Three fabric arrangements were used: (1) single-layer specimens flat; (2) multiple-layer combinations flat; and (3) multiple-layer combinations arranged to simulate use.

IJCST 11,5

266

Multiple-layered specimens were assembled in the selected order (Table I(a)) and allowed to settle for five minutes before thickness was measured. Ten replicates were taken for (1) and (2), and a factorial, partially randomised two replicate design was used for (3) (factors and levels are described in Table I). Firmly tucked and swaddled bedding was secured along the side of the mattress using lead weights. Layers were added singly, tucked into position and left to settle for five minutes before thickness was measured. This procedure was repeated for all subsequent layers of bedding with the side of the bed from which tucking was initiated being randomised. Thickness of each layer in the bedding assembly (that is, the textile plus the air layer) was determined by subtracting thickness of the previous layer from the total thickness. Where the layer was a sheet, the profile across the under-sheet and Variable

Description

(a) Bedding combination S A T D SA ST SAA SAT STA STT SAAD SATD STAD STTD

14 single- and multiple-layer combinations Sheet Air-cell blanket Twill blanket Duvet Sheet, air-cell Sheet, twill Sheet, air-cell, air-cell Sheet, air-cell, twill Sheet, twill, air-cell Sheet, twill, twill Sheet, air-cell, air-cell, duvet Sheet, air-cell, twill, duvet Sheet, twill, air-cell, duvet Sheet, twill, twill, duvet

(b) Manikin sleep position Lateral Right side uppermost Prone Ventral surface downwards and head turned to the right Supine Ventral surface and face uppermost (c) Method of tucking Loose Firmly Swaddled (d) Mattress type Board Foam (e) Measurement site Table I. Experimental variables

Bedding draped over the manikin, excess bedding folded so it lay on top of the mattress with blanket edges extending inwards Bedding tucked firmly over the manikin Swaddling cloth wrapped around the manikin and secured at the back of the neck. Bedding is then firmly tucked A non-compressible material A standard cot mattress Represented points across the bed where thickness was measured. Eleven sites (50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550) with site 300 indicating the centre of the bed and manikin mid line

over the infant was used as the base line. All materials were conditioned for 24 hours and tested under standard testing conditions (20 28C and 65 2 per cent RH) (British Standard Institution, 1992). Thermal resistance (dry) of each bedding combination was calculated as follows (International Organization for Standardization, 1993): Rct

Tm ÿ Ta :A ÿ Rct0 H ÿ Hc

where Rct0

Tm ÿ Ta :A H ÿ Hc

and Hc / Tm ÿ Ts Rct

=

Tm =

dry thermal resistance (m2.K/W) temperature of the measuring unit (8C)

Ta

=

air temperature in the test enclosure (8C)

A

=

area of the measuring unit (m2)

H

=

the heating power supplied to the measuring unit (W)

Hc =

a correction term linearly related to the difference in temperature between the measuring unit and thermal guard plate

Rct0 =

thermal resistance of the bare plate (m2.K/W)

/

=

the slope of the linear regression of heating power versus the difference in temperature between the measuring unit and thermal guard

Ts

=

temperature of the guard unit (8C).

Thermal resistance was calculated over a three-hour period which started one hour after equilibrium (35 0.18C). Under equilibrium conditions, energy supplied to the plate equalled that passing through the specimen across the test area. The upper-most surface of test specimen(s) was exposed to the ambient air for the duration of the investigation. The plate was maintained at equilibrium for 18 hours prior to testing. Statistical analysis Data on thickness of bedding during simulated use were screened to determine whether adjacent data or possible irregularities (i.e. at adjacent measurement sites) were correlated in any way. As thickness at each measurement site across the bed was determined as independently as possible, data were considered to

Estimating thermal resistance 267

IJCST 11,5

268

be unrelated. Differences in variance, particularly between layers and across measurement positions, were found and while weighting procedures to overcome differences in variance were attempted, no satisfactory procedure was identified. Data were first analysed in their entirety, then divided into two sets (lateral and prone/supine) for subsequent analysis. Results from the latter two sets are discussed. Descriptive statistics were determined for all variables. Thickness of multiple-layered combinations was estimated by adding thickness of the appropriate single layers. Differences among specimens when measured flat were investigated using one factor ANOVA, while MANOVA was used to analyse thickness of each layer during simulated use (Hair et al., 1987). Tukey's multiple comparison tests identified where significant differences occurred. Thickness and thermal resistance of multiple-layer combinations were described using regression analysis. Results and discussion Thickness of flat, single- and multiple-layer specimens Thickness of the various single layers of specimens (Table II) was significantly different F3;36 42; 754:50; p < 0:001 as was expected. Variability, while reasonably high, was within the accepted range for textile testing. The thickness of multiple-layer combinations (Table II) varied according to what layers constituted the assembly and the number of layers present F9;90 3; 147:72; p < 0:001; F2;97 10; 952:22; p < 0:001 respectively). High CVs reflect the pooling of all factors in this description of the data. Thickness varied significantly among both two- and three-layer combinations

X

Table II. Thickness of singleand multiple-layer bedding, measured and estimated (mm)

Flat

SD

(a)

Single layers Sheet (S) Air-cell blanket (A) Twill blanket (T) Duvet (D)

1.65 4.95 6.15 51.58

0.24 0.16 0.24 0.63

(b)

Multiple layers SA ST SAA SAT STA STT SAAD SATD STAD STTD

6.20 6.95 10.25 11.60 11.45 12.30 60.80 64.05 63.40 63.10

0.26 0.28 0.26 0.39 0.83 0.26 2.55 1.28 0.52 3.80

Simulated Site 300mm X SD

use Site 450mm X SD

Estimates N/A N/A N/A N/A

5.27 6.28 8.90 9.64 9.51 10.87 50.71 52.45 51.66 53.58

4.98 5.66 6.12 6.30 6.15 6.39 7.87 7.29 8.24 8.22

73.81 75.03 79.70 81.44 81.62 82.42 137.46 138.15 139.16 140.13

21.65 21.45 22.25 20.60 21.42 20.41 31.51 32.10 31.87 31.40

6.60 7.80 11.55 12.75 12.75 13.95 63.40 64.60 64.60 65.80

F1;18 38:21; p < 0:001; F3;36 29:49; p < 0:001, although, actual differences were small (0.75 and 2.05mm respectively) (Table II). The difference between three-layer combinations including twill and air-cell blankets was not significant, confirming that the order of different blanket types within this bedding assembly did not significantly affect total thickness. Thickness of four-layer bedding combinations was not significantly different F3;35 2:59; NS suggesting addition of the duvet masked differences identified among three-layer combinations. This masking effect was possibly because, while lightweight and compressible, the duvet was also stiff, bulky and less likely to follow the contours of underlying layers. Hence the duvet appeared to behave independently of underlying layers. Thickness of multiple-layer combinations ± estimated Multiple layers of textiles for bedding provide a greater opportunity for trapping air between and/or under layers than occurs with single layers of textiles. Thus data obtained by measuring directly might be expected to exceed those obtained by estimates based on thickness of the various individual layers. However, estimates were up to 13 per cent greater than the actual values t9 5:68; p < 0:001 (Table II(b)) with the greatest differences observed when bedding consisted of a sheet and two blankets (10-13 per cent). Addition of a duvet to the bedding reduced these differences to 1-4 per cent. Prediction of thickness of multiple-layer bedding from estimates Comparison of measured with estimated thickness (means) showed they differed by a constant and could be described as: Total thickness flat multiple-layers (actual) = ±0.960 + 0.987 estimated total thickness. This model accounted for 99.9 per cent of the variance F1;8 13; 129:04, p < 0:001. Thermal resistance of single- and multiple-layer bedding measured flat The relationship between thickness and thermal resistance described by Morris (1955) held true for both single-layer and multiple-layer assemblies (Table III). The best prediction of dry thermal resistance from thickness involved fitting one equation that adjusted for the presence of a duvet. Dry thermal resistance Rct of flat multiple-layer combinations was therefore: Rct = 0.051 + 0.023 thickness of the total assembly ± (0.469 if duvet present) F2;11 1; 607:52; p < 0:001 Rct = 0.006 + 0.025 estimated thickness of the total assembly ± (0.628 if duvet present) F2;7 = 2,971.79, p < 0.001). Each statistical model was equally effective for predicting thermal resistance, accounting for 99.9 per cent of the variance.

Estimating thermal resistance 269

IJCST 11,5

Predicted

Measured X SD

270

Table III. Dry thermal resistance of flat single- and multiple-layer bedding predicted from thickness m2 K=W

Present study

Holocombe Peirce and and Reese Weatherall Hoschke (1946) (1983) (1983)

(a) Single layers Sheet (S) Air-cell blanket (A) Twill blanket (T) Duvet (D)

0.09 0.14 0.18 0.79

0.02 0.00 0.01 0.02

0.09 0.16 0.19 0.77

0.07 0.20 0.25 2.07

0.05 0.14 0.17 1.45

0.03 0.13 0.16 1.42

(b) Multiple layers SA ST SAA SAT STA STT SAAD SATD STAD STTD

0.18 0.20 0.28 0.32 0.32 0.38 0.96 1.00 1.02 1.02

0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.02 0.04 0.03

0.19 0.21 0.29 0.32 0.31 0.33 0.98 1.06 1.04 1.03

0.25 0.28 0.41 0.46 0.46 0.49 2.42 2.55 2.53 2.51

0.17 0.19 0.29 0.32 0.32 0.34 1.70 1.79 1.78 1.77

0.16 0.18 0.27 0.31 0.31 0.33 1.67 1.76 1.74 1.73

±2.38 p < 0.05

±2.28 p < 0.05

(c) Difference between measured and predicted thermal resistance Paired t tests (df = 9) ±0.92 ±0.94 Significance NS p < 0.05

Given the difficulty of measuring thickness in the field, estimated thickness may continue to be the preferred method for estimating thermal resistance. Differences between the findings of Morris (1955) and those reported here probably reflect use of a classifying variable to accommodate the effect of product type in the analysis. That is, data were categorised as those combinations including or excluding a duvet, resulting in two distinct parallel and linear regressions being fitted as opposed to the single, non-linear data set identified by other researchers. Results confirmed the physical structure of the bedding significantly affected thermal resistance. Predictions of thermal resistance using various formulae are compared with those from this study in Table III. While predicted values are comparable for bedding combinations which did not include a duvet, where the duvet and other product types were not accounted for separately, significant overestimation of resistance resulted. Characteristics of bedding during simulated use In use, bedding drapes over the body creating a tent-like structure. A crosssection of the bedding under conditions of simulated use shows two general arrangements of textiles and air (loosely described in this paper as total thickness):

(1) ``steep'', between the manikin and the edge of the mattress, where large air spaces are formed between layers; and (2) ``flat'', immediately over the manikin, where minimal separation occurred (Figures 1-3).

Thickness (mm)

Thickness immediately over the manikin most closely resembled thickness of materials measured flat and in multiple-layers (Table II). Combinations measured at site 300mm during simulated use were significantly less than when measured flat (13-19 per cent; t9 4:31; p < 0:01 or when estimated (19-25 per cent; t9 5:19; p < 0:001. In contrast, thickness of site 450mm was up to 1,090 per cent thicker than actual and 1,018 per cent thicker than estimated thickness determined from flat specimens, emphasising the height of the air space incorporated into the structure adjacent to the body. It is the effect of air trapped within the total bedding which is ignored if thickness of flat single-layers is used to estimate thickness during use. Lateral

Prone

Supine

400

400

400

300

300

300

200

200

200

100

100

100

0

271

0

0 0 100 200 300 400 500 600

Estimating thermal resistance

0 100 200 300 400 500 600

0 100 200 300 400 500 600 Measurement site (mm)

Thickness (mm)

Key Duvet/air

Blanket 2/air

Blanket 1/air

Sheet/air

Infant

Sheet/mattress

Lateral

Prone

Supine

400

400

400

300

300

300

200

200

200

100

100

100

0

0

0 0 100 200 300 400 500 600

Figure 1. Profile of loose tucking during simulated use

0 100 200 300 400 500 600

0 100 200 300 400 500 600

Measurement site (mm) Key Duvet/air

Blanket 2/air

Blanket 1/air

Sheet/air

Infant

Sheet/mattress

Figure 2. Profile of swaddled tucking during simulated use

272

Thickness (mm)

IJCST 11,5

Lateral

Prone

Supine

400

400

400

300

300

300

200

200

200

100

100

100

0

0 0 100 200 300 400 500 600

Figure 3. Profile of firmly tucked during simulated use

0 0 100 200 300 400 500 600

0 100 200 300 400 500 600

Measurement site (mm) Key Duvet/air

Blanket 2/air

Blanket 1/air

Sheet/air

Infant

Sheet/mattress

The closest relationship between the thickness of flat specimens and that which occurs during use is with the area immediately over the manikin. Thickness immediately over the manikin (site 300mm) could be described statistically using measured and estimated thickness of flat specimens: Total thickness over the body, during use = 0.362 + 0.823 thickness, F1;8 11; 104:11; p < 0:001; and Total thickness over the body during use = ±0.442 + 0.813 estimate of thickness F1;8 29; 171:68; p < 0:001. Each equation accounted for 99.9 per cent of the variance. However, the proportions of the bed width described as flat accounted for only 18 per cent in the lateral and 36 per cent in the more rounded prone and supine body positions. These differences confirm the inappropriateness of extrapolating from measurements using flat specimens to describe the entire bedding during simulated use. Such a practice is likely to overestimate thickness immediately over the body and grossly underestimate thickness (and resistance) at those sites adjacent to the body. Findings confirm the need to accommodate the effect of factors other than thickness. Variables affecting thickness of the bedding during simulated use Thickness of the bedding during simulated use varied according to the measurement site, and relative order and type of layer. Irrespective of body position, the first layer contributed the most to thickness of the assembly, varying significantly according to measurement site F10;462 3; 896:97; p < 0:001; F10;924 3732:79; p < 0:001 lateral and prone/supine respectively). Subsequent blanket layers (i.e. layers two and three) followed the contours of the first (sheet) layer (Figures 4 and 5). While thickness of the duvet also varied significantly among measurement sites F10;264 145:58; p < 0:001; F10;528 52:16; p < 0:001 lateral and prone/supine respectively) the contours of the duvet behaved relatively independently of other layers. Thickness of the duvet was most affected by the manikin position and tucking method.

120

Key Textile

100

Estimating thermal resistance

Air

273

Thickness (mm)

80

60

40

20

Sheet

Blanket 1

50 150 250 350 450 550

50 150 250 350 450 550

50 150 250 350 450 550

50 150 250 350 450 550

0

Blanket 2

Duvet

Measurement sites 50-550 mm for layers 1-4 120

Figure 4. Thickness of the air and textile component of each bedding layer during simulated sleep: lateral sleep position

Key Textile

100 Air

60

40

20

Sheet

Blanket 1

Blanket 2

Measurement sites 50-550 mm for layers 1-4

50 150 250 350 450 550

50 150 250 350 450 550

50 150 250 350 450 550

0 50 150 250 350 450 550

Thickness (mm)

80

Duvet

Figure 5. Thickness of the air and textile component of each bedding layer during simulated use: prone-supine sleep position

IJCST 11,5

274

The main effect of tucking was on the relative positioning of the blanket layers and the size of the air space formed under the sheet F2;462 1; 224:33; p < 0:001; F2;924 301:44; p < 0:001 in the lateral and prone/supine positions respectively). The air space was affected by both tucking and body position with the size of this air space being greater in the lateral than the supine or prone positions. The effect of loose tucking was to incorporate larger air spaces throughout the bedding assembly. However, as a consequence of draping the bedding over the body the height of the air space immediately under the sheet was reduced closest the body (Figure 1). Air spaces between subsequent layers were thicker when loosely tucked, as the bedding did not follow the contours of the layer below, possibly reflecting the absence of tension typical of firm and swaddled tucking. Bedding was thicker and trapped more air at the edge of the bed, probably because loose tucking involved turning edges under and resting them on top of the mattress. This technique resulted in a thicker yet flatter profile towards each edge of the mattress than found for firm and swaddled tucking. Swaddled bedding had a rounded smooth profile, probably due to the swaddling cloth bulking out of the manikin form (Figure 2). Thickness differences between the prone and supine positions occurred when bedding was firmly tucked but not when swaddled, possibly because of the swaddling masking any differences in surface geometry between the prone and supine positions. Mattress type affected thickness of the sheet layer only F1;462 60:74; p < 0:001; F1;924 295:70; p < 0:001 lateral and prone/supine respectively) with bedding measured as thicker when on the non-compressible surface than on foam (5.9 per cent in the lateral and 14.8 per cent in the prone/ supine positions). These differences reflect the compression of the foam by the manikin and the method used to measure and calculate thickness. In the absence of significant interactions between mattress type and other variables, the effect of mattress type was considered negligible. Conclusions There are three main conclusions from this study. First, the need to distinguish those assemblies of bedding which include a duvet from those which do not when predicting thermal resistance using thickness has been demonstrated. Second, estimates of bedding thickness can be used to predict thickness and thermal resistance of multiple-layer bedding measured flat and immediately over the manikin during simulated use. But extrapolating from flat single-layer thickness is inappropriate. During use, manikin position and tucking method significantly affect thickness of the assembly through the formation of air spaces. It is these spaces which are not estimated if flat single-layer thickness measurements are used as an indicator of thickness, and therefore thermal resistance. Third, an improved model has been developed which improves the accuracy of predicting thickness and thermal resistance of bedding immediately over the

infant body. Thickness and thermal resistance (dry) is more accurately estimated by adding the thickness of each component of the assembly, measured flat, and adjusting for the presence of a duvet in the assembly as follows:

Estimating thermal resistance

Total thickness over the body during use = ±0.442 + 0.813 estimate of thickness, and Rct over the body = 0.006 + 0.025 estimate of thickness ± (0.628 if a duvet is present).

275

Thickness and thermal resistance of bedding during use predicted using these equations provides an estimate of the minimum amount of resistance offered by the system only. However, these equations do at least provide a basis for comparison among different bedding and sleep configurations. References Bacon, C., Scott, D. and Jones, P. (1979), ``Heatstroke in well wrapped infants'', The Lancet, Vol. 1 No. 8113, pp. 422-5. Bolton, D.P.G., Nelson, E.A.S., Taylor, B.J. and Weatherall, I.L. (1996), ``A theoretical model of thermal balance. Implications for the sudden infant death syndrome'', Journal of Applied Physiology, Vol. 80 No. 6, pp. 2234-42. British Standards Institution (1987), BS 2544: British Standard Methods for Determination of RBS 2544: British Standard Methods for Determination of Thickness of Textile Material Thickness of Textile Materials, British Standards Institution, London. British Standards Institution (1992), BS EN 20139 (ISO 139: 1973): Textiles ± Standard Atmospheres for Conditioning and Testing, British Standard Institution, London. Cowan, S.F. (1990), ``How do Christchurch parents keep young babies warm on cold nights?'', New Zealand Medical Journal, Vol. 103 No. 884, pp. 71-3. Epps, H.H. and Song, M.K. (1992), ``Thermal transmittance and air permeability of plain weave fabrics'', Clothing Research Journal, Vol. 11 No. 1, pp. 10-17. Fan, J. and Keighley, J.H. (1991), ``An investigation on the effects of: body motion, clothing design and environmental conditions by using a fabric manikin'', International Journal of Clothing Science and Technology, Vol. 3 No. 5, pp. 6-13. Hair, J.F., Anderson, R.E. and Tatham, R.L. (1987), Multivariate Data Analysis, 2nd ed., MacMillian Publishing, New York, NY. Holcombe, B.V. and Hoschke, B.N. (1983), ``Dry heat transfer characteristics of underwear fabrics'', Textile Research Journal, Vol. 56 No. 6, pp. 368-74. International Organization for Standardization (1993), ISO 11092: Textiles ± Physiological Effects ± Measurement of Thermal and Water-vapour Resistance Under Steady-state Conditions (Sweating Guarded-hotplate Test), International Organization for Standardization, GeneÂve. Kerslake, D.M. (1991), ``The insulation provided by infants bedclothes'', Ergonomics, Vol. 34 No. 7, pp. 893-907. Lotens, W.A. (1989), ``The actual insulation of multilayer clothing'', Scandinavian Journal of Work Environment & Health, Vol. 15 Suppl. 1, pp. 66-75. McCullough, E.A. and Wyon, D.P. (1983), ``Insulation characteristics of winter and summer indoor clothing'', ASHRAE Transactions DC-83-11, Vol. 89 No. 2B, pp. 327-52. Morris, M.A. (1955), ``Thermal insulation of single and multiple layers of fabrics'', Textile Research Journal, Vol. 25 No. 9, pp. 766-73.

IJCST 11,5

276

Nelson, E.A.S. and Taylor, B.J. (1989), ``Infant clothing, bedding and room heating in an area of high postneonatal mortality'', Paediatric and Perinatal Epidemiology, Vol. 3 No. 2, pp. 146-56. Peirce, F.T. and Rees, W.H. (1946), ``The transmission of heat through textile fabrics ± part II'', Journal of the Textile Institute, Vol. 37 No. 9, pp. T181-T204. Ray, R.D. (1981), ``The thermal performance of a continental quilt'', Journal of Consumer Studies and Home Economics, Vol. 5 No. 1, pp. 37-43. Snyder, R.G., Schneider, L.W., Owings, C.L., Reynolds, H.M., Golomb, D.H. and Schork, M.A. (1977), Anthropometry of Infants, Children and Youths to Age 18 for Product Safety Design, Report No. SP-450, Highway Safety Research Institute, The University of Michigan. Spencer-Smith, J.L. (1977), ``The physical basis of clothing comfort ± part 2: heat transfer through dry clothing assemblies'', Clothing Research Journal, Vol. 5 No. 1, pp. 3-17. Tuohy, P.G. and Tuohy, R.J. (1990), ``The overnight thermal environment of infants'', New Zealand Medical Journal, Vol. 103 No. 883, pp. 36-8. Wailoo, M.P., Petersen, S.A., Whittaker, H. and Goodenough, P. (1989), ``The thermal environment in which 3-4 month old infants sleep at home'', Archives of Disease in Childhood, Vol. 64 No. 4, pp. 600-4. Weatherall, I.L. (1983), ``Thermal properties of bedding'', in Story, L. (Ed.), Measurement, Construction and Performance, Proceedings of the Eleventh Annual Conference of the Textile Institute, New Zealand Section (Wool Research Organisation of New Zealand Inc.), Lincoln College, Christchurch, pp. 106-16. Wilson, C.A. (1992), ``Commonly used clothing and bedding combinations'', personal communication. Wilson, C.A., Taylor, B.J., Laing, R.M., Williams, S. and the New Zealand Cot Death Study Group (1994), ``Clothing and bedding and its relevance to sudden infant death syndrome: further results of the New Zealand cot death study'', Journal of Paediatrics and Child Health, Vol. 30 No. 6, pp. 506-12.

The current issue and full text archive of this journal is available at http://www.emerald-library.com

Multiaxial determination of the resistance to creasing of clothing wool fabrics M.D. NikolicÂ, Lj. M. Simovic and T.V. Mihailovic

Textile Department, Faculty of Technology and Metallurgy, University of Belgrade, Belgrade, Yugoslavia Keywords Clothing, Wool, Fabric

Determination of resistance to creasing 277 Received August 1997 Revised June 1999 Accepted June 1999

Abstract In this paper, values of deformation components (elastic, viscoelastic, plastic) of clothing wool fabrics by measuring the crease recovery angle in various directions to the warp direction (08-warp, 308, 458, 608, and 908-weft) were determined. The size as well as the change of deformation component from warp to weft direction was presented through polar diagrams. On the basis of the results of investigation it is possible to conclude that all investigated fabrics (plain, 2/2 twill, cross twill), regardless of the biaxiality concerning quickly reversible (elastic) deformation, tend toward isotropy in total reversible deformation (elastic + viscoelastic). Concerning the plastic deformation value, mentioned investigated fabrics also express tendency toward isotropy.

Introduction Studying the elasticity of woven fabrics is becoming of importance when the estimation of their quality during wearing is concerned[1,2]. During the exploitation, clothing fabrics are exposed to conditions of various mechanical forces which can provoke the change of a fabric's surface. These phenomena lead to the general aesthetic impression being disturbed. However, aesthetic impression is a category of great importance on the basis of which the decision concerning the choice of clothing fabric is made. Stability of the woven construction as well as the reliability of the final product's shape play an important role in the case of clothing fabrics. These characteristics, in essence, depend on elastic properties of fabrics. The fabric relaxation velocity might serve as one of the indicators of a fabric's behavior after the cessation of certain kinds of force action. Earlier investigations of resistance to creasing of clothing wool fabrics showed that, no matter what the structural characteristics, all fabrics after some period of time showed approximately the same relaxation velocity[3]. Also, all fabrics suffer higher deformation under action of pressure than under action of axial load. Besides, fabrics exposed under bending deformation are harder to recover regardless of their higher values of relaxation velocities[4]. Keeping in mind that woven structures, according to their construction, belong to biaxial materials, numerous standard methods for determination of the mechanical characteristics of fabrics are planned in such way that properties of fabrics are investigated in two directions (warp direction and weft direction).

International Journal of Clothing Science and Technology, Vol. 11 No. 5, 1999, pp. 277-286. # MCB University Press, 0955-6222

IJCST 11,5

278

Concerning the creasing of fabrics under action of bending force, it is interesting to observe the effect of direction of material on crease recovery angle, more exactly, on relaxation ability in various directions of fabric[5-7]. Experiment Nine commercially-produced clothing wool fabrics in three variants of weaves were used as experimental material. Fabrics were made from yarn fineness in measurements of 27.5 2 tex to 21.5 2 tex, weave density in limits from 160 184 threads per dm, weave crimp in limits from 4:47 9:54 per cent and mass in intervals from 169 194gmÿ2 . The basic characteristics of investigated fabrics are given in Table I. The method, which consists of measuring the angle () which appears with the straightening of unloaded fabric, used for determining the crease resistance of fabrics (Figure 1), represents the modified standard method[8,9]. Crease recovery angle () represents the measure of a fabric's resistance to creasing. If the angle () is bigger, the fabric has less tendency to crease. Investigated samples in dimensions of 20 50mm are folded through the narrow side at 1808 (Figure 2). The length of the folded part, for fabrics whose mass is within the limits of 100 500gmÿ2 , is 10mm. A load of 9.81N (pressure of 49kPa) is applied on a folding part of fabric for a period of 60 minutes. After the removal of the load, angle () is measured in degrees after five minutes (5 ) and after 60 minutes (60 ). The angle appearing immediately after unloading the investigated sample, angle of leap (0 ), which is hard to measure precisely, is calculated according to the formula[3,8,9]: 60 log 0 log 60 ÿ 3:5 log 1 5 Modification of the described method of determining the crease resistance consists of introducing the measurements of crease recovery angle () in a period of time of 10, 15, 30, 45, 60 and 1,440 minutes (24 hours). Besides usual structural directions (warp-08 and weft-908), the crease recovery angle was measured at different inclinations (308, 458, 608) to the warp direction. Type of Sample weave

Table I. Basic characteristics of investigated fabrics

1 2 3 4 5 6 7 8 9

Plain 2/2 twill Cross-twill

Density, dm±1 Warp Weft

Fineness, tex Warp Weft 23.0 23.0 27.0 24.5 23.0 27.0 23.5 21.5 23.0

2 2 2 2 2 2 2 2 2

24.5 24.0 23.0 23.0 27.5 23.5 24.5 23.0 23.5

2 2 2 2 2 2 2 2 2

183 176 170 171 170 165 180 171 171

179 172 184 181 170 181 175 160 172

Crimp, % Fabric weight Warp Weft gm±2 4.92 6.46 4.59 5.67 5.79 5.88 5.87 5.96 5.81

4.47 5.59 8.48 5.42 6.22 9.54 6.73 7.94 5.90

183 175 192 180 170 194 180 169 179

Measurements of crease recovery angle in the range of 90-1808 were conducted Determination of in the same way. The measurements were imposed on ten various samples for resistance to each of the investigated fabrics. After that, the average values for each creasing investigated direction were calculated. A greater number of measurements of the crease recovery angle from the moment of unloading the samples were used for determination of the extent of 279 deformation components: elastic, viscoelastic and plastic. Angles of observation of 08, 308, 458, 608 and 908 were used for monitoring the change of the fabric's elasticity from warp to the weft direction. Elastic deformation component is determined as relative percentage ratio of calculated angle of leap 0 (equation (1)) and recovery angle measured after 24 hours (1440 ). Viscoelastic deformation component is given as a relative percentage ratio of the difference of angles 60 and 0 60 ÿ 0 and recovery angle 1;440 . The plastic deformation component is given as a supplement to total deformation of 100 per cent (Figures 3-5). Results and discussions The average values of crease recovery angle measured after five minutes (5 ), 60 minutes (60 ) and 1,440 minutes (1;440 ) as well as the calculated values of angle of leap (0 ) for investigated directions of 08-warp direction, 308, 458, 608 and 908-weft direction are shown in Table II. Also the values of coefficient of variation (CV) are given in this table. The zone of crease recovery angles in the range of 90-1808, according to the measuring results, was shown to be symmetrical in regard to the monitoring zone of 0-908. Calculated values of deformation components (elastic, viscoelastic and plastic) on the basis of the results of measuring the crease recovery angle of samples cut at different angles to the warp direction are shown in Figures 3-5. A polar diagram, which is the best for demonstrating the change of all three deformation components in various directions of observation as well as the total deformation of material, was chosen for presentation of the measuring results (Figure 6). In polar the diagram, radius (r3 ) represents the value of total deformation (100 per cent). The remaining marks in Figure 6 are:

Figure 1. Crease recovery angle

α

180°

α = 0°

Figure 2. Determination of crease recovery angle

IJCST 11,5

Key Plastic deformation

100 90

Viscoelastic deformation

280

Deformation, %

80 70

Elastic deformation

60 50 40 30 20 10 0

Figure 3. Deformation components ± plain weave

0

90 0 30 45 60 Fabric 2

90

0 30 45 60 90 Fabric 3

Angle of investigation, °

Deformation, %

Figure 4. Deformation components ± 2/2 twill weave

30 45 60 Fabric 1

Key Plastic deformation

100 90 80 70 60 50 40 30 20 10 0

Viscoelastic deformation Elastic deformation

0 30 45 60 90 Fabric 4

0 30 45

60 90 0 30 Fabric 5

45

60 90 Fabric 6

Angle of investigation, °

r1 "e

± the average value of elastic deformation calculated on the basis of average values of elastic deformation for each monitoring direction;

r2 "e "v ± the average value of reversible deformation (quickly reversible deformation + slowly reversible deformation); "v

± represents calculated average value of deformation for each monitoring direction.

viscoelastic

The diagram is constructed in such a way that it is possible to perceive the size of all three kinds of deformation:

Key Plastic deformation

100

Deformation, %

90

Viscoelastic deformation

80 70

Elastic deformation

60 50

Determination of resistance to creasing 281

40 30 20 10 0 0 30 45 60 90 Fabric 7

0 30 45 60 90 0 30 45 60 90 Fabric 8 Fabric 9

Angle of investigation, °

r1

± represents the percentage average value of elastic deformation for measured samples calculated on the basis of values of instantaneous crease recovery angle (0 );

(r2 ÿ r1 )

± represents the percentage average value of viscoelastic deformation;

(r3 ÿ r2 )

± represents the percentage average value of plastic deformation determined on the basis of values of crease recovery angle measured after 24 hours from the moment of unloading the samples.

The average values of all measurements for a definite deformation component served for drawing circuits of appropriate radius on a polar diagram (Figure 6). Polar diagrams of fabrics in plain weave, 2/2 twill weave and cross twill weave are presented in Figures 7-9. The diagrams are constructed on the basis of the average values of corresponding kinds of deformation as well as the angles of investigation (Figures 3-5). The change of elastic deformation of fabrics of the same type of weave for a definite direction of observation in regard to the average value of elastic deformation (circle drawn with dash-dot line) of all investigated directions is presented by a line shaded zone. Concerning the viscoelastic deformation, deviations from the average value are small (for plain weave 0.8 per cent, 2/2 twill weave 0.3 per cent, and cross 2.0 per cent), so individual values of viscoelastic deformation of definite direction are approximated with the average value of deformation (circle drawn with dashed line). Finally, the dot shaded zone represents the value of plastic deformation.

Figure 5. Deformation components ± cross twill weave

IJCST 11,5 Fabric

282

Plain 1

2

3

Twill 2/2 4

5

6

Cross twill 7

8

Table II. Average values of crease recovery angle and coefficient of variation

9

Average values of crease recovery 8/coefficient of variation, % CV, % 60 CV, % 1;440

Calculated values of angle of CV, % leap (0 )

Direction of investigation 8

5

0 30 45 60 90 0 30 45 60 90 0 30 45 60 90

112 120 117 126 135 125 117 120 113 127 131 124 120 122 138

3.2 3.6 3.7 4.2 2.6 1.6 2.5 0.0 2.5 2.3 5.8 3.2 3.1 6.9 2.9

130 135 135 139 147 145 137 132 127 148 146 134 133 135 149

0.0 1.1 3.7 5.8 2.7 5.9 1.8 5.1 3.2 1.9 2.1 3.0 2.0 5.5 2.5

138 138 138 142 151 147 138 136 132 150 147 136 135 137 157

3.0 1.4 5.5 5.4 1.4 5.2 2.1 2.9 1.6 0.4 1.7 4.8 0.4 4.4 6.6

77 89 81 98 108 86 78 94 85 85 100 103 94 96 112

0 30 45 60 90 0 30 45 60 90 0 30 45 60 90

144 105 85 93 146 127 156 161 165 142 140 122 109 108 145

0.8 8.2 10.2 3.1 2.8 2.3 2.6 0.7 3.0 2.0 0.4 8.5 6.9 2.7 3.4

155 114 101 110 155 143 167 167 169 150 150 133 126 127 158

0.4 7.2 4.0 0.0 1.0 4.2 1.7 1.6 0.7 0.0 0.3 5.4 4.8 3.6 4.1

156 118 106 111 158 148 169 169 172 152 154 135 131 131 164

0.4 4.9 6.7 1.0 1.0 1.8 3.0 1.0 1.7 1.6 1.6 5.9 10.0 3.4 4.3

121 86 56 62 124 94 131 146 155 123 116 97 76 72 116

0 30 45 60 90 0 30 45 60 90 0 30 45 60 90

143 144 120 125 126 123 122 116 113 143 136 141 136 117 149

4.0 2.4 4.2 4.0 0.9 5.2 5.4 0.9 3.1 4.5 3.6 2.7 5.8 4.9 2.4

161 159 145 142 144 143 140 128 129 155 147 151 152 128 162

2.0 0.6 1.0 1.8 1.2 1.4 3.6 1.6 1.2 3.7 1.8 2.3 3.3 4.5 1.8

163 161 147 146 146 145 142 133 132 158 150 158 172 132 164

1.5 0.6 1.8 1.8 1.0 2.8 4.3 2.2 0.4 3.6 1.5 2.9 0.3 1.9 1.0

108 112 75 93 91 86 87 90 81 115 113 120 104 92 121

Determination of resistance to creasing 283

Figure 6. Polar diagram of deformation components

Figure 7. Polar diagram of deformation components of plain weave fabrics

The percentage average values of bending deformation components as well as the coefficient of anisotropy expressed through the ratio of deformation components in warp direction (08) and in weft direction (908) are shown in Table III. On the basis of investigations of the creasing of fabrics through their relaxation after removal of the action of bending force, it can be stated that plain weave fabrics expressed outstanding biaxiality concerning the elastic deformation. Elastic deformation shows positive deviation from the average value in the weft direction and negative deviation in the warp direction. Coefficient of this anisotropy "E0 ="E90 is 0.9. In the diagonal direction, in the scope of 308 up to 458 to the warp direction, elastic deformation coincides with the average value of 65.42 per cent. Positive deviation of elastic deformation begins in the range of 45-608 to the warp direction.

IJCST 11,5

284

Figure 8. Polar diagram of deformation components of 2/2 twill weave fabrics

Figure 9. Polar diagram of deformation components of cross twill weave fabrics

Table III. Average values of bending deformation components and coefficient of anisotropy

Fabric Plain weave

Average value "E (%) "E0 ="E90 65.42

0.90

Average value "V (%) 32.14

Average "V0 ="V90 value "P (%) 1.19

2.44

"P0 ="P90 1.34 (continued)

Values of viscoelastic deformation are complementary with quickly reversible deformation. Higher values of viscoelastic deformation in the warp direction in regard to the weft direction are noticed (coefficient of 1.19). However, total value of elastic and viscoelastic deformation ("e "v ), regardless of the direction of observation, tends toward approximately the same average value

of 97.56 per cent, which means approximately the same value of plastic Determination of component of total deformation. In such a way, it is possible to state that resistance to regardless of outstanding biaxiality concerning the quickly reversible creasing deformation, isotropy of investigated samples in total reversible deformation as well as in the extent of plastic deformation is shown. Some different phenomena concerning the reaction of fabrics, with 285 conditionally speaking diagonal structure (2/2 twill and cross twill), on the action of bending force can be noticed. All twill weave samples show positive deviation from the average value of elastic deformation (71.38 per cent for 2/2 twill weave and 65.89 per cent for cross twill weave) both in the warp and weft direction. Coefficient of anisotropy ("E0 ="E90 ) is 0.94 for 2/2 twill weave and 0.95 for cross twill weave. Also, for the weft direction, higher deviations in regard to the average value of elastic deformation can be noticed. More (cross twill) or less outstanding (2/2 twill) negative deviations from the average value of elastic deformation at the angle of 458 (diagonal directions) appeared. The sum of elastic and viscoelastic deformation ("e "v ), regardless of the direction of observation, tends toward approximately the same average value of 97.86 per cent for twill weave and 96.93 per cent for cross twill weave, which also means approximately the same value of plastic deformation component. In the scope of 0-308 as well as in interval of 60-908 to the warp direction, deviation of elastic deformation from the average value is positive, which means the smaller value of viscoelastic deformation. Elastic deformation shows negative deviation from the average value at the angle of 458. This observation means the higher value of viscoelastic deformation. Therefore, both for 2/2 twill weave and cross twill weave fabrics it can be stated that, regardless of outstanding biaxiality concerning the quickly reversible deformation, investigated samples show isotropy in total reversible deformation but the same phenomenon in the extent of plastic deformation. Conclusion Imposed investigations, aimed at finding the behavior of clothing fabrics as a function of their resistance to creasing after cessation of bending force action in various directions to the material, showed the following: . Elastic deformation of investigated plain weave fabrics show positive deviation from the average value in the weft direction, but negative deviation in the warp direction. . Investigated samples of 2/2 twill weave and cross twill weave fabrics show positive deviation from the average value of elastic deformation both in the warp and weft direction. . For investigated samples with diagonal structure, negative deviations from the average value of elastic deformation appeared at the angle of 458 to the warp direction, in contrast to fabrics with square structure

IJCST 11,5 .

286

whose elastic deformation coincides with the average value in intervals of 30-458 to the warp direction. Total reversible deformation (sum of elastic and viscoelastic deformation) of all three kinds of weaves, regardless of the direction of observation, tends toward approximately the same average value. This statement also means approximately the same value of plastic deformation component of investigated samples.

In spite of the fact that, from the aspect of construction, we are practically discussing two groups of fabrics (plain weave with square structure and twill weave with diagonal structure), all investigated fabrics, regardless of the biaxiality concerning quickly reversible (elastic) deformation, show a tendency toward isotropy in total reversible deformation (elastic and viscoelastic) but also isotropy in regard to the extent of irreversible (plastic) deformation. Multiaxial determination of the resistance to creasing of investigated clothing fabrics showed expressive different sizes quickly reversible deformations, while these differences in total reversible deformation with identical plastic deformation, regardless of the type of construction and direction of observation, are noticeably reduced. The way of imposed investigation enables determination the elastic properties of fabrics (resistance to creasing) in the case of special demands of their use according to requirements for certain forms of clothes. References 1. Wortmann, F.J., ``Aspects of the crease recovery of wool fabrics'', Melliand Textilberichte, No. 1, 1985, p. 78. 2. Wortmann, F.J., ``The construction of a wool fabric as a factor influencing the crease recovery'', Melliand Textilberichte, No. 12, 1985, p. 852. 3. Mihailovic, T.V., NikolicÂ, M.D. and Simovic, L.M., ``Resistance to creasing of clothing wool fabrics'', International Journal of Clothing Science and Technology, No. 4, 1995, p. 9. 4. NikolicÂ, M.D., Mihailovic, T.V. and Simovic, L.M., ``Deformation of wool fabrics'', The Indian Textile Journal, No. 3, December 1996, p. 88. 5. Dianick, M.M., Lebedinskaya, G.I. and Valentinov, V.A., ``Study of the crease resistance of linen/Lavsan and linen/Lavsan/triacetate fabrics during repeated creasing'', Tekhnologiya Tekstil'noi Promyshlennosti, No. 4, 1979, p. 22. 6. Nosov, M.P., Pavlov, V.I. and Miroshnikov, A.E., ``Anisotropy of the breaking characteristics of fabrics'', Tekhnologiya Tekstil'noi Promyshlennosti, No. 5, 1976, p. 10. 7. Scardino, F.L. and Ko, F.K., ``Triaxial woven fabrics. part I: behavior under tensile, shear and burst deformation'', Textile Research Journal, No. 2, 1981, p. 80. 8. International Organisation for Standardisation (ISO), ISO 2313-1972 (E), ISO, 1972. 9. German Standard DIN 53890.

The current issue and full text archive of this journal is available at http://www.emerald-library.com

Simulation modeling of a garment production system using a spreadsheet to minimize production cost M.R. Rotab Khan

Mechanical Engineering Department (IE Programme), Faculty of Engineering, King Saud University, Saudi Arabia

Garment production system 287 Received November 1996 Revised June 1999 Accepted June 1999

Keywords Garments, Simulation, Production Abstract A methodology of structuring a garment production simulation model using a spreadsheet is described to minimize the average daily production cost through the investigation of various man-machine combinations. The capability and usability of an easily available modern spreadsheet Excel 7.0 to simulate a simple garment production system is accessed with an attempt to demonstrate the simulation model building in a user friendly environment rather than learning and using costly simulation programming languages or simulation software packages. Simulation has evaluated the resource utilization and measured the system performance and developed strategies for taking operational decisions in a logical and better way to minimize the garment production cost. It may also assist and benefit the garment production managers to plan, design and operate their systems in an efficient manner in a competitive environment.

Introduction Spreadsheets have extensively been used for managerial decision making in trade, finance and other commercial areas since the last decade, but recent developments of formulations and functions of modern spreadsheets have provided an interactive modeling environment in which the user can take the opportunity to apply powerful quantitative management tools, like simulation technique, to develop models for the purpose of systems analysis. This paper reports and demonstrates the methodology of structuring a spreadsheet simulation model of a garment production system without using any simulation language. The main objective is to assess the practical simulation modelling capability and usability of a modern spreadsheet to simulate a production system and the reliability of spreadsheet simulation results. The major advantage of using a spreadsheet is its commercial availability, simplicity and ease of building a simulation model in a familiar and user friendly environment and quick update of data to test monitor the simulation for its ``what if'' questions rather than using simulation languages or simulation software packages which are costly and difficult to learn. The assistance and cooperation of Engr. Al-Waleed Al-Sheikh is acknowledged and appreciated. The author wishes to thank the anonymous referees for their valuable comments and suggestions.

International Journal of Clothing Science and Technology, Vol. 11 No. 5, 1999, pp. 287-299. # MCB University Press, 0955-6222

IJCST 11,5

288

The spreadsheet simulation model has investigated various man-machine combinations of the garment production situation with a view to minimizing the average daily production cost through the selection of best man-machine combination. The output performance of the spreadsheet simulation model has been compared with those of the same production simulation model developed by using the simulation language GPSS/H[1] through the statistical validation of simulation results. Since garment production systems have become increasingly competitive and complex, due to the use of modern technology and high capital investment, so a better understanding of the total system behaviour is required to ensure a smooth and uninterrupted flow of production, eliminating the bottlenecks and problems. The only way in which such problems can be investigated in advance is by experimentation on models adequately representing the real production systems rather than using the conventional analytical methods to analyze and tackle. Computer simulation allows investigators to construct models to represent the real system to study the total system behaviour. It can evaluate the performance of the production system without risking the disruption of existing operations and can help to develop efficient operating rules and scheduling techniques and effective and proper allocation of resources to increase manufacturing productivity. Computer simulation technique In production and manufacturing industries nowadays computer simulation has been adopted and emerged as an advanced, sophisticated and flexible management analysis tool which is able to take account of the complexities and dynamic changes within the production environment. It mimics and analyzes the stochastic behaviour of the production system for measuring its performance in terms of its overall strategy with a view to assisting the management in arriving at a better decision after evaluation of various alternative results obtained from the simulation. A large number of leading industrial organizations simulate their manufacturing systems for solving practical production problems relating to their daily operations because of bitter experiences of system performance and unrealized expectation of system output. It has compelled many companies to carry out detailed simulation to thoroughly test out various production possibilities before taking and implementing any key decisions. In most cases, it is not possible to carry out experiments on the real production system for its improvement and change since it would be a costly exercise with high financial risk. A simulation model benefits the production management of a factory through gaining its critical insights in respect of resources (man-machine) utilization, production output etc. The most commonly used definition of simulation is given by Maisel and Gnugnoli[2] as it is the technique of designing a model of a real system, and conducting experiments with that model, for the purpose of either understanding the behaviour of the system or evaluating strategies for the

operation of the system over a period of time. Simulation is considered most often as the only technique to analyze a complex dynamic stochastic system when it is mathematically intractable and analytical modeling is almost impossible or has no practical numerical solutions. Simulation can solve the ``what if'' questions. As an identifiable numerical problem solving technique, simulation is being widely used in manufacturing industries, production and process industries, inventory systems, maintenance systems, distributed systems, resource allocation, airlines, hospitals, financial modeling, agriculture, biology and medicine and government and military organizations etc.[2,3]. In textile and apparel problems several cases have been found[4-6] where simulation techniques have been used. Computer simulation programs can be written in many ways, either using a high level language (e.g. FORTRAN, BASIC, etc.) or by using a simulation software package, and in the latter case, although the modeling is shorter and faster, one has to overcome the difficulties and bear the pains to acquire the knowledge and the package handling skill to develop simulation models. Owing to the gradual development of mathematical functions and formulations of spreadsheets and their recent usage in mathematical programming and modelling areas, such as linear and dynamic programming, forecasting, decision trees and waiting line theory to be used in physical and applied sciences, efforts are being made to use the modern spreadsheet for the development of simulation models to make the modeling process easier. A few works on spreadsheet simulation of production systems, including textile and apparel, have been reported in the literature[7-11]. Description of the problem A case problem of a garment production system is taken from the literature[1] where the development of a simulation model using GPSS simulation software package was reported. The schematic diagram of the garment production system is shown in Figure 1, where four stages of machine cycles are depicted; 50 sewing machines are operating eight hours a day for five days a week. Each machine is subject to failure and the distribution of running time between failures is assumed arbitrarily by the author[1] to be 157 25 hours uniformly distributed, although it should be a Weibull or exponential. Each machine is replaced immediately with a backup machine or with one as soon as it becomes available after repair. Meanwhile, the failed machine is sent to a repair shop where it is repaired and then sent to be a backup machine. The machine repair time is 7 3 hours which is also assumed to follow a uniform distribution rather than a Weibull or exponential. It is to be noted that the Weibull distribution is a family type distribution and can represent a wide variety of distributions by having different shape parameter values. For b = 0.5, 1.0, 2.0, 2.5, and 3.5 it approximates hyper exponential, negative exponential, Rayleigh,

Garment production system 289

IJCST 11,5

Backup machines ready for use .....

290

Machines in production Failed machines in repair

Figure 1. Schematic diagram of a garment production

Repair queue

lognormal and normal distributions respectively. Weibull distribution can be used to represent both machine failure time and repair time distributions because of its versatility and wide applicability[12,13]. Initially the system is started with 50 owned production machines and three hired repairmen and three rented backup machines. There are provisions to hire more repairmen and rent more backup machines. The wage of a repairman is $3.75 per hour and the rent of a backup machine is $30.0 per day. The hourly lost production cost penalty due to having fewer than 50 machines in production at any instance of time is estimated at $20.0 per machine[1]. The wages and machine rental constitute the fixed cost and the variable costs are computed from the machine utilization and lost production penalty. In this paper, a simulation model of the aforementioned garment production system using a modern spreadsheet EXCEL 7.0[14] has been developed and described to demonstrate the production simulation methodology within the user-friendly spreadsheet environment for producing a large number of stochastic scenarios of various man-machine combinations in repetitive experiments to minimize the average daily production cost. Simulation methodology used The principal aim of this simulation is to find out the minimum production cost for a suitable combination of ``repairmen and backup machines'' provisionally available to supplement the 50 owned production machines for the above given rate of wage of repairman, rent of backup machine and lost production cost (if there are less than 50 machines in production at any time) respectively. The simulation methodology is developed and used here depending on the working principles of the spreadsheet and accordingly a program flow-chart has been prepared to build the simulation model of the garment production system and is shown in Figure 2.

Initialize the variables and the clock time, Set simulation period(SIMTIME) & number of runs (TOTRUN) Number of iterations ITRN=NFAILR number of failures

Garment production system

Initialize some variables and clock time for each run

Generate failure times of 53 machines with 157+25 hrs.uni.distn.

291

(i) (ii) (iii) (iv)

Sort failure time array and find out earliest failure time Identify the failed machine Generate repair time for the failed machine with 7+3 hrs.uni.distn. Engage a repairman, if free, to repair the failed machine, otherwise put it in waiting queue of repair (v) For failed m/c compute m/c down time (repair time+waiting Time), restart time and operating Time (vi) Update the clock when an event (failure/restart) is occurred (vii) Compute the number of m/cs in production, in repair, in queue waiting for repair and ready for use as back up after repair (viii) Compute resource utilization % of (machine and repairman) and production cost

Revise the failure time array for next iteration Is

No

CLOCK>=SIMTIME ? Yes Compute average resource utilization % (machine & repairman) and production cost for each run

No

Is NRUN>=TOTRUN ? Yes

Compute grand average resource utilization % (machine & repairman) and production cost for all the runs

The methodological steps of the spreadsheet simulation are briefly described below: (1) Generate failure times for each machine following the given distribution and stored in an array.

Figure 2. Flow chart of spreadsheet simulation model of a garment production system

IJCST 11,5

292

(2) Sort the failure time array and find out the earliest failure time and identify the machine that failed. (3) Generate repair time for the failed machine following the given distribution. (4) Engage a repairman, if free, to repair the failed machine, otherwise put it in the repair queue. (5) For failed machine compute downtime (repair time + waiting time, if any), restart time and operating time. (6) Compute number of machines in production, number of backup machines ready for use, number of machines in repair, number of failed machines in queue waiting for repair. (7) Update the clock whenever an event (failure or restart) occurs. (8) Compute the resource utilization (machine and repairman) and the cost of production. (9) Revise the failure time array for next iteration. (10) Check the clock time with the specified simulation period (SIMTIME). If the clock time is lower, repeat steps (2) to (10). (11) Compute average resource utilization (machine and repairman) and production cost for each simulation run. (12) Repeat steps (1) through (11) for the specified number of simulation runs. Compute average utilization of machines and repairmen and the production cost for the selected combination of ``hired repairmen and rented backup machines''. All nine ``hired repairmen and rented machines'' [3,3], [3,4], [3,5], [4,3], [4,4], [4,5], [5,3], [5,4] and [5,5] combinations have been simulated. Simulation modelling with spreadsheet Using the spreadsheet EXCEL-7.0 the simulation model has been developed and run on a Gateway 2000-586 personal computer (120mHz) with CD-ROM. All the statistics for a simulation run are arranged to display at a glance in the final worksheet shown in Figure 3. It would take several pages if all the Excel commands used for all the steps to build the model were described. Some of the important commands are briefly outlined below. In each iteration the failure times of machines are generated are through the generation of uniform random variates with the following Excel commands: A1= RAND( ) *0.999

A2= ROUND(((A1*50)+132),2)

B1= RAND( )*0.999

B2= ROUND(((B1*50)+132),2)

BA1= RAND( )*0.999

BA2= ROUND(((BA1*50)+132),2)

Garment production system 293

Figure 3. Final worksheet of spreadsheet simulation model

IJCST 11,5

The following Excel commands are used to sort the failure time array to find out the earliest failure time to identify the failed machine: A5= IF(A5=SMALL(A5:BA5,1),A5+A2,A5) B5= IF(A5=A6,IF(B5=SMALL(A5:BA5,1),B5+B2,B5),B5)

294

: BA5= IF(AND(AND(A5=A6,B5=B6,C5=C6,D5=D6,E5=E6,F5=F6,G5=G6, H5=H6,I5=I6,J5=J6,K5=K6,L5=L6,M5=M6,N5=N6,O5=O6,P5=P6,Q5=Q6, R5=R6,S5=S6,T5=T6,U5=U6,V5=V6,W5=W6,X5=X6,Y5=Y6,Z5=Z6, AA5=AA6,AB5=AB6,AC5=AC6,AD5=AD6),AND(AE5=AE6,AF5=AF6, AG5=AG6,AH5=AH6,AI5=AI6,AJ5=AJ6,AK5=AK6,AL5=AL6, AM5=AM6,AN5=AN6,AO5=AO6,AP5=AP6,AQ5=AQ6,AR5=AR6, AS5=AS6,AT5=AT6,AU5=AU6,AV5=AV6,AW5=AW6,AX5=AX6, AY5=AY6,AZ5=AZ6)),IF(BA5=SMALL(A5:BA5,1),BA5+BA2,BA5),BA5) A6= A5 : BA6= BA5 A11= MATCH(SMALL(A5:BA5,1),A5:BA5,0) The repair time of the failed machine is then generated in the same way as the generation of machine failure times by the following commands: A9= RAND( )*0.999 C11= ROUND(((A9*6)+4),2) The commands to compute the waiting time, down time and restart time of the failed machine and update simulation clock are D13= IF(A11=C13,D11,IF(D11
IF(E11
Garment production system

G65= IF(D65=0,`` '',D65+E65+F65) The computation of machine statistics and resource utilization is accomplished by a large number of commands using various cells, some of which are shown below. The computations include number of machines in production, in repair, in repair queue and the backup machines ready for use and the machine and repairman utilization. For next iteration, the failure time array of the machines is revised by updating the next failure time of the currently failed machine incorporating its down time. B69= 53±(COUNTIF(D13:D65,0)) C69= IF(B68>=3,3,B68) D69= IF(B68<=3,0,B68±3) E69= IF(COUNTIF(D13:D65,0)>50,COUNTIF(D13:D65,0)±50,0) F69= IF(COUNTIF(D13:D65,0)<=50,COUNTIF(D13:D65,0),50) A72= C69+A72 : K72=IF((53±COUNTIF(D13:D65,0))<=3,G13, IF(K72=SMALL(K127:K179,1),`` '',IF(G13=K181,K72,G13))) K73=IF((53±COUNTIF(D13:D65,0))<=3,G14, IF(K73=SMALL(K127:K179,1),`` '',IF(G14=K182,K73,G14))) : K124=IF((53±COUNTIF(D13:D65,0))<=3,G65, IF(K124=SMALL(K127:K179,1),`` '',IF(G65=K233,K124,G65))) K127= K72 : It is important to note that once the model has been created, it should be saved immediately before any iteration so that it can be restarted with the initial conditions when desired. Iteration of the model The following steps are required to iterate the model: (1) Open the saved model.

295

IJCST 11,5

(2) Open the Tools menu on the top of the screen. (3) Click Option. (4) Click Calculation.

296

Click on the iteration icon and enter the number of iterations for each run and the degree of accuracy needed. For one by one manual iteration the number of iterations should be equal to 1. The RECALCULATION key (F9) is to be pressed for each iteration. Click OK . The model has now been ready to iterate with the ``three hired repairmen and three rented machines'' combination to investigate the different statistics of interest. Several iterations have been made to reach the steady-state. The iterated model might be saved at any time during iterations or after each run but must have been saved with a name other than its original. In order to iterate the model again with the initial conditions the model must not be saved in its original name. All other ``hired, rented'' combinations of ``3, 4'', ``3, 5'', ``4, 3'', ``4, 4'', ``4, 5'', ``5, 3'', ``5, 4'', ``5, 5'' have been simulated simply by changing the number of machines and repairmen respectively. Additional cells are added in each working row and column to compute and display those additional machines and repairmen statistics. Results and discussion In order to obtain the steady state results the simulation model is run 50 times for a period of 45 weeks (1,800 hours) and it has been observed that the steady state is reached after roughly 23 weeks. The transient and steady state behaviours of machine utilization results for the period of 40 weeks are graphically shown in Figure 4.

Figure 4. Transient and steady state results of machine untilization

The average machine utilization results of the spreadsheet simulation for 20 weeks under steady state conditions and the corresponding production cost ($/day) of all nine ``hire, rent'' combinations are presented in Table I and Figure 5. The results indicate that the ``four hired repairmen and four rented backup machines'' combination is the best and minimizes the overall production cost. GPSS simulation model, taken from the literature [1], has also produced the same ``hire rent'' combination as the best to minimize the production cost. With a view to validating the spreadsheet simulation model the output of the GPSS model is considered as actual field result and the model validation has been accomplished through the hypothesis tests for means of the machine utilization with 95 per cent confidence interval by the statistical procedure suggested by Hines and Montgomery[15] as follows: The following hypotheses are: H1 : GPSS=H 6

Spreadsheet

H0 : GPSS=H

Spreadsheet

Garment production system 297

and

the test is if t0 > t=2;n1n2ÿ2 , we would accept the null hypothesis H0 Repairmen hired Machine utilization 3 4 5 Production costs ($/day) 3 4 5

3

Machines rented 4

5

0.986 0.987 0.986

0.988 0.994 0.996

0.992 0.998 0.998

292 314 352

306 288 302

304 366 316

5

400 ($ / day)

Table I. Output of spreadsheet simulation model for 20 weeks (800 hours) under steady state condition

300

4

200 100

3

0 3 Repairman Hired

4

5

Backup machines

Figure 5. Total production cost ($/day) of all nine combinations under steady state condition (20 weeks) by spreadsheet simulation model

IJCST 11,5

where, t0

298

Sp2

G ÿ S q Sp n1G n1S

NG ÿ 1SG 2 nS ÿ 1SS 2 nG nS ÿ 2

G = Mean of GPSS model S = Mean of Spreadsheet model SP 2 = Pooled mean's variance nG and nS are number of runs of the models The average machine utilization of GPSS model was found G 0:9853 with variance SG 2 0:0025 and that for the spreadsheet model is found S 0:9824 with the variance SS 2 0:0017 after running the models for a total of 50 times n 50. Hence, SP 0:064 which makes t0 0:226 and from the ``t'' table (95 per cent C.I.) t=2;n1n2ÿ2 t0:025;98 1:96 Since, t0 < t=2;n1n2ÿ2 it implies that there is no significant difference between the means, therefore, the spreadsheet simulation model is valid. Conclusions The methodology of a spreadsheet simulation modelling of a garment production system, without using any simulation program or language, has been developed and described. The capability of a modern spreadsheet EXCEL 7.0 to simulate a complete production system is demonstrated. The spreadsheet simulation provides a sophisticated and user-friendly means of structuring a production simulation model and work in a familiar modelling environment. Various simulated man-machine combinations have been evaluated as production alternatives to take better managerial decisions, logically choosing the best one to minimize the daily production cost in order to improve manufacturing productivity. Statistical validation of the model has been done to show that the spreadsheet simulation of a garment production situation can be developed and used with confidence. References 1. Schriber, T.J., Simulation Using GPSS, John Wiley & Sons, New York, NY, 1974.

2. Maisel, H. and Gnugnoli, G., Simulation of Discrete Stochastic System, Science Research, Chicago, IL, 1972. 3. Neelamkavil, F., Computer Simulation and Modeling, John Wiley & Sons, New York, NY, 1987. 4. Bobbin, ``Industry-specific software packages'', Editorial, June, 1986, pp. 86-98. 5. Kuwada, H. and Akira, H., ``Simulation of weaving operation and determination of optimal number of operating machines'', Journal of the Textile Machinery Society of Japan, Vol. 32 No. 1, 1986, p. 27. 6. Branstrator, J. and Melissa, M., ``Computer simulation justifies construction of Ciba-Geigy dyestuff production facilities'', Industrial Engineering, Vol. 21 No. 5, May 1989, pp. 17-20. 7. Larry, M., ``Introducing simulation with a spreadsheet example'', Decision Line, March 1992, pp. 10-11. 8. Taqi, N. Al-Faraj, et al., ``Simulating waiting line problem: a spreadsheet application'', International Journal of Operations and Production Management, Vol. 11 No. 2, 1990, pp. 49-53. 9. Lloyd, G. et al., ``Using spreadsheet simulation to generate a distribution of forecasts for electric power demand'', Journal of Operations Research Society, Vol. 42 No. 11, 1991, pp. 931-9. 10. Peniston Bird, D., ``Using balancing algorithms and simulation in tandem for team working'', Journal of Clothing Technology and Management, Vol. 11 No. 2, 1994, pp. 68-79. 11. Fozzard, G. et al., ``Simulation of flow lines in clothing manufacture'', International Journal of Clothing Science and Technology, Vol. 8 No. 4, 1996, pp. 17-27. 12. O'Connor, P.D.T., Practical Reliability Engineering, Heyden and Sons, London, 1981. 13. Rramakumar, R., Engineering Reliability: Fundamentals and Applications, Prentice-Hall, Englewood Cliffs, NJ, 1993. 14. Walkenback J., Excel for Windows 95 Bible, IDG Books Worldwide, Foster City, CA, 1995. 15. Hines, W.W. and Montgomery, D.C., Probability and Statistics in Engineering and Management Science, John Wiley, New York, NY, 1980.

Garment production system 299

The current issue and full text archive of this journal is available at http://www.emerald-library.com

IJCST 11,5

300 Received March 1997 Revised June 1999 Accepted June 1999

An experimental study of needle heating in sewing heavy materials using infrared radiometry Evangelos Liasi and Ruxu Du

Department of Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, Ontario, Canada

Dan Simon

General Motors Technical Center, Warren, Michigan, USA, and

Jasmina Bujas-Dimitrejevic and Frank Liburdi

Delphi Interiors and Lighting Systems, Windsor, Ontario, Canada Keywords Sewing, Needle, Infrared, Materials Abstract This paper presents an experimental study on needle heating in sewing heavy materials such as upholstery fabrics. In the experiments, infrared (IR) radiometry, high speed line scanning IR radiometry, and high speed IR radiometry are used to obtain thermal images of the needle during sewing. In particular, IR radiometry was used in lower speed sewing (approximately 500rpm). High speed IR and high speed line scanning IR radiometry were used for medium speed sewing (1,000-2,000rpm). Using Taguchi's design of experiment method, the effects of various factors are studied including needle conditions (sharp or blunt), sewing speeds, number of stitches per inch, material being sewn, and thread tension. It is found that even with air vortex cooling the needle may still reach high enough temperatures that may affect the sewing quality and even cause thread breakage. This was confirmed via a thread tensile testing experiment. An empirical model of the mean needle temperature is also proposed and tested.

1. Introduction In industry sewing, the term ``sewing problems'' sums up all those problems that appear during the sewing. The problems found most frequently include (Schmetz, 1994): bursting of weave threads, needle heating, difficulties when sewing elastic material, sewing thread breakage, skip stitches, and seam puckering. To solve the sewing problems, much research has been conducted in the past. For example, the seam puckering problem is addressed in Gershon (1990) and Stylios (1990), needle damage during seam formation in Soundhelm (1953), sewing forces in Liu and Du (1996), Matthews and Little (1988) and Muramatsu (1989) and needle vibration in Gershon and Grosberg (1992) and Lyon (1987). The list above is obviously far from complete but it has been

International Journal of Clothing Science and Technology, Vol. 11 No. 5, 1999, pp. 300-314. # MCB University Press, 0955-6222

The presented research is partially supported by Natural Science and Engineering Research Council of Canada, CRD Grant No. 661-159/95 and by General Motors of Canada Ltd. Many thanks go to Mr J.A. Vanderpark of Delphi Interiors and Lighting Systems for his assistance with the sewing thread tensile testing and Mr Tom Pitre, also of Delphi Interiors and Lighting Systems who was our sewer during all sewing experiments.

agreed that needle heating is a primary problem and is the source connected to An experimental many other sewing problems (especially to thread breakage). This is study of needle particularly true for sewing heavy materials such as upholstery fabrics used in heating automotive seating and interior. It has been identified (Howard and Parsons, 1968; Hurt and Tyler, 1973) that needle heating is affected by sewing needle condition, sewing speed, feed, 301 material sewn, thread tension and fabric finishing. In the mass production environment of industry sewing, needle heating depends mainly on the sewing speed as the other factors are relatively unchanged. As sewing speeds accelerate to over 4,000 stitches per minute in modern industry sewing, the needle temperature may rise quickly to more than 2008C unless special precautions are taken (some of these precautions will be addressed later). Such a high temperature is detrimental to the hardness of the needle and also causes harmful heating to the sewing thread as well as the material being sewn in the region of the needle penetration. Although natural fabrics (e.g. wool and cotton) can sustain needle temperatures above 3508C for a short period of time, fabrics with many weaves may not stand such high temperatures. As a result, significant damage may occur. In particular, when the fabrics to be sewn consist of synthetic materials (e.g. polyester and nylon) high needle temperatures (about 2008C) should not be exceeded. According to the literature survey, most research works on needle heating are based on experiments using thermal couples (Schmetz, 1994; Hurt and Tyler, 1973). Since the thermal couples cannot be placed on the needle tip or needle eye, their accuracy is limited. In addition, they cannot sense the localized heating zones. As a result, some important information may be missing. Infrared (IR) technology has also been used in research (Hersh and Grady, 1969), however, it is limited in using IR radiometry, which has relatively low accuracy. This paper presents an experimental study on needle heating using several advanced IR technologies. It consists of four sections. Section 2 describes the experiments using IR technologies to acquire the needle temperature information. Following Taguchi's (1991) design of experiment method, the effects of various factors are studied. Based on the experiment results, Section 3 analyzes the needle heating mechanisms and the needle cooling techniques. One of the major effects of needle heating is the thread tensile strength, which is also discussed in this section. An empirical equation for mean needle temperature is proposed as well. Finally, Section 4 contains the conclusions that emerged from this study. 2. Experiment results 2.1. Experiment setup The goal of the experiments was to evaluate how sewing conditions (sewing speed and feed), thread, needle condition, and materials being sewn affect needle heating in sewing heavy materials. The experiments were conducted on a Pfaff industrial sewing machine. The materials being sewn were A300 foam

IJCST 11,5

302

backed tri-laminate and 5mm thick plastic (J-retainer), as they are commonly used in automotive seating. The swing needles used were Schmetz No. 23 needles and the threads were 91577X92 bonded type thread from America Eagles. All the sewing operations were carried out by an expert sewer. During the experiments, needle temperatures were measured by IR radiometry (for low sewing speed approximately 500rpm), high speed IR line scanning and high speed IR radiometry (for sewing speed above 1,000rpm). The experiment setups were the same as depicted in Figure 1. The equipment was first calibrated following the standard recommended by the manufacturers. During the experiments, infrared thermal energy imaging equipment was first used to scan the needle during sewing. The thermal energy images were then converted to visible pictures and displayed on a conventional video equipment. Quantitative (and qualitative) temperature analysis was then carried out. As an example, Figure 2 shows an IR image of sewing. From the

Imaging equipment

Camera

Figure 1. The setup for infrared radiometry experiments

Figure 2. An IR image showing the sewing process

figure, the needle and holes punched by the needle can be clearly seen, though An experimental some holes are blocked by the pressure foot. Figure 3 shows a high speed line study of needle scanning IR image of a needle taken at a sewing speed of 1,000rpm. From the heating figure, it is seen that the highest temperature occurs on the needle eye. Figure 4 shows a high speed IR radiometry picture taken at a sewing speed of 2,000rpm. The high speed IR radiometry pictures were taken at 1,000 frames per second. 303 It shows the instantaneous temperature variation of the needle. This type of temperature variation as a function of needle position is consistent with the results presented in Dorkin and Chamberlain (1963).

Figure 3. A typical line scanning IR image of a needle at a sewing speed of 1,000rpm

Figure 4. A typical high speed IR image of a needle at a sewing speed of 2,000rpm

IJCST 11,5

304

2.2. The design of the experiment In order to study the factors that affect the needle heating, the experiments follow the Taguchi's design of experiment method (Hicks, 1993; Montgomery, 1991; Taguchi, 1991). In the DOE, the dependent is the needle temperature. As shown in Figures 3 and 4, the temperature varies along the needle. We are particularly interested in the temperature at the needle eye as it is always the highest and has a direct impact on the thread. The dependent factors and their levels are depicted in Table I. The first three factors, needle cooling, needle condition, and feed (stitches per inch), have two levels each and the other three factors, sewing speed, thread tension, and material being sewn, have three levels each. The conditions listed in the table are commonly used in automotive seating making. Other factors, such as fabric finishing conditions, needle size and finishing conditions and the constructions of yarns may also affect the needle heating. However, it is believed that they play a relatively minor role in sewing heavy materials. 2.3. Experiment results The data obtained from the experiments suggested that, even with air cooling, the needle may still reach rather high temperatures (over 1508C). Figures 5-7

Factors A B C Table I. Factors and levels used D E in the design of the F experiment

Needle cooling Needle condition Stitches/inch Sewing speed Thread tension Material being sewn

Levels

1

2

3

With cooling Blunt High (8) Low (500rpm) Low 2 ply

Without cooling Sharp Low (5) Medium (500rpm) Medium 1 ply + plastic

N/A N/A N/A High (2,000rpm) High 2 ply + plastic

Temperature vs Sewing Time (DOE #1) 80

Figure 5. Maximum thread and needle temperatures at a sewing speed of 1,000rpm (material being sewn: 2 ply A300 trilaminate, without cooling)

temperature (deg. C)

70 60 50 40 30 20

Key needle temperature

10

thread temperature

0 0

3

6

9

12

15

18

sewing time (s)

21

24

27

30

33

An experimental study of needle heating

Temperature vs Sewing Time (DOE #5) 160

Key needle temperature

140 temperature (deg. C)

thread temperature 120

305

100 80 60 40 20 0 0

1

3

4

7

11

15

16

sewing time (s)

Figure 6. Maximum thread and needle temperatures at a sewing speed of 1,000rpm (material being sewn: 2 ply A300 trilaminate, with cooling)

temperature (deg. C)

Temperature vs Sewing Time Plot (DOE #3) 200 180 160 140 120 100 80 60 40 20 0

Key needle temperature 0

1

2

3

thread temperature 4

5

6

sewing time (s)

depict typical temperature versus sewing time at different sewing speeds. In summary, Table II depicts the maximum needle temperatures obtained for each run of the design of experiment (four sets of experiments were conducted for each run and their average was taken as the maximum needle temperature in the table). Table III confirms what is already known. That is, the needle temperature is dependent on the sewing speed and the materials being sewn. The higher the sewing speed the higher the needle temperature. Also the needle temperature increases as the thickness and/or the toughness of the material sewn increase. The interactions between the sewing speed and materials being sewn are shown in Figure 8. From the figure, it is seen that the interactions are significant. However, the thread tension has an inclusive effect. Table IV is an ANOVA table of the experiments.

Figure 7. Maximum thread and needle temperatures at a sewing speed of 2,000rpm (material being sewn: 1 ply A300 trilaminate and plastic, with cooling)

IJCST 11,5

From the ANOVA table (Table III), it is seen that the major factors that affect needle heating, in order of importance, are: (1) sewing speed (factor D); (2) material being sewn (factor F); (3) thread tension (factor E); and

306

(4) the other factors (factor A, B and C). Needle cooling (factor A), needle condition (factor B) and feed (factor C) combined contributed 28.5 per cent of the variations. Among these factors needle cooling

Table III. The averaged maximum needle temperature under different sewing speeds, thread tension and material being sewn

Figure 8. The interactions between the sewing speed and thread tension

ABC

D

1 2 3 4 5 6 7 8 9

111 112 121 122 211 212 221 222 222

1 2 3 1 2 3 1 2 3

Factor

E

F

Maximum needle temperature

1 2 3 2 3 1 3 1 2

2 1 3 3 2 1 1 3 2

75 113 183 126 135 119 100 111 184

Factor

Level 1

Level 2

Level 3

Sewing speed Thread tension Material being sewn

100.33 101.67 110.67

119.67 141.00 131.33

162.00 139.33 140.00

max. needle temperature

Table II. The experiment results (maximum needle temperature observed)

Run

200 180 160 140 120 100 80 60 40 20 0

Key low thread tension medium thread tension high thread tension

0

1

2

3

sewing speed setting 1=low, 2=medium, 3=high

4

affects the needle heating the most, followed by the feed and finally the needle An experimental condition. Also, it should be pointed out that in the design of the experiment, study of needle 6 SSA SSB SSC . Thus, the these factors are not orthogonal. Hence, SSABC heating variation cannot be used for elucidation of their effects precisely. 3. Experiment results analysis 3.1 Needle heat generation and dissipation Table V presents a comparison between the findings in Hersh and Grady (1969) and Howard and Parsons (1968) and our finding in terms of the factors affecting needle heating. This comparison is interesting. First, it is all agreed that speed and the materials are the most influential factors. However, we have a different view on the role of the length of sew. As shown in Figures 5-7, the needle temperature stabilized after a few seconds. As a result, the length of sew is less important. Instead, the thread tension plays a significant role. This is perhaps due to the fact that the effect of thread tension on the maximum temperature (mainly on the needle eyes) cannot be accessed without using IR technology. According to Hersh and Grady (1969), within one stitch, the source of heat generated in sewing is mainly attributed to the frictional heating of the needle passing through the fabric. The energy required to form the hole in the fabric through which the needle passes might be considered a secondary source of heat generation. Of the total energy, as reported in Hersh and Grady (1969), nearly 70 per cent of the heat generated is a result of the needle penetrating the fabric and nearly 30 per cent is a result of the needle withdrawal from the fabric. As far as the heat dissipation is concerned, according to the study by Howard and Parsons (1968), radiation plays a very minor role ± heat lost by Source

SS

df

Variance

P-ratio

D E F Total

127,806.25 118,910 127,806.3 438,886.2178

1 1 1

127,806.25 118,910 127,806.3

25.25 20.9 25.25 100

Importance ranking

Hersh and Grady (1969) and Howard and Parsons (1968)

Our experiments

1 2

Speed Material (fabric structure and thickness) Length of sew Needle geometry Fabric finish Thread

Speed Material (fabric structure and thickness) Thread tension Needle cooling Feed (thread/inch) Needle condition

3 4 5 6

307

Table IV. The ANOVA table

Table V. A comparison between the literatures and our finding

IJCST 11,5

308

radiation is only 2-5 per cent depending on the needle finish. Another 10 per cent is a result of convection. On the other hand, conduction accounts for up to 85 per cent of the total heat loss when the machine is stopped. When the machine is running, however, most heat loss is a result of convection (up to 60 per cent of the total heat loss). These results are also discovered by Hurt and Tyler (1973): their experiments indicate that the heat loss by contact between the hot needle and cold fabric is significant. The studies above suggest several methods of reducing needle heating. These include: addition of lubricant, introduction/increase of air flow, reduction of the sewing speed, shortening of the sewing stroke, using pilot needles, improving needle finish, and improving the needle design (Hersh and Grady, 1969). Clearly, not all of these methods can be practiced owing to the cost and engineering limitations. For example, reducing the sewing speed will reduce needle heating. However, because of the reduced productivity it can only be used as a last resort. In the discussion that follows, only those approaches that can be practiced will be discussed, namely: addition of lubricant and air flow. First, in order to reduce needle heat, lubricants (e.g. grease, wax) can be added to the needle, the sewing thread, and/or the fabric. All of these act to reduce friction and, consequently, the needle heating. Various lubricating methods have been suggested (Hosiery Times, 1965; Dow Corning Corporation, 1957; Schrum and Queen, 1967). Lubricants may be applied to the needle or to the sewing thread either above or below the throat plate. Soaking the sewing thread in water or lubricant has also been suggested as a cooling technique (Textile Merchant, 1957; Dorkin and Chamberlain, 1963). The other effective method of needle cooling is to apply compressed air over the needle. For example, directing 1 cubic foot of air over the needle may reduce the needle temperature by as much as 34.3-70.68C depending on the thread and fabric used (Dorkin and Chamberlain, 1963), though the effective zone is limited as shown in Figure 8 The other disadvantage is that the air stream sometimes distorts the thread, which may cause skip stitches. In summary, an effective and inexpensive needle cooling method remains as a topic of research. 3.2. Needle heating and thread strength One of the most important findings of our study is the correlation between needle heating and thread strength. During the experiments it was found that when sewing was interrupted, the thread quickly soaked up heat. This is evident from Figure 9. As a result, the thread temperature reaches excessive levels at the end of a sewing run. The excessive heat causes the tensile characteristics of the thread to deteriorate, which in turn will introduce problems at the beginning of the next sewing run (e.g. possible thread breakage). This observation necessitates the further study of the effects that the needle temperature has on the thread strength. The thread strength experiment setup is shown schematically in Figure 10. The goal of the experiment is to determine tensile properties of monofilament, multifilament, and spun yarns, either single,

An experimental study of needle heating

Temperature vs Sewing Time 140

temperature (deg. C)

120 100

sewing interrupted

309

80 60 40 Key 20

thread temperature

needle temperature

0 0

1

2

12

21

28

30

32

36

40

48

53

57

58

60

67

sewing time (s)

Figure 9. Thread soaking up heat when sewing is interrupted

Figure 10. The effect of air vortex

plied, or cabled, with the exception of yarns that stretch more than 5 per cent when tension is increased from 5-10mN/tex (0.5gf/tex). In order to simulate the effect of the needle heating, a round metal rod (which simulates the smooth needle eye) and a square metal plate (which simulates the rough needle eye) were heated and used as local heat sources to supply heat to the thread, the same way that the needle eye would be a heat source to the thread during sewing. The tension-testing machine is a CRE type with cold clamp jaw No. 400. For each type of thread, three samples were taken at least 9m apart from the same roll of thread. The tension testing machine is first adjusted in the starting position to a distance of 250 3mm from nip to nip of the clamps along the specimen axis. Secure one end of the specimen in one of the clamps of the machine. Place the

IJCST 11,5

310

other end in the clamp, applying 5 1mN/tex pre-tension to close the second clamp, which is considered satisfactory to remove any slack or kinks from most yarns without appreciable stretching. During the test, the test machine is operated at an extension rate of 305 10mm/min until breaking force is reached. The breaking force and the elongation at break are then recorded. During the experiments, the testing temperatures ranged from 22 to 2428C. The mean breaking loads at each temperature setting are recorded as shown in Figure 11 together with a fourth order polynomial curve fits. The fourth order polynomials describing the breaking load of the thread as a function of temperature are given by equation (1) for the round rod and equation (2) for the square plate: L ÿ7:8325 10ÿ8 T 4 4:223 10ÿ5 T 3 ÿ 0:0073T 2 0:2959T 65:4829 L ÿ1:9768 10ÿ7 T 4 6:5947 10ÿ5 T 3 ÿ 0:0067T 2 0:0132T 72:1221

1

2

where L is the break load. The correlation coefficients are R2 0:97 for equation (1) and R2 0:94 for equation (2). The significance of the fourth order polynomial fit is that the correlation between the break load and the temperature is not linear. Take the curve corresponding to the square plate for example, the curve can be decomposed into three sections. In the first section (room temperature to 508C), the break load changes little. In the second section (50-1008C), with the increase of the temperature, the break load reduces linearly. Finally, when the temperature passes 1408C, the break load quickly deteriorates. This implies we should avoid the needle heating above 1408C to ensure the strength of the thread. Figure 12 shows the correlation between the energy required to break the thread and the temperature together with a linear curve fit. The linear pull thread

Figure 11. Thread tensile test experiment setup (a) simulating a needle with a smooth eye; and (b) simulating a needle with a sharp edge on eye

heater

(a)

(b)

An experimental study of needle heating

Breaking Load vs Temperature

breaking load [N] 80 70 60

311

50 40 30

Key circular rod heat source fourth order polynomial fit square plate heat source fourth order polynomial fit

20 10 0

0

50

100

150 200 temperature [deg. C]

250

Figure 12. Thread tensile testing results

300

equations that describe these linear fits are depicted as follows: E ÿ1:0015T 283:5169 for round rod

3

E ÿ1:0576T 262:3765 for square plate

4

where E is the energy required to break the thread. The corresponding correlation coefficients are R2 1:00 for both cases. In summary, temperature has a significant effect on the strength of the thread, and hence the thread breakage. As the temperature increases to 2008C for the round rod, or to 1408C for the square plate, the breaking energy required is reduced by as much as 40 per cent. Also, according to Schmetz (1994), the needle eye geometry (round or square) has a significant effect on the thread. As the thread continuously rubs the needle eye, the needle eye may wear out from the original round shape to form a sharp edge, which wears the thread. Consequently, under the same sewing condition, there may be a larger chance of thread breakage. Therefore, it may be worthwhile to change the needle regularly (Figure 13). 3.3. An empirical model for needle heating As pointed out earlier, needle heating has a profound effect on sewing operations, and it is desirable to limit the needle temperature to below 1408C. However, measuring needle heating is rather complicated and expensive. Therefore, developing a needle temperature model would be very helpful. The proposed needle temperature model is based on dimensional analysis and the experimental results presented above. According to the experimental

IJCST 11,5

Work to Break vs Temperature Plot Work done [N - mm] 300

250

312

200

150

100

Key round rod square plate and linear fits

50

Figure 13. Work to break versus temperature

0

0

50

100 150 temperature [deg. C]

200

250

results presented in the previous section, it is assumed that the mean needle temperature is a function of the factors listed in Table VI. That is, the needle temperature is affected by: . .

. .

the sewing speed (No. 1 factor); the specific sewing energy, which is characterized by the material being sewn (No. 2 factor); the thread tension (No. 3 factor); and the thermal conductivity and volume specific heat of the material being sewn (it is used to balance the dimensions).

The other factors, such as the type and size of the needle, the finishing of the needle, the thread, and the fabric, are considered as secondary and hence, not included. This is true in sewing heavy materials such as upholstery fabrics used in automotive seating.

Table VI. List of quantities for mean needle temperature model

Quantity

Symbol

Dimension

Mean needle temperature Sewing speed Specific sewing energy Thread tension Thermal conductivity and volume specific heat of the work

n V u T KC

LT ÿ1 FLÿ2 F F 2 Lÿ2 T ÿ1 ÿ2

An experimental study of needle 5 heating

Using the principle of dimensional homogeneity, it follows that: n

T

0:25 0:75

u

V

C

0:5

0:5

where the exponential constants are determined by interpolation from the experiment data. Moreover, when sewing identical materials with the same thread tension (a common practice in sewing heavy materials), the mean needle temperature depends only on the sewing speed, that is: 0:5 n1 V1 6 n2 V2

313

Equation (6) is easy to use. As long as the temperature at a given sewing speed is known, the temperature at any other sewing speed can be predicted. From the experiment data obtained, the validity of the model as a means of predicting the mean needle temperature at various cutting speeds was tested. The results are detailed in Table VII. The large prediction errors may be due to the fact that the model is somewhat oversimplified and hence, additional factors shall be considered. In a recent study (Li, 1998), several accurate, but complicated models are delivered. 4. Conclusions Based on the discussions above, the following conclusions can be drawn: (1) In sewing heavy materials, the needle may reach rather high temperatures (1508C) even with the use of air vortex cooling. (2) In sewing heavy materials, the needle heating is affected, ranked according to its importance, by the following factors: .

the sewing speed;

.

the material being sewn (the thickness and the type); and

.

the thread tension.

(3) Needle heating may significantly weaken the thread (as much as 40 per cent at 1408C), and as a result, may cause possible thread breakage. Therefore, it is suggested that the needle temperature shall be limited to below 1408C all the time.

Sewing speed (rpm) ~500 ~1,000 ~2,000

Needle temperature (from experiments) (8C)

Predicted needle temperature (8C)

Percent error

81 91 100

64 114 128

±26.0 20.6 22.3

Table VII. Evaluation of the empirical needle temperature model

IJCST 11,5

314

(4) In order to reduce the needle heating, various methods can be practiced. These include: . use lubricant; . use air cooling; . use different types of thread (i.e. those which can withstand higher temperatures); and . change the needle regularly. References Dorkin, C.M.C. and Chamberlain, N.H. (1963), ``The facts about needle heating'', Technological Report No. 13, Clothing Institute, London. Dow Corning Corporation (1957), ``Lubrication of sewing threads'', British, 788, December, p. 143. Gershon, D. (1990), ``Parallel process decomposition of a dynamic manipulation task: robotic sewing'', IEEE Transactions on Robotics and Automation, Vol. 6 No. 3, June, pp. 357-67. Gershon, D. and Grosberg, P. (1992), ``The buckling of fabrics during feeding into automatic sewing stations'', J. Text. Inst., Vol. 83, pp. 35-44. Hersh, S.P. and Grady, P.L. (1969), ``Needle heating during high speed sewing'', Textile Research Journal, Vol. 39 No. 2, February, pp. 101-20. Hicks, C.R. (1993), Fundamental Concepts in the Design of Experiments, 4th ed., Saunders College Publishing, New York, NY. Hosiery Times (1965), ``Problems of needle cooling'', Vol. 38 No. 431, pp. 81-3. Howard, G.M. and Parsons, D. (1968), ``Sewing needle temperature, part I: theoretical analysis and experimental techniques'', Textile Research Journal, Vol. 38, pp. 606-14. Hurt, F.N. and Tyler, D.J. (1973), ``Influence of fabric finishing conditions on sewing needle temperature'', Clothing Research Journal, No. 1, pp. 47-52. Li, Q.W. (1998), ``A study of needle heating in industrial sewing'', MS thesis, University of Windsor, Windsor, Ontario, Canada. Liu, Z. and Du, R. (1996), ``On the dynamics of sewing machines and sewing operations'', Computer Modeling and Simulation in Engineering, Vol. 1, pp. 435-51. Lyon, R.H. (1987), Machine Noise and Diagnostics, Butterworths, Boston, MA. Matthews, B.A and Little, T.J. (1988), ``Sewing dynamics: part I: measuring sewing machines forces at high speeds'', Textile Research Journal, July, pp. 383-91. Montgomery, D.C. (1991), Design and Analysis of Experiments, John Wiley & Sons, New York, NY. Muramatsu, N. (1989), ``Transient torque produced in the arm shaft of an industrial sewing machine'', 1989 ASME Design Technical Conferences ± 15th Design Automation Conference, Montreal, Quebec, Canada, 17-21 September, pp. 75-80. Schmetz, A.G. (1994), User's Guide to Sewing Techniques. Schrum, F. and Queen, L. (1967), ``Polyethylene as a softener for durable press'', Am. Dy. Report 56, No. 24, pp. 74-7. Soundhelm, W. (1953), ``Causes of seaming damage'', J. Text. Inst., pp. 580-85. Stylios, G. (1990), ``Prognosis of sewability problems in garment manufacture using computer based technology'', Proceedings of the 1990 IEEE International Conference on System Engineering, Pittsburgh, PA, 9-11 August, pp. 271-373. Taguchi, G. (1991), System of Experimental Design, Vol. I and II, 4th printing, Quality Resources, New York, NY. Textile Merchant (1957), ``New lubricant for sewing thread'', Vol. 136 No. 3535, May, p. 897.

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

No title

Recommend Documents