Frontiers of Assembly and Manufacturing: Selected papers from ISAM'09'

Frontiers of Assembly and Manufacturing

Sukhan Lee, Raúl Suárez, and Byung-Wook Choi

Frontiers of Assembly and Manufacturing Selected Papers from ISAM 2009

ABC

Prof. Sukhan Lee School of Information and Communication Sungkyunkwan University 300 Chunchun-Dong, Jangan-Ku Kyunggi-Do 440-746 Korea E-mail: [email protected] Dr. Raúl Suárez Feijóo Researcher IOC-UPC Av. Diagonal 647, planta 11 08028 Barcelona, Spain E-mail: [email protected]

ISBN 978-3-642-14115-7

Dr. Byung-Wook Choi International Affairs Center and Principal Researcher Div. of Advanced Robot Technology and Director, Korea IMS Center Korea Institute of Industrial Technology (KITECH) 1271-18, Sa-1-dong, Sangrok-gu Ansan-si 426-791 Korea Email: [email protected]

e-ISBN 978-3-642-14116-4

DOI 10.1007/978-3-642-14116-4 Library of Congress Control Number: 2010929749 c 2010 Springer-Verlag Berlin Heidelberg  This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Data supplied by the authors Production & Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India Printed on acid-free paper 987654321 springer.com

Preface

The technologies for product assembly and manufacturing evolve along with the advancement of enabling technologies such as material science, robotics, machine intelligence as well as information and communication. Furthermore, they may be subject to fundamental changes due to the shift in key product features and/or engineering requirements. The enabling technologies emerging offer new opportunities for moving up the level of automation, optimization and reliability in product assembly and manufacturing beyond what have been possible. We see assembly and manufacturing becoming more Intelligent with the perception-driven robotic autonomy, more flexible with the human-robot coupled collaboration in work cells, and more integrated in scale and complexity under the distributed and networked frameworks. On the other hand, the shift in key product features and engineering requirements dictates the new technologies and tools for assembly and manufacturing to be developed. This may be exemplified by a high complexity of micro/nano system products integrated and packaged in 3D with various heterogeneous parts, components, and interconnections, including electrical, optical, mechanical as well as fluidic means. The objective of this volume is to show how the assembly and manufacturing technologies evolve along with the advancement of enabling technologies and how the emergence of a high complexity of micro/nano system products dictate the development of new technologies and tools for their assembly and manufacturing. To this end, we have chosen 19 papers, top-rated yet relevant, out of the 76 papers accepted to present at the 8th IEEE International Symposium on Assembly and Manufacturing. The 19 papers chosen are further revised into the final manuscripts for book chapters that are organized into three parts: Part I: Fixture, Grasping and Manipulation in Assembly and Manufacturing, Part II: Micro/Macro Assembly and Disassembly, and Par III: Manufacturing System Scheduling and Control. Part I, II and III are reviewed and organized by the co-editors of this volume, Prof. Raul Suarez, Prof. Sukhan Lee and Dr. Byungwook Choi, respectively. Wishing that readers find this volume stimulating and informative … Sukhan Lee Raúl Suárez Byungwook Choi

Contents

Chapter I: Fixturing, Grasping and Manipulation in Assembly and Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary by Ra´ ul Su´ arez

1

Dual Arm Robot Manipulator and Its Easy Teaching System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chanhun Park, Kyoungtaik Park, Dong IL Park, Jin-Ho Kyung

5

Calibration of Relative Position between Manipulator and Work by Point-to-Face Touching Method . . . . . . . . . . . . . . . . . . . . Toru Kubota, Yasumichi Aiyama

21

Cutter Accessibility Analysis of a Part with Geometric Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Masatomo Inui, Kazuhiro Maida, Yuji Hasegawa

35

Automatic Determination of Fixturing Points: Quality Analysis for Different Number of Points and Friction Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jan Rosell, Ra´ ul Su´ arez, Francesc Penalba Contact Trajectories for Regrasp Planning on Discrete Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M´ aximo A. Roa, Ra´ ul Su´ arez

53

69

Modeling of Two-Fingered Pivoting Skill Based on CPG . . . . . Yusuke Maeda, Tatsuya Ushioda

85

Chapter II: Micro/Macro Assembly and Disassembly . . . . . . . . Summary by Sukhan Lee

97

VIII

Contents

Assembly of 3D Reconfigurable Hybrid MOEMS through Microrobotic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanty Rabenorosoa, Sylwester Bargiel, C´edric Cl´ecy, Philippe Lutz, Christophe Gorecki

99

Modified Assembly Systems and Processes for the Mounting of Electro-Optical Components . . . . . . . . . . . . . . . . . . . . 113 J. Franke, D. Craiovan Factory Level Logistics and Control Aspects for Flexible and Reactive Microfactory Concept . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Eeva J¨ arvenp¨ a¨ a, Riku Heikkil¨ a, Reijo Tuokko Development of Structured Light Based Bin–Picking System Using Primitive Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Jong-Kyu Oh, KyeongKeun Baek, Daesik Kim, Sukhan Lee Airframe Dismantling Optimization for Aerospace Aluminum Valorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Julie Latremouille-Viau, Pierre Baptiste, Christian Mascle A Monitoring Concept for Co–operative Assembly Tasks . . . . 171 Jukka Koskinen, Tapio Heikkil¨ a, Topi Pulkkinen Chapter III: Manufacturing System Scheduling and Controlling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Summary by Byung-Wook Choi Printing Pressure Control Algorithm of Roll-to-Roll Web System for Printed Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Kyung-Hyun Choi, Tran Trung Thanh, Yang Bong Su, Dong-Soo Kim Adding Diversity to Two Multiobjective Constructive Metaheuristics for Time and Space Assembly Line Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 ´ Manuel Chica, Oscar Cord´ on, Sergio Damas, Joaqu´ın Bautista Construction and Application of a Digital Factory for Automotive Paint Shops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Yang Ho Park, Eon Lee, Seon Hwa Jeong, Gun Yeon Kim, Sang Do Noh, Cheol-woong Hwang, Sangil Youn, Hyeonnam Kim, Hyunshik Shin Resource Efficiency in Bodywork Parts Production . . . . . . . . . . 239 Reimund Neugebauer, Andreas Sterzing

Contents

IX

Self-Tracking Order Release for Changing Bottleneck Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Matthias H¨ usig Integrated Operational Techniques for Robotic Batch Manufacturing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Satoshi Hoshino, Hiroya Seki, Yuji Naka, Jun Ota A Mathematical Model for Cyclic Scheduling with Assembly Tasks and Work-In-Process Minimization . . . . . . . . . 279 Mohamed Amin Ben Amar, Herv´e Camus, Ouajdi Korbaa Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293

Chapter I

Fixturing, Grasping and Manipulation in Assembly and Manufacturing Summary by Raúl Su
Assembly and manufacturing automation involves different problems, most of them derived from the physical interaction between parts, where any imprecision could produce important failures and consequently an increasing of the manufacturing costs. Properly task planning and programming, as well as the calibration of the involved systems, are quite frequently more complex than the task execution itself, and this is somehow strengthened by the continuous development of more and more versatile tools, with sophisticated features and high flexibility in their capabilities. Typical robotized tasks as manipulating an object or making some action on it require an appropriated constraint of the object degrees of freedom, either with respect to a gripper or to a specific static reference system, and this implies, for instance, the correct positioning of the object, the determination of the proper fixturing points, the calibration of the involved hardware and the planning and programming of the tool movements, problems that may be really time consuming and that usually demand high skill and practice from the human operator. Then, solving these problems in an automatic way is quite relevant in order to fully automate and optimize several assembly and manufacturing tasks, not only regarding their execution but also their planning and programming as well. This chapter includes some contribution in this line. It consists of six papers dealing with: a teaching system to simplify the robot programming, a calibration procedure to reduce position uncertainties, an approach to analyze object manufacturability in front of shape uncertainties with respect to the CAD model, a software tool to find and analyze the quality of fixturing points, a procedure to determine contact trajectories for object regrasping, and a model for two finger graspless manipulation of an object. A brief presentation of each paper is given below. The first paper, with title Dual Arm Robot Manipulator and Its Easy Teaching System by Chanhun Park, Kyoungtaik Park, Dong IL Park and Jin-Ho Kyung, presents a dual arm robot manipulator composed of two arms with six degrees of freedom each one mounted on a torso with two additional degrees of freedom and a teaching system to program it. The dual arm robot manipulator was designed for precision assembly of mechanical parts, but programming it with traditional teaching systems becomes a complex task, thus, the authors propose a more practical

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teaching system based on the guidance of one of the arms and making the other to follow the proper trajectories in accordance with the manipulated object. The second paper, with title Calibration of Relative Position between Manipulator and Work by Point-to-Face Touching Method by T. Kubota and Y. Aiyama, introduces a calibration procedure that estimates the relative position error between a manipulator and a workpiece. The procedure is based on a set of touching actions between a point of an arm tip tool and a face of the workpiece, the touching information obtained from each contact is iteratively processed to reduce the error in the estimation of the object position and orientation, with a limit imposed by the existing uncertainty in the position of robot end effector, which is assumed to be known. The approach needs to solve a linear programming problem in each iteration, but it requires neither advance tool calibrations nor a skilled operator for high precision calibration, thus it could reduce the calibration time and reduce production costs. The third paper, with title Cutter Accessibility Analysis of a Part with Geometric Uncertainties by Masatomo Inui, Kazuhiro Maida and Yuji Hasegawa, proposes a methodology to analyze the manufacturability of a holder part produced by working on a raw cast object, which usually has some shape differences with respect to the corresponding CAD model. This shape uncertainty may result in unmachinable features due to, for instance, collision between the cutter and the raw cast object. Detecting these problems at the design stage can strongly reduce the cost and waste time due to a later redesigning of a new holder part. The proposed approach has two basic steps, first, the CAD model is properly modified expanding it according to potential shape errors and, second, the un-machinable regions are detected with a cutter accessibility analysis that looks for possible interferences between the cutter and the non-ideal holder part model. The fourth paper, with title Automatic Determination of Fixturing Points: Quality Analysis for Different Number of Points and Friction Values by Jan Rosell, Raúl Suárez and Francesc Penalba, presents a software tool that implements a procedure to search for fixturing points on an object surface and also allows a quality analysis of any given set of fixturing points. The automatic search procedure can be applied to 2D and 3D free-form objects, respectively represented by a sequence of points or a triangular mesh, considering any number of fixturing points and a variable friction coefficient at the contacts; the procedure is based on a uniform exploration of the object boundary to identify contact points that iteratively increases the quality of the fixturing. The quality analysis allows to determine, for instance, how many points are necessary for a given friction coefficient in order to fix the object with a given desired quality, or, whether increasing the number of contact points means a significant improvement of the quality. The fifth paper, with title Contact Trajectories for Regrasp Planning on Discrete Objects by Máximo A. Roa and Raúl Suárez, provides a procedure that, given an initial and a final desired grasps characterized by a set of contact points on the object boundary, returns a path for each contact ensuring that the current grasp can resist external disturbances at any time during the regrasp motion. The approach can be applied to the regrasp planning of 2D and 3D objects whose boundaries are represented by a finite, but large, number of points. A sampling-based method together

Fixturing, Grasping and Manipulation in Assembly and Manufacturing

3

with the concepts of Independent Contact Regions and Non-Graspable Regions are used to speed up the search of the grasp space for a continuous path between the initial and the final grasp. Grasp changes are critical in manipulation tasks, therefore the proposed approach is an interesting step towards its automation. The sixth and last paper of the chapter, with title Modeling of Two-Fingered Pivoting Skill Based on CPG by Yusuke Maeda and Tatsuya Ushioda, deals with a particular problem of graspless manipulation: the translation of a cuboid by pivoting it using two fingers. This skill is modeled using Central Pattern Generators to obtain the commands for the index finger and the thumb of a virtual hand such that they push the cuboid producing a periodic pivoting. The results obtained with dynamic simulation are promising, and motivates the study and potential developments of other skills as well as their physical validation.

Dual Arm Robot Manipulator and Its Easy Teaching System Chanhun Park, Kyoungtaik Park, Dong IL Park, and Jin-Ho Kyung*

Abstract. The dual arm robot manipulator has been developed and it easy teaching system has been developed also. The manipulator consists of two industrial 6DOF arms and one 2-DOF torso and it was designed for the assembly automation of the automotive parts. Two-arm robot system has more advantageous than the traditional single arm robot system. But it is more difficult to teach the dual arm robot system. In this paper, the research results on the dual arm robot manipulator and its easy teaching system will be introduced.

1 Introduction Traditional single arm robot has just one arm to handle the object so it can’t perform its role in the workplace where the human worker does his jobs with his two arms. The robot manipulator needs to have two arms to have the function of cooperation to assembly mechanical parts. Recently, this is motivating some robot company to develop the robot system with two arms on one torso. With the same reason, industrial dual arm robot manipulator for precision assembly of mechanical parts has been developed by the authors and its research results have been already introduced [1]. The developed manipulator has two industrial 6-DOF arms and one 2-DOF torso. Left-arm and right-arm can be used to manipulate the workpiece in the cooperation task and each single arm can be used as a stand-alone 6DOF manipulator at the same time. The robot manipulator is very accurate and has enough power to lift up big and heavy workpiece. But it is difficult to make the manipulator understand the Chanhun Park . Kyoungtaik Park . Dong IL Park . Jin-Ho Kyung Department of robotics and intelligent machinery, Korea Institute of Machinery & Materials, 104 Sinseongno, Yuseong-gu, Daejeon, 305-343, Korea e-mail: [email protected], [email protected], [email protected], [email protected] *

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operator’s intention because it does not have enough intelligence. Usually, teaching pedants are used to teach the manipulator. But it is not easy to use them for the naive operators. So, the intuitive teaching methods have been introduced by many researchers and the direct teaching is one of the good candidates [2-6]. Two-arm robot system has more advantageous than the traditional single arm robot system as mentioned above. But it is more difficult to teach the dual arm robot system because the left arm and the right arm and the torso have to be taught separately. Furthermore it is very difficult the relative motion between the left arm and the right arm using the traditional teaching pendants. With the traditional teaching pendant system, the operator has to define the motion of the left arm. And then he has to teach the motion of the right arm. If the torso is worked while the two arms are working, the teaching process gets more complicated. So the easy teaching system for the developed dual arm robot manipulator has to be developed. For this reason, the industrial dual robot manipulator that it is possible to easily teach using easy teaching system has been developed. In this paper, the research results and experimental results will be introduced.

2 Robot Design and Analysis The developed dual arm manipulator has been developed for the automation of the mechanical parts. Transmission assembly and constant velocity joint assembly (Fig 1) are the first application target of the developed dual arm robot manipulator. The assembly is composed of the parts of Table 1. The major parts of the assembly are shown in Fig. 2.

Fig. 1 Transmission assembly line(left) and constant velocity joint assembly line (right)

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7

Fig. 2 Transmission drawings and important parts

Table 1 Part list of transmission assembly line Part

Number

Unit

1

PINION ASS’Y-MAIN DRIVE SUB

-

1EA

2

BEARING-NEEDLE ROLLER(M/D)

43229-4A060

1EA

3

RING-SYNCHRO(4&5)

43384-4D000

1EA

4

SHAFT-MAIN SUB ASS’Y

-

1SET

5

GEAR-COUNT SHAFT CLUSTER SUB

-

1SET

6

PLATE ASS’Y-INTERMEDIATE

-

1SET

7

BEARING-DOUBLE ANGULAR BALL

43226-4A040

1EA

8

SLEEVE REVERSE GEAR BEARING

43234-4A010

1EA

9

BEARING-ROLLER(SPACER)

43295-4D040

1EA

10

RETAINER-BEARING RR.

43144-4A001

1EA

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Table 2 Requirement specification of developed dual arm robot manipulator

specification Out-reach Payload Max Speed Repeatability DOF

values 1.5m 10kg/arm 150deg/s 0.1mm 6/Arm, 2/Torso

For the consideration of assembly process and the layout of the transmission assembly line (Fig. 3), the kinematic parameters of the manipulator are defined following Table II. Out-reach of the robot manipulator is 1.5m, the payload is 10kgf. The repeatability is 0.1mm.

Fig. 3 Brief layout of transmission assembly line

The kinematic structure of the developed manipulator is shown in Fig 4. The developed manipulator has two industrial 6-DOF arms and one 2-DOF torso. The kinematic structure of the left/right arm has the same as one of the traditional industrial 6-DOF robots.

Fig. 4 Kinematic structure of developed dual arm robot manipulator

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Fig 5 shows the picture of the developed dual arm robot manipulator. As it is introduced [1], the shoulder mechanism is specially designed for easy attach and detach of the left and right arm. That means the left and the right arm can be used a stand-alone 6 DOF manipulators and it can be used parts of the dual arm robot manipulator. The detail things can be referenced in [1].

Fig. 5 1st prototype of dual arm robot manipulator

Fig. 6 The cooperation workspace of the developed dual arm robot manipulator

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The workspace of the developed dual arm robot manipulator is shown in Fig 6. The height of the workspace is about 2800mm and the width is about 3500mm. The depth is 2600mm. The workspace for cooperation task can be shown with the red line in Fig 6. The size is about 1000mm x 2600mm for the bird view.

3 Kinematics Analysis There are two ways to define the motion of the left and the right arm. The first thing is the absolute motion and the next is the relative motion. The absolute motion means that the motion of the left arm and the right arm is defined with respect to the base coordinates. The left arm’s motion and the right arms motion are defined separately. But for the cooperative jobs, it is more convenient to use the relative motion definition, . The relative motion means that the left arm’s motion is defined with respect to the coordinates on the endeffector of the right arm as shown Fig. 7.

Fig. 7 Definition of the position and orientation of the end-effector of Arm 2 with respect to Arm 1

The transformation matrix for Arm 1 and Arm 2 is defined as the follows. ⎡n1 U 1i = ⎢ i ⎣0

o1i

a1i

0

0

p1i ⎤ ⎡n2 , U 2i = ⎢ i 1 ⎥⎦ ⎣ 0

o 2i

a 2i

0

0

p 2i ⎤ 1 ⎥⎦

(1)

Subscription i means the joint number. U1 means the transformation matrix from end effecter of Arm 1 to Joint i of Arm 1, and U2 means the transformation matrix from end effecter of Arm 1 to Joint i of Arm 2. Now, Jacobian matrix can be calculated by the following equation. The derivation procedure can be referenced in the paper [1] by the authors. ⎡−a1 ×(P2 −P1) " −a16 ×(P27 −P16) a21×(P27 −P21) " a26 ×(P27 −P26)⎤ J1,2 =⎢ 1 7 1 ⎥ a21 a26 −a11 −a16 " " ⎣ ⎦

1

(2)

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The superscript 1 means the end-effetor coordinates of the arm 1 is used for the reference coordinates for the expression of the Jacobian. If the left arm is arm 1 and the right arm is arm 2, the relative motion can be defined by Eq.3. ⎡ l p l ,r ⎤ l ⎡ q l ⎤ ⎢ l  ⎥ = J l ,r ⎢ ⎥ ⎣q r ⎦ ⎣ Φ l ,r ⎦

(3)

The superscript l means the end-effetor coordinates of the left arm is used for the reference coordinates for the expression of the Jacobian and the vectors. Here, ⎡ q l ⎤ ⎢q ⎥ = [q11 " q16 ⎣ r⎦ ⎡ l p l ,r ⎤ ⎢ l  ⎥ = x ⎣ Φ l ,r ⎦

[

T q 21 " q 2 6 ] .

z φ θ ψ

y

(4)

]

(5)

T

Eq. 2 is very useful to define the relative motion between the left and the right arms. But the calculation time would be problem. That is, it takes much time to calculate the inverse calculation of Eq. 2, so the real time calculation is not possible if the computing power is not sufficient. If the absolute motion of the left arm including the base motion is defined first, the joint motion of the left arm can be expressed by Eq. 7. The superscript bo means the reference coordinate of the torso base is used for the reference coordinates for the expression of the vector. ⎡ bo p l ⎤ bo ⎡ q l ⎤ ⎢ bo  ⎥ = J l ⎢  ⎥ = ⎣ qb ⎦ b ⎣ Φl ⎦b q l =

[[

bo

Jl

] ] ⎡⎢⎡⎢ +

l

[[ J ] [ J ] ]⎡⎢qq ⎤⎥ bo

(6)

l

bo

l l

l b

⎣ b ⎦b

[ J ] q ⎤⎥

p l ⎤ −  ⎥ Φ l⎦ ⎣⎢ ⎣ bo

(7)

bo

l b

bo

b

⎦⎥

Now, the required relative motion of the right arm (Eq. 5) is defined. Then, using Equation 3, the relationship between the defined relative motion and the required joint motion of the right arm and the left arm can be expressed by Eq. 8. ⎡ l p l ,r ⎤ ⎢l  ⎥ = ⎣ Φ l ,r ⎦

[[ J ] [ J ] ] ⎡⎢qq ⎤⎥ q = [ J ] q + [ J ] q l

l

l

l ,r l

l ,r r

l

⎣ r ⎦b

(8)

l

l ,r l

b

l

l ,r r

r

The absolute motion of the left arm is firstly defined by Eq. 7. Thus, the required motion of the right arm can be derived by Eq. 9. q r =

[[ J ] ]

+

l

l ,r r

⎡ ⎡ l p l ,r ⎤ l ⎤ ⎢ ⎢ l  ⎥ − J l ,r l q l ⎥ Φ ⎥⎦ ⎣⎢ ⎣ l ,r ⎦

[

]

(9)

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If the two arm is working independently, the joint motion of the two arm can be determined independently. But sometimes, the motion of the one arm and the torso is defined. In this case, the motion of the torso has to be considered to complete the motion of the other arm. Suppose that the motion of the left arm and the torso is firstly defined by Eq. 10, and then the motion of the right arm is defined by Eq. 11.  ⎡ q l ⎤ + ⎡ pl ⎤ ⎢q ⎥ = J l ⎢ω ⎥ ⎣ l ⎦b ⎣ b⎦

(10)

⎡ p r ⎤ ⎡ q r ⎤ ⎢ω ⎥ = J r ⎢q ⎥ = [[J r ]r ⎣ r⎦ ⎣ b⎦

q r ⎤ ⎥ = [J r ]r q r + [J r ]b q b q ⎣ b ⎦

[J r ]b ] ⎡⎢

(11)

Therefore, the required joint motion for the right arm is expressed by Eq. 12.

[

]

r ⎤ ⎤ + ⎡⎡ p q r = [J r ]r ⎢ ⎢ ⎥ − [J r ]b q b ⎥  ⎦ ⎣ ⎣Φ r ⎦

(12)

The result can be used to calculate the required joint speeds to implement the relative motion by Closed loop inverse kinematics algorithm (Eq. 13). q = J A+ ( X d + Ke) + ( I − J A+ J A ) q0

X = [x y z φ θ ψ ] e = Xd − X

T

(13)

Figure 8 shows an example of the relative motion of the developed dual arm robot manipulator.

Fig. 8 Example posture for cooperation task of developed dual arm robot manipulator

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4 Direct Teaching The controller structure for the developed dual arm manipulator is shown in Fig. 9. IBM compatible PC with dual core is used for the main computer system. Windows XP of Microsoft is used as an operation system. Windows XP is very useful to develop an application programs but it is not good for real time computation. So Real Extension, RTX of Ardence to extend the kernel for the real time computation is used. The application program for user interface is programmed and working in Windows XP environment but the real time control program is programmed and working in the RTX environment. Motion control board of Motion Engineering is used to control the motion of the each joint. The kinematic calculation and the control algorithm are done in the PC environment and the control commands are transferred to the motion control board. The user interface in Windows XP environment and the real time control program in RTX environment can communicate some information of user command and the state of the robot manipulator. The communication is implemented by the shared memory and it is working based on event. The required motion can be taught by a teaching pendant. But it is not easy to use teach pendant for the naive operators especially for the dual arm manipulator. The authors want to make it easy to teach the dual arm manipulator. For this reason, The force/moment sensor is equipped on the end-effector of each arm and the teaching handle (rod) is equipped on the force/torque sensor. The manipulator is controlled to comply with the teaching force to push or pulls the end-effector of the two arms. For this easy teaching control, virtual spring concept is used.

Fig. 9 Contorller structure for the developed dual arm robot manipulator

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Virtually, a spring is setup on the end-effector which the operator pushes or pulls. Then the virtual spring is starched or suppressed by the operator’s force and moment and it makes virtual displacement which is incremental position command for the manipulator (Fig. 10). If the motion of the left arm and the motion of the right arm are taught separately, the teaching method is summarized by Fig. 11. As it is shown, each arm is

Fig. 10 Virtual spring and virtual displacement

Fig. 11 Direct teaching algorithm in the case of the separated motion teaching

Dual Arm Robot Manipulator and Its Easy Teaching System

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controlled separately to comply with the teaching force/moment which the operator applies to each end-effector. The control algorithm is divided into two parts, “manual controller” and “dual arm robot controller”. In this figure, virtual spring and virtual displacement concept is used for “Manual controller”, and the generated reference trajectory is transferred to the “Dual arm robot controller”. The reference trajectory is expressed on the Cartesian space and it is interpolated and mapped into the Joint space in “Dual arm robot controller”. The interpolated joint reference is micro-interpolated for joint controllers. Simple independent joint controller based on PID is used for “Dual arm robot controller”. If the cooperative motion between the right arm and the left arm, teaching method is different. Since the relative relation is already defined by Eq. 5, the operator can teach the absolute motion of the one arm, left or right. In this case, the operator pushes or pulls just one end-effector, then the rest arm is moving to keep the relative relation between the left arm and the right arm. For this purpose, if the absolute motion of the left arm is directly taught, then Equation 9 is used to control the motion of the right arm to keep the relative relation. In this case, the direct teaching control algorithm is summarized as it is shown in Fig. 12. Here, as you see, the reference trajectory for the arm which is not taught directly is automatically generated by Eq. 9. “Dual arm robot controller” is the same as Figure 11.

Fig. 12 Direct teaching algorithm in the case of cooperated motion teaching

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Figure 13 and figure 14 show the experimental results. It is impossible or very difficult to teach the left and right arm at the same time with the traditional type of a teaching pedant system. But the developed dual arm robot manipulator can be taught manually, it is very convenient way to teach two arms at the same time. In figure 13, the teaching tools on the end-effector of the two arms are shown (red color). Figure 13 shows the operator is pushing and orientating the left arm and the right arm at the same time. Then the dual arm robot manipulator is controlled to comply with the teaching force and moment. During teaching process, the manipulator memorizes the trajectory taught by the operator, and it can playback the memorized trajectory after the teaching process is finished. Figure 14 shows the dual arm robot manipulator is doing playback with the reference trajectory by the operator after the teaching process is finished. Without the direct teaching method, it would be very difficult to teach the cooperation task of the left arm and the right arm by the traditional teaching pendant system. Figure 15 and figure 16 show another experimental result. In this experiment, the left arm and the right arm are taught separately. In this case, the direct teaching method is still useful. The teaching processes are done separately for each arm, but the playback can be done at the same time. Figure 15 shows the teaching process for the left arm only. The right arm can be taught with similar process. During each teaching process for the left arm and the right arm, the manipulator memorizes the each trajectory for each arm taught by the operator, and the manipulator can playback the memorized trajectories at the same time after the teaching process is finished.

Fig. 13 Direct teaching of the right and the left arm simultaneously

Dual Arm Robot Manipulator and Its Easy Teaching System

Fig. 14 Playback of the reference trajectory directly taught

Fig. 15 Direct teaching of the right and the left arm separately

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Fig. 16 Playback of the reference trajectory directly taught

5 Conclusion Two-arm robot system has more advantageous than the traditional single arm robot system. But it is more difficult to teach the dual arm robot system. In this paper, the research results on the dual arm robot manipulator and its easy teaching system for the developed manipulator are introduced.

References [1] Park, C., Park, K.: Design and kinematics analysis of the dual arm robot manipulator for precision assembly. In: IEEE International Conference on Industrial Informatics (INDIN 2008), Daejeon, Korea, July 2008, pp. 430–435 (2008) [2] Tsumugiwa, T., Yokogawa, R., Hara, K.: Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task. In: Proc. IEEE Int. Conf. on Robotics and Automation, Washington, DC, pp. 644–650 (2002) [3] Tsumugiwa, T., Yokowawa, R., Hara, K.: Variable impedance control with virtual stiffness for human-robot cooperative task (human-robot Cooperative peg-in-hole task). In: Proc. 41st SICE Annual Conference, Osaka, pp. 2329–2334 (2002)

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[4] Tsumugiwa, T., Sakamoto, A., Yokowawa, R., Hara, K.: Switching control of position/torque control for human-robot cooperative task – human-robot cooperative carrying and peg-in-hole Task. In: Proc. IEEE Int. Conf. on Robotics and Automation, Taipei, pp. 1933–1939 (2003) [5] Rahaman, M., Ikeura, R., Mizutani, K.: Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans. International Journal of JSME, Series C 45(2), 510–518 (2002) [6] Cetin, A.E., Adli, M.A.: Cooperative control of a human and a robot manipulator for positioning a cart on a frictionless plane. Mechatronics 16(8), 461–469 (2006) [7] Yamanaka, E., Murakami, T., Ohnishi, K.: Cooperative motion control by human and mobile manipulator using equivalent mass matrix and virtual impedance. IEEJ Transactions on Industry Applications 123(10), 1227–1233 (2003)

Calibration of Relative Position between Manipulator and Work by Point-to-Face Touching Method Toru Kubota and Yasumichi Aiyama

Abstract. This paper presents a new technique for simple, highly accurate calibration of relative position between a robot arm and a target work. This technique can shorten calibration time and reduce production cost. In this study, we adopted point-to-face touching for simple, highly accurate calibration. Point-to-face touching means to touch work surface with one point of an arm tip tool. Robot arms can get constraints of work position by difference between actual touching point and ideal one, and can calculate work position error with linear programming problem. Effectiveness of the calibration technique has been verified by experiments. By repeating experiments without changing environment and conditions, repeatability of the proposed calibration technique is verified by confirming dispersion of the calibration results. Precision is also verified by trying of robot arm to examine peg-in-hole task without force control.

1 Introduction Conventional industrial robot system usually assumes mass production system such as a car production plant. In such system, the initial start-up time and the initial operation volume is not large problem because it does not affect much cost to each production. But recently, life cycle of production becomes very short and production style becomes small production with large varieties. In such cases, it is not good to use teaching-playback method at the plant itself because it requires the plant to stop for a long period. It usually brings higher production cost. Then off-line teaching methods which use computer simulation with models of manipulators and environment become important to shorten stopping time of the plant [7]. But even if simulation models become much precise, there must exist positioning errors to locate robot arms, palettes and works to be manipulated since, for example, Toru Kubota · Yasumichi Aiyama Univ. of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan e-mail: [email protected]

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there is a margin between a hole and a screw to tighten a manipulator to the plant. Each individual manipulator has dispersion with its link parameters from the ideal designed values. So there exists danger of failure of operations just by simulation model without calibration between the ideal models and the actual plant. Then we need some calibration between the ideal models and the actual plant at least. To modify designed motion by simulation, relative position and orientation between a manipulator and a work space is important. So it is not needed to calibrate the individual properties of each manipulator such as link length etc. like as many traditional researches [6, 5, 4]. That is not effective for this purpose. It is effective to calibrate the relative position and orientation directly. As direct methods to calibrate the relative position and orientation between an arm and a work space, it is popular to adopt a manual operation to adjust an endeffecter of the arm to a certain position on the work space with seeing by a human operator. This method does not require any additional equipments and preparation, but the precision depend on skills of the operator. It is hard to increase the precision manually. As another method, there are some researches which use vision sensor to measure a base mark position on a work and calculate work position with difference from the ideal base mark position[2], which calculate faces and edges of work by touching an arm-tip touching tool to some work faces[3] and so on. These methods bring automatic calibration with good precision, but the position of tools and sensors must be completely known. So before the work calibration, we must calibrate tools and sensors independently. As a result, we need many steps at the plant to calibrate. The purpose of our study is to develop a simple and precise calibration method. The motivations are that it is not good if the precision is not enough with manual seeing because we need more operations to achieve required precision, and that if tools and sensors must be calibrated for each manipulator to be calibrated, we must repeat the operation as much as the number of manipulators. So we would like to solve these problems. In this paper, we propose and verify a new precise calibration method without skillful operators nor advance preparation. With such calibration method, it is possible to shorten calibration (direct teaching) time after off-line teaching with simulation. It may lower production cost.

2 Calibration by Point-to-Face Touching 2.1 Abstract of Calibration Point-to-face touching means to touch work surface with an end-effecter (tool) which is attached to the tip of the arm like as a gripper or a probe. Since it is touching to face, precise position is not required. With rough (nearly equal ethe idealf) information of work position, a robot manipulator can touch the face automatically and precisely. When point-to-point positioning is required, since it requires actual precise position of the point, some sensors like as vision sensor or manual operation with seeing are needed.

Calibration of Relative Position between Manipulator and Work

23

There is another problem to use simulated off-line teaching motion. In actual robot manipulator, there exists absolute positioning error with very large size. In simulations, there is no such kind of errors, but actually it becomes a large problem. But with the calibration method, since actual end-effecter position is used as manipulator position, the absolute errors are cancelled automatically and then we do not need to take care of it.

2.2 Assumptions Here, we will show some assumptions for the proposed calibration method; • The ideal position between an arm and a work is known. • There exist positioning errors in actual arms and works, but the range of the errors are limited and are known. • Arms have repetitive errors but the range are limited and known. • There exist located errors between arm-tip flange and a tool on an end-effecter, but the range are limited and known. Error factors of relative position between an arm and a work are, for example, positioning errors by installation of them, dispersion of arm link parameters, and so on. In this study, for simplification, we put these errors together and consider just an error of work position according to arm coordinate. According to this way, the limitation of the range of each parameter as stated above should be wider than the actual.

2.3 Coordinate System Settings For modeling of our calibration method with point-to-face touching, we set coordinate systems of an arm and a work. Fig. 1 shows the coordinate system configuration. Σ m is a coordinate setting on an arm base. Σ e is a coordinate on an arm-tip touching tool. Σ w is a coordinate on a work base. Σ t is a coordinate on an operation target point on the work such as connectors, cylinder axis etc. Σ c is a coordinate on a contact point by point-to-face touching calibration. T s with arrows in the figure mean transform matrices between two coordinates. However, there exist positioning errors with arm and work position. The tool on the arm-tip also has located errors. So even if the ideal T s are given by off-line simulator, the actual relative transformations are not obtained precisely. Fig. 2 shows modified coordinate system relationship with some errors. Here we set position errors of the work relatively based on the arm base as Δ w, and set the arm-tip tool location errors as Δ e. These two parameters are 6-dimensional vectors with translation and rotation parameters. Σ w is a coordinate of actual work base with translation and rotation with Δ w from ideal work coordinate Σ w. Σ e is a coordinate of actual arm-tip tool with translation and rotation with Δ e from ideal arm-tip coordinate Σ e. Σ c is a coordinate on an actual touching point on the work.

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e w

Te

Tt

c

m

Fig. 1 Coordinate system of arm and work

ࡢ࡯ࠢ

Robot arm

Hand of robot arm

Work

Touch point c´

e´ e

c

equal

Fig. 2 Relationship between coordinate systems

Tw

Base of robot arm

Tc ´ w

Tc

Te m

t

Tc

Tw

w

Work

Tt

t´ t

Tt Target

2.4 Calibration Method The information which is obtained when an arm-tip tool contacts with work surface is just one parameter of distance in the normal direction of the touching face. If there are no relative work position error Δ w, no arm-tip tool error Δ e and no link parameter errors, arm-tip tool coordinate Σ e and contact point coordinate Σ c are ideally consistent. But actually, there exist these kind of errors, contact point is different from the ideal position as shown in Fig. 3. In the figure, Δ is a second order small factor against these errors. Then we ignore this factor in the calibration. With this assumption, dx, distance between the ideal contact position and its actual position in the normal direction, is nearly equal d, distance between the ideal contact position and the actual contact position in the normal direction. Here, there exist repetitive errors in arm-tip position. With setting the size of the repetitive error as Δ c. we obtain; d + Δ c > dx > d − Δ c (1) As mentioned in the assumptions, range of Δ w and Δ e are limited and are known;

Δ wmax > Δ w > Δ wmin

(2)

Calibration of Relative Position between Manipulator and Work

25

Actual work c´

Shifted touch target

e´p c´ =

dy d

Touch tool

d

[

]

dx

e´

Fig. 3 Information obtained by point-to-face touching

Touch target for robot arm

Ideal work

Actual work ∑ t´

Robot arm

m

p t´ m

∑m

Δt

Ideal work ∑t

∑w´

pt

Δyw

Δxw

∑w

Δθw

Fig. 4 Target position and its error Δ t

Δ emax > Δ e > Δ emin

(3)

Here, as we set the relative position error of work position enough large, many other error factors such as absolute positioning error, individual product errors of arms and works, etc. can be merged. But, practically, if the distance between the operation target position and touching position is very far, the modeling error becomes large. So these two should be located nearby. Adding the initial condition inequalities (2) and (3), each touching operation brings one additional inequality (1). By these additional inequalities, the range of Δ w and Δ e are shrunk. Next, we will show description of the range of work target position error Δ t which is the most important factor for target operation. We set translational component of Δ t as Δ tx and rotational component as Δ tθ . As shown in Fig. 4, Δ tx is the difference between the actual target position m pt  and the ideal target position m pt and Δ tθ can be described with rotational matrices;

Δ tx = m pt  − m pt RΔ t θ = m Rt  m RtT

(4)

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Existence range of target

´

∑t

Δt xmin

Fig. 5 Target position existence range

Δt xmax

Δt ymax Δt ymin ∑t

As shown in Fig. 5, the range of the target error is obtained as the range of each axis. Unknown parameters in equations (1) to (4) are Δ w and Δ e. With describing these equations with Δ w and Δ e, these problems can be solved by linear programming problem with setting equations (4) as the objective function and setting (1) to (3) as constraint functions. From this linear programming problem, we can easily obtain the range of target position error Δ t.

2.5 Touching Strategy If there is no repetitive errors, all error parameters are obtained by same number of touching times. But actually, according to number of times for touching and their positions, the range of target varies. If we chose reasonable touching points, the range can be shrunk as same size as the arm repetitive error. For this, touching positions should be distant. However, when the operation target position and touching positions are quite distant, modeling error may badly affect. So touching positions should be near to the target position.

3 Solusion with Linear Programmign Problem In this section, to write down the calibration method to a linear programming problem as shown in subsection 2.4, we rewrite the constraints functions (1) to (3) and the objective functions (4) with Δ w and Δ e.

3.1 Constraint Functions Here, we rewrite the constraint function (1) with Δ w and Δ e. From Fig. 2, since −1 −1 −1 Te = Tw Tc , TΔ−1 e Te Tw TΔ w Tc becomes TΔ e Tc TΔ w Tc . So, e e

−1 pˆc = TΔ−1 e Tc TΔ w Tc pˆ0

(5)

pc = RTc (RΔ w − I)pc + RTc pΔ w − pΔ e

(6)

Calibration of Relative Position between Manipulator and Work

p0 = [0 0 0]T ,

 T pˆx = pTx 1 ,

27

 Tx =

Rx p x 0 1

By first order approximation of equation (6), we obtain ⎤ ⎡ Δ ex   e pc = − I RTc − RTc [pc ×] ⎣ Δ wx ⎦ Δ wθ ⎤ ⎡ 0 − xz xy 0 − xx ⎦ [x×] = ⎣ xz − xy xx 0



(7)

as a constraint function by one time touching. Here, Δ wx and Δ wθ are translational component of Δ w and rotational component respectively. By considering touching approach direction as x direction, x component of equation (7) means dx in equation (1). Then we obtain one linear constraint function for Δ w and Δ e. As repeating touching, the range of the unknown parameters Δ w and Δ e can be shrunk.

3.2 Objective Functions As same as the last subsection, we rewrite equation (4) with Δ w and Δ e. Δ t can be rewritten as; Δ tˆx = m pˆt  − m pˆt = Tw TΔ wTt pˆ0 − Tw Tt pˆ0

Δ tx = Rw (RΔ w − I)pt + Rw pΔ w

(8)

RΔ tθ = m Rt  m RtT = Rw RΔ wRTw

(9)

By first order approximation of equations (8) and (9), we obtain    Rw − Rw [pt ×] Δ wx Δt = 0 Rw Δ wθ

(10)

as the range of the operation target position error. By calculating the maximum and minimum of each component of Δ t, we can obtain the calibrated position of the operation target.

3.3 Algorithm of Calibration The flowchart of proposed calibration method with linear programming problem is as shown in Fig. 6. At first, we set the initial conditions with equations (2) and (3). With just these conditions, we can obtain the initial target position range before calibration by solving the maximum and the minimum of equation (10). Next, after the first point-to-face touching operation, we obtain one constraint equation (7) with the touching information. Adding this equation to the initial

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Start Set initial condition 9 9

sub to: Eq.(2)

Touch get new condition(Eq.(6)) from touch point

Calculation

solve the linear programming problem Change touch point NO

Fig. 6 Flowchart of calibration in LP problem

YES

End

conditions, we solve the linear programming problem of maximum and minimum of equation (10) again with constraints (2),(3) and one (7). If this solution of the range is enough small for target operation, the calibration is complete. But if it is not, we change the touching point and repeat this process. By repeating this process, we obtain more constraint function (7). We repeat this process until the solution becomes within a permissible range.

3.4 Operation Range of Proposed Algorithm In the proposed method, we use linear programming problem to solve the range, we use first order approximation for constraint functions obtained by touching. So, when work position error Δ w and tool position error Δ e are large, the solution may have large error against the real position. Then we have verified operation range of the proposed calibration algorithm. As pointed out at above, when orientation of Δ w becomes large, the solution becomes out of range of the actual position. When we checked, the limitation is about 10[deg]. This angle is not quite small. But, in production plant, work stands are often fixed with anchor bolt. In such cases, 10[deg] is easily occurrable error. So the limitation angle should be wider.

3.5 Expansion of Operation Range The reason why good calibration result cannot be obtained is because Δ w, the parameters of constraint functions are too large. Fig. 7 shows such case. In the left figure, the position of the actual work is very distant from the ideal work. In this case, information of touching point by one touching operation is also different from the ideal position. But during repeating the touching and solving the LP problems,

Calibration of Relative Position between Manipulator and Work

Ideal touch position

Fig. 7 Modification of ideal work position

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Actual touch position

Actual work Ideal work Before modification of ideal work position

Modified ideal work After modification of ideal work position

the position of the ideal work position is fixed. So all information is distant from the ideal value. It is one of the important reasons. So, after each trial of touching and solving LP problem, we modify the gidealh work position to the estimated position (average of the maximum and the minimum position). With this modification, measured position can be close to the ideal position. One more artifice is as shown in the right figure in Fig. 7. Not only just modification of ideal work position, the information of touching direction should be also modified. Because in this algorithm, we assume that touching direction is normal to the contact face. By simple modification of only work position, there exist contradictions about this assumption. This modification is effective because each information obtained by one touching does not deteriorate the estimation. Then modification by each trial does not deteriorate the result. With this expansion, operation range or the algorithm is improved to the limitation about 40[deg].

4 Experiments for Algorithm Evaluation 4.1 Abstract of Experiment To validate the effectiveness of the proposed calibration method, this section shows experiments for repeatability and precision. As repeatability evaluation, we have several times experiments without changing environment and conditions and check the variation of the calibration result. As precision evaluation, we have some calibration experiments, and after this, the manipulator tries to assemble a part at the target position by position control. By success ratio, we evaluate the precision. We cannot use a ruler to evaluate the calibration result because the result also consists absolute error of the arm.

4.2 Experiment Environment Experiment environment setup is as shown in Fig. 8. MOTOMAN-HP3J, a 6-d.o.f. manipulator of Yaskawa Electric Corporation is used as a robot arm. The repetitive

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Fig. 8 Experiment environment setup

error this arm is 0.03[mm]. At the wrist, a 6-axis force/torque sensor Nitta 70M35AM50B is attached to sense a touching probe tool contacts with the target work. As a touching tool, spherical probe is attached at the side of the gripper. For repetitive evaluation, the target work for calibration is a 120[mm] × 70[mm] × 30[mm] stainless block with 6 face milling cut. For precision evaluation, with a peg-in-hole insertion experiment, the target work is 28[mm] × 34[mm] × 30[mm] stainless blocks with oilless bush as a hole. We use two types of the bush; one has a diameter of 12 (+0.016 to +0.034) [mm] and the other has a diameter of 12 (+0.060 to +0.120) [mm]. We also use two types of stainless peg; one has a diameter of 12 (-0.017 to -0.006) [mm] and the other has a diameter of 12 (-0.043 to -0.016) [mm].

4.3 Experiment for Repeatability Evaluation As for repeatability evaluation, we have several times experiments without changing environment and conditions and check the variation of the calibration result. We locate the work ideally at (x, y, z) = (390[mm], 160[mm], 50[mm]) position. The results of 5 time experiments are as shown in Table 1 which shows each calibration result of work position and tool location error Δ e. Standard deviation of work position is about 0.04 to 0.09[mm] for position and about 0.02 to 0.04[deg] for orientation. Since the repetitive error of the manipulator is 0.03[mm], then this result shows the same order dispersion. We tried several experiments with different conditions. The tendency of results is not changed according to the conditions. In point of tool location error Δ e, standard deviation is about 0.02 to 0.03[mm]. The result of calibration of tool position seems to be well. So, we verify that even if precise tool position is not known, calibration is well achieved by the proposed way. Of course, to calibrate tool position, we need more trial of touching.

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Table 1 Result of experiment for evaluation of repeatability

ideal 1st result 2nd result 3rd result 4th result 5th result standard deviation average

X 390.00 391.57 391.47 391.54 391.52 391.50 0.04 391.52

Work position [mm / deg] Y Z Roll Pitch Yaw 160.00 50.00 0.00 0.00 0.00 165.24 51.12 0.01 -0.20 0.01 165.18 50.97 0.01 -0.31 0.05 165.25 51.15 0.03 -0.27 0.02 165.32 51.18 -0.07 -0.27 -0.01 165.25 51.02 0.00 -0.31 0.03 0.05 0.09 0.04 0.04 0.02 165.25 51.09 0.00 -0.27 0.02

Tool pos. error [mm] X Y Z 0.00 0.00 0.00 -2.01 -2.28 0.77 -1.98 -2.29 0.80 -2.02 -2.27 0.81 -2.02 -2.23 0.80 -1.94 -2.26 0.80 0.03 0.02 0.02 -1.99 -2.27 0.80

4.4 Experiment for Precision Evaluation As for precision evaluation, we tried peg-in-hole experiment with proposed calibration method. For the operation, it does not use force control. Position control is used for the operation. The manipulator has a 6-axis force/torque sensor on its wrist just to measure actual force and to stop the manipulator when the force becomes too large. If the operation is achieved, it can be said that position precision is within dimensional tolerance that is a gap between peg and hole; Case 1: Case 2: Case 3: Case 4:

0.076 to 0.163[mm] 0.066 to 0.137[mm] 0.032 to 0.077[mm] 0.022 to 0.051[mm].

Fig. 9 shows the result. These graphs show peg position for insertion and occurred force during the insertion for each case. (a) to (d) show Case 1 to Case 4 respectively.

59 57 2 55 53 0 51 -2 x-force 49 y-force -4 47 z-force peg position 45 -6 0 1 2 3 4 5 6 7 8 9 10 time[sec] 4

(a) case1 64 62 60 58 0 56 -2 x-force 54 y-force -4 52 z-force peg position 50 -6 0 1 2 3 4 5 6 7 8 9 10 time[sec]

Force[N]

4

Fig. 9 Contact force in peg-in-hole task

2

(c) case3

(b) case2 6

64 62 60 58 0 56 -2 x-force 54 y-force -4 52 z-force peg position 50 -6 0 1 2 3 4 5 6 7 8 9 10 time[sec]

Peg position[mm] Force[N]

6

Peg position[mm]

Force[N]

6

4 2

(d) case4

Peg position[mm]

59 57 2 55 53 0 51 -2 x-force 49 y-force -4 47 z-force peg position 45 -6 0 1 2 3 4 5 6 7 8 9 10 time[sec] 4

Peg position[mm] Force[N]

6

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Table 2 Success ratio for peg-in-hole operation Case 1 5/5

Case 2 5/5

Case 3 5/5

Case 4 1/5

In these graphs, z-force is the force against insertion direction. In Case 1 and 2, the peg contacts with the bush at 53[mm] position, and in Case 3 and 4, at 58[mm]. In Case 1, 2 and 3, small force about 2[N] occurred to perpendicular direction against insertion. But it does not disturb the insertion operation. But in Case 4, the contact force becomes larger than 4[N], then we stop the manipulator. We did each Case for several times, and almost all time has similar result as Fig. 9. Especially, in Case 1 sometimes, there is no contact force. Table 2 shows success ratio of each case experiments. With these results, we consider that position precision of 0.163 to 0.076[mm] is almost achieved. It is not enough to very precise task, but for usual assembly task with a position controlled manipulator, it seems to be well calibrated.

5 Conclusion In this paper, we have proposed a new calibration method of relative position between a manipulator and a work by point-to-face touching. It does not require advance preparation such as tool calibration. It does not require skilled operator for high precision calibration. To validate this method, we did experiments of repeatability and precision evaluation. With these experiments, effectiveness of the proposed method is verified. As future work, we want to achieve enhancement of precision of the calibration. And in this time, touching positions for calibration are manually designed. The operator considers distance between each touching point and between touching point and operation target position. Theoretically calibration precision does not depend on the touching position. But actually, convergence speed to required size of the range of target position error changes according to the touching position. So we would like to propose formulation of optimal problem where manipulator should touch to calibrate the relative position. Acknowledgements. This study is the result of a cooperative research with Yaskawa Electric Corporation. This study is sponsored By NEDO as Project for Strategic Development of Advanced Robotics Elemental Technologies.

References 1. DAIHEN CORP., NACHI FUJIKOSHI CORP.: Control Method of Industrial Robot. JP2006-293826, Japanese Patent, 2006-10-26 (2006) 2. FANUC LTD.: Teaching Position Correction Apparatus. JP2007-115011, Japanese Patent, 2005-5-10 (2005)

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3. FANUC LTD.: Apparatus for Correcting Robot Program. JP2006-293826, Japanese Patent, 2006-10-26 (2006) 4. Kin-Huat, L., Low, K.H.: Industrial Robotics: Programming, Simulation and Applications. I-Tech (2007) 5. Nakamura, H.: Industrial robot Calibration Method and Its Application for Production line. Journal of Robotics Society of Japan 15(2), 178–182 (1997) (in Japanese) 6. Roth, Z., Mooring, B., Ravani, B.: An Overview of Robot Calibration. IEEE Journal of Robotics and Automation 3(5), 377–385 (1987) 7. Yamamoto, N.: Development of Off-line Teaching System. ISCIE Journal of Systems, Control and Information 42(4), 189–194 (1998)

Cutter Accessibility Analysis of a Part with Geometric Uncertainties Masatomo Inui, Kazuhiro Maida, and Yuji Hasegawa*

Abstract. In designing a holder part of a large stamping die, designers must consider not only the functional property of the part, but also its manufacturability. The holder part is usually produced by cutting and engraving table, wall, slot and pocket features into a raw cast object. The raw cast object has inevitable large shape errors. It generally has 5 to 10mm shape difference from the nominal CAD model. This shape uncertainty causes various manufacturability problems in the milling process. The most serious problem is unexpected collisions between a cutter and raw cast object. They cause possible tool breakages and become obstructions to the cutter access to some regions on features. Since such features are not properly machined, costly re-designing the holder part is necessary. In this paper, the authors propose a manufacturability analysis system which can detect such unmachinable features caused by the shape uncertainty of the raw cast object. Proposed system computes a geometric model of a holder part with the maximum shape error by modifying the CAD model. Inverted offsetting and cutting simulations are successively applied to the model to extract the un-machinable region on the features. A system is implemented and some computational experiments are performed.

Masatomo Inui Department of Intelligent Systems Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan e-mail: [email protected]

*

Kazuhiro Maida Body Production Engineering Department, Mazda Motor Corporation, 3-1 Shinchi, Fuchu, Hiroshima 730-8670, Japan Yuji Hasegawa Department of Systems Engineering, Graduate School of Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan

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M. Inui, K. Maida, and Y. Hasegawa

1 Introduction To follow frequently changing customer preferences and to quickly distribute cars with high market competitiveness, automobile manufactures make efforts to shorten the production preparation time as much as possible. In the automobile production preparation process, making of stamping dies for large body parts is a very time consuming task. Curved shape part of a stamping die is usually fixed on a holder part. For precisely and stably positioning the curved shape part, hundreds of form features such as tables, walls, slots and pockets are defined on a holder. In figure 1, a holder part for a large side frame stamping die is shown. In this figure, table features are illustrated in white color. The holder part is usually fabricated by cutting and engraving the features into a raw cast object with a vertical cutter. Dark blue surface regions in figure 1 represent the un-machined surface of the raw cast object.

Fig. 1 Table features on a holder part of a large side frame stamping die

The raw cast object is produced by using “lost model casting” method. As a preparation, a model of the object is manually made by cutting, shaping and pasting some blocks of foamed styrene. The model is then buried in the casting sand and the melted metal is poured into the sand. The foamed styrene model is vaporized and its shape is replaced into the metal object. Since the foamed styrene model is soft and fragile, and it is manually made, the raw cast object of the holder part generally has 5 to 10mm shape errors from its nominal CAD model. In designing a holder part, designers must consider not only the functional property of the holder, but also its shape errors and their effects on the manufacturability.

Cutter Accessibility Analysis of a Part with Geometric Uncertainties

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Fig. 2 An example of un-machinable features caused by some shape errors in casting. (a) CAD model of features, and (b) configuration of features with possible maximum shape error and a flat end cutter.

Since designers are not experts of machining methods, they often fail the manufacturability evaluation and design a holder part with some un-machinable form features. An example of typical un-machinable form features is shown in figure 2. White color shape shown in figure 2(a) represents a circular table feature and a rectangular table feature being adjacent to a vertical wall feature. The circular feature locates above the rectangular feature. These features are fabricated by engraving the raw cast object with a vertical flat end cutter. As shown in figure 2 (b), a specified flat end cutter can machine the rectangular feature without colliding the circular feature if they have nominal shape. In the actual machining process, the cutter may collide with the circular feature if it has possible maximum shape error, and some part of the rectangular feature may remain un-machined (see green area in the figure). In the current practice, these un-machinable features are usually detected in a later machining preparation stage. They are then reported to the designer and are resolved by re-designing the holder part. In a large and complex holder part, number of possible un-machinable features often becomes more than 50. Cost and time loss for re-designing a new holder part is a serious problem in many automobile manufactures. In this paper, the authors propose a manufacturability analysis system which can automatically detect such un-machinable features caused by the shape error. Proposed system computes a geometric model of a holder part with the maximum shape error by modifying a nominal CAD model of the part. Inverted offsetting and cutting simulations are successively applied to the model to extract the unmachinable region on the features. By using this system, holder part designers with less machining knowledge can detect such features with manufacturability

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problems in early designing stage. The analysis result is displayed in a few minutes, so designers can modify the holder shape in a short time period. A system is implemented and some computational experiments are performed.

2 Prior Studies Many Computer-Aided Manufacturing (CAM) programs provide some functions for detecting un-machinable regions on the workpiece based on the CAD data and the milling cutter specification (for example, [6]), but they do not evaluate the effect of the shape error of the raw cast object. The manufacturability evaluation of the mechanical part was actively studied in 1990s [3]. An application of the knowledge engineering method [11], some scoring methods of the machining easiness of features based on the template knowledge [2], evaluation of the machining cost based on the removal volume of the workpiece and cutter path length [4,5], and a manufacturability evaluation method based on an assemblability evaluation technology [8] are known. These methods analyze the manufacturability of a part by applying some rules or procedures about the machining process to a nominal geometric model of the part. Since these methods do not evaluate the effect of the shape error of the raw cast object, they are not applicable to our cutter accessibility analysis problem.

3 Algorithm Outline 3.1 Input and Output Our analysis system requires a CAD model of a holder part with form feature definitions and the milling cutter specification as input data. After the computation, the system displays detected un-machibnable regions on the CAD model. The detail description of the input data is as follows; (1) A CAD model of a holder part with form features: This model represents the nominal shape (without casting errors) of a holder part to manufacture. In the model, some specifications of form features such as tables and walls are defined. Definitions of the features are given later. The authors assume that the model shape is represented as a set of triangular polygons. Most CAD systems provide a function to output the model data as a group of polygons, for example in the STL format. (2) Milling cutter specification: A milling cutter usually has 3 components, which are cutting edge, shank, and cutter holder. In our study, ball end cutter, flat end cutter and radius end cutter are assumed as cutting edge shape. Most automobile companies standardize the cutters for the holder machining. They are usually listed in an electric catalogue style, so even a designer without the expert knowledge of the milling operation can select the most preferable cutter for machining the holder part.

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Fig. 3 Form features considered in our manufacturability analysis system. They are represented with a set of triangular polygons.

3.2 Feature Definitions In our study, 7 types of form features are considered as shown in figure 3. These features are represented as a group of mutually connecting triangular polygons. They are selected based on interviews to several automobile companies in Japan; 1. 2. 3. 4. 5. 6. 7.

Table: Horizontal planar surface (see figure 3 (a)). Wall: Vertical surface. Usually it has flat shape or cylindrical shape (b). Slope: Planar surface except table and wall features (c). Pocket: Horizontal planar region completely or partly surrounded by wall features (d). Slot: Special pocket feature having two mutually parallel adjacent wall features (e). Hole: Vertical holes are only considered in our study (f). Non-vertical holes are usually machined in a special manner. Curved surface: A group of polygons approximating a curved shape (g).

In the above definition, pocket and slot features can be recognized as special table features with adjacent walls, therefore they are treated in a same manner in the following discussion. A vertical hole feature is considered as a special wall feature in a similar reason. Polygons not used in the form features represent the raw cast surface. They are not machined in the holder fabrication.

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3.3 Basic Processing Flow Our analysis algorithm finds the manufacturability problem of form features in the following 2 steps; Step 1: CAD model of a holder part is modified in consideration of the shape error of the raw cast object. Un-machinable regions on form features occur because of possible interferences between the cutter and the holder part with shape errors (see figure 2). In order to completely detect such interferences, the maximum volume shape of the holder part allowed in the shape error range is computed by “expanding” the CAD model. The detail of the expansion method is given in the next section. Step 2: Based on the expanded CAD model, un-machinable regions on form features are detected. Our developed algorithm [6] is used in the detection. This algorithm extracts the regions by successively applying the inverted offsetting and milling simulations. In our implementation of the algorithm, geometric models in the Z-map representation are used [1]. Since Z-map cannot represent the vertical shape precisely, this algorithm is unable to detect un-machinable regions on wall and hole features. Improvement of the algorithm for precisely analyzing the wall and hole feature case is discussed in section 5.

4 Step1: Modification of Holder Part In this section, the expansion method of the holder part model to obtain its maximum volume shape is explained. A holder model is represented as a set of triangular polygons. They are classified into the following 2 groups; 1. 2.

Polygons representing form features such as tables, walls, slots, pockets and curved surfaces. Other polygons representing the raw cast surface.

Different expansion methods are applied to each polygon group.

4.1 Expansion of Raw Cast Surface The maximum shape of the raw cast surface is represented by offsetting the surface with the possible largest shape error. Since raw cast object is known to have 5 to 10 mm errors, the raw cast surface area of the holder CAD model is offset by 10mm in our implementation. This shape is equivalent to a Boolean union shape of spheres, cylinders and prisms being placed on all polygons of the raw cast surface as follows (see figure 4); • Spheres of radius ε are placed on all vertices of the surface. ε means the maximum shape error of the raw cast object. • On each edge e, a cylindrical pin shape of radius ε is placed so that its center axis and e become coincident. • On each polygonal face p, a prism shape of the same area and thickness of 2ε is placed so that the center plane of the prism and p become coincident.

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Fig. 4 (a) Placement of a sphere on a vertex v, (b) a cylinder on an edge e, and (c) a prism on a triangular polygon p of a raw cast surface model

4.2 Extension of Form Features Form features such as tables and walls are also expanded by the maximum shape error of the raw cast object. Different from the raw cast surface, the surface area of the form feature is machined in the exact shape. For example, horizontal surface area of a table features is machined so that it has the exact flatness and height. Therefore, the “expansion” of a form feature is actually realized as “extension” of the feature surface in its tangential direction. In our study, each form feature is represented as a group of triangular polygons. Since table, slope, slot, pocket, wall and hole features can be recognized as a special type of the curved surface feature, the extending method of a curved surface feature is only explained. As a preparation, each boundary curve of a curved surface feature is traced in counter clockwise order based on the adjacency information between polygons. Edges and vertices on the boundary are indexed as ei and vi in their traced order (see figure 5). The extension of the curved surface in its tangential direction is realized by simply attaching a small quadrilateral and a part circle to each boundary edge and vertex in the following manner; Attachment of small quadrilateral: For each boundary edge ei, a small quadrilateral Qi is attached. Consider a vector ui from vi to vi+1 and the normal vector n of a form feature polygon being adjacent to ei. As a cross product of ui and n, a vector si representing the tangential extending direction of the feature at ei is obtained. Qi is defined by shifting ei in the si direction by ε meaning the maximum shape error. Attachment of small part circle: A boundary vertex vi has two adjacent boundary edge ei-1 and ei. By using the method mentioned above, quadrilaterals Qi-1 and Qi are defined for ei-1 and ei respectively. Consider extending direction vector si-1 for Qi-1 and another vector si for Qi. If cross product of si-1 and si, and the normal vector of the form feature at vi are in the same direction, there is a gap between Qi-1 and Qi as shown in figure 5. This gap is fixed by a part circle Ci whose center point is at vi and containing the vectors si-1 and si, and of radius ε.

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Fig. 5 Definition of quadrilaterals and part circles for extending a curved surface feature

4.3 Resolution of Feature Interferences Table features are usually defined on the top of protrusion shape of the raw cast object. So most table features are surrounded by raw cast surfaces as shown in figure 6. Therefore, the offset shape of the raw cast surface and the extended shape of the table feature often have interferences. Similar problems happen between form features also. For example, a slot feature is defined as a flat surface with its adjacent vertical wall features. When the flat surface part of the slot feature and the wall features are extended, their attached quadrilaterals and part circles have interferences. After the expansion of the raw cast surface and the extension of the form features, these interferences must be resolved. Resolution method is different for (1) feature and raw cast surface interference case and (2) feature and feature interference case.

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Fig. 6 Modification of the offset raw cast surface intersecting the extended table feature

Interference resolution between feature and raw cast surface: The extended shape of the form feature and the offset shape of the raw cast surface are compared, and a part of the offset surface which appears above the extended feature is detected and removed as shown in figure 6.

Fig. 7 Modification of mutually intersecting extended table and extended wall features

Interference resolution between extended form features: These interferences are further classified into the following two cases. Convex intersection: Two extended features intersect so that they make a convex angle at the intersection curve. Concave intersection: Two extended features make a concave intersection curve. In figure 7, a table feature connects two wall features named wall0 and

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wall1. Intersection between the extended table and the extended shape of wall0 organizes a convex intersection. Another intersection between the extended table and the extended shape of wall1 is a concave intersection. In the convex intersection, the extended part of wall0 appearing above the table and the extended part of the table appearing above wall0 are erased. On the other hand, the extended part of wall1 appearing below the table and the extended part of the table appearing below wall1 are erased in the concave intersection. These operations are implemented in our solid modeling system based on the Boolean intersection method between surfaces [10]. Our system has the offset function of the raw cast surface and the feature extension functions also. Z-map is used as the solid object representation format in this system. In the Z-map, the object surface is represented by a height map of vertical segments which are defined on rectangular grids in the XY plane. Therefore, the object surface must have unique z value for every (x, y) grid point in the Z-map representation. Form features on a holder part are machined with a vertical cutter, so their shape basically satisfies this condition. Since the top portion of the raw cast surface only affects the accessibility of the vertical cutter to the form features, Z-map representation is sufficient for defining the effective raw cast surface and its offset result. Vertical features such as walls and holes are exceptions. Handling method of these features is explained in the next section. Input model of a holder part is given to our modeling system to obtain its maximum volume shape. The result shape is recorded in the Z-map format and used in the following cutter accessibility analysis.

5 Step2: Cutter Accessibility Analysis 5.1 Basic Algorithm Based on the maximum volume shape of the holder part, the accessibility of a milling cutter is checked and un-machinable regions on extended form features are extracted. In this subsection, the outline of the accessibility analysis is explained. The detail of the method is given in our prior paper [6]. Our algorithm achieves the cutter accessibility analysis in the following 2 steps; Step 2.1: Compute a cutter path which completely covers the maximum volume model of the holder. The path must be computed so that the cutter moving along the path can engrave the model shape as exact as possible. Such cutter path is easily generated on the inverted offset surface of the model shape (see figure 8(a)). The inverted offset surface is equivalent to the top surface of the result shape of Minkowski sum operation between the maximum volume model and the inverted cutter. Step 2.2: Execute geometric milling simulations using the cutter path obtained in step 2.1 and the cutter model. Milling operation is geometrically equivalent to a Boolean subtraction of the swept volume of the cutter moving along the path from a solid model representing the stock shape. After the simulation, the maximum

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Fig. 8 Un-machinable region extraction method. (a) Minkowski sum operation with an inverted cutter, and (b) milling simulation process.

volume model of the holder and a model obtained as the simulation result are compared. As shown in figure 8(b), an un-machinable region on a form feature is extracted as a region where some material remains on the extended feature surface. Both the inverted offsetting and the milling simulation are very time consuming task. The authors developed a GPU (graphics processing unit) based method for accelerating these computations. In this method, the inverted offsetting and the milling simulation are translated into hidden surface elimination problems of polygon rendering [7]. Once the translation is done, the depth buffer mechanism of GPU can perform the computations very rapidly. Since GPU is now a standard component of most computers, this technology is available with no additional hardware cost.

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Fig. 9 (a) A cutter path for milling a wall feature, and (b) a flat end cutter moving along the path

5.2 Vertical Feature Case The analysis algorithm mentioned above is not suitable for extracting unmachinable regions on form features with vertical surfaces, such as wall and hole features. Remained shape after the milling simulation usually has a vertical pillar like shape as shown in figure 8(b). The remained shape tends to have rather large numerical errors after the complex inverted offsetting and milling simulation. Furthermore, Z-map cannot represent the vertical wall feature and hole feature precisely. Therefore, shape comparison between the vertical features and vertical remained shape is difficult. The authors develop a different method for analyzing the cutter accessibility to wall and hole features. Machining method of a vertical wall feature is standardized in most companies. In this method, a flat end cutter is selected and the cutting edge on its cylindrical side face is used to generate vertical shape. A cutter path for milling a wall feature with this cutter is generated so that the center point of the cutter moves along the offset curve of the boundary of the extended wall feature. Cutter radius is set to the offset value. The cutter moving along the top part of the path is not effective in milling the wall, so only the bottom part of the path is selected as shown in figure 9(a). Side face of a flat end cutter moving along the path can completely scan the wall feature (see figure 9(b)). A vertical hole is usually machined with a drill of the same radius to the hole, or a flat end cutter with smaller radius. In the drilling case, the trajectory path of the drill becomes identical to the center axis of the hole. In the flat end milling case, the same path generation method for milling a wall feature is applicable.

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Fig. 10 (a) Placement of a cutter model. It intersects the maximum volume model of a holder part. (b) Interference resolution by lifting up of the cutter model.

After the path generation, the path is subdivided by inserting many points on the path. The distance between points is set to be 0.1mm in our study. A cutter (a flat end cutter or a drill) model is then placed to each point, and the intersection between the cutter and the maximum volume model of the holder part is checked (see figure 10(a)). Since the Z-map is adopted in our model representation, the intersection check is achieved by simply comparing the end points of Z-map segments and the cutter model. This comparison can be efficiently executed by using the parallel computation capability of GPU. If the cutter and the holder model intersect, some regions on the wall feature are not machinable. Then the cutter is moved upward until the cutter and the holder model do not have any intersections. In this motion, the trajectory of the center point of the cutter is recorded and mapped to the wall feature as shown in figure 10(b). This computation is repeated to all points on the cutter path and the un-machinable region on the wall feature is detected. Un-machinable region on a hole feature is detected in a similar manner.

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5.3 Special Case Analysis Our cutter accessibility analysis method detects the un-machinable region by comparing the maximum volume model of the holder and the milling simulation result. This method cannot detect some un-machinable regions originated in the maximum volume model itself. Figure 11 illustrates this problem. Holder part designer sometimes define a placement of form features as shown in figure 11(a). This figure shows a condition that two table features are connected by a vertical raw cast surface. When these features and raw cast surface are expanded by using our modification method, a shape shown in figure 11(b) is obtained. In this shape, a part of the lower side table feature is covered by the extended upper side table feature so the covered region (a green color region in figure 11(b)) cannot be machined by using a vertical cutter. Such un-machinable region is difficult to detect by our analysis program because the shape shown in figure 11(b) itself is possible to machine. Another trouble case is given in figure 12. Our modeling system uses the Z-map representation for recording the shape data. In the Z-map representation, a placement of table feature and wall feature as shown in figure 12(a) cannot be properly represented because they organize an “overhang” shape. The result shape after the extension of the table and wall features becomes as shown in figure 12(b). In the original model, a part of the table feature exists under the wall feature. Since the vertical milling cutter cannot access such covered region on the table (a green color region in the figure), it remains un-machined. This covered region disappears in the modified model, so our analysis system cannot detect this un-machinable region. In order to overcome these limitations, the authors introduce a function to directly compare the extended shape of the table feature and the maximum volume holder part model. If some regions of the extended table is being contained within the maximum volume model as shown in figure 11(b) and 12(b), such regions are extracted and recorded as the un-machinable regions.

Fig. 11 Un-machinable region on a table feature which is difficult to detect using the method given in figure 8

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Fig. 12 (a) CAD model representation and (b) its modification result using Z-map based modeling system. An un-machinable region disappears in (b).

6 Computational Experiments By using the technology mentioned above, a maximum volume holder model computation program and a cutter accessibility analysis program are implemented using Visual C++, OpenGL, and a GPU language CUDA [9], and some computational experiments are performed. Application results of the programs to a holder CAD model are illustrated in figure 13 and 14. Figure 13 shows a nominal holder model of a stamping die. Its size is 4462mm x 2061mm x 845mm. This part is usually machined with a flat end cutter of radius 40mm. This cutter is attached to a cutter holder of radius 150mm. In the maximum volume model computation and the accessibility analysis, a Z-map model which is defined on a 7000 x 7000 resolution grid in the XY plane is used. Our program can check the cutter accessibility for all features on a holder part at one time, or check the accessibility for each feature in one-by-one manner. In checking all features at one time, whole part shape is represented in a single Zmap model, therefore the gird size of the Z-map becomes 0.42mm for the holder part shown in figure 13. The grid size of the Z-map means the accuracy of the computation. The accuracy is much improved in checking the accessibility for each feature one by one. The maximum size of the table shown in figure 13 is 400mm x 400mm, so the Z-map model accuracy becomes 0.06mm. This accuracy is generally sufficient in our un-machinable feature detection purpose. This method is, however, requires several times more computation time for checking all features compare to the whole shape checking method. In order to efficiently

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Fig. 13 Un-machinable regions on table features in a holder part model

achieve the verification, designers should check the existence of un-machinable regions on all features by using the whole shape checking method at first, then apply the one-by-one checking method to each detected feature to understand the unmachinable regions precisely. A PC with Intel Core2 Duo Processor (3.33GHz) , 3GB memory and NVIDIA GeForce GTX-280 GPU is used in our experiments. For analyzing the cutter accessibility of all features appearing in figure 13, our program needs 14.99 CPU seconds for computing the maximum volume model, and 117.26 CPU seconds for analyzing the cutter accessibilities. 349 un-machinable regions are detected on form features defined on the holder part. 71 regions are on tables, 274 regions are on walls, and 4 regions on slots. The authors compare our computation result to another result manually done by a machining expert. Our result includes all the un-machinable features detected manually. In addition, it can detect some unmachinable regions which are not detected by the expert. In figure 13, all un-machinable regions detected on table features are illustrated in green color. Some large un-machinable regions are specified with white arrows. Close-up picture of an area enclosed by a white rectangle is given in figure 14. Figure 14(a) shows un-machinable regions detected on table features in this area, and (b) shows the regions on wall features. In these figures, detected unmachinable regions are superimposed on the features in the nominal shape. Since un-machinable regions usually exist on the extended part of the features, they sometimes appear in the outside of the nominal features. In the figure, red curves surrounding the features represent the boundary of the extended features.

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(a)

(b)

Fig. 14 Close-up picture of an enclosed part in figure 13. (a) Un-machinable regions on table features, and (b) such regions on vertical wall features.

7 Conclusion In this paper, the authors propose a manufacturability analysis system which can detect un-machinable features caused by the shape errors of raw cast objects. The following 4 technologies are developed for the system; 1. The maximum volume shape of a holder part is computed by expanding the raw cast surface of the part and extending its form features. 2. Expanded raw cast surface and extended form features often have mutual interferences. They are resolved by removing the intersection portions. 3. Un-machinable regions on the form features are extracted by applying the inverted offsetting and the milling simulation to the maximum volume shape.

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4. Un-machinable regions on vertical features are detected by directly checking the intersection between the cutter model and the maximum volume shape. The following is our future research subjects; 1. Reduction of the computation time: Current system requires some minutes in the accessibility analysis. Further improvement of the computation speed is necessary. 2. Displaying method: Current system can display only un-machinable regions detected on form features. This information is not helpful for the designer for understanding how to modify the holder part to resolve the un-machinable regions. Better displaying method which is much informative for the designer must be studied.

References [1] Choi, B.K., Jerard, R.B.: Sculptured Surface Machining, Theory and Applications. Kluwer Academic Publishers, Dordrecht (1998) [2] Cutkosky, M.R., Tenenbaum, J.M.: Toward a computational framework for concurrent design. In: 16th Annual Conference of IEEE Industrial Electronics Society, IECON 1990, pp. 700–706 (1990) [3] Gupta, S.K., Das, D., Regli, W.C., Nau, D.S.: Automated manufacturability analysis: A survey. Research in Engineering Design 9(3), 168–190 (1997) [4] Gupta, S.K., Nau, D.S.: Systematic approach to analyzing the manufacturability of machined parts. Computer-Aided Design 27(5), 323–342 (1995) [5] Hsiao, D.: Feature Mapping and Manufacturability Evaluation with an Open Set Feature Modeler. Ph.D Thesis, Mechanical Engineering, Arizona State University (1991) [6] Inui, M., Miyashita, T.: Hollow Shape Extraction: Geometric Method for Assisting Process Planning of Mold Machining. In: Proc. 2003 IEEE ISATP 2003, pp. 30–35 (2003) [7] Inui, M., Ohta, A.: Using GPU to Accelerate Die and Mold Fabrication. IEEE CG&A Magazine 27(1), 82–88 (2007) [8] Miyakawa, S.: Simultaneous engineering and producibility evaluation method. In: Proc. of the SME International Conference on Application of Manufacturing Technologies (1991) [9] NVIDIA: NVIDIA CUDA Compute Unified Device Architecture, Programming Guide Ver.1.1 (2007) [10] Satoh, T., Chiyokura, H.: Boolean Operations on Sets Using Surface Data. In: Proc. Symp. on Solid Modeling Foundations and CAD/CAM Applications, pp. 119–126 (1991) [11] Subramanyan, S., Lu, S.: The impact of an AI-based design environment for simultaneous engineering on process planning. International Journal of Computer Integrated Manufacturing 4(2), 71–82 (1991)

Automatic Determination of Fixturing Points: Quality Analysis for Different Number of Points and Friction Values Jan Rosell, Ra´ul Su´arez, and Francesc Penalba

Abstract. This paper copes with the automatic determination of fixturing points on 2D and 3D free-form objects, for any number of fixturing points and a variable friction coefficient at the contacts. An approach is proposed that, starting from an initial set of points, successively finds a better set by changing only one point at a time following an heuristic search procedure that uniformly explores the object surface. A software tool that implements this approach is also presented. This tool also allows to analyze the quality of any given set of fixturing points, which has allowed us to determine how many points are necessary for a given coefficient of friction in order to fix 2D and 3D objects with a given quality. The tool has been released as open software.

1 Introduction A key point in a manufacturing process is the proper fixturing of objects when they are going to be processed in any way, or some particular actions must be done on them. There is a number of well known examples, like for instance polishing, drilling, or just performing an assembly of subparts to form a more complete product, among several others. In this situations there is always at least one part that must keep its position despite the application of external forces on it, in order to successfully perform the desired action. There are several works dealing with the problem of object fixturing, considering different particular conditions and/or constraints, including a number of works presented in the field of grasping and manipulation, which has several points in common with the problem of fixturing. Jan Rosell · Ra´ul Su´arez · Francesc Penalba Institute of Industrial and Control Engineering (IOC), Technical University of Catalonia (UPC), Barcelona, Spain e-mail: [email protected] 

This work was partially supported by the Spanish Government through the projects DPI2007-63665 and DPI2008-02448.

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Relevant concepts in this field are the form-closure property (the position of the fixtures/fingers ensures the object immobility) and force-closure property (the forces applied by the fixtures/fingers ensure the object immobility) [2]. The force-closure constraint is more frequently required in grasping, since the movement of the object makes its own weight to act as an external perturbation, while the form-closure constraint is more frequently required in fixturing, where the object usually lies in a stable position while no operation in being performed on it. Some relevant works dealing with grasping and fixturing of objects based on this property are given below in Section 2. In this work we present a tool to decide which is the most convenient way to restrict the position of an object assuring a desired minimum quality in terms of the forces that the object can resist without loosing the position. We have extended a previous work [22] and made an implementation that allows the search and analysis of fixturing points on the object. The idea is to visualize the quality of potential fixturing configurations under different conditions, like the number of contact points and the friction coefficient at those points, and use this information to decide how to secure the object. The approach is valid for 2D and 3D free-form objects. The paper is organized as follows. After this introduction, Section 2 presents the approach used to find force and form closure fixturings and to evaluate their quality. Section 3 describes the implemented tool (software) developed to search and analyze object fixturings. Section 4 shows some application examples in order to illustrated the approach. Finally Section 5 summarizes the work, presents some conclusions and discusses future work and potential improvements.

2 Fixturing Search and Evaluation How to constrain the position of an object depends on several factors, which determine the approaches followed by different researchers. The most relevant ones are listed here, together with a review of the most used quality measures that evaluate this constraint satisfaction in terms of the forces that the object can resist without loosing the position. Afterwards, the proposed approach is presented, which is a generalization of the work presented in [22].

2.1 Related Background How to constrain an object in a desired position depends on a number of factors, being the most relevant: the dimension of the object, i.e. 2D or 3D, the object shape, i.e. polyhedral or non-polyhedral, the type of contact between the fixtures (or fingers) and the object, i.e. frictionless, frictional or soft contact, and the number of contacts, which for arbitrary 3D objects must be equal or larger than 2 when soft contacts are considered, equal or larger than 4 for frictional contacts (exceptionally, 3 contacts may be enough for some particular objects), and equal or larger than 7 for frictionless contacts. See [3] for a review of these factors. These different cases are addressed in several relevant works, considering, for instance, 2D

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polygonal objects [11], 2D non-polygonal objects [5], 2D discrete objects [18], 3D polyhedral objects [19, 6], 3D non-polygonal objects [24, 23], and 3D discrete objects [12, 17, 20]. Arbitrary shaped objects are frequently modeled with a finite (but large) number of points, either using clouds of points as samples of the object surface or any type of mesh. These models are quite convenient when the object boundary is obtained using range sensors, or some vision systems based on structured light [1, 4], and they can also easily be obtained from any other representation. In this work we consider the boundary of a 3D object to be described by a triangular mesh, while a 2D object boundary is directly described by a finite sequence of discrete points.

2.2 Quality Measures Several different quality measures have been presented in the literature to evaluate the performance of a given fixture or grasp. See [21] for a survey on grasp quality measures. The quality measures that take into account the object properties (shape, size, weight), friction constraints and form and force closure conditions to quantify the grasp quality, can be classified into three subgroups. The first two groups do not consider limitations in the magnitudes of the forces applied at the contact points, one group considers only algebraic properties of the grasp matrix (for instance the value of its minimum singular value that indicates how far is the grasp from a singular configuration [10]), and the other group considers geometric relations in the grasp (for instance the shape [8] and the area [16] of the polygon defined by a three contact point fixture). The third group considers limitations in the magnitudes of the forces applied to constrain the object, thus being more realistic for practical applications. The quality measures used in this work belong to this third group, although constraints derived from the indeterminate friction forces in a quasi-static analysis [13] are not included. Given the forces that can be applied on the object at the contact points, the produced wrenches on the object are known, and they are used to compute the following quality measures: a) The radius of the largest hypersphere centered at the origin of the wrench space and fully contained in the Convex Hull of the wrenches that can be applied on the object at the contact points [7, 9], which indicates the maximum wrench that the constrained object can resist independently of the wrench direction. This measure depends on the reference point used to compute the torques. b) The volume of the the Convex Hull of the wrenches that can be applied on the object at the contact points [14], which gives an idea of the amount of wrenches that the object can resist, and is constant independently of the reference system used to compute the torques.

2.3 Implemented Approach The approach used to compute a set of fixturing points and to evaluate its quality is a generalization of the work presented in [22], which deals with the case of seven

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frictionless contact points for 3D discrete objects. That work is extended to allow the application to 2D and 3D objects, and using any number of contacts, either frictionless or frictional. Some other added features are described below. The main algorithm, valid for 2D and 3D objects and any number of frictionless or frictional contacts, is as follows: Step 1: Step 2: Step 3: Step 4: Step 5: Step 6:

Generate an initial set G of m fixturing points and evaluate its quality. Select another point p j on the object surface. Select a particular point pi ∈ G Evaluate the resultant quality when pi is replaced by p j in G. If the quality grows then update G replacing pi by p j . While a finishing condition is not satisfied go to Step 2.

The generation of the initial set G of m fixturing points in Step 1, as well as the other points in Step 2, is done using a sampling procedure that tries to pick points uniformly distributed over the object surface. Random and deterministic sampling algorithms were used for this purpose. The first point of G is randomly selected, and the remaining m − 1 points of G and the rest of the points in Step 2 are either randomly selected or selected maximizing the distance to the already selected points, in this latter way the object surface is better uniformly sampled (Fig. 1). The distance between points can be measured in different ways as, for instance, using a Euclidean distance between any two points or using the number of points in the mesh between them (details about the implemented solutions are given in next section). This generation of samples is iteratively repeated until a termination condition is satisfied. The evaluation, in Steps 1 and 4, of the fixturing quality produced by the set of contact points G is computed using any of the two criteria presented in Subsection 2.2.

Fig. 1 Illustration of the deterministic sampling (top) and random sampling (bottom) on the object surface of a pawn model for 20, 30 and 40 samples from left to right, respectively. Deterministic sampling obtains a better uniform coverage of the object surface.

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The selection of a particular point pi ∈ G in Step 3 is done such that, once a new point p j is selected on the object surface, the direction of the wrench wi produced by the normal force applied on pi is the closest to the direction of the wrench w j produced by the normal force applied on p j . This criterion tends to minimize the change in the directions of the potential wrenches applied on the object and facilitates the convergence of the algorithm. Step 5 is straightforward, and, finally, the finishing condition in Step 6 can be any of the followings: • • • •

A given desired minimum quality is obtained. A given number of steps without improving the quality were performed. A given number of points on the object surface were visited. All the points on the object surface were visited.

3 A Tool to Analyze the Fixtures There exists a powerful tool, Graspit! [15], that is focused on grasp planning, providing procedures to find the best grasp of a given object with a given mechanical hand. In our work we are interested in analyzing some properties of fixturings only from the object point of view, in particular the relation between the number of points, the friction coefficients at the contacts and the fixturing quality that can be obtained. For this reason, a tool called Grasp Analysis Tool (GAT) has been implemented to find fixturing or grasping points for a given object following the algorithm presented in the previous section. The tool can also be used to evaluate the quality of any set of given fixturing or grasping points. It has basically been implemented with an analysis aim and, thus, the user can define many parameters related to the object models used, the type of fixtures, the quality measures or some parameters of the search algorithm. They are detailed in the following subsections. The software package can be downloaded from http://iocnet.upc.edu/usuaris/JanRosell/GAT/GAT.html.

3.1 Object Models The Grasp Analysis Tool works for free-form objects in two and three dimensions: 2D objects are defined as a closed line described by a sequence of points; 3D objects are defined as a closed volume described with a triangular mesh. The segments defined by two consecutive points in 2D, or the triangles in 3D, are called elements. Their geometric center define the candidate fixturing or grasping points. Therefore, the search algorithm obtains better results with models composed of many uniform elements. The tool has an option that allows to refine the models by subdividing all their segments or triangles as illustrated in Fig. 2. Also, a parameter λ is defined to scale the objects. Assuming unitary forces at the contact points, the parameter λ is used to scale the torques, i.e. wi = (fi , λ τ i ).

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Fig. 2 Model refinement process on a dodecahedron. From left to right, model composed of 60, 240 and 960 triangles, respectively.

3.2 Type of Fixtures Fixtures vary as a function of the number of fixturing points and as a function of the force directions that can be exerted at them, which is determined by the friction coefficient at the contacts. In the Grasp Analysis Tool, the effect of friction is introduced using the Coulomb friction model, considering the friction coefficient μ equal at all the contact points. For the 3D case, the friction cone is approximated by a polyhedral convex cone with eight sides. The frictionless option is also available. For the frictionless case the number of points ranges from 4 and 7 for the 2D and 3D cases, respectively, up to the number of elements in the object model. When friction is considered, the minimum number of points is set to 3 and 4, respectively. Fig. 3 shows the interface devoted to the configuration of these parameters.

Fig. 3 Interface to determine the type of fixture: number of points and friction coefficient

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3.3 Quality Measures The two quality measures used have been described in Section 2.2. A combination of them has also been implemented as a third option, although it is not used in the search algorithm shown in Section 2.3 but only provided for evaluation purposes. They are labelled as: • Q1 : The radius of the maximum hypersphere. • Q2 : The volume of the Convex Hull. • Q3 : The ratio between Q1 and Q2 .

3.4 Parameters of the Searching Algorithm The Grasp Analysis Tool tool allows the searching algorithm to be run with different sampling strategies and with different distance measures: a) Sampling strategies: Deterministic or random sampling strategies can be chosen, as illustrated in Fig. 4, being the number of points to be sampled also variable. The manual selection of the candidate points is also possible.

Fig. 4 Interface to select the desired sampling strategy and the distance measure to be used

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Fig. 5 Discrete distance from a given triangle (white) to the other triangles in the mesh, computed using standard neighborhood (left) and extended neighborhood (right)

For objects with few elements considered as potential fixturing points, the algorithm can be run in an exhaustive way, i.e. all the combinations are tested and the one with the best quality is chosen. b) Distance measure: Distance between two elements is computed by the propagation of the distance between neighbor elements, thus, several alternatives can be chosen, as illustrated in Fig. 4. First, for 3D models two type of neighborhood can be selected: a) Standard neighborhood: two triangles are considered neighbors if they share an edge; b) Extended neighborhood: two triangles are considered neighbors if they share at least one vertex (Fig. 5). Second, the distance between neighboring triangles can be defined in two ways: a) Discrete distance: neighboring triangles are at a distance one; b) Euclidean distance: the distance between neighboring triangles is computed as the Euclidean distance between their centers.

4 Analysis of Fixturing Quality: Examples This section uses the GAT tool to analyze how the fixturing quality depends on the number of fixturing points and on the friction coefficient. The examples are based on the application of the searching algorithm on 2D and 3D models with the following parameters (see Subsections 3.3 and 3.4): • • • • •

Quality measure: Q1 . Sampling sequence: Deterministic. Distance measure: Euclidian distance combined with extended neighborhood. Friction values: 0.01, 0.05, 0.10, 0.15, 0.20 and 0.25. Number of fixturing points: from 3 to 11 for the 2D case and from 4 to 11 for the 3D case.

Since the search algorithm is heuristic, no optimal result can be guaranteed. Therefore, each example has been run three times starting each time with a different element on the object surface. The chosen starting elements are: a) the closest element to the geometric center of the object; b) the furthest element to the geometric center of the object; c) a randomly selected element. For each example, the quality obtained by the algorithm for each friction value and for each number of fixturing points is graphically reported. This quality is the maximum obtained by running the algorithm from the three considered starting points.

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4.1 2D Examples Two 2D examples have been considered: a rectangle and an ellipse. In both cases, the algorithm has been run until all the points on the object perimeter have been visited. The rectangle has an aspect ratio 3 × 1. The initial model, composed of eight uniform elements, has been refined up to 256 elements, resulting each element with a size lower than the 0.4% of the total perimeter. The ellipse has an aspect ratio 2 × 1. The model is composed of 400 non-uniform elements, being their size lower than the 0.35% of the total perimeter. Figures 6 and 7 show the results. As expected, it can be seen that the quality increases with the number of fixturing points and with the friction coefficient. In both cases this increase is not relevant for more than 6 fixturing points, being even for the ellipse not much relevant from 4 fixturing points. The increase in the friction coefficient is, on the other hand, always relevant, irrespective of the number of fixturing points used.

Fig. 6 Experiment results with the Rectangle: (top) quality Q1 vs. number of fixturing points for different friction coefficients; (bottom) quality Q1 vs. both number of fixturing points and friction coefficients

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Fig. 7 Experiment results with the Ellipse: (top) quality Q1 vs. number of fixturing points for different friction coefficients; (bottom) quality Q1 vs. both number of fixturing points and friction coefficients

4.2 3D Examples Three 3D examples have been considered (Fig. 8): two regular polyhedra (a tetrahedron and a dodecahedron), and an irregular object (a pawn). For the tetrahedron, the algorithm has been run until all the points on the object surface have been visited. For the other two examples the number of visited points has been limited due to the computational time needed for the complete exploration, and of the very slow

Fig. 8 3D examples

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Fig. 9 Experiment results with the Tetrahedron: (top) quality Q1 vs. number of fixturing points for different friction coefficients; (bottom) quality Q1 vs. both number of fixturing points and friction coefficients

increase in the quality that is obtained once a representative number of points have been visited. The initial model of the tetrahedron, composed of only four triangles, has been refined up to 256, resulting each triangle with an area lower than the 0.4% of the total area. The initial model of the dodecahedron, composed of 60 triangles, has been refined up to 960, resulting each triangle with an area lower than the 0.15% of the total area. The maximum number of sampled triangles was set to 200. The initial model of the pawn, composed of 304 triangles, has been refined up to 1216, resulting each triangle with an area lower than the 0.4% of the total area. The maximum number of sampled triangles was set to 300. Figures 9, 10 and 11 show the results for the tetrahedron, the dodecahedron and the pawn, respectively. As in the 2D examples, the quality increases with the number of fixturing points and with the friction coefficient. For the tetrahedron, the grasping quality presents a staircase shape with respect to the number of fixturing points, i.e. there are flat regions between 4 and 6 points and between 8 and 10. Therefore it makes nonsense to use 5 or 6 points instead of

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Fig. 10 Experiment results with the Dodecahedron: (top) quality Q1 vs. number of fixturing points for different friction coefficients; (bottom) quality Q1 vs. both number of fixturing points and friction coefficients

4 since the quality is nearly the same, and for the same reason it makes nonsense to use 9 or 10 points instead of 8. For the dodecahedron there is a very important increase of quality when incrementing the number of fixturing points from 5 to 7, which motivates the use of a number of fixturing points equal to or larger than 7. For the pawn it can be observed that for low friction coefficients there is a considerable quality step between 6 and 7 points, as expected since seven points are required for the frictionless case. Therefore for low friction coefficients the reasonable number of fixturing points is 7 or more. On the other hand, for high friction coefficients the behavior of the quality is almost linear with the number of fixturing points, from 4 up to 9 points. This suggests the use of as many points as possible in this range. In all cases, there is a linear increase as a function of the friction coefficient, irrespective of the number of fixturing points, although this linearity is not so clear for the case of the pawn. Then, the quality is always directly increased by an increase in the friction coefficient.

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Fig. 11 Experiment results with the Pawn: (top) quality Q1 vs. number of fixturing points for different friction coefficients; (bottom) quality Q1 vs. both number of fixturing points and friction coefficients

5 Conclusions This paper has analyzed how the fixturing quality of 2D or 3D free-form objects depends on the number of fixturing points and on the friction coefficient at those points. Fixturing points are found by an heuristic algorithm previously proposed by the authors that has been generalized to both 2D and 3D objects, to friction or frictionless contacts, and to a variable number of fixturing points. A software tool has been implemented to automate this analysis. The results allow to select in each case the minimum number of fixturing points and friction coefficient required to achieve a given desired minimum quality.

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References 1. Alexa, M., Behr, J., Cohen, D., Fleishman, S., Levin, D., Silva, C.: Computer and rendering point set surfaces. IEEE Trans. Visualization and Computer Graphics 9(1), 3–15 (2003) 2. Bicchi, A.: On the closure properties of robotic grasping. Int. J. Robotics Research 14(4), 319–344 (1995) 3. Bicchi, A., Kumar, V.: Robotic grasping and contact: A review. In: IEEE Int. Conf. on Robotics and Automation, pp. 348–352 (2000) 4. Campbell, R., Flynn: A survey of free-form object representation and recognition techniques, computer vision and image understanding. Computer Vision and Image Understanding 81, 166–210 (2001) 5. Cornell`a, J., Su´arez, R.: On computing form-closure grasps/fixtures for non-polygonal objects. In: IEEE Int. Symp. on Assembly and Task Planning, pp. 138–143 (2005) 6. Ding, D., Liu, Y., Wang, S.: Computation of 3-D form-closure grasps. IEEE Trans. Robotics and Automation 17(4), 515–522 (2001) 7. Ferrari, C., Canny, J.: Planning optimal grasps. In: IEEE Int. Conf. on Robotics and Automation, pp. 2290–2295 (1992) 8. Kim, B., Oh, S., Yi, B., Suh, I.: Optimal grasping based on non-dimensionalized performance indices. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 949–956 (2001) 9. Kirkpatrick, D., Mishra, B., Yap, C.: Quantitative Steinitz’s theorem with applications to multifingered grasping. J. Discrete and Computational Geometry 7(3), 295–318 (1992) 10. Li, Z., Sastry, S.: Task-oriented optimal grasping by multifingered robotic hands. In: IEEE Int. Conf. on Robotics and Automation, pp. 389–394 (1987) 11. Liu, Y.: Computing n-finger form-closure grasps on polygonal objects. Int. J. Robotics Research 19(2), 149–158 (2000) 12. Liu, Y., Lam, M., Ding, D.: A complete and efficient algorithm for searching 3-D form closure grasps in the discrete domain. IEEE Trans. Robotics 20(5), 805–816 (2004) 13. Maeda, Y., Oda, K., Makita, S.: Analysis of indeterminate contact forces in robotic grasping and contact tasks. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1570–1575 (2007) 14. Miller, A., Allen, P.: Examples of 3D grasp quality computations. In: IEEE Int. Conf. on Robotics and Automation, pp. 1240–1246 (1999) 15. Miller, A., Allen, P.K.: Graspit!: A versatile simulator for robotic grasping. IEEE Robotics and Automation Magazine 11(4) 16. Mirtich, B., Canny, J.: Easily computable optimum grasps in 2D and 3D. In: IEEE Int. Conf. on Robotics and Automation, pp. 739–747 (1994) 17. Niparnan, N., Sudsang, A.: Fast computation of 4-fingered force-closure grasps from surface points. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3692–3697 (2004) 18. Niparnan, N., Sudsang, A.: Computing all force-closure grasps of 2D objects from contact point set. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1599–1604 (2006) 19. Ponce, J., Sullivan, S., Sudsang, A., Boissonat, J., Merlet, J.: On computing fourfinger equilibrium and force-closure grasps of polyhedral objects. Int. J. Robotics Research 16(1), 11–35 (1997)

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20. Roa, M., Su´arez, R.: Finding locally optimum force-closure grasps. J. Robotics and Computer-Integrated Manufacturing 25(3), 536–544 (2009) 21. Roa, M., Su´arez, R., Cornell`a, J.: Revisi´on de medidas de calidad para la prensi´on de un objeto. Revista Iberoamericana de Autom´atica e Informatica Industrial 5(12), 66–82 (2008) 22. Su´arez, R., Rosell, J.: Searching form-closure fixturing points on objects described by triangular meshes. In: IEEE Int. Symp. on Assembly and Manufacturing, pp. 7–12 (2007) 23. Zhu, X., Ding, H.: Planning force-closure grasps on 3-D objects. In: IEEE Int. Conf. on Robotics and Automation, pp. 1258–1263 (2004) 24. Zhu, X., Wang, J.: Synthesis of force-closure grasps on 3-D objects based on the Q distance. IEEE Trans. Robotics and Automation 19(4), 669–679 (2003)

Contact Trajectories for Regrasp Planning on Discrete Objects M´aximo A. Roa and Ra´ul Su´arez

Abstract. Manipulation tasks, in general, require a grasp change on the object during its execution. The manipulation problem can be solved by simply rolling or sliding the fingers on the object surface, the so-called regrasping approach. This paper provides an algorithm for regrasp planning of 2D and 3D discrete objects, such that the regrasp trajectory of each contact ensures a force-closure grasp (i.e. a grasp that resists external disturbances) while the regrasp motion is performed. The proposed approach takes advantage of a sampling-based method that quickly explores the grasp space, and relies on the use of independent contact regions and non-graspable regions, which provide large regions of the force-closure or non force-closure subspaces starting from a single sample. Application examples are included to show the relevance of the results.

1 Introduction A manipulation problem appears when an object grasped by a multi-fingered hand needs a grasp change during the execution of a task; it implies the hand ability to change the position and orientation of the manipulated object from an initial to a final position. The final position can be achieved by simply moving the object inside the hand’s workspace, changing the position of the hand joints. The range of possible M´aximo A. Roa Institute of Robotics and Mechatronics, German Aerospace Center (DLR e.V.) D-82234, Wessling, Germany e-mail: [email protected] Ra´ul Su´arez Institute of Industrial and Control Engineering (IOC), Technical University of Catalonia (UPC), 08028 Barcelona, Spain e-mail: [email protected] 

This work was partially supported by the Spanish Government through the projects DPI2007-63665 and DPI2008-02448.

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movements that can be imparted on the object without changing the actual grasp is determined by the physical limits in the finger joints and the possible collisions between the fingers and the object. If the final position is not achievable in this way, then the contacts between the fingers and the object must be changed at some point during manipulation, and at least one contact point must be located in a different position; in this way the object can achieve a wider range of positions. Under this assumption, two different ways of manipulation can be established: finger gaiting and regrasping. Finger gaiting (or finger repositioning) involves the relocation of one or more fingers on the object surface while keeping the force-closure (FC) grasp with the remaining fingers (at least 2) [10]. The change of a grasp from n to n − 1 fingers involves a change in the problem conditions, as the degrees of freedom of the handobject system may increase when one contact is lost. The sequence of movements starts with an n − 1 finger grasp. The object is rotated without changing the contact points until one of the fingers reaches its workspace limits, then, a redundant (free) finger must be located to generate a grasp that allows lifting the limiting finger such that a new n − 1 FC grasp is obtained [7]. Another approach simply changes between different FC grasps by using one or more free fingers, rotating the object until the desired position is reached [9]. On the other hand, the regrasping approach (or multi-fingered manipulation) solves the manipulation problem by simultaneously using all the available fingers; the positions of the fingers can only be changed by rolling or sliding them along the object surface. The theoretical basis for rolling contacts has been established considering the finger-object system [10] and also including the hand kinematics [8]. Manipulation by rolling has been simulated, even using wheeled fingertips [11], but real applications are limited to simple experimental setups such as a 2-finger hand manipulating a ball [6], mainly due to control and stabilization problems, as well as limitations in the workspace of the fingers [1]. Finger sliding is a process that repositions the fingers by sliding them along the object surface; the theoretical basis for this process has been studied [2, 5], but it is hard to be mechanically implemented as the fingers must touch the surface during all the sliding movement [13], which requires tactile sensors with high accuracy and a very controlled hand dynamics. Assuming that the manipulation is performed at low velocities, then the interaction forces between the fingers and the object are dominant compared to the inertial forces, and the manipulation can be considered as quasi-statical, which simplifies the problem formulation. A dexterous manipulation planner that takes advantage of the quasi-statical formulation for 3D smooth objects has already been proposed [3]. However, computation of regrasp trajectories for general 3D objects has only been recently tackled. When dealing with 3D arbitrary shaped objects, a common approach to describe their surface is by using a cloud of points or a triangular mesh. A method was proposed to avoid dealing with the large amount of data involved in these representations; the approach starts with a triangular mesh, which is simplified and used to build a regrasp roadmap [12].

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This work discusses the problem of searching the trajectories for the fingertips on an object surface, in order to change from an initial FC grasp to a final desired one while ensuring the FC condition (i.e. ensuring the resistance to external disturbances) during the finger movements. A solution to the problem is presented based on the concepts of independent contact regions (ICRs) and non-graspable regions (NGRs) [15]. ICRs are defined such that the positioning of a finger in each ICR ensures an FC grasp, independently of the exact position of each finger. NGRs are defined such that a finger contact in each NGR always produce a non-FC grasp, independently of the exact position of each finger. The approach used in this work focuses only on the object geometry and the FC property to find the trajectories for the fingertips on the object surface, i.e. it is object-centered. The kinematics of the grasping device is not considered. It is assumed that the manipulation is performed at low velocities, therefore the manipulation can be considered quasi-statical. The rest of the paper is organized as follows. Section 2 provides a background for the regrasp planning problem. Section 3 describes the approach proposed to plan a regrasp movement on a discrete object, and discusses the problems that appear when the approach is applied to 3D objects. Section 4 shows two examples to illustrate the approach, and, finally, Section 5 presents the conclusions of the work.

2 Background This section presents the assumptions regarding the object modeling and the type of contacts considered, as well as a description of some relevant basic concepts used in the new developments, like the Independent Contact Regions and Non-Graspable Regions and their representation in the grasp space, which are key points in order to improve the efficiency and allow a practical implementation of the proposed approach.

2.1 Assumptions The following assumptions are considered in this work. There is a frictional punctual contact between each finger and the object, with friction being modeled according to Coulomb’s law. The object surface is discretized with a large enough set Ω of points pi , whose positions are described by one or two parameters u for 2D or 3D objects, respectively. The normal direction nˆ i pointing toward the interior of the object at pi is known. Besides, each point is connected with a set of neighboring points forming a mesh of interconnected points on the object surface.

2.2 Independent Contact Regions and Non-graspable Regions Independent contact regions and non-graspable regions are defined on the object surface in such way that a finger located in each ICR or NGR, independently of

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Fig. 1 ICRs for a discretized ellipse: a) FC grasp in the wrench space (the convex hull contains the origin O); b) ICRs on the ellipse

the exact finger position, always ensures that an FC or non-FC grasp is obtained, respectively. Fig. 1 shows an example of an FC grasp on a discretized ellipse and in the wrench space; it also shows the ICRs for each one of the 4 grasping points on the ellipse. 3,920 different FC grasps can be obtained from the possible combinations of finger positions inside the ICRs. Fig. 2 shows a 4-finger non-FC grasp for the ellipse in the wrench space and on the ellipse boundary. For a non-FC grasp, different sets of non-graspable regions can be computed [15]; each set is called an NGRH. For the example in Fig. 2, NGRH1 and NGRH2 allow 44,100 and 2,313,441 different non-FC grasps, respectively. Algorithms to compute ICRs and NGRHs have been already presented in previous works [14, 15].

O p2 F2

NGRH3 |H1 = NGRH4 |H1 F1 p4

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NGRH1 |H1 = NGRH2 |H1

= NGRH3 |H2 = NGRH4 |H2

b)

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Fig. 2 Sets of NGRs for a discretized ellipse: a) Non-FC grasp in the wrench space (the convex hull does not contain the origin O), b) First set of NGRs (NGRH1 ); c) Second set of NGRs (NGRH2 )

2.3 Grasp Space An n-finger grasp G is described by the set of parametersui that define  the positions of the fingers on the grasped object surface, i.e. G = u1 , . . . , u p , with p = n for 2D objects and p = 2n for 3D objects. The p-dimensional space representing

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the position of the possible contact points defined by u1 , . . . , u p is called the grasp space G. The grasp space G is divided into two complementary subsets: the FC space, formed by the points that represent FC grasps, and the non-FC space, whose points represent non-FC grasps. Fig. 3 shows the grasp space G for an ellipse discretized with 64 points using 3 frictional fingers (i.e. each point of G defines 3 contact points on the ellipse). The grasp space G contains 643 = 262, 144 grasps, with 12.1% being FC grasps and 87.9% being non-FC grasps, as shown in Fig. 3b with dark and light colors, respectively. This ellipse will be used in this work to illustrate the proposed approach for solving the regrasp problem.

u=1

a)

b)

Fig. 3 Grasp space for a 2D object with 3 frictional contacts: a) Discretized ellipse; b) Grasp space

  The grasp space G has some symmetries, as any grasp G = u1 , . . . , u p accounts for K different grasps, where K = n! is the total number of possible permutations of the fingers on the object while keeping the same contact points, i.e. the fingers may change their positions with all the other fingers without changing the contact points and the obtained grasps on the object are the same (as long as there are no specific assignments of the fingers to the contact points). For instance, Fig. 4 shows the 6 symmetrical points for a 3-finger frictional grasp of a 2D object. The grasp space is actually divided in 6 sectors or subspaces, symmetrical with respect to each other. The 6 subspaces are also shown in Fig. 4. The ICRs computed on the boundary of the object correspond to an axis-aligned region in the grasp space, hereafter called BI region, which encloses a number of FC grasps. Due to the symmetry described above, the ICRs computed for one grasp are actually mapped to K axis-aligned regions BI in the grasp space, as shown in Fig. 5. A 3-finger grasp is used in the example to allow a graphical representation of the grasp space, which is 3-dimensional for this case. The NGRHs also correspond to axis-aligned regions, hereafter called BN regions, that enclose a number of non-FC grasps in the grasp space. Thanks to the symmetry of G, the NGRHs computed for a non-FC grasp are mapped to K axis-aligned regions BN, as shown in Fig. 6b for the non-FC grasp shown in Fig. 6a. Note that both the BI and BN regions are stored by using 2p parameters, representing the lower and upper limit of the correspondent box along each axis of G.

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Fig. 4 Symmetries in the grasp space: a 3-finger frictional grasp of an arbitrary 2D object provides 6 points in the grasp space. The symmetrical sectors are highlighted in each figure.

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b)

Fig. 5 ICRs: a) An FC grasp with the corresponding ICRs on the discretized ellipse; b) Correspondent BI regions in the grasp space

a)

b)

Fig. 6 NGRHs: a) A non-FC grasp on the discretized ellipse; b) Correspondent BN regions in the grasp space

3 Regrasp Planning The regrasp planning problem is formulated as follows: given an initial and a final FC grasps, Gi and G f respectively, find a trajectory for each finger contact on the object surface that allows the grasp change while continuously keeping the FC property (i.e. ensuring the resistance to any external disturbance appeared during the regrasp process). The sequence of movements corresponds to a path between the points Gi and G f in the grasp space G such that all the points in the path are FC grasps. Since a grasp is a combination of n discrete points on the object, the p-dimensional grasp space is discretized to represent the potential grasps. This requires a parametrization of the object surface, which should allow an easy way to identify and label the position of the contact points.

3.1 Parametrization The simplest way to obtain an object discretization is the creation of an uniform grid based on an ordered numeration of the points that represent the boundary of

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the object. This numeration is straightforward for 2D objects, as their boundary is a closed curve and the discrete points on the boundary can be uniquely identified by a single parameter u. The parameter that identifies each point is an ordinal number that starts from an arbitrary origin where u = 1. This numbering procedure produces a well-ordered set, where the neighboring relations (required to compute the ICRs) are obtained very easily: a point with code ui has two neighbors, ui+1 and ui−1 . The exceptions are the points u = 1 and u = N, neighbors in the physical space but not in the ordered set; this can be solved readily by identifying them as neighbors in the parameter space. A similar parametrization for 3D objects can be obtained by using a special subset of 3D objects called superquadrics, which are mainly used for solid object modeling and scene representation, and to generate 3D solids from an unstructured cloud of points [4]. There are four kind of superquadric surfaces: superellipsoids, supertoroids, and superhyperboloids of one or two sheets, but only the first two define closed surfaces. Superellipsoids are defined as ⎞ a1 cosε1 φ cosε2 η −π /2 ≤ φ ≤ π /2 s(φ , η ) = ⎝ a2 cosε1 φ sinε2 η ⎠ , −π ≤ η < π a3 sinε1 φ ⎛

(1)

with the parameters a1 , a2 and a3 being the size factors along the three coordinate axes, and ε1 , ε2 the parameters that determine the shape of the superellipsoid. The exponentiation with εi is a signed power function defined as cosεi φ = sign(cos φ ) |(cos φ )|εi . This compact representation allows the generation of a large amount of shapes, as shown in Fig. 7. In order to get convex shapes, the parameters should be ε1 ≤ 2, ε2 ≤ 2. Supertoroids are defined as ⎞ a1 (a4 + cosε1 φ ) cosε2 η −π ≤ φ < π s(φ , η ) = ⎝ a2 (a4 + cosε1 φ ) sinε2 η ⎠ , − π ≤η <π a3 sinε1 φ ⎛

(2)

where a4 is an additional parameter related to the radius of the supertoroid (Fig. 8). The normal direction to the object surface for both the superellipsoids and supertoroids is given by ⎞ ⎛ 1 2−ε1 φ cos2−ε2 η a1 cos ⎟ ⎜ s(φ , η ) = ⎝ a12 cos2−ε1 φ sin2−ε2 η ⎠ (3) 1 2−ε1 sin φ a3 For these objects, a point on the superquadric surface is considered to have 4 neighbors (up, down, left, and right neighboring points).

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ε2 = 2

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Fig. 8 Supertoroids with different shapes given by the parameters ε1 and ε2 (the size parameters a1 , a2 , a3 and a4 are kept constant)

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3.2 Algorithm The regrasp algorithm is based on the computation of ICRs and NGRHs (which define BI or BN regions in the grasp space) to find the regrasp path. Each BI region is represented as a node in an auxiliary regrasp graph (hereafter called RG). Two nodes representing a pair of contiguous BIs in the grasp space are connected with an arc between them in RG. First, the regrasp algorithm computes the BI regions for Gi and G f . Note that although several BIs are identified for a single FC grasp G, only one BI contains the point Gi or G f ; these BIs are identified as BIi and BI f , respectively. Then, the algorithm takes a sample grasp from G, identifies whether it is FC or not, and builds the corresponding region around it. If it is an FC grasp, then the computed region BI is added to RG, and the contiguity relations for the new BI are tested, i.e. new arcs are added to RG between the nodes representing BIs that intersect each other. If the sample grasp is a non-FC grasp, then all the possible grasps included in the corresponding region BN are labeled as non-FC grasps. The iterative procedure goes on until a continuous path is obtained in the regrasp graph RG (or, equivalently, in the grasp space G). The algorithm is as follows. Algorithm: Regrasp planning 1. For the initial and final grasps, Gi and G f respectively: a. Compute the ICRs that define the regions BIi and BI f b. Label all the possible grasps inside the BIs as FC grasps c. Represent BIi and BI f as nodes in a regrasp graph RG 2. Get a sample grasp Gs from G 3. If Gs has already been labeled, go to Step 2 4. If Gs is FC then a. b. c. d.

Compute the ICRs that define the region BIs Label all the possible grasps inside BIs as FC grasps Represent BIs as a new node in RG Determine the contiguity relations between BIs and the existing BIs in RG

Else (i.e. if Gs is non-FC) a. Compute the NGRHs that define the region BNs b. Label all the possible grasps inside BNs as non-FC grasps 5. If there is a path in RG between BIi and BI f then a. Find the intersections in the grasp space between each pair of contiguous BI regions b. Find the centroids of each intersection zone, Gc c. Compute the regrasp trajectory from Gi to G f passing through all the centroids Gc Else, go to Step 2

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Fig. 9 illustrates the algorithm for a hypothetical 2-dimensional grasp space. It is considered that the order of parameters u in the grasps Gi and G f respects a predefined assignment of fingers. The sampling method used in Step 2 is based on a structured grid that identifies each cell of G with a unique numerical code. The sample selection follows a deterministic sequence that ensures the completeness of the method (a complete deterministic sequence covers the whole grasp space) [16]. Step 5 checks whether there is a path between the initial and final grasp; this is performed using a Dijkstra algorithm applied to the regrasp graph RG. For a quicker convergence of the algorithm (to a solution or to completely cover G and decide that there is no solution at all), Step 5 could be executed every certain number of generated samples. When there is a path between the initial and final grasps, obtained as a sequence of BIs in RG, the regrasp trajectory must be computed in G; different criteria can be used to compute such trajectory (for instance, minimizing the number of finger movements). The regrasp trajectory that this planner provides is based on one-at-a-time movement of the fingers, i.e. the trajectory of the regrasp sequence in the grasp space follows the direction of the axis (Fig. 9a).

4 Examples To illustrate the proposed approach, the algorithm was implemented in Matlab on a Pentium IV 3.2 GHz PC. The first example shows the regrasp planning process

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for a 3-finger frictional grasp on a discretized ellipse, which provides further insight into the algorithm behavior. The second example shows a regrasp computation for a 3D object.

4.1 Example 1 The first example uses the discretized ellipse previously presented in Fig. 3. The FC grasp space explored while searching for the regrasp sequence is shown in Fig. 10a. Fig. 10b shows the regrasp path inside the contiguous BIs that connect the initial and final grasp. As a reference, Fig. 10c shows the whole FC grasp space.

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Fig. 10 Regrasp planning for Example 1: a) FC space explored while searching the regrasp sequence; b) Contiguous BIs that provide the regrasp path between the initial and final grasp; c) Total FC space for the example

In 20 trials of regrasp computations between the same initial and final grasp, the averaged total time ellapsed to get the regrasp sequence was 17.1 s, and 101 evaluations of ICRs and 61 evaluations of NGRHs were required. Table 1 shows these

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results and the averaged results for the exploration of the whole grasp space using the deterministic sampling process [16]. Note that the regrasp computation provides a feasible trajectory in a very short time when compared to the time required for the total exploration of the grasp space. Fig. 11 illustrates the regrasp sequence between the initial and final grasp for this example.

Table 1 Results for the regrasp computation in Example 1 Parameter Regrasp computation Total grasp space time [s] 17.1 2,871 Number of samples 3,115 262,144 ICRs computed 101 566 % of the FC space 66.7 100 NGRHs computed 61 313 % of the non-FC space 97.7 100 % of the grasp space 94.0 100

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Fig. 11 Sequence of grasps for Example 1: a) Initial grasp Gi ; b) and c) Intermediate grasps; d) Final grasp G f

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4.2 Example 2 The second example computes a regrasp trajectory for a superellipsoid, shown in Fig. 12a, with 4 frictional fingers. Fig. 12b shows the final grasp, with the trajectories of the fingers on the object surface. The total computational time required to solve the regrasp problem is 8,875 s.

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Fig. 12 Superellipsoid for Example 2: a) Initial grasp Gi , with its corresponding ICRs (each color represents points within a given ICR); b) Final grasp G f , with the finger trajectories on the object surface (each color represents the trajectory for one finger)

5 Conclusions This paper has presented an approach to generate a regrasp trajectory in the grasp space for discretized objects with any number of fingers. The proposed method is based on the concepts of independent contact regions (ICRs) and sets of nongraspable regions (NGRHs). The approach is based on a discretization of the grasp space, and a sampling method that provides grasp samples, used to build regions of the FC or non-FC space. With a low number of samples, a large portion of the grasp space is covered. The search of a regrasp path is converted into a graph search in a regrasp graph, that keeps trace of the contiguity relations between different portions of the FC space.

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The algorithm presented in the paper has been implemented and some application examples are given. Although the procedures are valid for 3D objects with highdimensional grasp spaces, its application requires an efficient way to save the data, because the grasp space has a high dimensionality (for instance it is 8-dimensional for a 4-finger frictional grasp on a 3D object). The development of an efficient storage method to speed up the application of the proposed algorithm to 3D discrete objects is an interesting line of future work.

References 1. Bicchi, A.: Hands for Dexterous Manipulation and Robust Grasping: A Difficult Road Towards Simplicity. IEEE Trans. Robotics and Automation 16(6), 652–662 (2000) 2. Brock, D.L.: Enhancing the Dexterity of a Robot Hand Using Controlled Slip. In: Proc. IEEE Int. Conf. Robotics and Automation - ICRA, pp. 249–251 (1988) 3. Cherif, M., Gupta, K.K.: 3D In-Hand Manipulation Planning. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems - IROS, pp. 146–151 (1998) 4. Chevalier, L., Jaillet, F., Baskurt, A.: Segmentation and Superquadric Modeling of 3D Objects. J. Winter School of Computer Graphics, WSCG 11(1) (2003) 5. Cole, A.A., Hsu, P., Sastry, S.S.: Dynamic Control of Sliding by Robot Hands for Regrasping. IEEE Trans. Robotics and Automation 8(1), 42–52 (1992) 6. Han, L., Guan, Y.S., Li, Z.X., Shi, Q., Trinkle, J.C.: Dextrous Manipulation with Rolling Contacts. In: Proc. IEEE Int. Conf. Robotics and Automation - ICRA, pp. 992–997 (1997) 7. Han, L., Trinkle, J.C.: Object Reorientation with Finger Gaiting. In: Proc. 2nd IMACS Int. Conf. Computational Engineering in Systems Applications (1998) 8. Han, L., Trinkle, J.C.: The Instantaneous Kinematics of Manipulation. In: Proc. IEEE Int. Conf. Robotics and Automation - ICRA, pp. 1944–1949 (1988) 9. Hong, J., Lafferriere, G., Mishra, B., Tan, X.: Fine Manipulation with Multifinger Hands. In: Proc. IEEE Int. Conf. Robotics and Automation - ICRA, pp. 1568–1573 (1990) 10. Montana, D.J.: The Kinematics of Multi-Fingered Manipulation. IEEE Trans. Robotics and Automation 11(4), 491–503 (1995) 11. Noohi, E., Moradi, H., Ahmadabadi, M.N.: Manipulation Using Wheeled Tips Benefits and Challenges. In: Proc. 39th International Symposium on Robotics, pp. 442–447 (2008) 12. Phoka, T., Niparnan, N., Sudsang, A.: Hierarchical Simplification for 5-Fingered 3D Regrasp Planning on Triangular Mesh Objects. In: Proc. IEEE Int. Conf. Robotics and Biomimetics, pp. 571–576 (2007) 13. Phoka, T., Pipattanasomporn, P., Niparnan, N., Sudsang, A.: Regrasp Planning of FourFingered Hand for a Parallel Grasp of a Polygonal Object. In: Proc. IEEE Int. Conf. Robotics and Automation - ICRA, pp. 791–796 (2005) 14. Roa, M.A., Suarez, R.: Computation of Independent Contact Regions for Grasping 3D Objects. IEEE Trans. Robotics and Automation 25(4), 839–850 (2009) 15. Roa, M.A., Suarez, R., Rosell, J.: Grasp Space Generation Using Sampling and Computation of Independent Regions. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems - IROS, pp. 2258–2263 (2008) 16. Roa, M.A., Suarez, R., Rosell, J.: Influence of the Sampling Strategy on the Incremental Generation of the Grasp Space. In: Proc. 11th Int. Conf. on Climbing and Walking Robots - CLAWAR, pp. 812–819 (2008)

Modeling of Two-Fingered Pivoting Skill Based on CPG Yusuke Maeda and Tatsuya Ushioda

Abstract. Two-fingered pivoting is a typical example of human dexterous manipulation interacting adaptively with the environment. In this paper, we construct a skill model of two-fingered pivoting manipulation based on CPG (Central Pattern Generator) with five neurons. The CPG drives a virtual hand, which consists of a wrist, a palm, a thumb and an index finger, rhythmically to achieve robust pivoting through the interaction with the environment. Its adaptability is demonstrated in dynamic simulation of box pivoting. The virtual hand successfully adapts to environmental changes such as sudden decrease of the friction coefficient of the floor.

1 Introduction It is known that rhythmical adaptive motions found in biped walking of humans and quadruped walking of other animals are generated by CPG (Central Pattern Generators) [2]. The activity of the CPG is easily entrained by external periodic signals, which enables adaptive motion control by using sensory feedback from the environment. It is natural that the idea of the CPG has been applied primarily to the locomotion control of robots. However, the CPG is applicable not only to robotic locomotion but also to robotic manipulation; this is because human multifingered manipulation may be realized by the CPG [8]. Previous studies applied CPG-based control only to a few kinds of manipulation, such as crank rotation by a three-link manipulator [3] and cylinder rotation by a multifingered hand [4, 5]. Graspless manipulation [1], a method to manipulate an Yusuke Maeda Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501 Japan e-mail: [email protected] Tatsuya Ushioda Lenovo Japan Ltd.

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object by utilizing contacts between the object and the environment, requires motion control that adapts to environmental situations due to the existence of the contacts between the object and the environment. Therefore CPG-based robot control for graspless manipulation is promising, but there have been no such studies. In this study, we focus on pivoting [1], a typical graspless manipulation, and construct a CPG-based model for two-fingered pivoting skill.

2 Pivoting Task In pivoting operation (Fig. 1), a vertex of the manipulated object is used as a pivot around which the object is rotated. The object is in one-point contact with the ground in pivoting and therefore the manipulator does not have to support all the weight of it. Thus, pivoting is suitable for manipulation of heavy objects. Repeating small rotations around two vertexes of the object by turns, the object can be moved to any locations. The pivoting operation can be analyzed into the following steps: 1. Raise the vertex B and make the object be in point contact with the floor at the vertex A. 2. Rotate the object around an axis that passes through the vertex A. 3. Put the vertex B down and make the object be in line contact with the floor at the edge AB. 4. Exchange the vertex A and B, and go back to the step 1. Smooth pivoting requires rhythmical and adaptive motion through the interaction with the environment. There are a few studies on robotic pivoting. Aiyama et al. studied vision-based two-fingered pivoting by a robot hand [1]. Yoshikawa and Watanabe presented a dynamic control law for pivoting by two-fingered robot hands with soft fingertips [10]. Yoshida et al. studied whole-body pivoting by a humanoid robot [9]. However, they are based on not CPG but the conventional scheme of robot control.

Fig. 1 Pivoting

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Fig. 2 Hand model

3 Hand Model We use a hand model composed of a palm, an index finger and a thumb (Fig. 2). For simplicity, we ignore kinematic constraint on the palm by the arm. The index finger is composed of three revolutionary joints: MP (metacarpophalangeal), PIP (proximal interphalangeal) and DIP (distal interphalangeal). The thumb is composed of five revolutionary joints: CMC1 (carpometacarpal, abduction/adduction), CMC2 (flexion/extension), MP1 (abduction/adduction), MP2 (flexion/extension) and IP (interphalangeal). In order to simplify the motion of the hand model, we assume that the index finger and the thumb are in charge of the above step 1 and 3, and the wrist that moves the palm is in charge of step 2. The control references of the fingers and the wrist are determined by the output of CPGs as shown in the next section. The index finger and the thumb pinch the object and put it up and down. The wrist rotates the palm around a vertical axis, and the translational motion of the palm is passively determined in pivoting operation.

4 Design of CPG 4.1 Neuron Model We use Matsuoka’s model [7] for neurons that compose CPGs. The dynamics of the i-th neuron is given by the following equations: n 1 x˙i + xi = − ∑ ai j y j − bi zi + ui + Si Tri j=1

(1)

1 z˙i + zi = yi Tai

(2)

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yi = max(0, xi ),

(3)

where xi is the internal state of the neuron, yi is the output, zi is the fatigue state, ui is the external input, Si is the feedback input, ai j is a inhibitory connection coefficient from the j-th neuron to the i-th neuron, bi is a fatigue coefficient, and Tri and Tai are time constants.

4.2 CPG Configuration and Its Adaptability Successful pivoting requires good synchronization of the following: • Rotation of the wrist. • Lifting up and down of the object. • Switching of the pivot vertex. In this study, we control the hand model with CPGs in order to synchronize the above motions and tune the amplitude of pivoting step for adapting to the environmental situations. We assume that the following information are available for motion control: • Rotation angle of the wrist. • Which vertex is grounded. • Presence or absence of slippage at the pivot. We use two CPGs for driving the hand model: wrist CPG for driving the palm, and finger CPG for driving the index finger and thumb (Fig. 3). These two CPGs have no direct signal connections, but they can mutually entrain through the physical interaction among the palm, the fingers and the environment. We design the CPGs so that their mutual entrainment enables pivoting of the object. For reference in constructing a CPG-based pivoting model, we measured motions of human hands in pivoting operation with a data glove, a 6D positioning sensor and a 6-axis force sensor (Fig. 4) [6].

Wrist CPG The wrist CPG consists of three neurons: N1 , N2 and N3 (Fig. 3). The reference of the wrist angle, θwrist,ref , is determined from the outputs of N1 and N2 as follows:

θwrist,ref = p1 y1 − p2y2 − θ0 ,

(4)

where θ0 is the neutral angle of the wrist, and p1 and p2 are constant parameters. The external stimuli inputted to the neuron N1 and N2 are given by the following equation: (5) Si = ki si + sV (i = 1, 2),

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where k1 and k2 are constant parameters, and s1 and s2 are grounding signals as follows:  s1 = 1 and s2 = 0 when the index-finger-side vertex is grounded, (6) s1 = 0 and s2 = 1 when the thumb-side vertex is grounded. The CPG outputs of N1 and N2 (y1 and y2 ) are entrained to the timing of exchange of grounded vertexes, which enables appropriate control of the rotation of the wrist. The amplitudes of y1 and y2 are dominated by sV in (5), which is given by: sV = p3 y3 ,

(7)

where p3 is a positive constant parameter. The neuron N3 is placed to tune the amplitudes of the outputs of the neuron N1 and N2 according to slippage at the grounded vertex in pivoting. The excitation of N3 is controlled by the feedback input S3 as follows: (8) S3 = k3 s3 , where k3 is a negative constant parameter, and s3 is a slippage signal as follows:  s3 = 1 when slippage occurs, (9) s3 = 0 otherwise. When the friction coefficient of the floor is large enough to suppress slippage, N3 is excited by the constant input u3 . On the other hand, when slippage occurs, inhibitory stimuli (S3 ) is inputted to N3 and the output y3 becomes small. Because y3 is used as excitatory input to N1 and N2 , it affects the amplitudes of the outputs of these neurons. When slippage occurs, N3 is inhibited according to the time of the slippage and the wrist motion becomes small. This helps to achieve stable pivoting on a slippery floor and adaptive pivoting to the changes of a friction coefficient of the floor.

Finger CPG The finger CPG consists of two neurons, N4 and N5 (Fig. 3). The output of N4 (y4 ) is used to calculate the reference of MP and PIP joint angles of the index finger. The output of N5 (y5 ) is used to calculate the reference of CMC joint angles of the thumb. With the measured motions of human pivoting [6] as a guide, we determined the mapping between the CPG outputs and the reference of finger joint angles by trial and error as follows:

θindex,MP,ref = 1.256y4 − 0.833 [rad] θindex,PIP,ref = −1.256y4 − 0.542 [rad] θindex,DIP,ref = 0.785 [rad] θthumb,CMC1,ref = 1.57y5 + 0.833 [rad]

(10) (11) (12) (13)

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θthumb,CMC2,ref = 0.393y5 + 0.833 [rad] θthumb,MP1,ref = 0.523 [rad] θthumb,MP2,ref = 0.0938 [rad] θthumb,IP,ref = 0.785 [rad].

(14) (15) (16) (17)

All the joints are compliance controlled and the motion of the fingers causes lifting up and down of the object. As the wrist angle becomes large, the neuron driving the pivot-side finger is inhibited and that driving the opposite-side finger is excited as follows: S4 = k4 (θwrist − θ0 ) S5 = −k5 (θwrist − θ0 ),

(18) (19)

where θwrist is the current wrist angle, and k4 and k5 are constant parameters. The finger CPG causes finger motion according to the wrist angle, which leads to the exchange of pivot vertexes. Then the exchange makes the wrist CPG be entrained. Thus the synchronization between the finger CPG and the wrist CPG is achieved and therefore pivoting of the object is realized. When slippage occurs, finger motion becomes small according to the entrainment to the wrist CPG. That helps to suppress the slippage.

5 Simulation of Pivoting 5.1 Simulation Conditions We performed dynamic simulation of two-fingered pivoting using the above CPGbased model on an open source simulation engine, ODE (Open Dynamics Engine).ODE (version 0.9) can simulate friction, but has no functionality to represent soft finger contacts (rotational frictional contacts). Thus we placed pseudo contacts around the original contact point (Fig. 5). Here we used 15 pseudo contacts for each original contact point. The manipulated object is a cuboid of 310 [g] and its size is 50 × 50 × 100 [mm]. Table 1–5 show the parameters of the neurons of the CPGs. The joints of the hand are compliance controlled with limitations on maximum torques as shown in Table 6 in the simulation. original contact point additional pseudo-contacts Fig. 5 Surface contact approximated by point contacts

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Table 1 Neuron Parameters of Wrist CPG N1 N2 N3

Tri [s] Tai [s] bi ui 0.4 1.2 2.5 0.5 0.4 1.2 2.5 0.5 12 2.4 0.25 1.25

Table 2 Neuron Parameters of Finger CPG Neuron Tri [s] Tai [s] bi ui N1 0.4 1.2 2.5 0.5 0.4 1.2 2.5 0.5 N2 12 2.4 0.25 1.25 N3 0.6 1.2 7 1 N4 0.6 1.2 7 1 N5 Table 3 Connection Coefficients of Wrist CPG (ai j ) ai j N1 N2 N3

N1 0.0 1.5 0.0

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N3 0.0 0.0 0.0

Table 4 Connection Coefficients of Finger CPG (ai j ) ai j N4 N5 N4 0.0 1.5 N5 1.5 0.0 Table 5 Other CPG Parameters p1 p2 p3 k1 k2 k3 k4 k5 θ0 0.471 [rad/s] 0.471 [rad/s] 40 8 8 −80 8 [1/rad] 8 [1/rad] 0.6 [rad] Table 6 Maximum Finger Torque [Nm] Index finger Thumb MP PIP DIP CMC1 CMC2 MP1 MP2 IP 10 6 5 12 9 7.5 5 3

5.2 Simulation Results We performed simulations of pivoting when the friction coefficient between the object and the floor is constant. In this case, pivoting can be performed stably. Fig. 6 shows screenshots of the simulation when the friction coefficient is 0.5.

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Next, we checked the adaptability of pivoting by causing sudden decrease of the friction coefficient during pivoting operation. Concretely, we decreased the friction coefficient from 0.5 to 0.01 uncontinuously at 8 [s] after the start of pivoting. For comparison, we constructed another CPG model without adaptation functionality as a control. It is almost identical to the proposed model but k3 = 0, which disables the adaptability of pivoting step in the proposed model. The wrist angle, the outputs of the neurons of the wrist CPG, and the status of slippage in simulations are shown in Fig. 7 (proposed CPG model) and Fig. 8 (CPG model without adaptability). The shaded areas in the figures indicate that slippage occurs. y3 is around 1 both in the proposed and control model when the friction coefficient is large. However, after the sudden decrease of the friction coefficient, the excitation of N3 is suppressed only in the proposed model. The control model with k3 = 0 continues to perform pivoting allowing slippage (Fig. 7). On the other hand, in the proposed model, y3 is suppressed according to the slippage, which leads to small wrist motion. Because of this, slippage is suppressed. y3 becomes smaller as the slippage occurs, and the amplitude of θwrist becomes smaller until the S3 and u3 are balanced. After the balance, slippage can be found only around the instants of the exchange of pivot vertexes (Fig. 8). Thus, the CPGs worked as designed and achieved adaptive pivoting.

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6 Conclusion In this paper, we modeled a skill of two-fingered pivoting with interactions with the environment by using CPGs. The constructed CPGs cause mutual entrainment and achieve rhythmical and stable pivoting motion in dynamic simulation. The CPGbased model also shows adaptability of pivoting step to the sudden decrease of the friction coefficient. Future work should address the improvement of our CPG model based on the comparison between simulation with our CPG model and measured human pivoting. Developing CPG models for skills of other types of graspless manipulation such as tumbling is also necessary. Acknowledgements. This work was partly supported by The Ministry of Education, Culture, Sports, Science and Technology of Japan, Grant-in-Aid for Scientific Research on Priority Areas (Mobiligence), No. 18047010.

References [1] Aiyama, Y., Inaba, M., Inoue, H.: Pivoting: A new method of graspless manipulation of object by robot fingers. In: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 136–143 (1993) [2] Ijspeert, A.J.: Central pattern generators for locomotion control in animals and robots: A review. Neural Networks 21(4), 642–653 (2008) [3] Kondo, T., Somei, T., Ito, K.: A predictive constraints selection model for periodic motion pattern generation. In: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 975–980 (2004) [4] Kurita, Y., Ueda, J., Matsumoto, Y., Ogasawara, T.: CPG-based manipulation: Generation of rhythmic finger gaits from human observation. In: Proc. of 2004 IEEE Int. Conf. on Robotics and Automation, pp. 1209–1214 (2004) [5] Kurita, Y., Nagata, K., Ueda, J., Matsumoto, Y., Ogasawara, T.: CPG-based manipulation: Adaptive switchings of grasping fingers by joint angle feedback. In: Proc. of 2005 IEEE Int. Conf. on Robotics and Automation, pp. 2528–2533 (2005) [6] Maeda, Y., Ushioda, T.: Hidden markov modeling of human pivoting. J of Robotics and Mechatronics 19(4), 444–447 (2007) [7] Matsuoka, K.: Sustained oscillations generated by mutual inhibiting neurons with adaptation. Biological Cybernetics 52(6), 367–376 (1985) [8] Taguchi, H., Hase, K., Maeno, T.: Analysis of the motion pattern and the learning mechanism for manipulating objects by human fingers. Trans. of Japan Soc. of Mechanical Engineers (Series C) 68(670), 1647–1654 (2002) (in Japanese) [9] Yoshida, E., Poirier, M., Laumond, J.P., Kanoun, O., Lamiraux, F., Alami, R., Yokoi, K.: Pivoting based manipulation by a humanoid robot. Autonomous Robots 28(1), 77–88 (2010) [10] Yoshikawa, T., Watanabe, T.: Dynamic control of soft-finger hands for pivoting an object in contact with the environment. In: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 324–329 (2000)

Chapter II

Micro/Macro Assembly and Disassembly Summary by Sukhan Lee

The technologies for assembly and disassembly in manufacturing are rapidly advancing toward meeting the new requirements and challenges emerged from the following fronts: 1) The need to fabricate a high complexity of micro systems integrated and packaged in 3D with various heterogeneous parts, components, and interconnections, including electrical, optical, mechanical as well as fluidic means. 2) The drive to establish intelligent/smart assembly and disassembly for manufacturing to reach a new level of autonomy, quality and productivity by taking full advantage of the advancement in robotics and machine intelligence. This chapter, Micro/Macro Assembly and Disassembly, have chosen 6 papers to present, covering the state-of-the-art technologies in assembly and disassembly developed for the above two fronts: the first 3 for micro assembly and the latter 3 for intelligent assembly and disassembly. The first paper entitled “Assembly of 3D Reconfigurable Hybrid MOEMS through Microrobotic Approach,” co-authored by Kanty Rabenorosoa, Cedric Clevy, Philippe Lutz, Sylwester Bargiel and Christophe Gorecki, presents a 3D micro assembly station as well as its assembly strategy for the assembly of reconfigurable free space micro optical benches. The micro assembly station presented consists of two robotic manipulators: one for coarse positioning of assembly and optical parts, such as holders, ball lenses, etc., with a piezo gripper at the proximity of the substrate, and the other for fine positioning the substrate and correcting the trajectory during the assembly and guiding processes. The fabrication of a micro optical bench is done by assembling the micro-fabricated holders carrying optical parts on a substrate based on tele-operation. The second paper entitled “Modified Assembly Systems and Processes for the Mounting of Electro-Optical Components,” coauthored by J. Franke and D. Craiovan, deals with the electro-optical printed circuit boards capable of indirect optical coupling as well as of installing electro-optical surface mounted devices. Not only the exact alignment between the light beam and the coupling element such as waveguides under multi-layer indirect coupling but also the surface mounting technology for packaging VCSEL directly onto an electro-optical PCB are presented. The third paper entitled “Factory Level Logistics and Control Aspects for Flexible and Reactive Microfactory Concept,” coauthored by Eeva Jarvenpaa,

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Riku Heikkila, and Reijo Tuokko, addresses the issues related to the realization of a large scale of integrated micro factory for the assembly of multi-part products. A special attention is given to the logistic aspects as well as the control concepts supporting flexibility and dynamic re-configurability of the system, where a micro factory system is modeled as a holonic manufacturing system enabling reactivity to sudden changes and failures. An integrated micro assembly factory with vision based flexible feeding, product invariant carriers and modular belt conveyors at the factory level is introduced. The forth paper entitled “Development of Structured Light Based Bin Picking System using Primitive Models,” coauthored by Jong-Kyu Oh, KyeongKeun Baek, Daesik Kim, and Sukhan Lee presents a system for automatically picking up parts in a bin that are randomly stacked in 3D. In order to handle the parts with no distinct geometric features and/or textures as the landmarks identifiable by cameras, active depth imaging based on structured light is used instead. To extend its applicability to a wide variation of parts without paying too much on building individual reference models, a generic model represented by a combination of surface patch primitives is introduced for recognition and pose estimation. The fifth paper entitled “Airframe Dismantling Optimization for Aerospace Aluminum Valorization,” coauthored by Julie L. Viau, Pierre Baptiste and Christian Mascle addresses a method of disassembly sequence planning for airframe dismantling based not only on the classification of connection types and fasteners but also on the aluminum recycling and recovery in such a way as to optimize the profitability of aircraft dismantling process by linear programming. The sixth paper entitled “Monitoring of Co-Operative Assembly Tasks: Functional, Safety and Quality Aspect,” co-authored by J. Koskinen, T. Heikkila, and T. Pulkkinen deals with human-robot cooperative assembly where a human operator and a collaborative robot or Cobot share the workspace to carry out assembly tasks. A generic assembly model is described for collaborative assembly tasks where two heavy parts are to be joined together, including functional, safety and quality aspects associated with cobot control as well as with software architecture.

Assembly of 3D Reconfigurable Hybrid MOEMS through Microrobotic Approach Kanty Rabenorosoa, Sylwester Bargiel, C´edric Cl´ecy, Philippe Lutz, and Christophe Gorecki

Abstract. Micro-assembly has been identified to be a critical technology in the microsystems technology and nanotechnology. Increasing needs of MOEMS (MicroOpto-Electro- Mechanical Systems) for microsystems conducts to development of new concepts and skilled micro-assembly stations. This paper presents a 3D microassembly station used for the reconfigurable free space micro-optical benches (RFSMOB) which are a promising type of MOEMS. Designed parts of RFS-MOB are assembled by using the developed micro-assembly station. The flexibility of the micro-assembly station provides the possibility to manipulate a variety of microcomponents. The RFS-MOB design enables to reduce adhesion forces effects during releasing operations. Experimental results are shown and validate the effectiveness of the micro-assembly station and micro-assembly strategies.

1 Introduction Over the last few years the request for miniature objects and devices has continuously increased. So the need for MEMS (Micro Electro Mechanical Systems) and MOEMS (Micro Opto Electro Mechanical Systems) in the field of telecommunication and sensor technology [16] has become more and more important. Miniaturization of optical components and the assembly of various components constitute Kanty Rabenorosoa · C´edric Cl´ecy · Philippe Lutz FEMTO-st Inst., UMRS CNRS 6174 - UFC/ENSMM/CNRS Automatic Control and Micro-Mechatronic Systems depart.(AS2M department), 24 rue Alain Savary, 25000 Besancon, France e-mail: {rkanty,cclevy,plutz}@femto-st.fr Sylwester Bargiel · Christophe Gorecki FEMTO-st Inst., UMRS CNRS 6174 - UFC/ENSMM/CNRS Micro Nano Sciences, and Systems depart. (MN2S department), 32 avenue de l’Observatoire, 25000 Besancon, France e-mail: {sbargiel,cgorecki}@femto-st.fr

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the principal challenge in hybrid MOEMS manufacture. For fabricating MOEMS, Free-Space Micro-Optical Benches (FS-MOB) represent a very promising solution. They consist in the mounting of several different micro-optical components coming from various sectors of manufacturing on a same substrate. The relative position of these components with the alignment of optical path enables the fabrication of complex hybrid products. Indeed, optical components (lenses, mirrors, fiber holders, detectors, beam splitters) are microfabricated using ”simple”, well known and reliable processes [10, 17]. These components are then assembled together permitting the fabrication of complex 3D microstructures [15, 5, 11, 9]. The understanding of the microworld phenomena [6, 13] and the automation of the micro-assembly tasks are currently under investigation. Recent results in the field of multiscale assembly, especially serial precise assembly demonstrate the validity of a new approach for fabricating complex 3D MOEMS based on micro-assembly [3, 4]. These results are accompanied by the development of micro-assembly stations which integrate flexibility, modularity, precision and repeatability. High yield assembly of microobjects depends on the compatibility of tolerances between micro-assembly station and micro-object dimensions. The micro-assembly station bring an interesting solution for fabricating 3D MOEMS which integrate some RFS-MOB and enable the design of 3D complex optical path. Due to the microfabrication tolerances and the inaccuracy of optical parameters coming from technological processes, the development of new micro-assembly station able to compensate them through the assembly of reconfigurable free space micro-optical benches (RFS-MOB) is proposed. This station enables the manipulation of generic components of RFS-MOB using active microgripper associated to a robotic micro-assembly system. Active gripping ensures reversible locking systems (which are not possible with passive gripping) and a fine control of tasks like [14]. For the precise control of position and the alignment of optical path, the 3D micro-assembly station comprises of 8 degrees of freedom (DOF) arranged into 2 manipulators with a vision system. In this paper we propose an original micro-assembly station equipped with an active microgripper. In the following, the concept of RFS-MOB is detailed in Section 1. Section 2 describes the robotic micro-assembly system. Section 3 presents the experimental results. Finally, Section 4 provides some conclusions.

2 The Concept of RFS-MOB The concept of RFS-MOB is composed of two bulk-micromachined silicon parts: a non-movable substrate (base), and a removable MOEMS chip (holder) with desired optical component (see Figure 1, 2). The hybrid free-space optical system can be built on the substrate by the assembly of the individual holders equipped with optical function on the precisely formed rails using an active microgripper . The substrate is a reference part for the others parts of the optical system and allows their alignment on micromachined rails, along the optical axis. The substrate can be designed for the simplest configuration with one straight rail or the complex form (two or three perpendicular rails). It is composed of two anisotropically etched

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V-grooves (guiding of movement) and a central runner with two vertical V-grooves (see Figure 4). Such a construction of the rail ensures the well defined surfaces of reference for the other parts of MOB. This surface permits the optical path alignment in Z direction which improve the performance of the optical bench (reduction of optical loss).

Fig. 1 RFS-MOB concept: general view

Fig. 2 RFS-MOB concept: Holder assembled on the substrate

The holder with appropriate optical component can be assembled on such a rail using a microgripper, accurately positioned by a 3D micro-assembly station. In such a configuration, the holder contacts with the rail by two protruding grooves with triangular cross-section, compatible to the guiding V-grooves. In order to fasten the holder in a chosen position onto the rail, it contains a mechanical snap connector with two folded springs, which shape is adjusted at the end to the shape of the

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vertical V-grooves (see Figure 1). Once the springs are pressed by the microgripper (see Figure 3), the holder can be inserted directly into the rail, adjusted to the optical setup, and then fastened by the release of the springs.

Fig. 3 (1) Gripping principle of the holder by the microgripper and (2) releasing in the groove

The mechanical contact of the holder with the substrate occurs between its two protruding rails and the V-grooves on the base. The snap connector is also in contact to a rhomboid-shaped runner formed on the carrier’s back side and they ensures the lock in Z direction (see Figure 2). Hence, once the adjustment of the all holders is finished, UV-curable glue can be used to definitely preserve their positions on the rail. The dimensions of the bench is 2.5 cm x 1 cm. The dimensions of the holder is 800 μ m x 1350 μ m x 50 μ m, and the cross section of the spring is 10 μ m x 50 μ m. The results of fabricated rails are shown in Figure 4. The substrate is obtained after the following microfabrication process: 1. 2. 3. 4. 5. 6. 7. 8.

Thermal oxidation of the double side wafer with 1.4 μ m, Photolitography 1 of the back side and SiO2 etching in BHF, KOH etching in back side for forming 100 μ m membrane, Thermal oxidation of the back side with 1.0 μ m, Photolitography 2 of the top side and SiO2 etching in BHF, Photolitography 3 of the top side and DRIE etching of 100 μ m membrane, After stripping and cleaning, silicon etching in KOH, SiO2 stripping in HF.

The results of fabricated holders are shown in the Figure 5. The available components are mirror, lens holder, and circular aperture. The substrate is obtained after the following microfabrication process: 1. 2. 3. 4. 5. 6.

Thermal oxidation of the double side wafer with 1.2 μ m, Photolitography 1 of the back side and photolitography 2 of the top side, SiO2 etching in BHF and photoresist stripping, DRIE etching of the top side, Silicon etching in KOH by protected the top side by a chuck, SiO2 stripping in BHF.

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Fig. 4 Microfabricated substrate using DRIE and KOH etching: (1) general view of the wafer, (2) bench with straight rail after dicing, (3) the rail with a ruler, and (4) view of V-groove and the vertical V-groove.

Fig. 5 SEM pictures of the designed lens holder

More details about the design and the microfabrication process are available in [1]. At the end of the process, the holders are maintained by a tether designed in [8]. The lens holder receives a special design for integrating a lens grip. A lens grip is two flexible cantilever equipped at the end by double hooks (see Figure 5). From a micro-assembly point of view, the RFS-MOB concept helps to reduce adhesion effects during releasing the holder. When the holder is locked into the rail (see Fig. 6), protruding rail and V-grooves are in contact, like the leg and the vertical V-grooves. These contacts are blocking the lateral movement of the holder during the releasing which occurs when the adhesion force is not dominated by another forces. In this sense, the RFS-MOB design constitutes an interesting strategy to overcome adhesion forces during releasing tasks. An attractive feature of the presented MOB technology is the ability to adjust the position of every optical part by active way. Thus, the inaccuracy of the optical

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Fig. 6 Effect of blocking during releasing the holder

parameters coming from technological processes, e.g. shift in a focal length of microlens, can be compensated. This feature makes the MOB an reconfigurable tool to build the optical systems at different level of complexity.

3 Micro-assembly Challenge and Micro-assembly System 3.1 Micro-assembly Challenge The micro-assembly of micro-optical benches needs a precise and an adapted microassembly station. In addition, the features of this station must match the required DOF for micro-optical bench assembly. For these reasons, the developed microassembly station integrates: 1. Coarse and fine positioning stages. The ability of the micro-assembly station for positioning the holder along the rail depends on the range of the stage. The fine positioning is used for compensating positioning errors from of the coarse manipulator,

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2. Two rotation stages (pitch and yaw) for micro-object orientation on the bench (rolling is not necessary because of the design of the components), 3. Views of assembly sequence (top view and side view) to enable teleoperated assembly, 4. Adapted end-effector of the microgripper which have a suitable dimension for the holder and ball lens gripping, 5. Reduction of the effects of adhesion forces during micro-assembly.

3.2 Micro-assembly System 3.2.1

Description of the Workcell

In order to perform serial assembly, the workcell comprises a robotic structure, vision system, and a microgripper. The proposed 3D microrobotic assembly system is a structure with eight DOF motorized stages arranged into two robotic manipulators. The kinetic scheme of this robotic structure is presented in Figure 7.

Fig. 7 The kinematic sheme of the microrobotic structure

The manipulator M1 is a large space positioning robot with four DOF. It is composed of linear coarse positioning stages from Physik Instrumente - M112.1 DG (with 25 mm of travel range) and a rotation stage SmarAct - SR-3610-S (with 1.1 μ ◦ of resolution). This robot permits to manipulate holder (break tether, pick, move, align to the groove and guide the holder) and other optical components. The manipulator M2 constitutes a support of the substrate and is composed of fine positioning robot with four DOF based on P-611.3 NanoCube XYZ Piezo Stage with nanometric resolution (with 100 μ m of travel range) and a rotation stage SmarAct -SR-3610-S. This robot carries the substrate during the assembly process and corrects the trajectory during the guiding operation. All of these stages are closed loop controlled. The robotic configuration is shown in Figure 8.

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Fig. 8 Developed micro-assembly station for micro-optical benches

3.2.2

High Voltage Piezogripper

For gripping holders, ball lenses,.. and ensuring reconfigurability, actuated microgripper which is the MMOC (Micromanipulator-Microrobot-On-Chip) piezogripper developed in FEMTO-ST Institute [12] was chosen. It has two active fingers and two DOF for each finger. Both fingers of the microgripper can independently move along Y and Z. It permits a stroke of 320 μ m in open-close motion(Y) and 400 μ m in up-down motion (Z). The resolution of the piezo actuator can attain 1.6 μ m/V consequently submicrometric accurate motions are achievable. In reference to [2], [7], the modularity of this microgripper is largely proved. Appropriate finger tips (tools) are chosen and installed on the MMOC. The microgripper is mounted at the end of manipulator M1 (see Figure 8). The grasping is done on the flexible part of the holder shown in Figure 3. This microgripper has to provide enough gripping forces during micro-assembly. The blocking forces is up to 100 mN in Y direction and 15 mN in Z direction for 100 V . 3.2.3

Control System of the Micro-assembly Station

The micro-assembly station is also equipped with a visualization system. It helps the alignment for entering in the groove, guiding, and supervising the whole

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assembly process (teleoperated mode in these works). This micro-assembly station is controlled via AP2M (French acronym of “Application for Controlling the MicroManipulation”), a home made software based on Borland C++ Bulder 6.0. AP2M is a software which makes a link between human (operator) and movable parts of the station. The modularity of this software enables the rapid development of the station. An AP2M module is developed for each element of the micro-assembly station (stages, microgripper,...). It enables teleoperated assembly by a joystick and automated pick and place tasks. Due to the flexibility of the AP2M, the integration of new devices (force sensor, position sensor, camera) is simplified.

3.3 Micro-assembly Sequence 3.3.1

General Assembly Sequence

The general assembly sequence gives a global view of tasks done after microfabrication of microparts. For holders, there are made by using a 4 Inches SOI wafer and at the end of the microfabrication process, each holder is maintained by the tether on the wafer. Substrates are done on 4 Inches wafer and separated by dicing. The substrate is firstly brought on the M2 robot and is used like a workplane during the holder assembly (Figure 9-1). Tethers are broken and the holder is picked and put on the holder storage (Figure 9-2). After that, the holder storage is put on the substrate and the assembly of the holder on the substrate is following (Figure 9-3).

Fig. 9 Description of the general assembly sequence

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Detailed Assembly Sequence

Micro-assembly process is the sequence of tasks to operate for obtaining complex heterogeneous devices. The sequence of tasks takes into account the specificities of the parts. The substrate is the reference part during assembly process of RFSMOB. Holders are sequentially assembled on it and a precise position control is very important for ensuring the optical features of the assembled systems. Each holder assembled to the substrate goes through six steps, which are: (1) the holder is picked by a microgripper, (2) the holder is removed from the chip wafer, (3) the holder is moved and rotated, (4) the holder is aligned to the groove and inserted on input port of the guiding rail, (5) the holder is guided on the rail, and (6) the holder is released. During the guiding, two strategies can be employed: - The contact between protruding rails and V-grooves is maintained. The overshoot of the contact force has to be controlled to avoid the sliding of the micro-parts between the microgripper and the breaking of the flexible part. - The contact is avoided and control laws which take into account forces during micromanipulation are developing. In this sense, force feedback during the guiding task is investigating. For the teleoperated mode, the operator manages the guiding task using views of cameras.

4 Experimental Results 4.1 Holder Assembly The assembly sequence is tested on the micro-assembly station. This experiment is done by teleoperated mode by using the joystick. The assembly sequence is followed and first results of assembly are shown in the Figure 10. During the releasing of the holder, the effectiveness of the blocking force for reducing the adhesion force is observed. The Z-lock rail and two folded springs ensure the fastening of the assembled holder.

4.2 Ball Lens Assembly The ball lens has to be inserted on the lens holder designed with the ball lens grip. The diameter of the ball lens is about 254 μ m. Two strategies can be used for the ball lens assembly: - The ball lens is previously put on the lens holder and after that the lens holder is assembled on the substrate. - The ball lens is put on the lens holder when this one is assembled on the substrate.

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Fig. 10 Assembly sequence for holder micro-assembly: (1) Pick of the mirror on the flexible part (springs), (2) Move on space after rotation, (3) Guide in the groove, (4) Release

Fig. 11 Assembly sequence of ball lens using a microgripper: (1) the ball lens is gripped by the microgripper and align to the lens grip, (2) the ball lens is inserted to the lens grip, and (3) the ball lens is released and maintained into the lens grip

This second strategy is chosen because the right position of the holder in the substrate enables a good accessibility on the lens grip. For assembling the ball lens in the lens holder, the ball lens is picked by the microgripper and inserted into the lens grip. The ball lens is correctly maintained when it is gripped on the center. The sequence of ball lens insertion in the lens grip is shown in Figure 11.

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4.3 Assembled Demonstrator First results conduct to the fabrication of a demonstrator. The assembled demonstrator is composed of a mirror and a lens holder. The ball lens is put on the lens holder after the assembly of the holder on the substrate. This demonstrator can be used for testing the focal length of the ball lens by changing the distance between the mirror and the lens holder. The distance which minimizes the spot size form the fiber laser source corresponds to the focal length. The complete view of the RFS-MOB demonstrator is shown in Figure 12.

Fig. 12 SEM pictures of the assembled demonstrator

4.4 Positioning Accuracy Positioning accuracy of components is an important criteria which influences optical microbenches. The developed micro-assembly station enables 1 nm resolution along x, y, and z and 3 μ ◦ in α and β . Fine positioning of components can be done by active positioning, in other words, optimized position corresponds to a minimal optical loss. The reversible locking enables to perform this step.

4.5 Observed Difficulties During Micro-assembly During the micro-assembly of the demonstrator, the principal difficulties concern the control of the guiding task. Indeed the contact appears between V-grooves of the substrate and protruding grooves of the holder. During the contact, the overtaking friction can break the flexible part of the holder. The use of force control can

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reduce the risk of breaking. In this case, the integration of force sensors in the microassembly station constitutes a future works. Additionally, the microscale forces like electrostatic, and pull-off force (included van der Waals, and capillary force) have to be taken into account and conducts to the development of microscale hybrid force position control for precise control of interactions.

5 Conclusion In this paper, we have proposed a micro-assembly station which permits the assembly of 3D MOEMS. The RFS-MOB is introduced to bring a response to a new generation of complex assembled MOEMS. Indeed the microfabrication of elementary optical components uses ”simple”, well known and reliable processes. This station enables the assembly of new micro-optical systems based on the micro-assembly of holders with optical features and the substrate. The use of active microgripper is a proposed solution for ensuring the reconfigurability of micro-benches. The micro-assembly system is developed and an eight DOF robotic configuration with nanometric resolution ensures the precise positioning of components. The principle is validated through successful assembly sequence of holders on the substrate in teleoperated mode. A demonstrator MOEMS with a mirror and a lens holder is assembled. Other type of optical components can be designed like diffractive lens, beam splitter, and others, which can also be assembled with the same micro-assembly station. At the end, complete MOEMS like microspectrometer, 3D confocal miniaturized microscope, and miniaturized goniometer can be obtained. The main advantage of the RFS-MOB concept is the reconfigurability and the use of generic optical components for obtaining rapidly complex and new hybrid microsystems. According to the reconfigurability of RFS-MOB, it can also be used like a tool for characterizing new optical components. Due to the compliance of the micro-objects, the use of the force sensor constitutes a promising solution for automated tasks like pick and place, insertion, guiding. The integration of force sensor in the workcell and hybrid force/position control law are in current investigation. The characterization of the assembled micro-bench should complete the project and future work will focus on that. Acknowledgements. This work has partially been supported by the Franche-Comt´e region under the MIAAMI Project and NEMO/Marie-Curie. The authors would like to thank David H´eriban for discussions and his help.

References 1. Bargiel, S., Rabenorosoa, K., Cl´evy, C., Gorecki, C., Lutz, P.: Towards micro-assembly of hybrid MOEMS components on a reconfigurable silicon free-space micro-optical bench. J. Micromech. Microeng. (in press) 2. Cl´evy, C., Hubert, A., Chaillet, N.: Flexible micro-assembly system equiped with an automated tool changer. Journal of micro-nano mechatronics (2008), doi:10.1007/s12213008-0012-z

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3. Das, A., Zhang, P., Lee, W.H., Stephanou, H., Popa, D.: m3 : Multiscale, deterministic micro-nano assembly system for construction of on-wafer microrobots. In: IEEE International Conference on Robotics and Automation, pp. 461–466 (2007) 4. Dechev, N., Cleghorn, W., Mills, J.: Microassembly of 3-d microstructures using a compliant, passive microgripper. Journal of Microelectromechanical Systems (2004), doi:10.1109/JMEMS.2004.825311 5. Descour, M.R., Karkkainen, A.H.O., Rogers, J.D., Liang, C.: Toward the development of miniaturized imaging systems for detection of pre-cancer. IEEE Journal of Quatum Electronics (2002), doi:10.1109/3.980264 6. Gauthier, M., R´egnier, S., Rougeot, P., Chaillet, N.: Analysis of forces for micromanipulations in dry and liquid media. Journal of Micromechatronics (2006), doi:10.1163/156856306777924699 7. H´eriban, D., Gauthier, M.: Robotic micro-assembly of microparts using a piezogripper. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4042– 4047 (2008) 8. H´eriban, D., Agnus, J., Petrini, V., Gauthier, M.: A mechanical de-tethering technique for silicon mems etched with a drie process. J. Micromech. Microeng. 19 (2009), doi:10.1088/0960-1317/19/5/055011 9. Kim, B., Kang, H., Kim, D.H., Park, J.O.: A flexible microassembly system based on hybrid manipulation scheme for manufacturing photonics components. The International Journal of Advanced Manufacturing Technology 28, 379–386 (2005) 10. Motamedi, M.E., Wu, M.C., Pister, K.S.J.: Micro-opto-electro-mechanical devices and on-chip optical processing. Optical Engineering (1997), doi:10.1117/1.601356 11. Nolan, M., Labs, Z.: Apparatus and methods of manufacturing and assembling microscale and nanoscale components and assemblies (2008) 12. Perez, R., Agnus, J., Cl´evy, C., Hubert, A., Chaillet, N.: Modelling, fabrication and validation of a high performance 2 dof microgripper. ASME/IEEE Transaction on Mechatronics 10(2), 161–171 (2005), doi:10.1109/TMECH.2005.844712 13. Rabenorosoa, K., Cl´evy, C., Lutz, P., Gauthier, M., Rougeot, P.: Measurement setup of pull-off force for planar contact at the microscale. Micro Nano Letters 4, 148–154 (2009a), doi:10.1049/mnl.2009.0034 14. Rabenorosoa, K., Das, A.N., Murthy, R., Cl´evy, C., Popa, D., Lutz, P.: Precise motion control of a piezoelectric microgripper for microspectrometer assembly. In: ASME 2009 International Design Engineering Technical Conferences (IDETC 2009) & Computers and Information in Engineering Conference (CIE 2009), San Diego, United States (2009b) 15. Rathmann, S., Raatz, A., Hesselbach, J.: Concepts for Hybrid Micro Assembly Using Hot Melt Joining. Springer, Boston (2008), doi:10.1007/978-0-387-77405-3 16. Tolfree, D., Jackson, M.J.: Commercializing Micro-Nanotechnology Products. CRC Press, Boca Raton (2006) 17. Wu, M.C., Lin, L.Y., Lee, S.S., Pister, K.S.J.: Micromachined free-space integrated micro-optics. Sensors and actuators 50, 127–134 (1995), doi:10.1016/09244247(96)80096-3

Modified Assembly Systems and Processes for the Mounting of Electro-Optical Components J. Franke and D. Craiovan*

Abstract. Optical interconnections have been used for years in long distance networks and gain more and more importance for optical applications on system and board level. With the integration of optical layers into printed circuit boards the functionality can be increased while the board size remains the same. The success of this technology depends in particular on the availability of efficient production solutions. Photonic packaging implicates basically three challenges for the placement of electro optical components. With a modified process sequence and optimized processes low cost mass production is possible. This article describes the conceptual design and the implementation of a continuous process chain into a modified standard assembly system.

1 Introduction Optical technologies used for on-board data transmission have recently received increasing interest due to its many advantages compared to electrical interconnects. Because of the properties of light, such as high frequency, short wavelength and large photon energy, some physical problems that limit electrical interconnections may be solved, e. g. cross-talk, signal distortion, wave reflection phenomena and voltage isolation. The potential is on the one hand a higher bandwidth density capability and on the other hand robust electronics for industrial, medical or aviation applications. The integration of optical interconnections at board-level is realized by polymer optical layers in addition to the electrical layers and provides potential for new and cost-efficient applications. [1] [2] The optical chip to chip communication i.e. is a promising technology and offers the possibility to process an enormous data volume on one single system board. Figure 1 shows an electro-optical PCB (EOPCB) with two CPU and three memory components. J. Franke . D. Craiovan Institute for Manufacturing Automation and Production Systems (FAPS) University of Erlangen-Nuremberg, Germany *

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Fig. 1 Electro-Optical Printed Circuit Board

Furthermore optical interconnections provide the possibility of local separation for high-voltage electronics. Figure 2 shows a possible application to guarantee the voltage isolation between the power stage and the control unit. An integration of a high-voltage optical sensor could also be possible.

Fig. 2 Long distance galvanic separation

A third exemplary promising application is the cost-efficient production of passive splitters. Fiber optic splitters and couplers are used to split or combine optical signals in various kinds of networks. Typical splitter modules are manufactured in a manual process by splitting a polymer waveguide. This process offers potential for rationalization by using a lithographic process and integration into a PCB. Figure 3 shows on the upper part a standard application with a conventional fiber splitter module and on the bottom part an optical splitter-PCB is shown. Although constant research activities in the field of optical signal transfer on board level are done, the electrical data transmission is still dominating. In fact one reason for the missing introduction into the market is the missing technology for mass production of electro-optical PCB and for photonic packaging. Another reason is a lack of technical maturity. It is not the purpose of this article to debate the low-cost manufacturing of electro-optical PCB, but to provide a solution for mass production by using a hybrid packaging technology considering the specific challenges of optical packaging.

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Fig. 3 Comparison between traditional fiber splitter and integrated one

This article describes a low-cost solution for the automated mounting of electro-optical components on substrates with integrated polymer wave guides by active alignment. After specific challenges for the new hybrid packaging are discussed, potential alternatives for mounting concepts will be compared by balancing their advantages and disadvantages. Finally the results of an experimental setup with a standard assembly machine will be presented.

2 Optical Interconnection To understand the new challenge of photonic packaging the following section shows the setup of the electro-optical components and its function. Figure 4 illustrates a fundamental electro-optical interconnection on board level.

Fig. 4 Setup of an optical interconnection on board level

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In general a data transmission system consists of a transmission path with its transmitter and receiver module. To prepare the data information for the transmission, it has to be modulated and after receiving the signals they need to be demodulated. Optical data transmission systems are set up in the same way, but unlike the electrical data transfer, the electro-optical transmitters transform the electrical signal into light pulses and emit the light into a wave guide. The wave guide transports the light to the receiver. There the photo diode retransforms the light pulses into an electrical signal. The most critical part of this signal path is the exact alignment between the light beam and the coupling element. Basically there are two types of couplingmethods, therewith further sub-classifications are possible: direct and indirect coupling. Direct coupling is based on optical boards with embedded fibers. With precision molded v-structures the light source can be mounted in front of the end facet of the optical fiber [4]. The main advantages are high performance and low loss regarding the light propagation through the optical layer. The indirect coupling-method is based on polymeric- layers applied in the stack of the multilayer printed circuit board [5]. The light is deflected by total internal reflection on a 45° phase in the optical layer; this leads the light from the top layer into the waveguide. The advantage is the exact adjustment of the mirror to the waveguide. To reach a high-performance optical coupling the light beam has only to be aligned to the mirror and the gap needs to be kept clean of environmental effects [5] [6].

Fig. 5 Different Coupling-Methods

Although the direct coupling using embedded glass fibers is very promising, most research work is done for polymeric-based layers due to the flexible design of the optical layout. For this reason this paper is focused on a specific assembly process based on substrates with integrated polymer-waveguide-layers. The process was developed at the Institute for Manufacturing Automation and Production Systems. Therefore an electro-optical PCB, prototypical transmitter and receiver were developed and processed in a modified assembly system.

3 Electro-Optical SMT-Components The following section describes the setup of the electro-optical SMT components developed and derivates the main requirements for the specific photonic packaging.

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3.1 Electro-Optical Printed Circuit Board The optical circuit board is designed for demonstrating the assembly process and provides an optical one-way channel at a data rate of 1.2 GHz. The PCB with the dimension of 100 mm x 160 mm (euro format) and a thickness of 2.4 mm provides a polymer layer which is embedded 500 microns below the top layer. The polymer waveguide has a square cross-section of 70 x 70 µm and a length of 104 mm. The optical attenuation is optimized for a wavelength of 850 nm. The 45° mirror at the coupling was realized in a cutting process, thus the light is deflected by 90° through total internal reflection. The position of the mirror is marked by very high precise local fiducials onto the polymer layer. [7]

Fig. 6 Demonstrator electro-optical printed circuit board

The global PCB fiducials and the reflow ability allow a built-up of an optical signal transmission by the usage of electro-optical surface mounted devices.

3.2 Optical Transmitter and Receiver Components For the electro-optical transformation Vertical Cavity Surface Emitting Laser (VCSEL) come into operation. They are especially characterized by their high light power emission and their small size. Generally VCSEL-packages are designed to emit the light out of the top side of the package. For the data transmission mentioned before a package is needed where the light is emitted out of its bottom side directly into the EOPCB. For this purpose a special component which can be processed in standard surface mounted technology (SMT) assembly systems was developed. Figure 7 shows the setup and the manual manufacturing process chain of this prototypical component.

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Fig. 7 Setup of the transmitter component (VCSEL)

The component consists of its top and its bottom housing. The bottom housing provides an aperture in its central for the beam exit. The carrier plate also has an aperture and serves to carry the glass submount on which the VCSEL is applied by flip chip technology. The electrical contact to the lead frame is realized by wire bonding. The condition of manufacturing allows the placement of the VCSEL with an accuracy of less than 50 µm relative to the lead frame. Furthermore an inclination of up to 4° of the laser-beam is possible. To increase the coupling quality a spherical lens is embedded into the aperture.

3.3 Light Coupling Into and Out of the PCB The transmission quality depends directly on the position accuracy of the transmitter. The photonic packaging requires an alignment of the laser beam referring to the mirror. Figure 8 (left) shows a typical profile of a VCSEL with lens. Due to the manufacturing tolerances the point of the laser output is not centered and makes a passive alignment impossible. To determine the coupling profile and derivate the required position accuracy a special measuring system was developed, which provides the possibility to hold the transmitter mechanically and activate it electrically. By a positioning stage, the light-beam can be moved horizontally on the transmitter side leading to a change in the coupling light intensity. For each position the transmitted light-power is measured at the output link. Illustrated in the middle of figure 8 are two PCB with diverse mirror qualities. Their light intensity - position graph is shown on the right. This demonstrates that the mirror quality depends directly on the cutting process and provides optimization potential.

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Fig. 8 Coupling analysis

The coupling profile shows that the components have to be positioned with an accuracy of less than 15 microns referring to the optical characteristics.

3.4 Requirements for the Photonic Packaging In comparison to the electrical standard smt packaging there are basically three increased challenges for the photonic packaging: higher positioning accuracy of the transmitter component, fixation of the optical alignment and protection of the optical path. As shown before the attenuation of the coupling depends particularly on the positioning accuracy of the components. To setup a well working data transmission system, a maximum attenuation of 3 dB per interface is allowed. Hence a maximum misalignment of 15 µm for the transmitter is permitted. In contrast the receiver needs only a positioning accuracy of 150 µm. In general the electrical footprints are aligned with an accuracy of more than 30 µm related to the electrical pads. To handle the positioning of the transmitter component, on the one hand the position of the laser aperture needs to be exactly known and on the other hand the assembly system placement accuracy has to be improved. The requirements for the positioning of the receiver component can be processed in the standard SMT process. In electronics production the self centering effect is well known. In the reflow process a misaligned component is centered by the surface tension of the liquid

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Fig. 9 Influence of the self-centering effect on the coupling

solder. Depending on the offset of the optics, the component can be displaced up to more than 100 µm from its ideal position referring to the lead frame as shown in Figure 9. Therefore the second challenge for the photonic packaging is the fixation of the transmitter during the placement process before entering the reflow oven, even if the self-centering effect is desirable for the electrical packaging. Furthermore, smallest pollution of the optical path could cause a malfunction of the optical data-transmission up to its total breakdown. Because of the high optical attenuation of filling materials, the optical path has to be protected without obstructing the optical path.

4 Photonic Packaging The standard electrical packaging key process is divided in three main process steps: paste application, placement and reflow. As described before the misalignment of the components is corrected by the self centering during reflow process and allows a lower placement accuracy of 50 100 µm. To reach this placement accuracy an optical centering is done. Here for the misalignment of the picked component’s package is measured by a camera into the placement machine and is considered when placing the component. This optical centering is optimized to align the electrical pins to the middle of the footprints and does not measure the position of an integrated optical element like the laser of the optical transmitter as shown in Fig. 7. Because the standard smt process does not fulfill the novel requirements which the photonic packaging demands, a modification of this assembly process is needed. The following describes the main modification of the process-steps.

4.1 Placement Concept with Active Alignment Basically, the placement of electrical SMD can be divided into three sequences: pick component, optical centering of the components by camera, place component.

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These process steps are applicable for almost all kinds of surface mounted components, but the optical centering for laser alignment is not realized in the standard process. For that reason the optical centering has to be modified. Figure 10 shows two methods using active alignment: the semi-active alignment and the active alignment.

Fig. 10 Active Alignment Concepts

The semi-active adjustment differs only in the method of identifying the laser beam characteristics respective to the standard electrical optical centering. Before placing the component onto the optical printed circuit board it is contacted electrically to a special laser measurement sensor. There it is activated and the position of the laser beam focus is measured by a camera. The active adjustment on the one hand is the most precise but, on the other hand, the most complex method. During the placement process the VCSEL is activated by a special gripper with electrical contacts. The emitted light signal is coupled into the waveguide and with a light power sensor at the receiver side the light intensity is detected. The position of the activated component will be adjusted by feedback control until the receiving light intensity reaches its maximum value. The semi active adjustment is the most promising concept because of its easy setup. In Addition this method provides flexible and fast production. For this reason the laser measuring sensor is the key system part for the modified placement process. As shown in Figure 11, it consists of a frame, a microcontroller, a CMOS-camera, a 6-times magnifying objective, a diffusing screen and an adapter-PCB. The construction of the frame gives the possibility of installing it into the placement machine and provides the necessary stability. The microcontroller is

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basically used for four tasks, controlling the camera, analyzing the projection, driving the connected VCSEL and data communication with the control-unit of the placement machine. The camera is an 8-bit grey-scale camera with a pixel dimension of 6 x 6 µm. Together with the 6-times magnifying objective a resolution of 1x1 µm can be achieved. The adapter-PCB provides the contacting pads to activate the laser.

Fig. 11 Setup of the laser measurement sensor

B. Fixation of the Optical Components Due to the self-centering effect the position of the optical components have to be fixed after their placement. Therefore diverse possible combinations of mechanical and electrical joining techniques are available. Electrical connections can be realized by soldering, conductive adhesive, wire-bonding, welding or by electrical connectors. For the mechanical fixation diverse techniques are applicable, i.e. fixation by clapping, by form fit, by adhesive application or by welding. Although the welding and the conductive adhesive are promising methods, soldering is the most used process in electronics production. For this reason a mechanical fixation has to be chosen which is applicable in combination with the soldering process. Fixation by clapping is too complex, fixation by form fit is unworkably because of the unknown position of the optical parts, the welding is a process too special and requires a high effort to implement it into standard machines. Thus the fixation with adhesive is the best solution. To guarantee a fixation directly after the placement a fast curing is necessary. Therefore a uv-curing adhesive or a 2kadhesive can be used. Figure 12 illustrates these processes. Both processes are promising and applicable for the photonic packaging and both can be processed with jet-systems for a high precision volume control.

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Exemplarily the uv-curing with its shorter process sequence was integrated into the novel process chain, and will be explained in the next section. C. Continuous Process Chain To provide a low cost mass-production of electro-optical printed circuit boards, an efficient process chain has to be developed. It needs to be similar to the standard processes to avoid a total reconfiguration of the assembly systems. In Figure 5 the modified process sequence is shown. The underlines process steps represent the standard process: pick-up of the transmitter – characterization of the package – placement (without uv-hardening).

Fig. 12 Fixation Process with Adhesive

At first, four dots of a dual-cure adhesive are applied next to the landing area of the transmitter component. The adhesive is based on an epoxy resin and contains both an ultraviolet and a thermal hardener. The dual-cure characteristic allows a very fast pre-curing while the complete curing is achieved in the final reflow soldering process. Afterwards, the location of the reflector is defined by the verification of the local fiducials. Subsequently, the transmitter component is picked up by the gripper. With a standard component camera the transmitter is characterized concerning its electrical footprint. The following characterization of the laser beam is done at the laser measurement sensor. For this purpose the transmitter is activated. Both the location of the reflector and the characterization of the laser beam allow a placement of the transmitter at highest quality regarding the resulting data transfer rate. After the gripper has deposited the transmitter on the EOPCB, the

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Fig. 13 Modified Assembly Process Chain for the photonic packaging (The standard electrical packaging process steps are bold and underlined)

adhesive wets the edges of the component. The pre-curing of the adhesive takes place while the transmitter is still held in place by the gripper. Therefore special optical waveguides, which are attached at the gripper, lead the ultraviolet light into the adhesive. After pre-curing the transmitter is fixed in its position so that the gripper releases and leaves the component at the EOPCB. Finally, a thermal hardening adhesive is applied at the edge of the mounted component. The viscosity of this medium is adjusted in such a way that the gap between the component package and the EOPCB is filled by capillary forces while the optical path between the component and the EOPCB remains unfilled.

5 Realization and Qualification To prove the presented concept for the photonic packaging the modified process chain was tested with a commercial high performance assembly machine (Siplace HF). Figure 14 shows the system with its main modification. To integrate the whole process chain into one placement machine, two working areas are needed. The placement machine Siplace HF is qualified because of its high positioning accuracy of the linear axis and its two working areas. As shown in Figure 6, within the first working area the adhesive application is processed with two jet-systems, one for the application of the dual-cure adhesive, the other

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Fig. 14 Modified Placement Machine for the mounting of electro-optical components

one for the application of the capillary adhesive. Within the second working area the laser measurement sensor is integrated. A special placement head with increased accuracy was integrated.

6 Concluding Remarks This paper presented the potential of electro optical printed circuit boards. Furthermore the setup of the components with its specific characteristic is described and the requirements for the photonic packaging shown. The required components like electro-optical surface mounted devices and EOPCBs are available but need further research and development activities. Especially signal processors with integrated optical functions are not available, yet. The described modification of a standard assembly machine has shown that the so far incompatible technologies of electrical and electro-optical electronics packaging can be combined within one continuous process chain with the same assembly systems. Thus a high-volume manufacturing is possible and gives the possibility for new applications for high data rates systems.

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Acknowledgments. Part of this work was carried out within the framework of the research project AMOB (Automated Assembly of optical components onto Substrates with integrated waveguides) carried by the “Bayerische Forschungsstiftung” (Bavarian foundation for research) under the direction of Prof. Dr.-Ing. Klaus Feldmann.

References [1] Miller, D.: Physical Reasons for Optical Interconnection. Int. J. Optoelectronics 11, 155–168 (1997) [2] Scheel, W.: Optische Aufbau- und Verbindungstechnik in der elektronischen Baugruppenfertigung. Verlag Dr. Markus A. Detert, Templin/Uckermark (2002) [3] Schröder, H., Kropp, J., Schrage, J., Bierhoff, T., Park, H., Franke, M., Offrein, B.: Elektro-Optische Baugruppenträger – Roadmaps, Koppelkonzepte und Stand der Technik. In: Proceedings of SMT/HYBRID/PACKAGING, Tutorial, vol. 20, pp. 2–15 (2005) [4] Schneider, M., Kuhner, T.: Coupling elements for optical printed circuit boards with precision molded alignment strucutres. In: Proceedings of the 58th Electronic Components and Technology Conference, ECTC 2008, pp. 276–282 (2008) [5] Van Daele, P., Hendrickx, N., Van Steenberge, G., Bosmann, E.: Coupling Light to and from Optical Boards. In: 2nd International Symposium on Photonic Packaging, Munich (2008) [6] Zolleiß, B.: Optimierte Prozesse und Systeme für die Bestückung mechatronischer Baugruppen. Dissertation Universität Erlangen. Maisenbach Verlag Bamberg (2007) [7] Neyer, A., Demmer, P.: Herstellung der polymer-optischen Wellenleiter, Industrielle Produktionstechnik für Baugruppen mit integrierten optischen Kurzstreckenverbindungen. Verlag Dr. Markus A. Detert, Templin (2005) [8] Offrein, B.J.: Board -Level Optical Interconnects For Computing Applications. In: 2nd International Symposium on Photonic Packaging, Munich (2008) [9] Luo, F., Cao, M., Zhou, X., Xu, J., Luo, Z., Yuan, J.: Optical interconnection technology on electro/optical PCB (EOPCB), Optoelectronic Devices and Integration. In: Proceedings of the SPIE, vol. 5644, pp. 821–828 (2005)

Factory Level Logistics and Control Aspects for Flexible and Reactive Microfactory Concept Eeva Järvenpää, Riku Heikkilä, and Reijo Tuokko*

Abstract. Micro assembly and micro manufacturing, as well as micro factories are currently widely studied around the world. However, the research is typically focusing on single machines and not so much on integration of single processes and machines into wider process chains and larger systems with integrated material logistics. This paper discusses issues related to the realization of a larger scale integrated micro factory for the assembly of multi-part products. Special attention is paid on the logistical aspects and control concepts supporting flexibility and dynamic reconfigurability of the system. A scenario of a microfactory system as a holonic manufacturing system enabling the reactivity to sudden changes and failure situations is also presented.

Keywords: Microfactory, micro assembly, flexible feeding, logistics, control, reconfigurability, holonic manufacturing systems.

1 Introduction Miniaturization of products has been a strong global trend for several years already. Along with that, discussion about the miniaturization of production equipment, facilities and the ecological footprint of production has also clearly strengthened. Today the miniaturized production systems, i.e. micro and desktop factories, are widely studied in several universities and research centers around the world. This trend of development is supported by the commitment of governments and large-scale companies to move towards more environmental friendly production. Mini, micro and desktop factories are expected to decrease the factory floor space, Eeva Järvenpää . Riku Heikkilä . Reijo Tuokko Tampere University of Technology Department of Production Engineering Korkeakoulunkatu 6, 33720, Tampere, Finland e-mail: [email protected] *

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reduce energy consumption and improve material and resource utilization [1] which is strongly supporting the new sustainable manufacturing paradigm. In addition, this type of miniaturization can answer also to many other technical challenges related to micro assembly. Microfactories can be seen as one type of solution to the point-of-need manufacturing of customized and personalized products, such as medical implants or hand-held consumer electronics. Products are typically featured by short lifecycle times, small batch sizes, increasing number of product variants and converge of new technologies. Changing customer orders cause altering requirements concerning the output capacity and the processing functions of the manufacturing systems. In order to be able to answer effectively to the variation in products and production volumes, the assembly systems and system components need to be modular, flexible and reconfigurable. Miniaturization of production equipment enables the development of construction kit type systems with higher ‘plug-and-play’ capability compared with traditional macro production equipment, which improves and speeds up reconfiguration [2]. Reconfiguration and reuse of microfactory systems represent a chance to comply both the economic objective of increasing cost efficiency and the ecological objective of increasing the efficient use of resources and decreasing waste. By far the research on the area of microfactories has concentrated on developing single machines or process modules in miniaturized size. However, in order to support the implementation of microfactory technologies into real factory environments, the integration of these technologies and control of the overall system needs to be considered. This paper aims to present a concept of a large scale micro assembly factory for multi-part products. The flexibility and reconfigurability of the system will be highlighted. The emphasis will be placed on the material logistics and control aspects at the factory level where a larger number of assembly cells or stations are integrated into assembly lines and even a larger factory.

2 Short Introduction to TUT-Microfactory© Concept The microfactory system presented in this paper is built from the TUTmicrofactory modules discussed in detail in [3]. The TUT-microfactory is a modular construction kit type concept with easy and rapid reconfigurability for the different manufacturing processes of hand held size or smaller products. The system structure is designed with an idea that a base module can work as an independent unit including all needed auxiliary systems. The outer dimensions of one module are 300x200x220 mm. Fig. 1 presents an example application of TUT-microfactory module for the assembly of a tiny spring in a MEMS-based product. In this application, a pocket delta robot is used for the assembly task. The base module of the TUTmicrofactory includes a clean room class work space, a control cabinet and the equipment needed by the clean room. The production module can be tailored to certain processes by placing process modules on top of the base module. In

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addition to the top side, both sides and the front side can be left open when adjacent cells compose one integrated work space (see Fig. 2). Feeders and other devices can be placed in the opening on the front side. All interfaces have been designed to be as simple as possible. The base modules can be locked next to each other side by side, front by side, or front by front allowing nearly unlimited numbers of factory topologies, ranging from a simple line type to a freely branching one. [3]

Fig. 1 Spring assembly in TUT-microfactory module

Fig. 2 Example of TUT-microfactory layout [3]

3 Factory Level Material Logistics for TUT-Microfactory Concept The overall concept of the microfactory assembly system consists of several different process modules, such as assembly, lubrication, screwing, quality control, and inspection modules. This paper won’t look into details of any technological solution but highlights more the material logistics and control aspects. This section

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discusses the material logistics on the microfactory system. Both the part feeding into the microfactory modules as well as the product transporting from one module to another on the whole factory level will be covered.

3.1 Feeding Methods Several different feeding methods are available for part feeding, incl. tray, tapeand-reel, bowl and machine vision based flexible feeding. Discussions with companies assembling miniaturized products have shown the authors that the most desired methods for feeding are tray feeding and machine vision based flexible feeding. Therefore only these two methods are discussed here. 3.1.1 Tray Feeding Tray feeding is a desirable feeding method for applications where delicate, fragile or high-quality parts are handled. It is very suitable for sensitive parts with almost any form or material. The negative aspect of tray feeding is that it requires the parts to be palletized on trays beforehand. Palletizing is a non-value-adding activity and is often manual. The trays also need a lot of space, both in the storage and on the assembly line. Tray feeding requires a tray changer mechanism which removes the empty trays from the microfactory module and then fills the tray fixture with a full one. A conceptual idea of the tray changer system can be seen in Fig. 3. This kind of changer mechanism also needs a lot of space and increases the size of the overall system. Other kind of issue related to tray feeding is that the tiny, lightweight parts may often move or drop off from the tray during transportation and handling.

Full trays Tray changing mechanism

Empty trays

Fig. 3 Tray changer concept

In order to increase the flexibility of the feeding and handling system all trays should have similar handling, positioning and locking interfaces. In order to keep the amount of trays reasonable, flexible trays should be used instead of dedicated ones. Flexible trays don’t have part specific caves for the parts and therefore allow different parts to be fed with the same tray. Flexible trays, however, require more

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intelligence from the picking robot, because it has to be able to recognize the exact position and orientation of the part before picking. This intelligence is usually implemented with machine vision systems. 3.1.2 Flexible Feeding For components without critical surface quality, a machine vision based flexible feeding method is a more desirable option, because the parts can be fed directly to the assembly cells without palletizing or pre-orientation. The working principle of flexible feeders (like Wisematic Minifeeder seen in Fig. 4) is based on using a machine vision system, which recognizes the part and allows the position and orientation to be calculated from the image of the system. All the parts are not suitable for being fed by this kind of machine vision based feeder. The shape and appearance of the component have to fulfill certain requirements. First of all it needs to contain geometrical features which allow it to be directed into a specific position in the feeder for the pick up. The features should also be recognizable by the machine vision to allow the detection of the component itself and its position and orientation unambiguously.

Fig. 4 Example of flexible feeder, Wisematic Minifeeder [4]

3.1.3 Analysis of the Feeding Methods The amount of components assembled in one microfactory module is limited mainly by the restricted space for feeding equipment and tool changing capability. In case of similar type of parts and process steps, the same robot can be used to assemble several parts without changing the tool. While using tray feeding with TUT-microfactory concept two 2 x 2 inch trays can be fitted into the module at the same time. With small sized flexible feeders there can be about six feeders placed side-by-side. The feeding capacity of flexible feeders is typically varying which must be taken into account in case of short cycle times. In order to always ensure the availability of a part two feeders can be used to feed the same part to minimize the risk of unavailability of parts.

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3.2 Carrier System for the Base Part Often in case of a well-designed product, parts (and sub-assemblies) are assembled on top of the product’s base part and the base part moves through the assembly line and through the needed process units. The authors have designed a concept of a carrier system presented in Fig. 5. Jigs are product specific, but the carriers are the same for all product variants. This supports the reuse of the carriers and allows the same transportation modules to be used for all products and product variants. Because in most cases the product requires also some manual operations, the developed carrier system concept enables the product to be removed easily from the assembly line for manual operations. The carrier system contains also an escort memory, such as RFID, which allows keeping track on the location of the carrier and the assembly operations that have been performed for the assembly.

Fig. 5 Carrier system for the base part

3.3 Conveying Method for Factory Level There are multiple conveying methods that can be used to handle the factory level logistics, meaning to transport the product through all the required process steps on the assembly line. A modular belt conveyor with standardised interfaces can be integrated into the TUT-microfactory modules. This ensures the flexibility and reconfigurability of the line. Fig. 6 illustrates a microfactory system for a specific assembly process chain of watch mechanism assembly with belt conveyor modules passing through the process modules. The most important thing in the material logistics regarding the reactivity of the line is that the carriers can be freely routed to any cell. This is especially important in case of failures on the line, e.g. if one process module breaks down, or if multiple modules provide the same process step. In the meanwhile when the process module is being rapidly exchanged, thanks to the modularity, the carrier can be routed to other process unit capable for performing the same step. For the rerouting purposes there is a “bypassing” conveyor, which allows the carriers to skip some process modules.

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Flexible feeder

“Bypassing” conveyor

Tray feeding

Bowl feeder

Fig. 6 Microfactory system for an integrated assembly process chain

Fig. 7 presents an example of a factory level assembly system layout with the belt conveyor going through all the microfactory modules and lines. The assembly factory is divided into lines or clusters of modules, which all perform one assembly process chain. Example of this kind of cluster was seen in Fig. 6. This kind of division supports easier management of the overall system in case the process consists of vast amount of assembly steps. For controlling the routing of the carrier systems on the assembly line, the selected method can vary between centralized and fully distributed control. In order to support the reactivity and dynamic reconfigurability of the system, distributed control is preferred. More about distributed control is discussed in section 4.

Buffers Manual staons

Carrier loading and unloading staon

Microfactory modules

Fig. 7 Example of an integrated micro assembly factory

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3.4 Interface between Manual Stations and Microfactory Modules In today’s sustainable manufacturing paradigm the goal is not to replace humans with full automation, but to keep the human in the loop and to support the human workers in their work by performing the boring, repetitive and stressing tasks automatically [5]. The human–machine interface must be optimized, to maximize the benefits of both human skills and machine capabilities. Therefore the operations that need special skills should be performed by humans and difficult tasks (because of small part size or high accuracy or quality) and boring repetitive tasks (such as component palletizing) by automated microfactory units. The manual stations have to be compatible with the microfactory system not only from their physical interfaces, but also considering the control on the factory level. Because the material logistics from one microfactory module to another is automatic, flexible use of the manual stations requires them to be integrated into the same material logistics system together with the microfactory modules. In the Fig. 7, the similar belt conveyors move the carriers through microfactory units and the manual stations. The manual stations have to also contain the RFID-readers in order to keep on track, which operations have already been accomplished for the assembly. The carrier system concept presented earlier aims to simplify the interface between the automatic and manual working modes. This kind of carrier system allows the product to be removed from the microfactory module for manual operations, either when the next assembly operation is too complex for automatic assembly or when failures have occurred and human operator needs to make inspection and corrections for the assembly. If the quality control system in any microfactory module recognizes a faulty assembly, that carrier will be routed automatically to a manual station, where the skilled human worker will analyze the problem and fix it. The microfactory system itself doesn’t have to understand what is wrong or how to fix it, just to notice that everything is not correct. After the assembly is fixed by a human worker, the carrier system is returned to the automated assembly process line. One aspect in the discussion of the human-machine interface is that the small size of the equipment complicates for example the maintenance operations. On the other hand, the modularity of the equipment makes it easy to change some subsystems from the line. One can for example just lift one microfactory module away and replace it with a different one, e.g. when the module is broken or reconfiguration is required. One benefit compared with traditional macro-size systems is that human can see with “one sight” bigger part of the factory and process chain and therefore it is easier to manage and understand.

4 Reactivity and Reconfigurability of the Microfactory Systems Reconfigurability is traditionally defined as the ability to adjust the production capacity and functionality of a manufacturing system to new circumstances through rearrangement or changing of the system’s components [6][7]. To be able to

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produce different variants of the product and on the other hand to react fast to the failures or other disturbances on the assembly line, the microfactory system has to be reconfigurable both in a static and dynamic way. In this paper reconfiguration is divided into two types: static reconfiguration and dynamic reconfiguration. Static reconfiguration is the change of the system design during the downtime of the system. Dynamic reconfiguration is the change of the system design and system adaptation during the operation. Static reconfiguration answers to the challenge of changing production requirements (e.g. new product or production volume), whereas dynamic reconfiguration is related to the production system’s ability to recover from disturbances on the line and to self-organize itself to balance the production flow. To be able to exploit these both types of reconfigurability and adaptivity, a truly adaptive control architecture is required. The TUT-microfactory concept has been developed to be reconfigurable from the outset. It answers well to the requirement for static reconfiguration. It is designed to be modular and the modules have standardized interfaces, which allow them to be plugged together easily. However on the control side they still require sophisticated information technologies (intelligence-based control systems) and powerful control devices that allow reconfiguring the systems rapidly. The hierarchical management and control philosophy should be broken down into intelligent, collaborative and autonomous production units, which are intelligent physical agents suitable for microfactory automation [8].

4.1 Holonic and Agent-Based Control Supporting Dynamic Reconfiguration of the Microfactory System Many manufacturing paradigms aim to meet the challenges of adaptivity, evolvability and dynamic reconfiguration. Two of these paradigms, agent-based manufacturing systems and holonic manufacturing systems (HMS) have received a lot of attention in academia and industry. The difference between these two concepts is very slight. Giret and Botti stated in [9] that these approaches differ mainly in their motivation summarizing that MAS (multi-agent system) is a broad software approach that can also be used for distributed intelligent control, whereas HS (holonic system) is a manufacturing-specific approach for distributed intelligent control. Basically, agent technology is used to implement holons. While agents are considered to be pure software entities, holons are agents that contain also physical manufacturing hardware. [9] The holonic concept was originally developed by the philosopher Arthur Koestler in order to explain the evolution of biological and social systems. The word “holon” is a combination of the Greek word “holos”, meaning whole, and the Greek suffix “on”, meaning particle or part. [10] Translated into the manufacturing world an HMS concept views the manufacturing system as one entity consisting of autonomous modules (holons) with distributed control. The holons are able to fulfill their own goals, as well as communicate and co-operate with other holons by means of some communication language. The goal has been to attain the

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benefits that holonic organization provides to living organisms and societies, in manufacturing, i.e., stability in the face of disturbances, adaptability and flexibility in the face of change, and efficient use of available resources. [9] According to the study performed by Pechoucek and Marik [11] all the applications of holonic approach document the efficiency of the HS concept especially in the real-time reconfiguration tasks. 4.1.1 Short Introduction to DiMS-Concept Somewhat different approach into holonic manufacturing has been taken at TUT in the FMS 2010 –project. Fig. 8 shows the holonic view of the Distributed Integrated Manufacturing System (DiMS) -concept developed in the scope of that project [13]. It is based on the Product-Resource-Order-Staff (PROSA) reference architecture by Van Brussel et al. [12], which describes a manufacturing system with three types of basic holons: resource holons, product holons and order holons. Resource holons are responsible for controlling and consuming resource capabilities. Knowledge of products, as well as processes needed to manufacture the products, are the responsibilities of product holons. Order holons hold the information on how to perform the right manufacturing task at the right time. While the resource holons have both physical and digital part, the order and product holons have typically only digital part. [12] The DiMS-concept goes beyond the production control and integrates the design and development of products, production systems and business processes [13].

Fig. 8 Holonic view to a Distributed Integrated Manufacturing System [13]

4.1.2 Agent-Based and Holonic Communication There is no central database that agents/holons can rely on during factory operations and there is no central coordinator to organize and direct the holons. Each holon must be programmed to coordinate with its peers to effect the appropriate product flow and assembly operations. In order to achieve this coordination, holons must know and understand common communications protocols. Because

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each interacting holon needs the same understanding about a particular vocabulary, ontologies are needed to describe and structure the knowledge related to the domain where the holons operate. [8] In recent years the rise of the XML-based formats with the emergence of ontological definitions along the semantic web have provided means for transferring knowledge into such form that can be understood and re-used by many. The ontological classification of the product models, manufacturing processes and systems offers a great approach to standardize the way people, software and machines communicate, embedding the meaning and the context within the message being sent. [14] Ontologies and XML-based formats are seen as a solution to represent the product, process and system related knowledge so, that it can be utilised for the rapid reconfiguration and adaptation of microfactory systems. They can be used both to structure the knowledge related to the holons themselves and the language the holons are using for their interaction. 4.1.3 A Scenario of a Holonic Microfactory System The following simplified scenario will show the reactivity and ability to recover from failures of the holonic approach in case of microfactory systems (see Fig. 9). There is OH1 (order holon 1) processing an operation to produce a PH1 (product holon 1). The production process is carried out in processing steps assigned to resource holons RH1, RH2, RH3 and so on, such that RH1 is the first processing step, RH2 the second, and so on. If for some reason the microfactory module represented by RH5 breaks down, the rest of the holons have to realize this situation which prevents the execution of their current plans. The resource holons will then negotiate with each other, either directly or through a broker, and decide if some of them are able to perform the task originally done by the broken RH5. Then the product holon is redirected to a capable module, in this case to the RH6.

Fig. 9 Example scenario of a holonic microfactory system

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Principles of the holonic microfactory system example: • •

• •

Each carrier system acts as an independent holon and makes its own decisions about where to move on the line. The physical carrier system itself doesn’t contain intelligence (processor), but it is represented as a computer program, which refers to the specific carrier by the RFID tag attached to the carrier. Together these form the product holon. Thanks to the RFID-technology, the product holon can now have both physical and digital part. The product holon has the recipe of the assembly process and the history data of the accomplished processes. The product holon negotiates with the process module holons requesting the services from the providers, following the SOA (service oriented architecture) principles (see e.g. [15]).

Another example of a SOA-based holonic microfactory system can be taken from a case when the carrier can be routed through different routes depending on the process modules it has to go through. This is the situation for example with the cross conveyors. When the carrier arrives at the cross conveyor the next process step to be performed will be read from the escort memory of the carrier. The information within the escort memory tag includes the assembly process recipe for the product, which is read by the reader embedded in the cross conveyor and later obtained by the agent coordinator. It then sends a query for the service broker asking for the names of the holons in charge of the modules that can accomplish the next process steps. Then the carrier is routed to that module. If there are more holons capable of accomplishing the same task, the holons will negotiate with each other to decide which will take the work. Here the work load and distance will be the main factors guiding the negotiation and decision making.

5 Conclusions Whilst single micro assembly robots and other microfactory technologies have already been widely studied by the academy and industry, their implementation into real factory environments is still rare. One reason for this is the lack of concepts for factory level integration and control that would support rapid reconfiguration of the system – a feature, which is usually connected to microfactories. This paper opens the discussion on the integration of single microfactory modules and stations together and how to control the material logistics over the assembly line and the whole factory. The paper has presented a conceptual idea for micro and desktop assembly at the factory level. The presented microfactory system supports assembly applications with vast amount of parts, such as watch mechanism assembly. The main attention has been placed on the logistical considerations. An agent-based and holonic control concept has been introduced to support the dynamic reconfigurability of the overall assembly system.

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References [1] Okazaki, Y., Mishima, N., Ashida, K.: Microfactory – Concepts, History, and Developments. Journal of Manufacturing Science and Engineering 126, 837–844 (2004) [2] Heikkilä, R., Huttunen, A., Vuola, A., Tuokko, R.: A Microfactry Concept for LaserAssisted Manufacturing of Personalized Implants. In: Proceedings of the 6th International Workshop on Microfactories IWMF 2008, Northwestern University, Evanston, Illinois, USA, October 5–7 (2008) [3] Heikkilä, R., Karjalainen, I., Uusitalo, J., Vuola, A., Tuokko, R.: Possibilities of a Microfactory in the Assembly of Small Parts and Products – First Results of the M4project. In: Proceedings of the ISAM 2007, IEEE International symposium on assembly and manufacturing, Ann Arbor, Michigan, USA, July 22-25, pp. 166–171 (2007) [4] Oy, W.: Wisematic Minifeeder System (2009), http://www.wisematic.com/Minif-system.htm (Viewed 5.4.2009) [5] Jovane, F., Westkämper, E., Williams, D.: The ManuFuture Road – Towards Competitve and Sustainable High-Adding-Value Manufacturing, p. 261. Springer, Heidelberg (2009) [6] Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., Van Brussel, H.: Reconfigurable Manufacturing Systems. Annals of the CIRP 48(2), 527–540 (1999) [7] EIMaraghy, H.A.: Flexible and reconfigurable manufacturing systems paradigms. International Journal of Flexible Manufacturing Systems 17(4), 261–276 (2005) [8] Martines Lastra, J.L., Insaurralde, C., Colombo, A.: Agent-based Control for Desktop Assembly Factories. In: Wang, L., Nee, A.Y.C. (eds.) Collaborative Design and Planning for Digital Manufacturing. Springer, London (2009) [9] Giret, A., Botti, V.: Holons and agents. Journal of Intelligent Manufacturing 15(5), 645–659 (2004) [10] Koestler, A.: The Ghost in the Machine. Arkana Books, London (1967) [11] Pechoucek, M., Marik, V.: Industrial deployment of multi-agent technologies: review and selected case studies. Autonomous Agents and Multi-Agent Systems 17(3), 397–431 (2008) [12] Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry 37(3), 255–274 (1998) [13] Nylund, H., Salminen, K., Andersson, P.: Digital Virtual Holons An Approach to Digital Manufacturing Systems. In: Proceeding of he 41st CIRP Conference on Manufacturing Systems, pp. 103–106 (2008) [14] Lanz, M., Kallela, T., Järvenpää, E., Tuokko, R.: Ontologies as an Interface between Different Design Support Systems. In: Proceedings of WSEAS Neural Networks, pp. 202–208 (2008) [15] Gottschalk, K.: Web Services architecture overview. IBM Developer Works, Whitepaper (2000)

Development of Structured Light Based Bin–Picking System Using Primitive Models Jong-Kyu Oh, KyeongKeun Baek, Daesik Kim, and Sukhan Lee*

Abstract. As a part of factory automation, bin-picking systems perform pick-andplace tasks for randomly oriented parts from bins or boxes. Conventional binpicking systems can estimate the pose of an object only if the system has complete knowledge of the object (e.g., as a result of the geometric features of the object being provided by an image or a computer-aided design model). However, these systems require the features visible in an image to calculate the pose of an object, and they require additional setup time for an operator to register the reference model every time when the workpiece is changed. In this article, we propose a structured light based bin-picking system using primitive models with small amount of prior knowledge. To obtain a reliable 3D range image for comparison with conventional systems, we use a structured light sensor with gray-coded patterns. With the 3D range image, the pose of the object is estimated with the use of primitive segmentation, rotational symmetric object modeling, and recognition. Through experiments using an industrial robot, we validate that the proposed method can be employed for a practical bin-picking system.

1 Introduction Applications of industrial robots have increased to improve the flexibility of factory automation systems. The work performed by industrial robots has broadened from spot welding to assembly, which requires more sophisticated control. The material handling process involves an especially high degree of difficulty, and it can have a noticeable impact on production output rates and costs; fixtures can be removed and thus save the amount of working space required in high-value manufacturing areas. Thus, there are increasing demands from manufacturers for a reliable sensing system and a system integration technology so that the robot system can be applied in factories. Jong-Kyu Oh . KyeongKeun Baek . Daesik Kim . Sukhan Lee Intelligent System Research Center of Sungkunkwan University, Gyeonggi, Korea e-mail: {jkoh,kk.beak,daesik80,lsh}@ece.skku.ac.kr *

Jong-Kyu Oh Electro-Mechanical Research Institute of Hyundai Heavy Industries Co. Ltd, Gyeonggi, Korea

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In the area of part feeding automation, some research has looked at bin-picking systems that perform pick-and-place tasks for randomly oriented parts from bins or boxes [1][2]. Ban proposed the laser-vision–based bin-picking system [3]. The proposed laser-vision sensor consists of a projector that projects a cross-type pattern and a camera to capture the image. This sensor calculates the X and Y values and the roll of the object with the use of 2D image processing; it then computes the Z value, the pitch, and the yaw by analyzing a projected image with a laser pattern. However, this system has a narrow sensing range, because the 3D position of the object is calculated only with regard to the surface on which the laser slit is projected. Bin-picking systems that make use of stereo vision have been widely researched. A study by Rahardja calculated the position and normal vector of randomly stacked parts with the use of a stereo camera [4]. However, this system required unique landmark features, which are composed of seed and supporting features to identify the target object and to estimate the pose of the object. To overcome the disadvantages of conventional bin-picking systems, Schraft proposed a pose estimation method that made comparisons between the computeraided design (CAD) model of a workpiece and the range image captured by a laser scanner [5]. However, this system has disadvantages in that the accuracy and speed of the pose estimation depend on the number of registered models in the related database. To tackle the aforementioned problems, in this article, we propose a structured light based bin-picking system that makes use of primitive models. The term primitive models refers to basic 3D shapes, such as a plane, a cylinder, a cone, and a sphere. Gray-coded patterns are employed to obtain a reliable range image, and the pose of an object is estimated with the use of primitive segmentation, rotational symmetric object modeling, and recognition. Because it makes use of the geometric primitives of target objects, this system can estimate the pose of the objects without having complete prior information about the objects (e.g., 2D geometric features, CAD models). Although all parts of the object are not visible or measurable, the system can estimate the positioning of those objects. The organization of this article is as follows. Section 2 describes the configuration of the proposed structured light based bin-picking system. Section 3 presents the operation procedure of the proposed system, including the structured light technique, the sensor calibration, and the pose estimation method. Experimental results are given in Section 4, and Section 5 is the conclusion.

2 Configuration of the Structured Light Based Bin-Picking System For the precise manipulation of complex objects in a bin, we configured a visionguided robotic system with two parts, as shown in Fig. 1. One part is the

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personal-computer–based structured light vision system, which includes an IEEE 1394 PCI board, an IEEE 1394 camera, a lens, and a projector. The other part is the robot system, which includes a robot controller. The robot controller and the vision system are connected via RS232C or Ethernet.

Fig. 1 Configuration of proposed bin-picking robot system

3 Operation Procedure of the Proposed System Fig. 2 represents the operation procedure of the proposed system. It consists of the preparatory stage, which includes the initial setup of the 3D vision system and the definition of the robot’s tasks, and the operation stage, during which the system automatically performs the pick-and-place task in the workspace by means of measured pose information. During the preparatory stage, the operator has to calibrate the sensor, register the 3D pose of the reference object, and assign the trajectory of the robot for the task. During the operation stage, the structured light sensor illuminates a gray-coded pattern on parts in a bin. The point cloud can be obtained after finding a corresponding pair of points between the projector and the camera, and then triangulates them. The pose of randomly stacked objects is estimated with the use of 3D edge detection, object segmentation, and primitive modeling. The 6-DOF variance between the reference object and the current workpiece is transmitted to the robot controller. Finally, the robot modifies its trajectory. The primary functions of the proposed vision system are the acquisition of a 3D range image and the estimation of the pose of the object to be grasped; these functions are explained in detail in the following paragraphs.

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Projector

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3D Reconstruction Correspondence Points Antipodal Gray Code

Feature Extraction

Subpixel Decoding uc ,u p

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Pose Estimation 3D Edge Detection Object Segmentation Primitive Modeling 3D pose of incoming workpiece

Calculation of 3D pose of reference workpiece

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Robot teaching with respect to reference workpiece

Trajectory Modification

Preparatory Stage

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Fig. 2 Diagram of proposed bin-picking system

3.1 3D Range Image Acquisition Several approaches have been developed to reconstruct objects and environments, such as stereo vision, focusing technique, and light and shadow analysis [6][7]. Although these approaches—especially stereo vision—are popular, in the case of the passive approach (which only makes use of a camera), the accuracy and reliability of the 3D reconstruction results are relatively low. The structured lighting technique is reliable for 3D reconstruction; it makes use of triangulation with a corresponding pair of points between the projection device and the camera [8].

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3.1.1 Structured Light System Our structured light system consisted of a DLP projector and a camera, as shown in Fig. 3. The projector illuminated the coded patterns, and the camera captured several patterned scenes. We employed the antipodal Gray code, which is the variant of the conventional Gray code, for our structured light system [9] and we improved the decoding method for finding the corresponding pair of points to obtain more accurate results.

Fig. 3 Structured light system

3.1.2 Camera/Projector Calibration A camera/projector calibration is required to establish the relationship between the 3D coordinates and their corresponding 2D image coordinates. Under perspective projection, 3D point X = [ X Y Z 1]T in space is projected to an image point

x = [u v 1]T in the image plane via a projection matrix P as follows, where P consists of the intrinsic parameter matrix K , the rotation matrix R , and the translation vector T [7].

x = K [ R | T] X = PX

(1)

To obtain the P matrix, we needed a calibration rig that could provide 3D position information in an unknown environment, so we used a calibration rig composed of square patterns, as shown in the left-hand image of Fig. 4.

Fig. 4 Calibration rig (left) and the detected corner points (right)

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The procedure for the camera/projector calibration was performed as follows. First, we put the rig within a common field of view in the structured light system, and we acquired the image without illuminating any light from the projector. Next, the corner points of the image were extracted, and the camera calibration was performed by mapping from the known corner points in 3D space to the corner points in image plane. The right-hand image of Fig. 4 shows the distribution of detected corner points in the calibration program. After calibrating the camera, we illuminated the coded-pattern to the calibration rig, captured the pattern, and decode the coded-pattern for calibrating the projector. Because we had already extracted the corner positions of the image and obtained the decoded pattern positions at those points, the relationship between the known corner points of the calibration rig and the projector’s pattern position could be computed. If the accuracy of the corner position in the camera image is under a sub-pixel, the corresponding pattern position may not be obtained directly. In this case, we computed the pattern position by interpolating the pixel around the corner position. The camera/projector calibration was performed just once during the preparatory stage. After the camera/projector calibration process, we acquired the projection matrices of the camera and the projector. At this point, the transformation between the calibration rig and the robot was computed from more than three corresponding points represented in each coordinate frame. As shown in Fig. 5, we taught the corner positions of the calibration rig to the robot’s end-effector.

Fig. 5 Transformation between the calibration rig and the robot can be computed by teaching the robot more than three corresponding points

Then, we can compute the rotation and translation using a unit quaternion, which may be converted in closed form to get a 3×3 conventional rotation matrix [10].

3.1.3 Reconstruction If the projection matrices of the camera and the projector are given and if the pair of corresponding points in the camera and the projector image plane is given, we can determine the coordinate of the scene point that corresponds with the points in the two-image plane.

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The relationship between point X in the scene and the corresponding points x and x′ in the camera and the projector image coordinates can be written as follows, where P and P ′ denote the projection matrix of the camera and the projector, respectively: x ≅ PX

(2)

x′ ≅ P ′ X

(3)

Equations (2) and (3) are expressed in terms of homogeneous coordinates:

MX = 0

(4)

Where ⎡ p11 − up31 ⎢ p − vp 31 M = ⎢ 21 ⎢ p11′ − u ′p31 ′ ⎢ ′ − v′p31 ′ ⎣ p21

p12 − up32

p13 − up33

p22 − vp32 ′ p12′ − u ′p32

p23 − vp33 ′ p13′ − u ′p33

′ − v′p32 ′ p22

′ − v′p33 ′ p23

X = [X

Y

Z 1]T .

p14 − up34 ⎤ p24 − vp34 ⎥⎥ , ′ ⎥ p14′ − u ′p34 ⎥ ′ − v′p34 ′ ⎦ p24

(5)

(6)

Thus, we can estimate the scene point through singular value decomposition (SVD)-related techniques. The four elements of the last column of V obtained by the SVD of M (i.e., M = UDV T ) are the homogeneous coordinates of X .

3.2 Pose Estimation The traditional industrial workpieces in the manufacturing assembly line contain geometric features such as the plane, the cylinder, the cone, and the sphere; some of them have rotational symmetric characteristics through the principal axis. Most pose estimation methods of vision-guided robotic systems make use of the complete knowledge of the parts, such as geometric features in an image and CAD models [11][12]. In this case, every time that the workpiece changes, an operator must register the features of the new object in the database, which reduces the productivity of the manufacturing line. Moreover, if the features of the object cannot be extracted, the pose of the object cannot be calculated. To overcome the problems of conventional methods, we propose a new poseestimation algorithm that calculates the pose of an object by fitting the geometric primitives (as mentioned previously), thus extracting the rotational symmetric features of industrial parts without requiring the prior complete knowledge of the workpiece. The process of pose estimation for the rotational symmetric object is divided into three phases. First is the process that segments the primitives by using detected 3D edges from a range image; the second process identifies rotational symmetric primitives among the separated primitives and creates a model; the

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third phase involves the estimation of the variance of the pose of the object by making comparisons between the reference object in the database and the modeled object. 3.2.1 Object Segmentation

To grasp a part among the stacked objects, the target object must be extracted. In this study, object segmentation is determined with the use of the 3D edges and the normal vector map. When compared with the 2D edges of an image, the 3D edges can segment out the object from a background or neighboring objects, regardless of texture, because this system makes use of the difference of depth rather than the difference of intensity [6]. To determine the 3D edge, we apply Wani’s 3D edge-detection method, which identifies three types of edges: the fold edge (FE), the boundary edge (BE), and the semistep edge (SE) [13]. The FE is the edge that corresponds with pixels where normal surface vector discontinuity occurs. BE is formed by pixels that have at least one immediate neighbor pixel that belongs to the background, that occurs where one object obstructs another object or a part of itself. To identify the 3D edges, normal vectors are calculated in whole areas of a scene with the use of the cross-product of a directional vector [6]. With the use of the difference of normal and the difference of depth, the three kinds of 3D edges are extracted. Among the three types of 3D edges, FE and BE are needed to distinguish objects from the background and from neighboring objects [14]. Using the FE and the BE, we were able to find the boundary of the surface that encloses the primitives in the range image. Finally, we divide the surface that is contained with the equivalent primitive features in the input image. Fig. 6 shows the results of 3D edge detection.

Fig. 6 Input image (left) and 3D edge image (right). Red, Fold edge; blue, boundary edge.

3.2.2 Primitive Object Modeling

After recognizing the primitive objects through the previous process, it is necessary to perform an additional modeling process to detect the rotational symmetric object. In this study, we used Baek’s modeling method [14].

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As shown in Fig. 7, if two points are on the surface of an object at the same height, the vectors whose directions are opposite to the ones of the normal vectors of two points will meet at a point on the axis of the object at the same distances from the surface.

Fig. 7. Features of a rotational symmetric object

We then calculated the center points at several heights to find the equation of the axis. T We defined two points on the surface of an object as P1=[x1 y1 z1] and P2=[x2 T T y2 z2] and two normal vectors on P1 and P2 as N1=[nx1 ny1 nz1] and N2=[nx2 ny2 T nz2] , respectively. The equations of the normal vectors are shown here:

⎡ x p1 − xc N1 = ⎢ ⎣ r

y p1 − yc

⎡ x p 2 − xc N2= ⎢ ⎣ r

y p 2 − yc

r

r

z p1 − zc ⎤ ⎥ r ⎦

T

z p 2 − zc ⎤ ⎥ r ⎦

T

(7)

(8)

T

The point Oc=[xc yc zc] is a point on the axis of the object, and r is the radius of the object. Because we know the values of P1, P2, N1, and N2, we can derive the following equations from Equations (7) and (8) and obtain the three radiuses rx, ry, and rz:

xc = (nx1 x2 − nx 2 x1 ) (nx1 − nx 2 ), yc = (ny1 y2 − n y 2 y1 ) (ny1 − ny 2 ),

(9)

zc = (nz1 z2 − nz 2 z1 ) (nz1 − nz 2 ) , rx = ( x1 − xc ) nx1 = ( x2 − xc ) nx 2 , ry = ( y1 − yc ) n y1 = ( y2 − yc ) n y 2 ,

(10)

rz = ( z1 − zc ) nz1 = ( z2 − zc ) nz 2 .

If both |rx - ry| and |ry - rz| are within the threshold (e.g., 2 mm), we can consider T the point Oc=[xc yc zc] to be a point on the axis.

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In practice, we randomly selected two points, P1 and P2; the first one is on the surface of the object, and the second one is every other point on the surface of the object. We then computed the point Oc. By iterating the process several times (e.g., for 30% of all of the object’s surface points), we can obtain the set of points that are considered to be on the axis. If there are more than a certain number of acquired points (e.g., 30 points), then the object is rotation symmetric. By calculating the covariance matrix from the set of points and performing SVD, we can get three directional vectors from three unique variables. The vector that corresponds with the largest value of the unique variables becomes the vector of the initial axis. There can be error in the initial axis as a result of noise. To fix this error, we set up the axis in parallel with the Z-axis (as shown in Fig. 8), divided the axis by 1 mm, obtained cross-sections of the object in the X-Y plane, and gathered the points from all cross-sections. The center of a circle that is fitted on the points of each cross-section becomes a point on the new axis. By gathering the center points of all of the cross-sections and fitting them into a line, we obtained an accurate model of the rotation symmetric object.

1mm

rotation

z

Fig. 8 Correction of the principal axis

3.2.3 Finding the Position of Rotation Symmetric Objects The results of the modeling of rotation symmetric objects are the direction of the axis, the points on the axis, and the radius of each cross-section. The direction of the axis shows the position of the object in 3D, and the center point of the axis shows the location of the object. To determine if the object is the target for bin-picking, the object should be compared with the information stored in the database. The information stored in the database is the radius of each cross-section, which means that whether the object is the target or not can be determined by comparing the radius information of each cross-section.

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Equation (11) is used to compare the values of the radius. Ri is the set of radius values of the cross-sections, and Rm is the radius of the modeling object. If Equation (11) is satisfied, we can confirm that the object is the target for bin-picking.

Threshold ≥ Average( R i − R m )

(11)

3.2.4 Variance between Reference Object and Modeling Object

The measured 3D pose of the reference object is registered in the database. During the operation stage, the shifted data between the 3D pose of the reference workpiece and the measured 3D pose of the incoming workpiece is transmitted via RS232 or Ethernet to the robot controller. According to the shifted data, the robot completes the task even though the workpieces are placed in an arbitrary fashion.

4 Experimental Results 4.1 Experimental Setup For our experiments, the Optoma EX330 projector and the PGR flea2 IEEE 1394 digital camera were used for the structured light system. The resolution of the projector was 1024 × 768, and the resolution of the camera was 640 × 480. The position of the camera was about 20 cm away from the projector. There were 256 code strings in a horizontal direction and 192 code strings in a vertical direction that were used for pattern projection. The distance between the camera/projector and the objects was about 0.6 m to 1 m (minimum and maximum distances). A HYUNDAI HA020 robot was used for the bin-picking task, and it picked an M12sized bolt from randomly stacked environments. After the camera/projector calibration process, we put the reference object on the pallet. The position and pose of the object were registered in database by means of the structured light sensor, as shown in Fig. 9. We then taught the trajectory of the robot for the bin-picking task with respect to the reference object. Fig. 10 shows the modeling result of the reference object.

Fig. 9 The position and pose of the refence object is registerd (left), and the trajectory of the robot is taught during the preparatory stage (right)

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Fig. 10 Modeling result of the reference object

During the operation stage, we randomly scattered the M12-sized bolts on the pallet, as shown in Fig. 11. The structured light sensor then acquired the 3D range image and calculated the pose of the workpieces. The right-hand image of Fig. 11 shows the 3D reconstruction image.

Fig. 11 Randomly piled objects (left) and modeling results (right)

4.2 Qualitative Results Fig. 12 explains the process of segmenting the target object and estimating its position. The left-hand image of Fig. 12 represents the primitives that were detected with the proposed object segmentation method; the gray regions are segmented objects, and the white regions are planes. The modeling results of the randomly scattered objects are shown in the right-hand image of Fig 12.

Fig. 12 Object segmentation(left) and primitive object modeling(right)

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The target object to be grasped was determined by the quantity of modeling. In most cases, the top object among the stacked objects was selected as the object to be picked up. After the object to be picked up was selected, the variance between the pose of the reference object and the selected object was transmitted via the RS232C. Finally, we examined whether the robot could successfully grasp the bolt. Fig. 13 shows the robot picking up an object from among the randomly piled bolts.

Fig. 13 The robot successfuly picks up a part from among the randomly piled objects

4.3 Quantitative Results To estimate the repeatability of the proposed method, we compared the error of the rotational symmetric object’s radius and orientation with the average value of 30 measurements. Tables 1 and 2 show the repeatability of the position and orientation for the proposed method, respectively. Table 1 Repeatability of the specimen radius (unit : mm) Radius ± 0.15

Repeatability

Table 2 Repeatability of the specimen orientation (unit : degree)

Repeatability

x ± 0.482

y ± 0.549

z ± 1.260

Through the qualitative and quantitative experiments performed with the use of the industrial robot, we verified that this system could be employed for certain material handling and automotive plant assembly applications.

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5 Conclusion In this article, we proposed a structured light based bin-picking system using primitive models. To obtain a reliable range image, we made use of a structured light sensor that consisted of a camera and a DLP projector, which illuminated 8bit antipodal Gray-coded patterns. In contrast with the conventional pose estimation method for a bin-picking system, which requires complete knowledge of the object (e.g., geometric features in an image, CAD models), we estimated the pose of the object with known geometric primitive information through primitive segmentation, rotational symmetric object modeling, and recognition. The experimental results obtained with the use of an industrial robot confirmed that the proposed system was able to be employed for a practical bin-picking system. In the near future, we will study the pose estimation method for complex objects with free-form surfaces, and we will look for methods to determine multiple grasp points without collision. Acknowledgments. This research was performed for the Intelligent Robotics Development Program, which is one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. This research was also supported by Hyundai Heavy Industries as a part of an industry–academic cooperative research program.

References [1] Iversen: Vision-guided Robotics: In Search of the Holy Grail. In: Automation World, February 2006, pp. 28–31 (2006) [2] Hardin, W.: Vision enables freestyle bin picking. Vision System Design 12(6) (June 2007) [3] Ban, K., Warashina, F., Kanno, I., Kumiya, H.: Industrial Intelligent Robot. FANUC Tech. Rev. 16(2), 29–34 (2003) [4] Rahardja, K., Kosaka, A.: Vision-based Bin-Picking: Recognition and Localization of Multiple Complex Objects using Simple Visual Cues. In: Proc. of the IEEE/RSJ international Conf. on Intelligent Robots and Systems, November 1996, vol. 3, pp. 1448–1457 (1996) [5] Schraft, R.D., Ledermann, T.: Intelligent picking of chaotically stored objects. Assembly Automation 23(1), 38–42 (2003) [6] Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, Englewood Cliffs (2003) [7] Trucco, E., Verri, A.: Introductory Techniques for 3-D Computer Vision. PrenticeHall, Englewood Cliffs (1998) [8] Jähne, B., Haußecker, H.: Computer Vision and Applications: A Guide for Students and Practitioners. Academic Press, London (2000) [9] Kim, D., Ryu, M., Lee, S.: Antipodal Gray Codes for Structured Light. In: Proc. of the IEEE International Conference on Robotics and Automation, May 2008, pp. 3016–3021 (2008)

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[10] Horn, B.K.P.: Closed-Form Solution of Absolute Orientation Using Unit Quaternions. Journal of the Optical Society of America A 4(4), 629–642 (1987) [11] Fujiwara, N., Onda, T.: Three -Dimensional Circle Detection and Location of Pipe Joints for Bin-Picking Tasks. In: Proc. of the IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, vol. 2, pp. 1216–1221 (1998) [12] Hema, C.R., Paulraj, M.P.: Segmentation and Location Computation of Bin Objects. International Journal of Advanced Robotic Systems 4(1), 57–62 (2007) [13] Wani, M.A., Batchelor, B.G.: Edge-Region-Based Segmentation of Range Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(3) (March 1994) [14] Baek, K.-K., Park, Y.-C.: Object/Environment Segmentation based on Generic Model for 3D Object Recognition and Modeling. In: Korean CAD/CAM Conference, pp. 414–418 (2008)

Airframe Dismantling Optimization for Aerospace Aluminum Valorization Julie Latremouille-Viau, Pierre Baptiste, and Christian Mascle*

Abstract. Seeking the most cost-effective process, dismantlers must continuously make decisions while they part and shear out a plane. The mathematical model presented optimizes the profitability of aircraft dismantling process by determining which airframe entities must be sheared and sorted prior to shred its components or which entities must be directly shredded. The model also identifies which shredded components should be sorted in order to upgrade recovered materials composition. Until now, disassembly sequences generation and disassembly planning methods have been elaborated based on assembly connection types and generic mathematical models have been published. Most of the time, these approaches are time consuming and require efforts to be adapted to a specific product. The model proposed here is aircraft-oriented and is not only based on assembly connection types and fasteners classification which are considered as being too restrictive. Knowing that an airplane is made of about 60 % of aluminum alloys and that aluminum recycling could considerably reduce the aerospace industry’s ecological footprint, the model focuses on aircraft aluminum recovery.

1 Introduction Aircraft retirement occurs when reparations or upgrades are too expensive [1]. In this case, aircraft dismantling can be an interesting option knowing that the aircraft design, its age, state and location, material collecting costs, infrastructure availability, recovery methods and consumer demand for used or remanufactured components have an important influence on the overall profitability. According to the president of Aircraft End-of-Life Solutions (AELS), more than 90 % of an aircraft can be reused, remanufactured or recycled [2]. Components and materials valorization rarely reach more than 60 weight percents of an airplane because of the lack of infrastructures and technologies inefficiency. Therefore, aircraft owners often prefer to warehouse obsolete airplanes or to dump them near an airport or in the desert. These unsecured storages can have important environmental impact such as soil contamination. Boeing has Julie Latremouille-Viau . Pierre Baptiste . Christian Mascle École Polytechnique de Montréal, Department of Mathematics and Industrial Engineering 2900, boul. Édouard-Montpetit, Montreal

*

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determined that about 7,200 civil airplanes will be retired in the next 20 years without considering military equipment. Actually, about 300 aircrafts of a hundred passengers’ capacity are dismantled each year [2] in order to reuse non obsolete components, to recover them and to recycle materials. The metallic airframe is a mix of aluminum, zinc and magnesium alloys [1]. According to Bartin Recycling Group, a specialized aircraft dismantling company, the metallic composition of an airplane contains between 65 and 75 % of aluminum alloys, 10 % of steel, 3 % of titanium and 2 % of copper [3]. The remainder is made of wood, isolating materials, plastics, glass and rubber. The primary aluminum production from bauxite is an energy-consuming process which needs important quantities of non-renewable natural resources. In addition, this process generates air and water emissions, byproducts and solid wastes. Each kilogram of aluminum produced from bauxite release 3.5 kg eqCO2 in the atmosphere [4]. Aluminum recycling provides many advantages compared to primary production because it reduces material consumption as energy, water, land occupation, waste disposal, and air and water emissions. The secondary aluminum production (aluminum recycling) requires between 80 and 95 % less energy, produces 90 % less wastes and reduces 90 % of air emissions (CO2, fluoride, SO2) by ton of aluminum produced compared to primary production [5]. According to London Metal Exchange (LME) historical data, secondary aluminum costs less than primary aluminum which is due to lower production costs. This observation applies only to basic aluminum alloys that are found in large amounts on the market and that are easily recycled because of their availability and large demand. It is perhaps more difficult to recycle specialized aluminum alloys due to their small demand and their mechanical properties which require more complex production processes. Aerospace aluminum alloys are in majority wrought alloys which have a lower tolerance to impurities than cast alloys. The most used alloys are specialized alloys from series 2xxx and 7xxx. The nominal composition of these alloys contains high quantities of copper and zinc which explain their highest market value compared to other series knowing that these alloying elements are in much lower proportions in other wrought alloys. According to reference [6], three options should be taken into consideration in aircraft dismantling strategies: reuse aluminum alloys from recycled aircrafts (1) in new aircrafts (closed loop), (2) in non-aircraft applications or (3) in aluminum castings (open loop). Even though cost-effective aircraft alloys recycling is complex because of high alloying element concentrations and the low level of impurities required, it is important to evaluate if the amount of aircrafts retired each year, their aluminum composition, the technology available and operation costs associated to the aircraft dismantling process can provide an economic source of recycled aluminum for the aerospace industry. In this paper, an algorithm is proposed in order to determine which aircraft dismantling sequence provides the most cost-effective aluminum value recovery for a specific aircraft given the three options mentioned above. A literature review is first presented in which an overview of research issues in the disassembly field is made, followed by the actual aerospace aluminum recirculation flow based on

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two well-known aircraft dismantling sites activities. Then, an airframe dismantling optimization method based on a mathematical optimization model is developed and tested in order to optimize aluminum valorization based on operation costs and aluminum market value.

2 Litterature Review Nowadays, the implementation of environmental legislations, increasing disposal fees, social responsibility, corporate imaging, environmental growing concern, production costs reduction and the awareness of conserving energy and material resources are encouraging original equipment manufacturers (OEM’s) to ensure an environmental friendly end-of-life treatment of their products according to the Extended Producer Responsibility principle (EPR) [7, 8, 9, 10]. This situation explains the growing interest for the dismantling and disassembly fields.

2.1 Research Issues in the Disassembly/Dismantling Field According to reference [11, 12], distinct levels of detail are used in dismantling planning (DP). The strategic (long-term) level focuses on reverse logistics: network planning and facility location planning. The tactical (mid-term) level is the field of disassembly planning and scheduling: master dismantling schedule and capacity planning. Finally, the operational (short-term) level is related to the detailed dismantling scheduling (DDS): shop floor control and task planning. Disassembly sequences generation and optimization are needed prior to disassembly planning. Disassembly sequences are network representations of admissible subsequent disassembly operations. Feasible sequences depend on the type of product to be dismantled, the techniques applicable, the objective of the dismantling effort [11] and the knowledge of connection types and preceding relations among disassembly actions. In many cases, incomplete disassembly can provide a most cost-effective option than complete disassembly. Technical constraints can also force an incomplete disassembly [13]. Disassembly sequences can be of two different natures: selective or partial. Selective disassembly emphasis on a particular component and optimizes a disassembly sequence for this specific component as mentioned by Wang et al. (2003) [24]. Instead of focusing on a particular component, partial disassembly determines the best disassembly sequence within feasibility limits, i.e. the best sequence and the best end-node of a whole product. Tripathi et al. (2009) have implemented this approach [25]. References [10, 12] present an overview of different dismantling sequences modeling strategies. Takeyama et al. (1983) assumed that the disassembly sequence of a product was the reverse of its assembly sequence [14]. Knowing that some assembly processes are not completely reversible, this approach can’t be applied for any product. Bourjault (1984) introduced the generation of all assembly/disassembly sequences using connection diagram, component-fasteners graph or direct graph elaborated on the study of connection types and fasteners classification.

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According to preceding relations among disassembly actions, disassembly choice diagrams can then be generated [15]. This approach has been used by Zhang, H. C. and Kuo, T. C. (1997) to generate a graph-based disassembly sequence planning for end-of-life (EOL) product recycling and they also introduced the use of disassembly precedence matrices [16]. In the case of complex assemblies, the amount of disassembly choice diagrams becomes hardly manageable. Furthermore, the model can’t be used when more than one component or subassembly can be disassembled simultaneously when one-to-many disassembly techniques are available. A near optimal disassembly sequences approach has been proposed in order to solve this problem [17]. De Fazio and Whitney (1987) elaborated a single disassembly graph based on connectivity states and a graph based on corresponding subassembly states also called diamond diagram [18]. Bourjault et al. (1987) have also proposed the use of Petri nets in disassembling sequences [19] when these sequences are already known. AND/OR graph representation have been introduced by Homen de Mello and Sanderson (1990) which is a compact representation of all feasible assembly sequences and can provide a selection tool for the optimal assembly plan [20]. In 1991, they adapted this strategy for the generation and selection of disassembly sequences [21]. Here again, the large amount of possibilities can complicate the implementation of the proposed strategy particularly if incomplete dismantling sequences must be included. To avoid this disadvantage, Lambert (2000) proposed a reduced disassembly graph [22]. A component-oriented approach integrating assembly and disassembly in a computer aided system which includes direction, physical properties, connections and fasteners analysis has also been elaborated by Mascle and Balasou (2001) [23]. Product-oriented approach has been proposed too for the automatic analysis of detachability of subassemblies from CAD-files or drawings, but the size of problems grows exponentially with the amount of components which requires the addition of geometric and technical constraints. Once the set of dismantling sequences is identified, mathematical programming methods (MP) such as Linear Programming (LP), Mixed Integer Programming (MIP), Dynamic Linear Programming (DLP) and the shortest path algorithm can provide a complete or incomplete optimal disassembly sequence based on costs and revenues or any other objective: minimum dismantling time, maximum recycling ratio, minimum environmental impact, etc. Krikke et al. (1998) have developed a DLP to optimize a recovery plan and its disassembly sequence by considering technical, commercial and ecological aspects [26]. Willems et al. (2004) have presented a LP model based on costs and revenues related to a particular network [27]. Artificial intelligence methods have also been implemented as genetic algorithm (GA), ant colony algorithm, neural networks and fuzzy logic but the generated solutions are often sub-optimum solutions.

2.2 Actual Situation in the Aerospace Industry Shearing and shredding equipments as excavator with shears combined with sorting technologies as magnetic sorting, air current sorting and laser induced

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breakdown spectroscopy (LIBS) can provide homogeneous aluminum lots. According to the aircraft dismantling approaches used by Bartin Recycling Group and Evergreen air center, two businesses which own an aircraft dismantling site, it is not the way things are actually done. Fig. 1 shows the actual aircraft dismantling parting out chain and Fig. 2 presents the actual recirculation diagram for aircraft aluminum components. First, the decontamination of the aircraft starts: kerosene, battery, pyrotechnic, electronic and electric materials removal. Then, airplane components as seats, reactors, engines and landing gears are collected (disassembled). Fig. 1 Aircraft dismantling parting out chain

Disposal companies

Smelters

Fluids, oils, plastics, composites

Banks

$

New components

Metals

Aircraft dismantling Reusable components Obsolete aircrafts companies

Aircraft owners

Regulations Reusable components

Regulatory institutions

Specialists overhaul

Reusable components

The remained structure, the airframe, is cut out and crushed with a shear. The scrap obtained is finally sent to smelters and melted without being sorted or after being sorted coarsely.

Fig. 2 Actual recirculation diagram for aerospace aluminum alloys

Bauxite extraction

Alumina production

Aluminum wrought alloys Aircraft critical components

Primary aluminum production Aluminum cast alloys

Aircraft non-critical components Non-aircraft applications Cast aluminum alloys

Secondary aluminum production

Because of the high impurity of the aluminum collected, the aluminum alloys obtained by this recirculation process can’t be reused in the aerospace industry for fracture critical aerospace components, but can be reused in non-fracture critical aircraft components and in non-aircraft applications as in the automobile industry.

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These alloys can also be reused in the production of cast aluminum alloys for which specifications are less restrictive and the tolerance to impurities is higher.

3 Algorithm and Model Description In papers, dismantling is often associated to disassembly. A precision must be made to differentiate both activities. Disassembly is the action of separating components of an assembly or a subassembly. On the other hand, a part could be dismantled in several pieces even if this part is made of a unique component. Equipment used to dismantle EOL products such as airplanes are not limited to assemblies and subassemblies concepts. This is why dismantling sequences generation must not be restricted to the study of joint types and fasteners classification. The algorithm proposed in this paper (Fig. 3) is based on dismantling sequences generation instead of being based on disassembly sequences generation which contrasts with every modeling strategy proposed in previous papers.

Fig. 3 Dismantling algorithm

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3.1 Algorithm Description The algorithm starts with the dismantling sequences generation. Even if it is time consuming, disassembly graphs can easily be generated because assemblies, subassemblies and components have been known since the product conception phase. In dismantling, the product’s entities are unknown because the process is not limited to disassembly possibilities. For this reason, dismantling sequences must be generated based on subsequent dismantling actions feasibility, therefore on human knowledge of the product and available technical resources. Each sequence generated must reach the maximal decomposition level according to technical and geometric limits. A decomposition operation is a shearing operation which separates an element in two or more elements. Once the dismantling sequences generation is completed, every sequence must be implemented in the binary linear program proposed. For each sequence, the model determines for which decomposition level the proposed sequence is optimal and the profitability associated. Using the given algorithm, the optimal complete or incomplete dismantling sequence is obtained. The main inconvenient of this approach is that it can be difficult to collect the required knowledge of the product to elaborate dismantling sequences but the proposed algorithm reduces the number of sequences generated to the most appropriate and can deal with the uncertainty of the EOL product state due to changes on the product during its usage phase. The main frame of this algorithm can be used for any disassembly or dismantling sequences generation and optimization, but the binary linear program is only applicable for airframes and products that can be dismantled without being restricted to their disassembly. Finally, the algorithm can also be used for a section of a product instead of an entire product.

3.2 Linear Programming Model Description The mathematical optimization model presented below offers the opportunity to recycle aircraft critical aluminum components in the aeronautic industry if shreds’ sorting is a cost-effective operation. The model determines which airframe entities must be sheared prior to shred its components or which entities must be directly shredded. The model also identifies which shredded components should be sorted in order to upgrade recovered materials composition. The word “entities” is used instead of assemblies and subassemblies for an evident purpose. Figure 4 presents the aspect of a dismantling sequence (dismantling tree) and its components designation. Six assumptions have been made: 1. Sorting is done by air current sorting, magnetic sorting and LIBS which sort aluminum by alloy types; 2. The shearing time is linearly proportional to the number of components included in the sheared entity;

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3. The shredding time of a component is linearly proportional to its volume; 4. The sorting time of a component is linearly proportional to its volume; 5. Shearing, shredding and sorting operation costs are determined by the user (cd, cs, ct); shred’s sorting costs are the only sorting cost considered.

Fig. 4 Example of an airframe dismantling tree

i=1

Product / Product section

i=2 i=4

Entities

i=3

i=4

i=6

i=7

i=8 (2)

i=9

i=10

i=11

i=12

i=13

Parts

Indices and Sets i: airframe components z: entities y: parts a: aluminum alloy types SAi: sets of components obtained from the shearing of component i Parameters ni: number of components obtained from the shearing of component i Va: Market value of alloy type a per weight unit cd: shearing cost per time unit cs: shredding cost per time unit ct: sorting cost per time unit Ni: quantity of different alloy types contained in component i vm: Non sorted aluminum market value by weight unit Hi: User defined parameter, 1 if the component i is made of only one kind of aluminum alloy, 0 else. Pai: total weight of alloy type a contained in component i

⎧user defined value, ⎪ Pa ,i = ⎨ n P ∑ k ak ⎪⎩k∈SAi

i ∈ y, ∀a i ∈ z , ∀a

VTi: Total aluminum market value contained in component i

(1)

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∀i,

Pai = ∑ Va Pai

165

(2)

a

Vi: Volume of component i

i ∈ y,

⎧user defined value, ⎪ Pa ,i = ⎨ n V ∑ kk ⎪⎩k∈SAi

i ∈ z,

(3)

TDi: Time needed to shear component i

∀i ∈ z , TDi =

∑n

k ∈SAi

k

(4)

C1 is a constant linked to aluminum shearing which must be determined by the user by experimentations. C1 varies according to the used shearing equipment. TSi: Time needed to shred component i

∀i,

TSi = C 2Vi

(5)

C2 is a constant linked to aluminum shredding which must be determined by the user by experimentations. C2 varies according to the used shredding equipment. TTi: Time needed to sort shreds of component i

∀i,

TTi = C 3Vi

(6)

C3 is a constant linked to aluminum sorting which must be determined by the user by experimentations. C3 varies according to the used sorting technologies. Variables

⎧1, Component i is sheared Di = ⎨ ⎩0, else

(7)

⎧1, Component i is shredded Di = ⎨ ⎩0, else

(8)

⎧1, shreds of component i sorted Di = ⎨ ⎩0, else

(9)

Object Function The objective of this mathematical model is to maximize the profitability of an airframe dismantling process. The object function contains two main parts: revenues associated with the aluminum recovery from sorted aluminum shreds or

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non-sorted aluminum shreds and operation costs linked to shearing (DiTDicd), shredding (SiTSics) and sorting (TiTTict). The last expression of the function must be added in order to consider only the shredding costs of first shredded entities of each branch of the dismantling tree which include the shredding costs of all their subsequent components.

⎛ ∑ niTiVTi + ∑ ∑ H k DzVTk + ∑ ∑ nk S zTS k cs ⎞ ⎜ i ⎟ z k∈SAk z k∈SAk ⎜ ⎟ ⎜ ⎛ ⎞⎟ max⎜ + vm⎜⎜ ∑ Pai − ∑∑ niTi Pai − ∑ ∑ ∑ H k Dz Pak ⎟⎟ ⎟ i a z k∈SAk a ⎝ a ⎠⎟ ⎜ ⎜ − n (D TD cd + S TS cs + T TT ct ) ⎟ i i i i i i i ⎜ ∑ ⎟ ⎝ i ⎠

(10)

Constraints A sheared component can’t be shredded, a shredded component can’t be sheared and every component must be sheared or shredded:

∀i,

Di + Si = 1

(11)

Last parts of the disassembly tree can’t be sheared:

∀i ∈ y,

Di = 0

(12)

If an entity is shredded, all its components are shredded too:

∀i ∈ z , k ∈ SAi ,

Si ≤ S k

(13)

Sorting operation costs are only considered for shreds sorting. A component can be sheared or sorted but can’t be both:

∀i,

Di + Ti ≤ 1

(14)

Shreds’ sorting is possible only for components made of more than one alloy type:

∀i,

N i ≥ 2Ti

(15)

Sorting is possible only for the first shredded component of each branch of the dismantling tree:

∀i ∈ z , k ∈ SAi ,

2 − (Si + S k ) ≥ Tk

(16)

Sorting is possible only for a shredded component:

∀i, ∀i,

Si ≥ Ti

(17)

Di , Ti , Si ∈ {0,1}

(18)

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4 Model Implementation The proposed model has been used to evaluate which components of an empennage (Fig. 5) should be sheared, shredded and/or sorted.

Fig. 5 Empennage dismantling sequence Empennage i=1 i= 3 V stabilizer with rudder

(2) H stabilizer with i= 2 elevator kit =4 Horizontal stabilizer

i= 5 Elevator kit

i= 8 Elevator

i= 6 Vertical stabilizer

i= 7 Rudder

i= 9 Trim tab

Using the Cplex solver of AmplStudio software, it took 0:10:10 seconds to solve a problem of 57 constraints and 27 variables. Table 1 contains the parameters’ value used to solve the problem. The solution obtained with shearing, shredding and sorting costs of 100 $/h is to shred and sort the empennage (i=1) for a total profit of 42 700 $. C1, C2, C3 and vm have been arbitrarily fixed to 1 h/component, 2 h/m3, 1 h/m3 and 1000 $/t. Table 1 Empennage problem parameters

Quantity per component (tons) Estimate Alloys value ($/ton) a1 3000 a2 3500 a3 2500 Estimate volume (m3)

4

6

7

8

9 1.0

2.0 2.0 1.0

1.0

2.0 1.0 1.0

0.5

1.0 0.5

C1, C2 and C3 have an impact on the generated solution and its profit. When C1 worth less than 0.42, the program propose to shear entities 1 and 3 for a greater benefit but when C1 is higher than 0.42, the optimal solution proposed and its profit remain the same as the original because from that point it is no more costeffective to shear an entity. In the original scenario, every entity is shred so a diminution of C2 leads to an augmentation of the profit without modifying the sequence. But, when C2 is greater than 60 h/m3 shearing becomes a cost-effective

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operation instead of shredding for entities 1, 2 and 3. Finally, lower values of C3 bring a higher profit but superior values of C3 leads to sequences without sorting operations.

5 Conclusion As mentioned before, the dismantling optimization model exposed in this paper could be used for the dismantling of any products which are not restricted to their disassembly possibilities. The model could be adapted for the valorization of any materials and could also be modified for the valorization of multiple materials. Further studies will be done in order to incorporate other elements in the object function such as the environmental impact and energy savings. Eventually, the algorithm will be adapted to determine which end-of-life strategy must be selected between the dismantling process, dumping or storage.

References 1. ATE&M, The end of the line – aircraft recycling initiatives. Aircraft Technology Engineering and Maintenance Magazine, pp. 28–33, April/May (2007) 2. Vlielander, S.: The end of an aircraft’s life. Auto Recycling Nederland, http://www.aels.nl/sites/aels/user_files/NewsArticles/ ARNArticleEnglishComplete.pdf 3. Itzkowitch, Z.: 1ère Plateforme européenne de démantèlement aéronautique. Dossier de presse Bartin AERO Recycling (Mars 2008), http://www.veoliaproprete.com/documents/ DP%20Bartin%20Aero%20Recycling_2008_HD.pdf 4. Reh, L.: Challenges for process industries in recycling. China Particuology 4(2), 47–59 (2006) 5. Schlesinger, M.E.: Aluminum Recycling, p. 225. Boca Raton, Florida (2007) 6. Das, S.K.: Recycling Aluminum Aerospace Alloys. Light Metals, 1161–1165 (2007) 7. Mutha, A., Pokharel, S.: Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers & Industrial Engineering 56, 334–346 (2009) 8. Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J.M., Van Wassenhove, L.N.: The impact of product recovery on logistics network design. Production and operations management 10(2), 156–173 (Summer 2001) 9. Toffel, M.W.: Strategic management of product recovery. California management review 46(2), 120–141 (Winter 2002) 10. Tang, Y., Zhou, M., Zussman, E., Caudill, R.: Disassembly modeling, planning and application: a review. In: Proceedings of the 2000 IEE International Conference on Robotics & Automation, San Francisco, CA, April 2000, pp. 2197–2202 (2000) 11. Schultmann, F., Sunke, N.: Planning Models for the Dismantling of Electrical and Electronic Equipment Under Consideration of Uncertainties. Progress in Industrial Ecology – An International Journal 5(1/2), 82–101 (2008) 12. Lambert, A.D.J.: Disassembly sequencing: a survey. International Journal of production Research 41(16), 3721–3759 (2003)

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13. Lambert, A.D.J.: Determining optimum disassembly sequences in electronic equipment. Computers & Industrial Engineering 43(3), 553–575 (2002) 14. Takeyama, H., Kojima, T., Inoue, K., Honda, T.: Study on Automatic Determination of Assembly Sequence. CIRP Annals 32(1), 371–374 (1983) 15. Bourjault, A.: Contribution a une approche méthodologique de l’assemblage automatisé: élaboration automatique des séquences opératoires. PhD Thesis, Besançon, France: Faculty of Science and Technology, Université de Franche-Comté (1984) 16. Zhang, H.C., Kuo, T.C.: A Graph-Based Disassembly Sequence Planning for EOL Product Recycling. 21st IEEE/CPMT Int. Electronics Manufacturing, 140–151 (October 1997) 17. Gungor, A., Gupta, S.M.: Disassembly Sequence Planning for Complete Disassembly in Product Recovery. In: Proc. of the Northeast Decision Sciences Institute Conf., Boston, MA (1998) 18. De Fazio, T.L., Whitney, D.E.: Simplified Generation of all Mechanical Assembly Sequences. IEEE Journal of Robotics and Automation 3(6), 640–658 (1987) 19. Bourjault, A., Chappe, D., Henrioud, J.M.: Élaboration automatique des gammes d’assemblage à l’aide de résaux de Pétri. RAIRO APII 21, 323–342 (1987) 20. Homen De Mello, L.S., Sanderson, A.C.: And/or graph representation of assembly plans. IEEE Transactions on Robotics and Automation 6(2), 188–189 (1990) 21. Homen De Mello, L.S., Sanderson, A.C.: A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE Transactions on Robotics and Automation 7(2), 228–240 (1991) 22. Lambert, A.J.D.: Optimum disassembly sequence generation. In: Proceedings of 2000 SPIE Conference of Environmentally conscious Manufacturing, pp. 56–67 (2000) 23. Mascle, C., Balazoiu, B.A.: Disassembly-assembly sequencing using feature-based life-cycle model. In: Proceedings of 2001 IEEE International Symposium on Assembly and Task Planning, pp. 399–403 (2001) 24. Wang, J.F., Liu, J.H., Li, S.Q., Zhong, Y.F.: Intelligent Selective Disassembly using the Ant Colony Algorithm. Artificial Intelligence for Engineering Design and Manufacturing 17, 325–333 (2003) 25. Tripathi, M., Agrawal, S., Pandey, M.K., Shankar, R., Tiwari, M.K.: Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self Guided Ants (ASGA). Robotics and Computer Integrated Manufacturing 25, 2197–2202 (2009) 26. Krikke, H.R., Harten, A.V., Schuur, P.C.: On a medium term product recovery and disposal strategy for durable assembly products. International Journal of Production Research 36, 111–139 (1998) 27. Willems, B., Dewulf, W., Duflou, J.: End-Of-Life strategy selection: a linear programming approach to manage innovations in product design. International Journal of Production Engineering and Computers 6(7), 45–53 (2004) 28. Yi, J., Yu, B., Du, L., Li, C., Hu, D.: Research on the selectable disassembly strategy of mechanical parts based on the generalized CAD model. International Journal of Advanced Manufacturing Technology 37(5-6), 599–604 (2008)

A Monitoring Concept for Co–operative Assembly Tasks Jukka Koskinen, Tapio Heikkilä, and Topi Pulkkinen*

Abstract. In this paper we present a monitoring concept for co-operative assembly tasks. In cooperative assembly a human operator and a collaborative robot –cobot - share the work place and carry out assembly tasks. A generic assembly model is described for collaborative assembly tasks where two heavy parts are joined together. A cobot monitoring concept was developed with a software architecture considering functional, safety and quality aspects. The developed software was integrated and tested in an experimental cobot cell. Keywords: co-operative robots, cobot, monitoring.

1 Introduction Co-operative assembly robots sharing a workplace with humans are called assist robots. Such a robot can be guided physically by a human or it can assist a human worker without physical guidance. In the previous case the robot is called a passive collaborative robot (cobot) [1] and in the latter case an intelligent assist robot [2]. This paper focuses on cobots. A typical example of a cobot application is an assembly task where a human lifts a heavy load in co-operation with a cobot; the human introduces motion intelligence and the cobot produces power assistance. High flexibility and improvements in ergonomics are two important reasons for applying assist robots [3]. The major motivation for the implementation of cobots is to improve the ergonomics and thus reduce physical stress on the operators. Manual handling or assembly of heavy parts covers a wide range of activities, including lifting, pushing, pulling and holding. Working operations including these Jukka Koskinen . Tapio Heikkilä . Topi Pulkkinen Technical Research Centre of Finland, Kaitovayla 1, FI-90571 Oulu, Finland e-mail: {Jukka.Koskinen,Tapio.Heikkila,Topi.Pulkkinen}@vtt.fi *

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types of work include often a substantial risk for injuries, especially musculoskeletal disorders [4]. Often, altering the workplace or working conditions using mechanical aids such as cobots are most potential way to go. When the operators are released from carrying heavy loads, they can focus on controlling the handling or assembly operations. Better quality can be achieved with better ergonomics. Many of the current industrial manual assembly tasks could be fully automated with conventional robots, but high flexibility is often difficult to achieve costeffectively with conventional robot systems [5]. Robotic systems are usually costeffective in assembling high-volume products, but flexibility in these assembly lines is low. Low and medium-volume products are often customized for customer needs and production times and volumes may vary depending on product demand. Therefore, high product flexibility (ease of new product introduction and product modification) and mix flexibility [6] (ability to change the relative proportions of different products within an aggregate output level) are required by manufacturing systems for low and medium-volume products. High flexibility can be achieved by co-operative robot-human systems rather than autonomous robot cells. This paper contributes to increasing flexibility by introducing a control concept for new machines or robots for assisting humans in assembly tasks. Safety plays an important role in interactive task execution because the human operator and cobot are sharing the same workplace. Safety sensors are used to detect instant safety-related errors and support safety precautions in order to reduce the risk of safety flaws. An assembly system that executes and monitors tasks where a cobot and a human work co-operatively needs special attention to monitoring and the related flexibility. The system should recognize and recover from abnormal situations like safety risks or system malfunctions by itself, without action by the operator. This paper presents the requirements for a cobot cell and a concept for controlling and, especially, monitoring a collaborative assembly task. The functional, safety and quality aspects are considered here and an overview of a cobot test system is presented.

2 Aspects for Monitoring Cobot Systems 2.1 Functional Aspect A collaborative assembly task execution with cobotic systems is basically done in a same manner as with intelligent assist robots. However, there are substantial differences between use of cobots and intelligent assist robots in the details: • In cobots, fine motions for attachment are typically guided and controlled by human operators; for assist robots, there is much more freedom (either the human or the robot can do the fine motion); for cobots, this is also a matter of reduced investment costs (the human replaces sensors).

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• In assist robot systems, sensors that can be used to guide placement and attachment motions never close the control loop, except for motion guidance by force control. In cobotic systems, the sensor information is always shown to the human operator who is guiding and controlling the fine motions of the cobot; for cobots, external sensors can still be used in a closed loop manner for automated parts of the work sequence, such as picking parts or billets with the cobot. • Use of cobots heavily relies on human skills to observe and analyze the environment and spatial relationships between billets and parts, while assist robots rely on either the stability of the environment or the use of external sensors; this makes cobots much more flexible, although more restricted in the sense of performance (capacity). The foundation of the cobotic assisted assembly is that a human works as part of the control loop with the cobot. Therefore, it is not advisable to constrain the user of the system. In addition, a robot does have its advantages, like precision, strength and repeatability. It also has its limitations, such as reachability and use of force where a subtle operation would be required. Humans have an advantage in visual perceptual ability and reasoning. With these two sides to consider, it is imperative that the optimal use of the cobot is found by utilizing the best ‘features’ of humans and robots. One way of doing this is to sequence the assembly process and figure out how the cobot can help the user in the best possible way. If the sequencing is successful, a partial assembly process has been created, which allows the user enough freedom to choose what to do but also encourages better utilization of the extra hand the cobot provides. Monitoring can then be focused on the task constraints in each task of the sequence. The general assembly operation can be decomposed into more detailed operations, here called “tasks”. The tasks “get part”, “pick part” and “install part” are considered here as general sub-operations of “assembly”. The decomposition of the general assembly operation into these tasks is illustrated with a state machine in Fig. 1. The assembly operation here is considered to be sequential, i.e. to refer to all affix operations that are needed to construct a product or product billet. For example, attaching two parts can be considered to compose one “assembly operation”. A precondition for this is that the result has been modeled/identified as such and also referred to a product billet in the assembly order description. The synchronization of the execution of the tasks is carried out by corresponding events. In general, these events can be created by any of the related subordinates or the human operator in the flexible assembly cell/station. Each task can be further decomposed into subtasks (or “actions”). Later in this section only the collaborative subtask “install” is illustrated in detail with a state machine diagram. The “get” task is decomposed into subtasks referring to feeder and sensor operations. Again, either a subordinate (feeder, robot, etc.) or the human operator

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Fig. 1 Decomposition of the “assembly” operation into “tasks”

can create the synchronization events. This is mainly because of the possibility to have automated picking of parts or billets by the cobot. From the monitoring point of view, this means that the validity constraints of “get”, or even its subtasks such as “feed part” or “locate part”, should be supported by a monitor. For example, observing feeder status or feeder behavior can be one monitoring task. Also, the status or behavior of a sensor can be one monitoring task. The “pick” task is decomposed into subtasks referring to cobot, human and sensor operations. Picking can be carried out as an automated operation by the cobot. Again, either a subordinate or the human operator can create the synchronization events. From the monitoring point of view, this again means that the validity of “pick”, or even its subtasks such as “move to grasp”, “grasp”, “depart from grasp” or “locate part in gripper”, should each be supported by a monitor. For example, free space status, feeder behavior or behavior of the location sensor can be monitoring tasks. The “install” task (Fig 2.) is decomposed into subtasks referring to cobot, human and sensor operations. Here, the collaborative subtasks where the cobot carries the load while installing but is guided interactively by the human operator are identified. Sensors can be used to guide the human operator in carrying out the fine motion guidance in the proper way (e.g., guiding arrows in the cobot GUI to show the desired direction of motion). Table 1 summarizes the functional monitors of the assembly task.

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Table 1 Examples of functional monitors for a cobot cell Monitor

Task/action

Sensor type

Feeder status

Get part, Pick part

Binary switch

Locating sensor status

Get part/locate part

Camera

Gripper status

Pick part, Install part Pressure sensor

Cobot free space status Get part, Pick part

Fig. 2 Decomposition of the “install” task into subtasks

Proximity sensor

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2.2 Safety Aspects By safety sensor system we refer to the safety-related sensors that are used to prevent collisions between robots and humans. Standardized safety sensors usually produce binary information for the safety monitoring systems and are hard-wired to the robot safety functions. When a safety sensor detects a person in the area in which the robot is operating automatically, the safety monitoring system automatically stops or halts the robot or restricts its direction of movement or slows down its movements. The operator is informed of such situations through the UI. Human actions are normally only needed when recovering from such situations. A flexible system should have the ability to make a decision by itself, i.e. whether to stop, halt or continue a task operation based on the safety sensor information. From the safety point of view, this means that the system should know the locations of humans more accurately – not just that a human is entering an operating area, as is the situation in conventional robot cells. Thus, in the monitoring of co-operative work we can speak of localization of humans rather than just collision monitoring. A safety sensor in a co-operative cobot cell can be a camera system, a laser scanner, a light curtain, a safety mat or any type of range or positioning sensor. Typically, redundancy should be used. This produces redundant information that is used to ensure safe operation. From the monitoring point of view, this means that two types of monitors are used for checking the human operator location in the cobot’s operating area to prevent collisions in the automatic part of the cobot operation, i.e. the human operator is not allowed to be in the area the cobot is moving to. Table 2 summarizes the monitors needed for safe operation of a cobot cell.

Table 2 Examples of safety monitors for a cobot cell Monitor

Task/action

Sensor type

Human entrance

Get part, Pick part

Safety mat, virtual fences

Human location

Get part, Pick part

Radio positioning

Human cobot collision

Install part

Proximity sensor

2.3 Quality Aspects In collaborative task operations, the human operator takes care of interactively controlling all the critical operations with the required accuracy, also targeting better quality and efficiency. There should be a measure that could be used to indicate improved quality and improved efficiency in the assembly tasks. An increasingly common method for this is to use Overall Equipment Efficiency (OEE).

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OEE is an indicator of how well the equipment or machinery is performing or being utilized within a time period and indicates which parts of the assembly line or machinery are not performing well. OEE is a tool that helps focusing on improvement activities. OEE is defined as product of three factors: availability, performance rate and quality rate. Availability is the ratio between the actual operating time and the planned operating time. Performance efficiency is the ratio between the actual rate and the designed rate. Quality rate is the ratio between the number of good quality items produced and the total number of items produced. Introducing a cobot to an assembly task will increase the quality rate and performance efficiency. Quality and efficiency in terms of OEE factors can be monitored by measuring variables from which the OEE factors are calculated [7]. However, OEE is not used for controlling robot or task execution, i.e. there is no feedback to the systems. It is just a measure of how well a machine or process is performing. Since the human and the cobot are working co-operatively, human actions also have an effect on process performance, i.e. OEE. However, a human’s, a machine’s or a cobot’s efficiency are indistinguishable directly from OEE factors. Reasons for lower efficiency can be inferred from the variables from which the OEE factors are calculated. Such variables are cycle times and operating times, which are used to define availability and performance efficiency. From the monitoring point of view, this means that a monitor is used to catch the events denoting the start and end of the assembly sequence, showing the active time of the assembly sequence and the idle time between assemblies. One way to define the operating times is to subtract a machine’s down time from its loading times [8]. The loading time is the time when the machine is in productive use. The difference between the functional and the safety monitors is that the user of the OEE monitor is outside the cobot cell, e.g. the SPC (Statistical Process Control) system. Table 3 summarizes the monitors for quality monitoring. Table 3 Examples of quality monitors for a cobot cell Monitor

Task/action

Sensor type

Cycle time

Assembly operation

Virtual sensors/process events

Operating time

Assembly operation

Virtual sensors/process events

3 Monitoring Architecture for Assembly Tasks/Operations In the decomposition models described earlier there are many “task” operations that are mainly moving and handling the parts. There are also sensing operations to get feedback from the part and environment relationships for guiding – or controlling – the task execution. Generally, each can be extended with further sensory operations, the purpose of which is to observe that (functional or safety) preconditions specific for the task are satisfied during the execution time. If such preconditions are not valid, the task

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execution should be halted or stopped. Taken that such observations will be determined according to the (mainly spatial) details of the task, we are applying the principles of programmable monitors defined in the so-called PEM concept (Planning, Execution, Monitoring) [9]. This approach has been applied earlier in the automatic execution of the paper roll piling tasks of an autonomous heavy duty manipulator [10], and also in distributed production control [11]. In the concept of programmable monitoring, each operation, e.g. a task or subtask, has a related monitor, the detailed behavior of which depends on the context and details of the operation. This is illustrated in Fig. 3.

Fig. 3 General operation model: extension with a programmable monitor

In nominal task execution, the monitor is evoked while the task is activated. Both the task execution and the monitor behavior are modeled as state machines and the synchronization of them is done with events representing the completion of the subtasks/sub-operations or with reports from the monitoring. The task

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execution proceeds as planned unless the monitor observes something conflicting with the preconditions of the task. If the monitor observes a conflict in the preconditions of the task, it reports an error to the task execution. The task execution is then responsible for deciding whether recovery operations can be tried and carried out or whether the task execution should be halted or stopped. In collaborative phases, all of these decisions should be made by the human operator.

Fig. 4 Safety monitor behavior

Detection of a human in an assembly area is an example of an automatically recoverable case. This means that in these situations the system should recover by itself. Such situations are monitored within an architecture that has the ability to deal with variations from task to task without first having to finish. A non-nominal execution of a subtask and a monitor is illustrated in Fig. 4 and in Fig. 5. The

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monitor checks whether a zone (sub area) of the cell is occupied or free. If the zone where the cobot is entering is occupied (an extra human in the area), the monitor sends to the executor the message “zone NOT OK”. The cobot is halted in such case until the zone is free again.

Fig. 5 Example of non-nominal execution of a subtask (shown in Fig. 2) with a programmable safety monitor

4 Experimental Cobot Cell For testing our monitoring concepts we developed a cobot cell and monitoring and control software that carries out task execution and monitoring of an assembly operation. The cobot cell was designed to carry out car rear screen installation assembly: in the screen installation the cobot gets and picks up a rear screen from a buffer and moves it to assembly area, where an operator guides the cobot arm to the rear door and installs (Fig. 6) the rear screen co-operatively. The assembly operation of the rear window is divided into three sub-tasks (get part, pick part and install part). The functional and safety aspects presented in this paper were implemented. The structure of the test system is presented in Fig. 7. The cell controller was implemented with C++ and is responsible for the execution and monitoring of the assembly tasks. The cell controller is based on the architecture presented in previous section. The human operator controls the cobot through a human machine interface (HMI) and mainly interacts by applying force to the cobot. The movement types (rotation, translation or their combinations) can be selected from the GUI. The

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Fig. 6 Co-operative screen installation

Fig. 7 Structure of the developed cobot like system

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directions of movements given by the operator are sensed with the force sensor as forces and torques [12]. Depending on the selected movement directions, the forces and torques are controlled on a position-based force control principle via the analog I/O to the robot controller, which acts accordingly. Two cameras are used in order to define the screen position in the cobot’s tools centre point. This data is used in the force control algorithm to compensate for the contact forces between the screen and rear door frame. This corner point detection enables screen installation by only one operator: When the operator guides the cobot to make contact of the screen corner to the door frame corner, the cobot keeps this contact and the operator can move to the other side of the door frame and complete the screen installation by flipping the screen into the door frame. Image processing routines are implemented in a PC with Matlab. The other functions of the robot controller change the robot’s movement types with regard to the user’s wishes and, when the assembly is over, automatically select the routines for collecting another screen from the buffer. Force controller’s outputs are connected to robot’s motion increments in the robot controller by a “function generator”, which feeds the interpolation loop of a continuous motion with the motion increments acquired from the force controller unit. Radio positioning technology was used for locating humans in the cobot cell and safety mats were used for entrance monitoring. Three safety mats were installed on the floor of the cobot cell. With radio positioning equipment, humans can be located indoors by installing an RF tag on their clothes. The position is calculated from the delay times of the radio waves between the tag and four base stations. The main advantage of radio positioning is that large areas can be monitored and the cobot’s cell can be divided into subareas. Modification of the shapes and sizes of the areas is straightforward in the safety master PC. For collision detection, a capacitive proximity sensor was tested to detect human presence close to the cobot’s arm. Table 4 shows the implemented monitors. All the sensors were integrated to the task execution control by wireless networks [13]. Table 4 Implemented Monitors for the cobot cell Monitor

Task/action

Sensor type

Human entrance

Get part/ Pick part

Safety mat

Human location

Get part/ Pick part

Radio positioning

Human cobot collision

Install part

Capacitive proximity sensor

5 Discussion In this paper, monitoring aspects using a Collaborative Robot (cobot) in an assembly task were presented. The functionality and safety aspects were then tested with a car rear window installing process, where the operator guides the window

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installation into its door frame using the cobot as an extra powered arm. We modified a heavy and large size robot so that it acted like a cobot. However, one should be careful while using this kind of robot as cobot in industrial use as the developed cobot-like was a research prototype to testing the functional feasibility of the approach and not to meet the safety standards. Our purpose was to develop a test environment in which the control and monitoring concepts, safety sensors and force control algorithms for human robot interaction could be tested. Cobots developed for industrial use are specially designed for handling, lifting and assembling materials - i.e. they may look different from traditional assembly robots or manipulators. Industrial cobots look more like cranes or other handling devices and can be moved with rails or wheels. The safety functions provided in the test system were used for illustrating the development of the monitoring part of the control system. Thus the safety sensors should be considered prototypes of future safety sensors. The wireless connection to the cell controller was a practical, though not safety-classified, choice. Acknowledgments. This work is partially funded by the European Union as part of the NMP-2004-3.4.3.12 PISA project.

References 1. Peshkin, A., Colgate, E.J., Wannasuphoprasit, W., et al.: Cobot architecture. IEEE trans Robotics and Automation 17, 377–389 (2001) 2. Bernhardt, R., Surdilovic, D., Katschinski, V., et al.: Next generation of flexible assembly systems. In: Azevedo, A. (ed.) Innovation in Manufacturing Networks. Springer, Boston (2008) 3. Akella, P., Peshkin, M., Colgate, E., et al.: Cobots for the automobile assembly Line. In: IEEE Int. Conf. on Robotics & Automation, pp. 728–733 (1999) 4. Code of practice for manual handling, Occupational Health and Safety Act, No. 25, Victorian Work Cover Authority, Australia (2000) 5. Hägele, M., Schaaf, W., Helms, E.: Robot assistants at manual workplaces: Effective co-operation and safety aspects. In: Int. Symp. on Robotics, pp. 404–409 (2002) 6. Baker, J.: Agility and flexibility: What’s the difference? Tech Rep SWP 5/96 (1996), https://dspace.lib.cranfield.ac.uk/handle/1826/1151?mode=f ull (Accessed February 3, 2010) 7. Ljungberg, O.: Measurement of overall equipment effectiveness as a basis for TPM activities. Int. J. of Operations and Prod. Management 18, 495–507 (1998) 8. Dal, B., Tugwell, P., Greatbanks, R.: Overall equipment effectiveness as a measure of operational improvement – A practical analysis. Int. J. of Operations and Prod. Management 20, 1488–1502 (2000) 9. Heikkilä, T., Röning, J.: PEM-Modelling: A framework for designing intelligent robot control. J. of Robotics and Mechatronics 4, 437–444 (1992) 10. Vähä, P., Röning, J., Heikkilä, T., et al.: Mechatronic system techniques for intelligent paper roll manipulator systems. In: Leoneds, C.T. (ed.) Mechatronic Systems Techniques and Application, Gordon and Breach Science Publishers Inc., Newark (2000)

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11. Heikkilä, T., Kollingbaum, M., Valckenaers, et al.: An agent architecture for manufacturing control: Manage. Comput in Ind. 46, 315–323 (2001) 12. Sallinen, M., Heikkilä, T., Koskinen, J.: Sensor based flexibility for robotics in manufacturing applications. In: IEEE Int. Symp. on Industrial Electronics, pp. 1422–1427 (2009) 13. Heikkilä, T., Rehu, J., Korkalainen, M., et al.: Location aware multihop wireless sensor network. In: IEEE Int. Conf. on Ultra-Modern Communication, pp. 1–7 (2009)

Chapter lll

Manufacturing System Scheduling and Controlling Summary by Byung-Wook Choi

This chapter reports on the recent advancement of manufacturing system scheduling and controling with 7 contributions. In their article entitled "Printing pressure control algorithm of roll-to-roll web system for printed electronics," Kyung-Hyun Choi, Tran Trung Thanh, Yang Bong Su, and Dong-Soo Kim propose a mathematical model of roll printing pressure control system and then design a full state feedback controller with gains determined optimally by using the modified genetic algorithm as well as the back-stepping approach. This study shows that, with the rapid development of sensor technology, electronic devices, and powerful computer, the proposed control algorithm of full state feedback controller can result in a control system with high precision. In their article entitled "Adding Diversity to Two Multi-Objective Constructive Meta-heuristics for Time and Space Assembly Line Balancing," Manuel Chica, Oscar Cordon, Sergio Damas, and Joaquin Bautista propose a new mechanism to introduce diversity into two multi-objective approaches based on ant colony optimization and randomized greedy algorithms solve a more realistic extension of a classical industrial problem: time and space assembly line balancing problem (TSALBP). This study shows promising results obtained by applying the designed constructive metaheuristics to ten real-like problem instances. In their article entitled "Construction and Application of a Digital Factory for Automotive Paint Shops," Yang-Ho Park, Eon Lee, Seon-Hwa Jeong, Gun-Yeon Kim, Sang-Do Noh, Cheol-Woong Hwang, Sangil Youn, Hyeonnam Kim, and Hyunshik Shin deal with a digital factory that is a single integrated model for virtual manufacturing in which diverse engineering activities such as design evaluation, process and material planning, production flow analysis, and ergonomic analysis can be brought together. They have suggested systematic methods, considerations, and expectations as the core basis for applying virtual manufacturing technologies to the engineering activities of an automotive paint shop in new car development. In their article entitled "Resource Efficiency in Bodywork Parts Production," Reimund Neugebauer and Andreas Sterzing discuss the question of what options are open to companies in the manufacturing industries for increasing resource efficiency. An opportunity emerges for reducing material deployment on the one

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hand and the amount of energy required for the production of components on the other. This paper shows a selection of approaches reducing the consumption of resources in the area of forming technology, particularly in the bodywork parts production sector. In his article entitled "Self-Tracking Order Release for Changing Bottleneck Resources," Matthias Huesig presents a self-tracking order release strategy for job shop production with well-defined routes. The release strategy is a combination of different known methods. This study shows that the proposed order release strategy could easily be implemented for complex shop problems with alternating routes and also used for cyclic job shop problems with multiple use of the same machine. In their article entitled "Integrated Operational Techniques for Robotic Batch Manufacturing Systems," Satoshi Hoshino, Hiroya Seki, Yuji Naka, and Jun Ota deal with a batch manufacturing system with multiple industrial robots, by proposing the operational techniques such as route planning approaches, operation dispatching rules, and reactive cooperation, on the basis of task-assignment that will reduce the effect of a bottleneck which is a constraint dominating the entire system performance. They have showed throughout the simulation experiments that the proposed operational techniques could effectively improve the shifting bottleneck and evenly spread a heavy workload. In their article entitled "A Mathematical Model for Cyclic Scheduling with Assembly Tasks and Work-in-Process Minimization," Mohamed Amin Ben Amar, Herve Camus, and Ouajdi Korbaa deal with cyclic scheduling problems with assembly/disassembly tasks and work-in-process minimization, by proposing a mathematical model of the scheduling issue of such systems which deals with the specificity of assembly systems, that is, synchronization of multiple tasks. This study shows that one can find an optimal scheduling (cycle time) for assembly/disassembly systems.

Printing Pressure Control Algorithm of Roll-to-Roll Web System for Printed Electronics Kyung-Hyun Choi, Tran Trung Thanh, Yang Bong Su, and Dong-Soo Kim*

Abstract. In the paper, a mathematical model of roll printing pressure control system using the pneumatic system is proposed. By writing the system of dynamic equations in strict feedback form and applying the back-stepping theory, a full state feedback controller is obtained with gains that are determined optimally by modified genetic algorithm (MGA). A printing pressure control algorithm is given by using the proposed mathematical development and controller. The simulation results employed in Matlab/Simulink show the stability and high precision of the proposed algorithm.

1 Introduction In recent years, there are many applications which employed roll-to-roll web technology for mass production such as web printing, papers machine, film processing, and textiles fabrics and so on to make cheaper production in shorter time. Especially, RFID and printed electronics use the principle of roll-to-roll manufacturing Kyung-Hyun Choi . Tran Trung Thanh School of Mechanical Engineering, Jeju National University, 66 Jejudaehakno, Jeju-si, Jeju-do, Republic of Korea Tel.: +82-64-754-3713; Fax: +82-64-756-3886 e-mail: [email protected], [email protected] Yang Bong Su School of Electrical Engineering, Jeju National University, 66 Jejudaehakno, Jeju-si, Jeju-do, Republic of Korea e-mail: [email protected] Dong-Soo Kim 171, Jang-Dong, Yeseung-Gu, Daejeon, KOREA Institute of Machinery & Materials/ Nano-Mechanical System Research Division, Republic of Korea, Tel.: +82-42-868-7152 Fax: +82-42-868-7176, C.P: +82 - 11-855-1816

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to make devices at high speeds for lower cost will have a big impact on the printed electronics and publishing industries. Several developments pushed the burgeoning printed electronics industry and up to now roll-to-roll system technology is seen as the key to producing flexible electronic components, such as organic thin film transistors and other applications. In order to improve the quality of product and the precision of roll to roll processing, many important aspects should be consider such as web tension [18], lateral error [9], web printing pressure and so on. In recent years, applications of gravure/offset printing technology for printed electronics to make micro-level devices have been taken attention by many researchers and scientists in all over the world. With the increasing demand of roll to toll manufacturing technology applicability [2, 5, 8, 10], it is needed to improve accuracy and quality of printed electronics technology. Thus, printing pressure control algorithms play an important roll in producing the product with high quality. Until now, almost all roll to roll web printing system setups based on offline calculations, supplemented by trial and error procedures during operation. Works in [6, 10, and 11] discusses about mathematical model of cold rolling and temper rolling process of thin steel strip by using the influence function method. This model can well predict not only the rolling load but also the large forward slip. An off-line model based controller and a process simulator are described in [2] by using the laws of physics. This model can predict the future behavior and stability of controlled process and model based control system is evaluated on a simulation model that represents accurately the dynamic of the process. However, these works are only applicable for steel strip processing with slow speed and relatively large thickness and not consider the effect of web tension during operation. In this paper, the goal is to develop the printing pressure control algorithm that controls the gap with micro-level precision between two rollers, maintains the precise web pressure and thickness, avoids the slippage and fast responds with disturbances. By considering two following assumptions: 1. The slippage and unsuitable pressure are main causes that affect on the printing quality. 2. In order to eliminate the slippage and keep web pressure at suitable pressure, the problem leads to find the pressure (or gap) that is high enough (small enough) to keep web no slippage and no destroying (or satisfying the durability condition of materials). Thus, proposed pressure printing control algorithm development is based on eliminating the slippage and keeping gap at suitable distance. A mathematical model of roll printing pressure control system using the pneumatic system is proposed. By writing the system of dynamic equations in strict feedback form and applying the back-stepping theory, a full state feedback controller is obtained with gains that are determined optimally by genetic algorithm. A printing pressure control algorithm is given by using the proposed mathematical development and controller. The simulation results employed in Matlab/Simulink show the stability and high precision of the proposed algorithm.

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2 Mathematical Model Development Figure 1 shows the model of offset printing pressure control system that consists of two air cylinders, nip roll, offset roll and ink roll. Cylinders with controlled valves are used to change pressure and move up and down nip roll to reference positions to make printing pressure at suitable level.

Fig. 1 The model of offset printing pressure control system

Figure 2 shows the model of roll printing pressure control system using the pneumatic system. It is assumed that the air is ideal and the process is isothermal.

Fig. 2 Model of roll printing pressure control system using the pneumatic system

- By assuming for a moment a positive input signal to the valve, a short line between valve and cylinder and chamber pressure of about half the supply pressure, mass flow rate and input pressure of cylinder is written as follows;

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ps uv 2 p  B = −K 2 s u v m 2  A = K2 m

(1)

Where K 2 : Valve coefficient

p s : Supply pressure ( psupply ) u v : input voltage - Using the equation of continuity and some assumptions, pressure dynamic equations in two cylinder chambers are written as follows [12, 15];

p A =

p A Ap Δx A m p0 V0 /2 + A p Δx ρ 0 V0 / 2 + Ap Δx

p B Ap Δx B m p0 + p B = V0 /2 − A p Δx ρ 0 V0 / 2 − Ap Δx

(2)

Where V0 : The total air volume

ρ 0 : The reference density (chosen according to ISO 6358) ρ 0 : Reference pressure (chosen according to ISO 6358) A p : The cross-section area of cylinder Δx : The piston displacement From the equations (1) and (2), we have:

p AB = p A − pB = −

ps Ap V0 / 2

Δx +

K 2 ps p0 uv V0 / 2 ρ 0

(3)

Where p AB : The different pressure between two chambers + The motion equation of piston, rod, and nip roll. By supposing that the frictional force is proportional to the speed ( FFric = − K F Δx ), we have;

Δx =

A p p AB − FL + K F Δx MP

(4)

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Where M s : The sum of mass of piston, rod, and half of nip roll

FL : The external forces FFric : The frictional force Δx : The acceleration of piston By using the equations (1) (2) (3), and (4) and supposing that the frictional force is proportional to the speed ( FFric = − K F Δx ); the motion equation of piston 1 and 2 are written as follows;

⎧M p1x 1 = A p1 p1 − F1 + K F1 x 1 ⎪ p s1 A p1 K 21 p s1 p 01 ⎨   x u1 = − + p 1 ⎪ 1 V /2 V /2 ρ 01 01 01 ⎩

⎧M p2 x2 = A p 2 p 2 − F2 + K F 2 x 2 ⎪ p s 2 Ap 2 ⎨ K 22 p s 2 p02   = − + p x u2 2 2 ⎪ V02 / 2 ρ 02 V02 / 2 ⎩

(5)

(6)

Where

x1 , x2 : The piston displacement of cylinder 1 and 2 K 21 , K 22 : Valve coefficients of cylinder 1 and 2 M p1 , M p2 : The sum of mass of piston and rod of cylinder 1 and 2, respectively; F1 , F2 : The external forces of cylinder 1 and 2, respectively p1 , p 2 : Different pressure between two chambers of cylinder 1 and 2, respectively K F1 , K F 2 : The frictional coefficient of cylinder 1 and 2, respectively

FFric : The frictional force V01 , V02 : The total air volume of cylinder 1 and 2 ρ 01 , ρ 02 : The reference density of cylinder 1 and 2 (according to ISO 6358) p 01 , p 02 : Reference pressure of cylinder 1 and 2 (according to ISO 6358) A p1 , A p 2 : The cross-section area of cylinder p s1 , p s 2 : Supply pressure of cylinder 1 and 2 ( psupply ) u1 , u 2 : input voltage of valve of cylinder 1 and 2

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Figure 3 and Figure 4 show the motion of pressure roller and relationship between displacement of cylinder 1 and 2 and rotation angle of pressure roller.

Fig. 3 Motional model of pressure roll

Fig. 4 Detailed motional model of pressure roll

By using the Lagragian equation II; we can write the equation of motion of roll;

⎧⎪M R xP = F1 + F2 − M R g + Fc ⎨  ⎪⎩ Jθ = − F1 L1 + F2 L2 + Fc L p

(7)

Where M R : The mass of pressure and connecting parts; g : The gravity

Fc : The contact force between printing roll and pressure roll J : The inertia moment of pressure roll θ : The angular displacement of pressure roll x P : The displacement of rotational center of pressure roll From Fig. 3 and Fig. 4, the relationship between x P , θ and sented as follows:

x1 , x 2 is repre-

Printing Pressure Control Algorithm of Roll-to-Roll Web System

x 2 − x1 ⎧ ⎪⎪θ = L ⎨ ⎪ x = L1 x + L 2 x ⎪⎩ P L 1 L 2

193

(8)

Figure 5 shows the cross-section area of contact model between web and rollers. By the assumption about uniform nip pressure and uniform tension on crosssection of web and the independent action that the effect of nip pressure and web tension is independent. Thus, the deformation sum under vertical direction due to nip pressure will be the sum of two components:

Fig. 5 Cross-section area of contact model between web and rollers

From the above considerations and using the elastic theory, Newton’s law, and it is assumed that rollers are rigid body and have the same radius, we have:

Δx Re f = h −

([F ] +

pW

2 ER1ϕw

)h

−μ

T EW

(9)

And

FLoad = [σ ]R1ϕw −

pw T +μ wh 2

Where FLoad pw

: Force acts on the pressure roll

: Gravity force of nip roll and connecting mechanisms

h : The thickness of web R

1 : Radius of nip roll μ : Poisson coefficient

T: web tension

(10)

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[σ ] : Allowable compression stress; w : The width of web ΔxRe f

: Convenient gap between pressure and offset roll E: Young module By letting:

⎧ y1 = x1 ⎧ y1 = x1 ⎪ y = x ⎪ y = x 1 1 ⎪ 2 ⎪ 2 ⎪⎪ y3 = p1 ⎪⎪ y 3 = p 1 ⇒⎨ ⎨ ⎪ y4 = x2 ⎪ y 4 = x 2 ⎪ y5 = x 2 ⎪ y 5 = x2 ⎪ ⎪ ⎪⎩ y 6 = p 2 ⎪⎩ y 6 = p 2

(11)

By combining the equations (4) (5) (8) (10) and (11), the dynamic equations of printing pressure control system are written under form as follows:

1 ⎡ y1 ⎤ ⎡0 ⎢ y ⎥ ⎢0 k 10 ⎢ 2⎥ ⎢ ⎢ y 3 ⎥ ⎢0 k14 ⎢ ⎥=⎢ 0 ⎢ y 4 ⎥ ⎢0 ⎢ y 5 ⎥ ⎢0 k 4 / k1 ⎢ ⎥ ⎢ 0 ⎢⎣ y 6 ⎥⎦ ⎢⎣0 ⎡0 ⎢0 ⎢ ⎢k + ⎢ 15 ⎢0 ⎢0 ⎢ ⎢⎣ 0

0 k8

0 0

0 k13

0

0

0

0

0

1

k 2 / k1 0 k 7 / k1 0

0

k16

0 ⎤ ⎡ y1 ⎤ k11 ⎥⎥ ⎢⎢ y 2 ⎥⎥ 0 ⎥ ⎢ y3 ⎥ ⎥⎢ ⎥ 0 ⎥ ⎢ y4 ⎥ k 5 / k1 ⎥ ⎢ y5 ⎥ ⎥⎢ ⎥ 0 ⎥⎦ ⎢⎣ y6 ⎥⎦

0⎤ 0 ⎡ ⎤ ⎢ k +k ⎥ 0 ⎥⎥ ⎢ 9 12 ⎥ ⎥ 0 ⎥ ⎡ u1 ⎤ ⎢ 0 ⎥⎢ ⎥ + ⎢ ⎥ 0 ⎥ ⎣u 2 ⎦ ⎢ 0 ⎥ ⎢(k 3 + k 6 ) / k1 ⎥ 0⎥ ⎥ ⎢ ⎥ k17 ⎥⎦ 0 ⎢⎣ ⎥⎦

Where

y1 , y 4 : The piston displacement of cylinder 1 and 2 y 2 , y 5 : The displacement speed of piston of cylinder 1 and 2, respectively.

(12)

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y 3 , y 6 : Different pressure between two chambers of cylinder 1 and 2, respectively. And

([F ] +

pW

⎡ X 1 ⎤ ⎡1 0 0 ⎢ X ⎥ ⎢0 1 0 ⎢ 2⎥ ⎢ ⎢ X 3 ⎥ ⎢0 0 1 ⎢ ⎥=⎢ ⎢ X 4 ⎥ ⎢0 0 0 ⎢ X 5 ⎥ ⎢0 0 0 ⎢ ⎥ ⎢ ⎣⎢ X 6 ⎦⎥ ⎢⎣0 0 0

0 0 0 1 0 0

ΔxRe f = h −

2 ER1ϕw

)h

T EW

(13)

0⎤ ⎡ y1 ⎤ 0⎥⎥ ⎢⎢ y2 ⎥⎥ 0⎥ ⎢ y3 ⎥ ⎥⎢ ⎥ 0⎥ ⎢ y4 ⎥ 0⎥ ⎢ y5 ⎥ ⎥⎢ ⎥ 1⎦⎥ ⎣⎢ y6 ⎦⎥

(14)

−μ

The output equation;

0 0 0 0 1 0

Where

M R L21

M R L1 L 2 M R L22 J J J + 2 , a 22 = − 2 a 11 = − 2 , a 12 = 2 2 2 L L L L L L Fc L p M R gL2 Fc L2 F L M gL F L + − b1 = b2 = − c p + R 1 − c 1 , L L L L L L 2 a12 − M p 2 − a 22 A p1 b1 k1 = , , k3 = − k2 = M p1 + a11 a12 M p1 + a11 Ap2 K F1 b K k4 = k5 = − , , k6 = 2 , k 7 = − F2 a12 M p1 + a11 a12 a12 − ( M p 2 + a22 ) k3 − ( M p 2 + a22 ) k4 k10 = , a12 k1 a12 k1 − ( M p 2 + a 22 ) k 5 A p 2 − ( M p 2 + a22 ) k6 b2 + , k12 = − , k 11 = a12 k1 a12 a12 k1 a12 p s 1 A p1 − ( M p 2 + a22 ) k7 K F 2 K 21 p s1 p 01 + , k 15 = , k13 = k 14 = − a12 k1 a12 V01 / 2 V 01 / 2 ρ 01 p s2 A p2 K p p k 16 = − k 17 = 22 s 2 02 V 02 / 2 ρ 02 V02 /2 k8 =

−(M p 2 + a 22 ) k 2 , a12 k1

k9 =

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By using the criterion of controllability or

r = Rank(B AB A 2 B A 3B A 4 B A 5B]) = 6

(15)

It is concluded that the system is controllable.

3 Full State Feedback Controller Design 3.1 Mathematical Basis Consider a control system under strict feedback form as follow [3]:

x 1 = f1 ( x1 , x2 ) x 2 = x3 + f1 ( x1 , x2 ) x3 = u + f1 ( x1 , x2 , x3 )

(16)

Step1: Imagine that we can use x2 to stabilize at 0 the first equation with a feedback law α1 ( x1 ) so that ∂V1 / ∂x1. f ( x1 ,α1 ) < 0 , for all x1 ≠ 0 , where Δx1 is known Lyapunov function. Note, however, that we can achieve x2 = α1 ( x1 ) only

= x2 − α1 ( x1 ) . Let us also denote z1 = x1 so that x1 and x 2 are known explicit functions of z1 and z 2 vice-versa. We now rewrite the first

with an error z 2

two system equations as:

z1 = f1 ( z1 ,α1 ( z1 )) + z2ϕ1 ( z1 , z2 ) z2 = x3 + f 2 ( z1 , z2 + α1 ( z1 )) − α1 Where ϕ1 ( z1 , z2 ) is known, because pressed as:

f1 is assumed to be differentiable and is ex-

f1 ( z1 , z2 + α1 ( z1 ) ≡ f1 ( z1 ,α1 ( z1 )) + z2ϕ1 ( z1 , z2 ) Another key observation is that α is also known explicitly. Step 2: imagine now that we can use x3 to stabilize at 0 the above ( x1 , x 2 )-system

with feedback law α 2 ( z1 , z2 ) . To design α 2 , we first construct a Lyapunov function:

V2 ( z1 , z2 ) = V1 ( z1 ) + 1 / 2 z22 ) With

x3 = α 2 ( z1 , z 2 ) we want to make V2 negative

Printing Pressure Control Algorithm of Roll-to-Roll Web System

Recall that the first term was made negative in step 1, so we choose to make

V2 negative or

α 2 ( z1 , z2 ) = − z2 −

197

α 2 ( z1 , z 2 )

∂V1 ϕ1 ( z1 , z2 ) − f 2 ( z1 , z2 + α1 ( z1 )) + β1 ( z1 , z2 ) ∂z1

However, since we cannot achieve x3

= α 2 ( z1 , z2 ) , there is an error

z3 = x3 − α 2 ( z1 , z2 ) and the actual V2 is: ∂V V2 = 1 f1 ( z1 , α1 ( z1 )) − z22 + z1 z3 ∂z1 We will take care of

z1 z 2 in step 3. Since we know z1 z 2 and z 3 as function of x1 ,

x 2 and x3 and vice-versa, our system can be written as: z1 = f1 ( z1 , α1 ( z1 )) + z2ϕ1 ( z1, z2 ) z2 = z3 + α 2 ( z1 , z2 ) + f 2 ( z1 , z2 + α1 ( z1 )) − β1 ( z1 , z2 )) z3 = u + f3 ( z1 , z2 + α1 ( z1 ), z3 + α 2 ( z1 , z2 )) − β 2 ( z1 , z2 , z3 ) Where

β 2 ( z1 , z2 , z3 ) is the known expression for α 2

Step 3: at this step there no need to imagine a fictitious control, because the actual control u is our disposal. A feedback law for u is now chosen to make the derivative of

V3 = V2 + 1 / 2 z32 negative. By doing so, the designed feedback law ob-

tained as follows:

u = − z3 − f 3 ( z1 , z2 + α1 , z3 + α 2 ) + β 2 ( z2 , z2 , z3 ) − z2

(17)

3.2 Application for Designing the Full State Feedback Controller By applications of the above mentioned theory, the full state feedback controller of the dynamic equations of printing pressure control system (12) is given:

u1 =

1 (−c3 ( y3 − α 2 ) − k14 y2 + α 2 ) k15

1 (−c6 ( y6 − α 4 ) − k16 y4 + α 4 ) u2 = k17

(18)

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Where

α1 = −c1 ( y1 − yRe f ) , α 1 = −c 1 y 2 α1 = −c1 (k 8 y 3 + k11 y 6 + k 10 y 2 + k13 y 5 + k 9 + k 12 ) 1 (−c 2 ( y 2 − α 1 ) − k11 y 6 − k 10 y 2 − k13 y 4 − k 9 − k12 + α 1 ) k8 1 α 2 = (c 2α 1 − (c 2 + k10 )(k 8 y 3 + k 11 y 6 + k10 y 2 + k13 y 5 + k 9 + k12 ) − α1 ) k8 α 3 = −c4 ( y4 − yRe f ) , α 3 = −c 4 y 5

α2 =

k k k k k2 k y3 + 5 y6 + 4 y 2 + 7 y5 + 3 + 6 ) k1 k1 k1 k1 k1 k1 k k k k k k α 4 = 1 (−c5 ( y5 − α 3 ) − 2 y3 − 4 y2 − 7 y5 − 3 − 6 + α 3 ) k1 k1 k1 k1 k1 k5 k k k k k k k α 4 = 1 (c5α 3 − (c5 + k7 )( 2 y3 + 5 y6 + 4 y2 + 7 y5 + 3 + 6 ) + α3 ) k1 k1 k1 k1 k1 k1 k5 The parameters c1 , c 2 , c3 , c 4 , c5 and c6 in full state feedback controller are posi-

α3 = −c 4 (

tive gains that are determined by using the MGA mentioned in chapter 4.

4 Applications of Genetic Algorithm for Optimal Gains Genetic algorithm (GA) is stochastic search method that mimics the metaphor of natural biological evolution. GA starts with no knowledge of the correct solution and depends completely on responses from its environment and evolution operators (i.e. reproduction, crossover and mutation) to arrive at the best solution [4, 14, 16, 17]. By beginning with the initial populations and searching in parallel, the algorithm avoids local minima and converging to sub optimal solutions. Figure 6 shows the structure of a GA. GA works on populations of individuals instead of single solutions. In this way the search is performed in a parallel manner. These problems may be overcome by the introduction of a mutation operator into the GA. Mutation is the occasional random alteration of a value of a string position. It is considered a background operator in the GA. In recent years, with the rapid development of digital computer, GA has been applied for many applications. However, almost algorithms are employed for the linear SISO system. In this paper, modified genetic algorithm (MGA) is proposed by using the approach of space state model. Fig. 7 is the diagram of MGA applied in this paper with parameters shown in Table 1.

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Table 1 Parameters of modified genetic algorithm Table. Parameters

Values

1

A number of generation

N=100

2

Size of population

3

The probability of mutation

S=10 pm =0.8

4

The probability of crossover

pc =0.3

5

Scale factors

γ 1 = 0 .5 ; γ γ 3 = 0 . 1; γ

2 4

= 0 .5 = 0 .1

Fig. 6 The structure of genetic algorithm

By using the proposed algorithm, the positive gains

c1 , c 2 , c 3, c 4 , c 5 , c 6 of

back-stepping controller (18) are optimally determined by using the MGA with parameters shown in table I to obtain the desired performance specifications with objective function shown in (19). N

( )

N

( )

N

( )

N

( )

J = γ 1 ∑ Δx12 i +γ 2 ∑ Δx22 i + γ 3 ∑ u12 i + γ 4 ∑ u22 i → Min i =1

i =1

i =1

i =1

Cm

(19)

m = 1,6; Where N is an integer number of iterations in control simulations, is the error between the operat-

γ 1 , γ 2 , γ 3 , γ 4 are scale factors, Δx1 = x1 − x ref ing and reference position of cylinder 1 and

Δx 2 = x 2 − x ref is the error between

200

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ui (i = 1,2) is input con-

trol of valve of cylinders.

Fig. 7 The diagram of modified genetic algorithm

5 Printing Pressure Control Algorithm Design In industrial applications, almost all gravure/offset printing systems use the gap control technology. By supplementing by trial and error procedures during operation with each type of web materials, the suitable gap is given. In other cases, pressure feedback control system is designed by using only feedback signal receiving from load cell. In reality, the obtained results are poor and the accuracy is low. With the rapid development of sensor technology, digital computer and computation speed, PC-integrated control systems take a special attention in control system design. Fig. 8 shows the block diagram of printing pressure control system using the full state feedback controller.

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Fig. 8 The block diagram of printing pressure control system

In this algorithm, the load cell, encoder, position sensors are used to feedback the signals of position, displacement speed, and pressure of nip roll. From the obtained signals, full state feedback controller (18) with optimal gains determined by the MGA in chapter 4 generates the control input to adjust the valve of cylinder and makes system obtain the performance specification.

6 Simulation 6.1 Simulation Parameters By the application of the proposed algorithm with the full state feedback controller in the (18) and It is assumed that two cylinders implemented in simulation have the same parameters that are shown in Table 2 and Table 3, the simulation outcomes can be implemented by using the Matlab/Simulink tool. The optimal gains c1 , c 2 , c3 , c 4 , c5 and c6 obtained by using the MGA mentioned in chapter 4 are shown below

c1 = 55.2; c 2 = 52.5, c3 = 53.0; c 4 = 49.3; c5 = 53.6, c 6 = 53.3 (20)

Table 2 Web material properties Table. Name

values

units

1

The thickness of web; h

0.001

m

2

Allowable compression stress 0.08 109

N / m2

3

Poisson’s coefficient

0.38

-

4

Web tension

40

5

Young’s module

2.7 10

6

The width of web

0.3

N 9

N / m2 m

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K.-H. Choi et al. Table 3 Parameters of cylinders and control valves Table. Name

Values

Units

1

Valve coefficient

0.01

-

2

Frictional coefficient

0.005

-

3

Supply pressure

7. 105

N / m2

4

Reference pressure

101325

N / m2

5

Reference density of air

1.2250

Kg / m 3

6

Cross-section area of cylinder 0.015386

7

Stroke of cylinder

0.2

m

8

The mass of cylinder

0.5

Kg

9

Total air volume of cylinder

0.0038465 m3

m2

6.2 Simulation Condition Simulation result is employed with web material that is PET with parameters in Table 2. Depending on the equation (13) and above parameters; we can calculate the reference position between nip roll and offset roll in (21): ΔxRe f = h −

([F ] +

pW

2 ER1ϕw

)h

−μ

T = 0.0009575(m) EW

(21)

It is assumed that the distance between the center of nip roll and center of offset roll is H 0 = 0.1(m) at initial time. So, we can determine the reference displacement of piston in (22):

H reference = H 0 − (2 R + h − ΔxRe f ) = 0.05995(m)

(22)

To estimate the effectiveness of proposed algorithm, simulation results are employed in three cases, the first one is done without the external load force with the same initial conditions, and the second one is without the external load force with the different initial conditions and the last one is implemented with external load force shown in Fig. 9. In the first second, no contact between pressure roll and web occurs. When time is more than 1 second, the beginning of contact between pressure roll and web occurs and external load force increases in time. When time is more than 2 second, the desired position of pressure roll is obtained and external load force is constant as shown in Fig. 9.

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Fig. 9 The change of external force on pressure roller

6.3 Simulation Results Case 1: Without external load force and same initial condition in two cylinders.

Fig. 10 The position change between pressure roll and offset/gravure printing roll

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Fig. 11 The speed change of nip roll or piston

Fig. 12 The pressure change of two cylinders

Simulation results without external load force and same initial condition shown in above Fig. 10, Fig. 11 and Fig. 12. The response of system reaches the stability and obtains the specification performances with no overshoot and steady-state error and settling time about 0.175 second. Also, the response of dynamic parameters of two cylinders is the same.

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Case 2: Without external load force and same initial condition in two cylinders

Fig. 13 The position change between pressure roll and offset/gravure printing roll

Fig. 14 The speed change of nip roll or piston

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Fig. 15 The pressure change of two cylinders

Simulation results without external load force and with different initial conditions, the response of system reaches the stability and obtains the specification performances with no overshoot and steady-state error in Fig. 13. The response of dynamic parameters of two cylinders is different in the first 0.2 second and obtains the desired value with settling time about 0.2 second in Fig. 14. Case 3: With change of the external load force in Fig. 9

Fig. 16 The position change between pressure roll and offset/gravure printing roll

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Fig. 17 The speed change of nip roll or piston

Fig. 18 Pressure change of two cylinders

Simulation results with external load force in Fig. 6.1 and with different initial conditions. At time interval 0 ≤ t ≤ 1 (s) , no contact between pressure roll and web occurs, so response of system is the same with case without load. At time interval 1 ≤ t ≤ 2( s ) , the external load force is generated and increase linearly in time by contacting of pressure roll and web. The response of system in this time is with no overshoot, settling time 0.1 second and steady-state error about 3.3 % shown in Fig. 6.8. When the external load force is constant, the steady-state error will be eliminated in 0.1 second.

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7 Conclusion From the above simulation results, it is clear that the proposed algorithm of using the full state feedback controller can be obtained the desired performance specifications of the high precision and stability with large bandwidth under the presence of different operating conditions. With the rapid development of sensor technology, electronic devices, digital computer, and computational speed, the proposed control algorithm of full state feedback controller can result in a control system with high precision and are useful for applications with high digital computational system. The following conclusions are made; 1. A mathematical model for printing pressure control system is proposed. 2. By using the proposed mathematical model and back-stepping approach, the full state feedback controller is designed with gains determined optimally by using the MGA. 3. A printing pressure control algorithm is given using the designed controller. The simulation results prove the reliability and high precision of proposed algorithm Acknowledgement. This study is supported by Ministry of Knowledge Economy through project “Component/Material Technology Development”.

References [1] Alexander, K., Konrad, K., Klaus, Z.: FE – Simulation of thin strip and temper rolling process. In: ABAQUS Austria Users’ Conference, pp. 24–25 (2003) [2] Bouazza, S.E., Abbassi, H.A.: Model based control system for hot steel strip rolling mill stands. Asian Journal of information Technology 6, 246–253 (2007) [3] Chen, C.: Back-stepping control design and its application to vehicle lateral control in automated highway systems. Doctor of Philosophy, University of California (1996) [4] Chen, H.C., Chang, S.H.: Genetic algorithm based optimization design of a PID controller for an active magnetic bearing. IJCSNS 6, 122–145 (2006) [5] Chung, D.T., Shin, K.H.: Transient wrinkling analysis of steel web rolling. In: IEEE IAS 2004, pp. 886–890 (2004) [6] Cui, Z., Xu, B.: Generalized mathematical model for hot rolling process of plate. J. mater. Sci. Technol. 19, 123–129 (2003) [7] Durovsky, F., Ferkova, Z.: Computation of rolling stand parameters by genetic algorithm. Acta Polytechnica Hungarica 5, 234–345 (2008) [8] Guan, L., Lin, J., Chen, G., Chen, M.: Study for the offset printing quality control expert system based on case reasoning. In: IEEE International conference (2006) [9] Hinge, K.C., Maniatty, A.M.: The effect of skew angle on the axial pressure distribution between flexible rubber-covered rollers. Int. J. Mech. Sci. 38, 607–619 (1996) [10] Lee, W.H.: Mathematical model for cold rolling and temper rolling process of thin steel strip. KSME International Journal 16, 1296–1302 (2002) [11] Lee, C.W., Shin, K.H.: A study on taper tension control considering telescoping in the winding system, pp. 398–403. IEEE, Los Alamitos (2007) [12] Li, S., Ruan, J., Yu, Z.Q., Zhu, F.M.: Electro-hydraulic synchronizing servo control of a robotic arm. Journal of Physics 8, 1268–1272 (2006)

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[13] Nevala, K.: Compensation for the effect of cylinder friction in the pressure control of the Nip load on a paper center winder. IEEE 6, 59–163 (2002) [14] Seyedkazaemi, M., Akbarimajd, A., Rahnamaei, A., Baghbanpourasl, A.: A genetically tuned optimal PID controller. In: Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Greece (2007) [15] Sun, H., George, T.C.C.: Motion synchronization for dual-cylinder electro-hydraulic lift systems. IEEE Transactions on Mechatronics 7, 171–181 (2002) [16] Renato, A.K., Joost, P.R.: Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE Transactions on Evolutionary computation 5, 78–82 (2001) [17] Zhunang, M., Atherton, D.P.: Automatic tuning of optimum PID controllers. IEE Proceeding-D 140, 216–224 (1993)

Adding Diversity to Two Multiobjective Constructive Metaheuristics for Time and Space Assembly Line Balancing ´ Manuel Chica, Oscar Cord´on, Sergio Damas, and Joaqu´ın Bautista

Abstract. We present a new mechanism to introduce diversity into two multiobjective approaches based on ant colony optimisation and randomised greedy algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. Promising results are shown after applying the designed constructive metaheuristics to ten real-like problem instances. Keywords: Time and Space Assembly Line Balancing Problem, Constructive Metaheuristics, Multiobjective Optimisation, Automotive Industry.

1 Introduction An assembly line is made up of a number of workstations, arranged either in series or in parallel. These stations are linked together by a transport system that aims to supply materials to the main flow and to move the production items from one station to the next one. Since the manufacturing of a production item is divided into a set of tasks, a usual and difficult problem is to determine how these tasks can be assigned to the stations fulfilling certain restrictions. Consequently, the aim is to get an optimal assignment of subsets of tasks to the stations of the plant. Moreover, each task requires an ´ Manuel Chica · Oscar Cord´on · Sergio Damas European Centre for Soft Computing, Mieres (Asturias), Spain e-mail: {manuel.chica,oscar.cordon,sergio.damas}@softcomputing.es Joaqu´ın Bautista Universitat Polit`ecnica de Catalunya, Barcelona, Spain e-mail: [email protected] Joaqu´ın Bautista Nissan Chair http://www.nissanchair.com

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operation time for its execution which is determined as a function of the manufacturing technologies and the employed resources. A family of academic problems –referred to as simple assembly line balancing problems (SALBP)– was proposed to model this situation [3, 15]. Taking this family as a base and adding spatial information to enrich it, Bautista and Pereira recently proposed a more realistic framework: the time and space assembly line balancing problem (TSALBP) [2]. This framework considers an additional space constraint to become a simplified version of real-world problems. The new space constraint emerged due to the study of the specific characteristics of the Nissan plant in Barcelona (Spain). As many real-world problems, TSALBP formulations have a multicriteria nature [4] because they contain three conflicting objectives to be minimised: the cycle time of the assembly line, the number of the stations, and the area of this stations. In this paper we have selected the TSALBP-1/3 variant which tries to minimise the number of stations and their area for a given product cycle time. We have made this decision because it is quite realistic in the automotive industry. The final aim is to provide the plant manager with a well spread Pareto front of solutions with different trade-offs between the number of stations and the area of these stations. This will allow the plant manager to choose the most appropriate one for his/her industrial context. TSALBP-1/3 has an important set of constraints like precedences or cycle time limits for each station. Thus, the use of constructive approaches like ant colony optimisation (ACO) [10] is more convenient than others like local or global search procedures. ACO is a constructive metaheuristic [13] inspired by the shortest path searching behaviour of various ant species. Many different ACO algorithms have successfully solved different combinatorial problems such as the travelling salesman problem, the quadratic assignment problem, the sequential ordering problem, production scheduling, timetabling, project scheduling, vehicle and telecommunication routing, and investment planning [10]. Due to the two aforementioned reasons, i.e., the multiobjective nature of the problem and the need to solve it through constructive algorithms, a sensible choice is to use a Pareto-based multiobjective ACO (MOACO) algorithm [12]. This family involves different variants of ACO algorithms which aim to find not only one solution, but a set of the best solutions according to several conflicting objective functions. In [7], we successfully tackled the TSALBP-1/3 by means of a specific procedure based on the multiple ant colony system (MACS) algorithm [1]. However, we noticed that intensification could be too high in a specific region of the Pareto front because of the station-oriented approach that was accomplished. In particular, the approximations to the obtained Pareto fronts showed a significant lack of diversity and an excessive convergence to the left-most region of the objective space. That is an undesirable situation for the plant managers who should be provided with all the configurations of their contextual interest in the objective space. In this paper we aim to introduce a new mechanism in two constructive metaheuristics in order to avoid that local convergence behaviour. On the one hand, we induce the generation of more diverse solutions by means of a multi-colony

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approach [14] according to different station filling rates in the MACS algorithm. On the other hand, the new filling threshold mechanism is also included on another method. In particular, we consider a multiobjective randomised greedy algorithm (MORGA), based on the first stage of the GRASP method [11] and proposed in [6]. It follows the same constructive scheme and Pareto-based approach used in the MACS algorithm. In this way, we have been able to compare the influence of incorporating the new diversity improvement in both approaches, the MOACO algorithm and the MORGA. These algorithms, with and without the new diversification component, will be tested on ten real-like TSALBP-1/3 instances. The paper is structured as follows. In Section 2, the problem formulation and the MOACO algorithm and the MORGA are explained. Then, the proposed multicolony approach to improve the proposals is described in Section 3. The experimentation setup as well as the analysis of results is presented in Section 4. Finally, some concluding remarks are discussed in Section 5.

2 Preliminaries In this section the problem preliminaries are presented first. Then, the main features of the MACS algorithm and the MORGA to tackle the TSALBP-1/3 variant are described.

2.1 The Time and Space Assembly Line Balancing Problem The manufacturing of a production item is divided into a set V of n tasks. Each task j requires an operation time for its execution t j > 0 that is determined as a function of the manufacturing technologies and the employed resources. A task j is assigned to a single station k. Each station k has thus assigned a subset of tasks Sk (Sk ⊆ V ), called its workload. Each task j has a set of direct predecessors, Pj , which must be accomplished before starting it. These constraints are normally represented by means of an acyclic precedence graph, whose vertices stand for the tasks and where a directed arc (i, j) indicates that task i must be finished before starting task j on the production line. Thus, if i ∈ Sh and j ∈ Sk , then h ≤ k must be fulfilled. Each station k presents a station workload time t(Sk ) that is equal to the sum of the tasks’ lengths assigned to the station k. SALBP [15] focuses on grouping tasks in workstations by an efficient and coherent way. There is a large variety of exact and heuristic problem-solving procedures for it [16]. The need of introducing space constraints in the assembly lines’ design is based on two main reasons: (a) the length of the workstation is limited in the majority of the situations, and (b) the required tools and components to be assembled should be distributed along the sides of the line. Hence, an area constraint may be considered by associating a required area a j to each task j and an available area Ak to each station k that, for the sake of simplicity, we shall assume it to be identical for every station and equal to A : A = max∀k∈{1..n}{Ak }. Thus, each station k requires a

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station area a(Sk ) that is equal to the sum of areas required by the tasks assigned to station k. This leads us to a new family of problems called TSALBP in [2]. It may be stated as: given a set of n tasks with their temporal t j and spatial a j attributes (1 ≤ j ≤ n) and a precedence graph, each task must be assigned to a single station such that: (i) every precedence constraint is satisfied, (ii) no station workload time (t(Sk )) is greater than the cycle time (c), and (iii) no area required by any station (a(Sk )) is greater than the available area per station (A). TSALBP presents eight variants depending on three optimisation criteria: m (the number of stations), c (the cycle time) and A (the area of the stations). Within these variants there are four multiobjective problems and we will tackle one of them, the TSALBP-1/3. It consists of minimising the number of stations m and the station area A, given a fixed value of the cycle time c. We chose this variant because it is quite realistic in the automotive industry since the annual production of an industrial plant (and therefore, the cycle time c) is usually set by some market objectives. For more information we refer the interested reader to [5].

2.2 TSALBP-1/3 Formulation According to the TSALBP formulation [2], the 1/3 variant deals with the minimisation of the number of stations, m, and the area ocuppied by those stations, A, in the assembly line configuration. We can mathematically formulate this TSALBP variant as follows:

f 0 (x) = m =

Min

UBm

∑

k=1

f 1 (x) = A =

max x jk ,

j=1,2,...,n

(1)

n

∑ a j x jk k=1,2,...,UBm max

(2)

j=1

subject to: Lj

∑

x jk = 1,

j = 1, 2, ..., n

(3)

k=E j UBm

∑

k=1

max x jk ≤ m

j=1,2,...,n

n

∑ t j x jk ≤ c,

(4)

k = 1, 2, ...,UBm

(5)

k = 1, 2, ...,UBm

(6)

j=1 n

∑ a j x jk ≤ A,

j=1

Adding Diversity to MO Constructive Metaheuristics for the TSALBP Li

∑

k=Ei

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Lj

kxik ≤

∑

kx jk ,

j = 1, 2, ..., n; ∀i ∈ Pj

(7)

k=E j

x jk ∈ {0, 1},

j = 1, 2, ..., n; k = 1, 2, ...,UBm

(8)

where: • n is the number of tasks, • x jk is a decision variable taking value 1 if task j is assigned to station k, and 0 otherwise, • a j is the area information for task j, • UBm is the upper bound for the number of stations m, • E j is the earliest station to which task j may be assigned, • L j is the latest station to which task j may be assigned, • UBm is the upper bound of the number of stations. In our case, it is equal to the number of tasks, and Constraint in equation 3 restricts the assignment of every task to just one station, 4 limits decision variables to the total number of stations, 5 and 6 are concerned with time and area upper bounds, 7 denotes the precedence relationship among tasks, and 8 expresses the binary nature of variables x jk .

2.3 Multiple ant Colony System MACS was proposed as a was proposed as a multiobjective extension of the ant colony system (ACS) [9]. MACS uses a single pheromone trail matrix τ and several heuristic information functions η k (in our case, η 0 for the operation time t j of each task j and η 1 for its area a j ). From now on, we restrict the description of the algorithm to the case of two objectives. In this way, an ant moves from node i to node j by applying the following transition rule:  arg max j∈Ω (τi j · [ηi0j ]λ β · [ηi1j ](1−λ )β ), if q ≤ q0 , (9) j= ˆ i, otherwise. where Ω represents the current feasible neighbourhood of the ant, β weights the relative importance of the heuristic information with respect to the pheromone trail, and λ is computed from the ant index h as λ = h/M, with M being the number of ants in the colony, q0 ∈ [0, 1] is an exploitation-exploration parameter, q is a random value in [0, 1], and iˆ is a node selected according to the probability distribution p( j): ⎧ ⎨ τi j ·[ηi0j ]λ β ·[ηi1j ](1−λ )β , if j ∈ Ω , (10) p( j) = ∑u∈Ω τiu ·[ηiu0 ]λ β ·[ηiu1 ](1−λ )β ⎩ 0, otherwise. Every time an ant crosses edge < i, j >, it performs the local pheromone update as follows: τi j = (1 − ρ ) · τi j + ρ · τ0 .

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Initially, τ0 is calculated by taking the average costs, fˆ0 and fˆ1 , of each of the two objective functions, f 0 and f 1 , from a set of heuristic solutions by applying the expression:

τ0 =

1 fˆ0 · fˆ1

(11)

However, the value of τ0 is not fixed during the algorithm run, as usual in ACS, but it undergoes adaptation. At the end of each iteration, every complete solution built by the ants is compared to the Pareto archive PA which was generated till that moment. This is done in order to check if a new solution is a non-dominated one. If so, it is included in the archive and all the dominated solutions are removed. Then, τ0 is calculated by applying equation (11) with the average values of each objective function taken from the current solutions of the Pareto archive. If τ0 > τ0 , being τ0 the initial pheromone value, pheromone trails are reinitialised to the new value τ0 = τ0 . Otherwise, a global update is performed with each solution S of the Pareto set approximation contained in PA applying the following rule on its composing edges < i, j >:

τi j = (1 − ρ ) · τi j +

ρ f 0 (S) · f 1 (S)

(12)

2.4 A MACS Algorithm for the TSALBP-1/3 In this section we describe the customisation made on all the components of the general MACS algorithm scheme to build our solution methodology. 2.4.1

Heuristic Information

MACS works with two different heuristic information values, η 0j and η 1j , each of them associated to one criterion. In our case, η 0j is related with the required operation time for each task and η 1j with the required area:

η 0j =

| Fj | tj · c maxi∈Ω | Fi |

η 1j =

aj | Fj | · UBA maxi∈Ω | Fi |

where UBA is the upper bound for the area (the sum of all tasks’ areas) and Fj is the set of tasks that come after task j. The second term in both formulae represents a ratio between the number of successors of the task j (the cardinality of the successors set Fj ) and the maximum number of successors of any eligible task belonging to the ant’s feasible neighbourhood Ω . Both sources of heuristic information range in [0, 1], with 1 being the most preferable. As usual in the SALBP, tasks having a large value of time (a large duration) and area (occupying a lot of space) are preferred to be firstly allocated in the stations. Apart from area and time information, we have added another information related

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to the number of successors of the task which was already used in [2]. Tasks with a larger number of successors are preferred to be allocated first. Heuristic information is one-dimensional since it is only assigned to tasks. In addition, it can be noticed that heuristic information has static and dynamic components. Tasks’ time t j and area a j are always fixed while the successors rate is changing through the constructive procedure. This is because it is calculated by means of the candidate list of feasible and non-assigned tasks at that moment. 2.4.2

Pheromone Trail and τ0 Calculation

The pheromone trail information has to memorise which tasks are the most appropriate to be assigned to a station. Hence, pheromone has to be associated to a pair (stationk ,task j ), being k = 1, ..., n and j = 1, ..., n. In this way, contrary to heuristic information, our pheromone trail matrix has a bi-dimensional nature since it links tasks with stations. In every ACO algorithm, an initial value for the pheromone trails has to be set up. This value is called τ0 and it is normally obtained from an heuristic algorithm. We have used two station-oriented single-objective greedy algorithms, one per heuristic, to compute it. These algorithms open the first station and select the best possible task according to their heuristic information (related either with the duration time and successors rate η 0j , or the area and successors rate η 1j ). This process is repeated till there is not any task that can be included because of the cycle time limit. Then, a new station must be opened. When no more tasks are to be assigned, the greedy algorithm finishes. τ0 is then computed from the costs of the two solutions obtained by the greedy algorithm using the following MACS equation: τ0 = f 0 (S )·1f 1 (S ) . time

2.4.3

area

Randomised Station Closing Scheme and Transition Rule

Our approach follows a station-oriented procedure, which starts opening a station and selecting the most suitable task to be assigned. When the current station is loaded maximally, it is closed and the next one is opened and ready to be filled. At the beginning, we decided to close the station when it was full in relation to the fixed cycle time c, as usual in SALBP and TSALBP applications. We found that this scheme did not succeed because the obtained Pareto fronts did not have enough diversity. Thus, we introduced a new mechanism in the construction algorithm to close the station according to a probability, given by the filling rate of the station: ∑i∈Sk ti (13) c This probability distribution is updated at each construction step. A random number is uniformly generated in [0, 1] after each update to decide whether the station is closed or not. If the decision is not to close the station, we choose the next task among all the candidate tasks using the MACS transition rule and the procedure goes on. p (closing Sk ) =

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Because of the one-dimensional nature of the heuristic information, the original transition rule (Equation 9) that chooses among all the candidate tasks at each step, has been modified:  arg max j∈Ω (τk j · [η 0j ]λ β · [η 1j ](1−λ )β ), if q ≤ q0 , (14) j= ˆ i, otherwise, where iˆ is a node selected by means of the following probability distribution:  τk j ·[η 0j ]λ β ·[η 1j ](1−λ )β p( j) = ∑u∈Ω τku ·[ηu0 ]λ β ·[ηu1 ](1−λ )β , if j ∈ Ω , (15) 0, otherwise.

2.5 MORGA Our diversification generation mechanism behaves similarly to a GRASP construction phase [11]. The most important element in this kind of construction is that the selection of the task at each step must be guided by a stochastic greedy function that is adapted with the pseudo-random selections made in the previous steps. We introduce randomness in two processes. On the one hand, allowing each decision to be randomly taken among the best candidates. On the other hand, closing the station according to a probability distribution. We use the same constructive approach, with closing probabilities at each constructive step, than in the MACS algorithm. The probabilistic criterion to select the next task that will be included in the current station is changed to be only based on heuristic information. Therefore, to make a decision among all the current feasible candidate tasks we use a single heuristic value given by:

ηj =

tj aj | Fj | · · c UBA maxi∈Ω | Fi |

(16)

The decision is made randomly among the selected tasks in the restricted candidate list (RCL) by means of the following procedure: we calculate the heuristic value of every feasible candidate task to be assigned to the current open station. Then, we sort them according to their heuristic values and, finally, we set a quality threshold for the heuristic given by q = maxη j −γ · (maxη j − minη j ). All the tasks with a heuristic value η j greater or equal than q are selected to be in the RCL. γ is the diversification-intensification trade-off control parameter. When γ = 1 there is a completely random choice inducing the maximum possible diversification. In contrast, if γ = 0 the choice is close to a pure greedy decision, with a low diversification. As MACS, the MORGA construction algorithm also incorporates a mechanism which allows us to close a station according to a probability distribution, given by the filling rate of the station (see equation (13)).

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3 Using a Multi-colony Approach on the MACS-TSALBP-1/3 and MORGA Algorithms The MACS-based TSALBP-1/3 algorithm proposed in [7] carries the problem of not providing enough intensification in some Pareto front areas, since there is a low probability of filling stations completely. Hence, there is a need to find a better intensification-diversification trade-off. This objective can be achieved by introducing different filling thresholds associated to the ants that build the solution. These thresholds make the different ants in the colony have a different search behaviour. Thus, the ACO algorithm becomes multi-colony [14]. In our case, thresholds are set between 0.2 and 0.9 and they are considered as a preliminary step before deciding to close a station. Therefore, the solution construction procedure is modified. We compute the station closing probability distribution as usual based on the station current filling rate (equation (13)). However, only when the ant’s filling threshold has been overcome, the random decision of either closing a station or not according to that probability distribution is considered. Otherwise, the station will be kept opened. Thus, the higher the ant’s threshold is, the more complete the station will be likely to be. This is due to the fact that there are less possibilities to close it during the construction process. In this way, the ant population will show a highly diverse search behaviour, allowing the algorithm to properly explore the different parts of the optimal Pareto fronts by appropriately spreading the generated solutions. We have also used the same filling thresholds technique for the MORGA. In the MACS algorithm, these filling thresholds are applied in parallel following the multicolony approach. Unlike the MACS algorithm, different thresholds are only used in isolation at each iteration in the case of the MORGA.

4 Experimentation 4.1 Problem Instances and Parameter Values Ten problem instances with different features have been selected for the experimentation: arc111 with cycle time limits of c = 5755 and c = 7520 (P1 and P2), barthol2 (P3), barthold (P4), heskia (P5), lutz2 (P6), lutz3 (P7), mukherje (P8), scholl (P9), and weemag (P10). Originally, these instances were SALBP-1 instances only having time information. However, we have created their area information by reverting the task graph to make them bi-objective (as done in [2])1 . We run each algorithm 10 times with different random seeds, setting the time as stopping criteria (900 seconds). All the algorithms were launched in the same computer: Intel PentiumT M D with two CPUs at 2.80GHz, and CentOS Linux 4.0. 1

Problem instances and more information available at http://www.nissanchair.com/TSALBP

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On the one hand, the values of the parameters used in all the MACS algorithms with and without the new diversification component are as follows. We consider ten different ants, β = 2, and ρ = 0.2. Different values of the transition rule parameter q0 are also studied. In particular: q0 = 0.2, 0.5, 0.8. On the other hand, the MORGA was launched with different diversification-intensification parameter values, γ = {0.1, 0.2, 0.3}. With respect to the parameters of our proposal on using different filling thresholds, there are two ants for each of the five ants’ thresholds considered: {0.2, 0.4, 0.6, 0.7, 0.9} in the MACS algorithm. The same threshold values were used for the MORGA.

4.2 Metrics of Performance We will consider two different multiobjective metrics [8, 17] to evaluate the performance of the two variants of the MACS-based TSALBP-1/3 algorithm and the MORGA. On the one hand, we selected the hypervolume ratio (HVR) from the first group. It can be calculated as follows: HV R =

HV (P) , HV (P∗ )

(17)

where HV (P) and HV (P∗ ) are the volume (S metric value) of the approximate Pareto set and the true Pareto set, respectively. When HV R equals 1, then the approximate Pareto front and the true one are equal. Thus, HV R values lower than 1 indicate a generated Pareto front that is not as good as the true Pareto front. We should notice that the true Pareto fronts are not known in our real-world problem instances. Thus, we will consider a pseudo-optimal Pareto set, i.e. an approximation of the true Pareto set, obtained by merging all the (approximate) Pareto sets Pij generated for each problem instance by all the existing algorithms for the problem in the different runs [5]. Thanks to this pseudo-optimal Pareto set, we can compute HV R and consider it in our analysis of results. On the other hand, we have also considered the binary set coverage metric C to compare the obtained Pareto sets two by two based on the following expression: C(P, Q) =

|{q ∈ Q ; ∃p ∈ P : p ≺ q}| , |Q|

(18)

where p ≺ q indicates that the solution p, belonging to the approximate Pareto set P, dominates the solution q of the approximate Pareto set Q in a minimisation problem. Hence, the value C(P, Q) = 1 means that all the solutions in Q are dominated by or equal to solutions in P. The opposite, C(P, Q) = 0, represents the situation where none of the solutions in Q are covered by the set P. Note that both C(P, Q) and C(Q, P) have to be considered, since C(P, Q) is not necessarily equal to 1 −C(Q, P). We have used boxplots based on the C metric that calculates the dominance degree of the approximate Pareto sets of every pair of algorithms (see Figure 1 and 2).

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Fig. 1 C metric values represented by means of boxplots comparing MACS with and without multi-colony scheme (i.e. variable filling thresholds)

Each rectangle contains ten boxplots representing the distribution of the C values for a certain ordered pair of algorithms in the ten problem instances (P1 to P10). Each box refers to algorithm A in the corresponding row and algorithm B in the corresponding column and gives the fraction of B covered by A (C(A, B)).

4.3 Analysis of Results The experimental results obtained by the two MACS variants with and without the diversity mechanism can be seen in the C metric boxplots of Figure 1 and in the HV Rvalues in Table 1. Some conclusions can be reached from the analysis of the C metric values: • Comparing both versions of MACS, the original one with a specific value of q0 and its counterpart multi-colony extension, we can see that significantly “better”2 results are provided by the latter MACS with thresholds. It happens regardless of 2

When we refer to the best or better performance comparing the C metric values of two algorithms we mean that the Pareto set derived from one algorithm significantly dominates that one achieved by the other. Likewise, the latter algorithm does not dominate the former one to a high degree.

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Table 1 Mean and standard deviation values (in brackets) of the HV R metric for the MACS algorithm. In each problem instance, the best mean value is in bold. A1: MACS 0.2 (without thr.), A2: MACS 0.5 (without thr.), A3: MACS 0.8 (without thr.) A4: MACS 0.2 (with thr.), A5: MACS 0.5 (with thr.), A6: MACS 0.8 (with thr.)

P1

P2

P3

P4

P5

A1 A2 A3 A4 A5 A6

0.5532 (0.023) 0.5549 (0.019) 0.5331 (0.008) 0.9051 (0.01) 0.8770 (0.009) 0.8353 (0.008) P6

0.6655 (0.009) 0.6600 (0.017) 0.6418 (0.014) 0.8962 (0.013) 0.8839 (0.016) 0.8522 (0.010) P7

0.6418 (0.026) 0.6331 (0.012) 0.6172 (0.016) 0.8852 (0.020) 0.8617 (0.016) 0.8285 (0.022) P8

0.4297 (0.043) 0.4475 (0.034) 0.4629 (0.061) 0.8176 (0.027) 0.7969 (0.024) 0.8191 (0.018) P9

0.9686 (0.006) 0.9660 (0.006) 0.9608 (0.007) 0.8695 (0.022) 0.8471 (0.013) 0.8114 (0.018) P10

A1 A2 A3 A4 A5 A6

0.6729 (0.022) 0.6833 (0.036) 0.6486 (0.036) 0.8430 (0.022) 0.8368 (0.016) 0.7284 (0.054)

0.8222 (0.315) 0.7101 (0.246) 0.6523 (0.239) 0.9723 (0.066) 0.8812 (0.058) 0.7330 (0.066)

0.5522 (0.019) 0.5480 (0.013) 0.5365 (0.019) 0.8979 (0.011) 0.8988 (0.013) 0.8656 (0.011)

0.6014 (0.017) 0.5968 (0.015) 0.6070 (0.019) 0.8941 (0.011) 0.8829 (0.012) 0.8506 (0.013)

0.7830 (0.019) 0.7819 (0.035) 0.7789 (0.014) 0.7674 (0.028) 0.7535 (0.037) 0.7067 (0.052)

Table 2 Mean and standard deviation values (in brackets) of the HV R metric for the MORGA. In each problem instance, the best mean value is in bold. A1: MORGA 0.1 (without thr.), A2: MORGA 0.2 (without thr.), A3: MORGA 0.3 (without thr.) A4: MORGA 0.1 (with thr.), A5: MORGA 0.2 (with thr.), A6: MORGA 0.3 (with thr.)

P1

P2

P3

P4

P5

A1 A2 A3 A4 A5 A6

0.5792 (0.012) 0.5779 (0.012) 0.5624 (0.026) 0.9258 (0.005) 0.9333 (0.007) 0.9542 (0.007) P6

0.6602 (0.018) 0.6550 (0.008) 0.6789 (0.017) 0.9093 (0.005) 0.9121 (0.005) 0.9385 (0.007) P7

0.6017 (0.023) 0.6294 (0.042) 0.6028 (0.019) 0.7560 (0.005) 0.6528 (0.008) 0.6488 (0.009) P8

0.4278 (0.04) 0.3957 (0.035) 0.4129 (0.017) 0.8457 (0.020) 0.9262 (0.019) 0.9366 (0.016) P9

0.9137 (0.007) 0.9294 (0.010) 0.9302 (0.009) 0.8642 (0.007) 0.8953 (0.038) 0.9149 (0.052) P10

A1 A2 A3 A4 A5 A6

0.5784 (0.020) 0.5909 (0.029) 0.6451 (0.043) 0.7611 (0.029) 0.8361 (0.033) 0.8847 (0.038)

0.6914 (0.223) 0.5447 (0.09) 0.6730 (0.237) 0.7034 (0.260) 0.7498 (0.039) 0.7466 (0.067)

0.5176 (0.015) 0.5316 (0.022) 0.5301 (0.026) 0.8769 (0.009) 0.8797 (0.008) 0.9011 (0.006)

0.5861 (0.012) 0.5807 (0.016) 0.5873 (0.017) 0.8606 (0.004) 0.8663 (0.004) 0.8610 (0.007)

0.7911 (0.026) 0.7939 (0.027) 0.7994 (0.031) 0.8568 (0.018) 0.8726 (0.017) 0.8837 (0.022)

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Fig. 2 C metric values represented by means of boxplots comparing the MORGA with and without using the variable filling thresholds

the value of q0 , and it is common in all the problem instances but P5 (heskia). This is because of the nature of that problem instance, whose pseudo-optimal Pareto front is not wide enough. Every solution of this problem instance is found in the central part of the objective space, so the diversity introduced by the filling thresholds is not useful. • We find less performance differences with a lower value of q0 . It makes sense since MACS with higher q0 values gives more importance to a higher intensification in the selection procedure and thus, the Pareto fronts are more similar. Hence, the algorithm does not take advantage of the diversity induced by the thresholds approach. • If we compare every MACS variant with and without thresholds, regardless of the value of q0 , the conclusion is that MACS 0.2 with thresholds is the best approach. It gets better results than MACS 0.5 and 0.8 with thresholds in every problem instance. It is only dominated by MACS 0.2 and 0.5 without thresholds in P5. Even in a non-common problem instance like P5, results are good enough. Hence, the diversity of the task selection procedure (a low value of q0 parameter) and the use of variable station filling thresholds are both important to solve the problem appropriately. Nevertheless, if we select MACS 0.8 with thresholds and MACS without thresholds with lower values of q0 (0.2 and 0.5) to be compared,

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Fig. 3 Pareto fronts of the MACS algorithm and MORGA for the P3 and P10 problem instances respectively

we can notice that the former algorithm outperforms the latter two in five and six problem instances respectively. On the contrary, the latter two are better in four of them. All of these algorithms have thus quite similar results. Consequently, the variable filling thresholds in isolation are not enough to get a good yield. There is also a demand for diversity in the randomised task selection procedure of the algorithm which requires a good diversification-intensification trade-off. On the other hand, we show the results of the MORGA with and without the diversity mechanism. In Figure 2, the boxplots of the C metric are shown. Similar conclusions can be obtained: • The MORGA variants with the diversity mechanism almost always achieve better performance than those without it. • Only in the P5 instance, there are solutions of the MORGA variants with the diversity mechanism which are dominated by the algorithms without the new approach.

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• It is clear how the MORGA with γ = 0.3 is the best of the MORGA variants, and its version with the diversity mechanism the best algorithm. In general terms, we can draw similar conclusions analysing the HV R metric values (see Tables 1 and 2). They are always higher in variants with thresholds as they better converge towards the true (i.e., pseudo-optimal) Pareto fronts. For example, that is shown in the Pareto fronts of Figure 3 that graphically shows the aggregated Pareto fronts corresponding to P3 and P10 instances for the MACS algorithm and MORGA.

5 Concluding Remarks In a previous contribution [7] we demonstrated that the use of multiobjective constructive metaheuristics to tackle the TSALBP-1/3, particularly a MACS algorithm, was a good choice. And the consideration of a stochastic procedure to decide when to close a station performed better choice than a pure station-based approach. Nevertheless, that solution still leads to situations where intensification was too high in a specific region of the Pareto front. That is an undesirable situation for the plant managers who should be provided with all the configurations of their contextual interest in the objective space. To solve this problem, in this contribution we showed a better intensificationdiversification trade-off. It could be achieved in a MOACO algorithm by introducing different filling thresholds associated to the ants that build the solution in order to provide a different search behaviour to the different ants in the colony. We also applied a modified version of this new diversity mechanism to a multiobjective randomised greedy algorithm (MORGA). Ten well-known problem instances of the literature were selected to test our proposal. From the obtained results we have found out that the best yield to globally solve the problem belongs to the new MACS-TSALBP-1/3 algorithm using the multi-colony scheme with q0 = 0.2. Likewise, the MORGA with additional diversity clearly outperforms the results of the basic one. In the future we aim to consider other multiobjective constructive metaheuristics and apply a local search to increase the current performance. Acknowledgements. This work is supported by the UPC Nissan Chair and the Spanish Ministerio de Educaci´on y Ciencia under project DPI2007-63026 and by the Spanish Ministerio de Ciencia e Innovaci´on under project TIN2009-07727, both including EDRF fundings.

References 1. Bar´an, B., Schaerer, M.: A multiobjective ant colony system for vehicle routing problem with time windows. In: 21st IASTED Conference, Innsbruck Germany, pp. 97–102 (2003) 2. Bautista, J., Pereira, J.: Ant algorithms for a time and space constrained assembly line balancing problem. European Journal of Operational Research 177, 2016–2032 (2007)

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3. Baybars, I.: A survey of exact algorithms for the simple assembly line balancing problem. Management Science 32(8), 909–932 (1986) 4. Chankong, V., Haimes, Y.Y.: Multiobjective Decision Making Theory and Methodology. North-Holland, Amsterdam (1983) 5. Chica, M., Cord´on, O., Damas, S., Bautista, J.: Multi-objective, constructive heuristics for the 1/3 variant of the time and space assembly line balancing problem: ACO and randomised greedy. Tech. Rep. AFE-09-01, European Centre for Soft Computing, Asturias (Spain) (submitted to Information Sciences) (2009) 6. Chica, M., Cord´on, O., Damas, S., Bautista, J.: A multiobjective GRASP for the 1/3 variant of the time and space assembly line balancing problem. In: 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2010), Cordoba, Spain (to appear, 2010) 7. Chica, M., Cord´on, O., Damas, S., Bautista, J., Pereira, J.: A multiobjective ant colony optimization algorithm for the 1/3 variant of the time and space assembly line balancing problem. In: 12th International Conference on Processing and Management of Uncertainty in Knowledge-based Systems (IPMU), M´alaga (Spain), pp. 1454–1461 (2008) 8. Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-objective Problems, 2nd edn. Springer, Heidelberg (2007) 9. Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997) 10. Dorigo, M., St¨utzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004) 11. Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109–133 (1995) 12. Garc´ıa Mart´ınez, C., Cord´on, O., Herrera, F.: A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. European Journal of Operational Research 180, 116–148 (2007) 13. Glover, F., Kochenberger, G.A. (eds.): Handbook of Metaheuristics. Kluwer Academic, Dordrecht (2003) 14. Middendorf, M., Reischle, F., Schmeck, H.: Multi colony ant algorithms. Journal of Heuristics 8(3), 305–320 (2002) 15. Scholl, A.: Balancing and Sequencing of Assembly Lines, 2nd edn. Physica-Verlag, Heidelberg (1999) 16. Scholl, A., Becker, C.: State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research 168(3), 666–693 (2006) 17. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

Construction and Application of a Digital Factory for Automotive Paint Shops Yang Ho Park, Eon Lee, Seon Hwa Jeong, Gun Yeon Kim, Sang Do Noh, Cheol-woong Hwang, Sangil Youn, Hyeonnam Kim, and Hyunshik Shin*

Abstract. To ensure competitiveness in today’s automotive market, growing emphasis is being laid on the collaboration of disparate engineering activities in manufacturing in the automotive industry. By applying virtual manufacturing, diverse engineering activities such as design evaluation, process & material planning, production flow analysis, and ergonomic analysis can be brought together to be performed in a single integrated model, viz., a digital factory. In this paper, we have suggested a procedure, consideration and expected effects for paint shop of automotive company. Therefore, we constructed a digital factory for a paint shop for an automotive company. By applying the digital factory to manufacturing engineering, it is expected that time and cost savings can be realized in many manufacturing engineering work in planning and new product development processes.

1 Introduction In recent years, manufacturers are under tremendous pressure to improve their responsiveness and efficiency in terms of product development, manufacturing preparation, planning, operations, and resource utilization along with transparency in production and quality control. Also, the time and cost for product development and production must be cut as much as possible to meet the changing demands of customers in different regions of the world. Therefore, most manufacturing companies need a new production paradigm that can achieve both competitiveness and fast production. Virtual manufacturing is an integrated computer model that represents the physical (characteristics), logical schema, and behavior of a real manufacturing Yang Ho Park . Eon Lee . Seon Hwa Jeong . Gun Yeon Kim . Sang Do Noh Department of Systems Management Engineering, Sungkyunkwan University, Korea *

Cheol-woong Hwang . Sangil Youn . Hyeonnam Kim . Hyunshik Shin Manufacturing Engineering Center, GM Daewoo Auto&Technology, Korea

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system. It provides the manufacturing engineering content and solutions to create, evaluate, monitor, and control for distributed agile manufacturing based on 3-D CAD, simulation, databases, and computer networks. Generally, virtual manufacturing can verify and optimize many decisions, plans, and operations in manufacturing engineering such as product design, equipment, jig, and fixture design, process planning, factory layout design, production and material flow analysis, and OLP (Off-Line Programming) for various equipment. As a result, it saves time and cost in product development and production [1, 2]. The General Motor corporation has a plan to apply virtual manufacturing technology to their manufacturing systems as part of a math-based manufacturing program that began in 1990. This program means that “every engineer necessitates implementing the manufacturing, assembly system creation, verification, design and operation by using a mathbased model before making prototypes,” [3]. In particular, many reports have investigated the effect of applying virtual manufacturing technology to automotive companies in application deployment and the strategic analysis of virtual manufacturing technology for an overall business process [4]. Using virtual manufacturing technologies, many activities in manufacturing can be integrated and realized into one system, and thus manufacturing cost and the time-to-market can be reduced and productivity can be improved dramatically. According to current reports on virtual manufacturing, the time and cost for making jigs and fixtures have been reduced by more than 75% in aerospace industries, errors in the design of molds and dies have decreased by about 50% in machine shops, and the total development time of production lines has been trimmed by more than 20% in the automotive sector [5]. Fig. 1 shows concepts and structure of virtual manufacturing.

Fig. 1 The concept and structure of a virtual manufacturing [6]

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In this research, we suggest a systematic and effective method for constructing a digital factory of a paint shop. Based on the method, we have constructed a sophisticated digital factory of an automotive paint shop. A real case study in the automotive industry and the effects of virtual manufacturing are presented. This paper aims to build a digital factory of a paint shop in an automotive company more systematically and effectively by analyzing the manufacturing process.

2 Digital Factories A digital factory is an integrated computer model that consists of models of products, machines, human workers, manufacturing processes, jobs, workstations and plants, and it could be a critical base of providing environments for applying virtual manufacturing technology to manufacturing activities that arise in the factory. A digital factory also includes processes, as classified by the range of application of the model and the levels of detail and related information [7]. The use of a digital factory makes it possible to execute a range of activities such as the design of machines, utilities, and tools, the planning of processes and schedules, decisions regarding the factory layout and cells, the establishment of logistics and the assignment of the storage area, OLP performance of production machines, decisions regarding assembly procedures and methods, worker education, task error prevention and improvement, collision checks, and so on; it can also reduce the production time and cost. A digital factory can be used as not only a virtual model of a real factory but also the central control for system audits, control, and decision-making. A digital factory is an integrated environment that is applied across all fields of production. If a digital plant is built and utilized, rapid verification of the production possibility in management processes and new idea development in product development can be possible. Also, product visualization, performance analysis, virtual examination through the production of a virtual prototype, and evaluation of the efficiency and ease of production can be possible in product design. In product manufacturing, the specification of manufacturing utilities, optimization of processes and utilities arrangement, productivity improvement, and cost reduction are possible [8].

3 Automotive Paint Shop and Workflow Analysis There are many engineering activities in developing a new car from “planning” to “start of production.” Fig. 2 shows the general car development process. The development process consists of 'planning,' 'development of the platform,’ 'styling,' 'prototype drawings,' 'prototype building' and 'production drawings,' 'product and process evaluation for a tryout and vehicle match,' 'pilot production,' and 'mass production'. Diverse engineering activities are performed concurrently in each process. A typical paint shop will consist of several painting processes. In rough order, these processes include the electro-coating, sealing, main color painting, inspection, and painting operations. The paint shop processes are such that many of the

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Fig. 2 General product development process in automotive company

operations can be performed without stopping the vehicle at a station. Therefore, the capability and reliability of material handling equipment is important in a paint shop [9]. Fig. 3 shows the typical manufacturing process of an automotive paint shop. The layout of the paint shop in this paper has been composed of three floors: the under, base, and top floors. The under coating, base coating, and top coating processes are conducted at each floor. In terms of storage, the paint shop has Colored Painted Storage (CBS) and Painted Body Storage (PBS). PBS and CBS are buffers in the processes. Especially the PBS area is for temporary storage, before the painted body is transferred to the final assembly line.

Fig. 3 Typical manufacturing process of automotive paint shop

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The manufacturing process of paint shop in this paper is composed mainly of five sub-processes: pretreatment, electro deposition, sealing and under coating, base coat, and top coat. At the stage of pretreatment, the purpose is the elimination of pollutants on the body to increase the quality of painting. Electrodeposition is the first coating on the car body using electronic plating. Sealing and under coating refers to finishing the joint of steel sheet or edge to prevent damage during movement. The next process is base coating. It is the second coating for improving painting. The top-coat process is the third coating for making the final color. Lastly, inspection and modification are performed in an automotive paint shop. In this process, various machines and equipment are required, such as robots and overhead and ground conveyors. As described above, because of continuous and automatic processes in the paint shop, the layout of robots and the conveyor car body is more important. But manual work, as well as automatic lines, exists in current paint shops for inspection and sealing, under coating, masking & sanding, wax coating, minor repair, and heavy repair. Because it is difficult to apply robots or other machines and equipment to these processes, these processes are performed manually. Fig. 4 shows the production flow of an automotive paint shop. Based on these workflow analyses, we analyzed the areas and engineering activities for the application of virtual manufacturing. Table 1 shows the areas, effects, and needed data pertaining to virtual manufacturing for paint shops.

Fig. 4 Production flow of an automotive paint shop

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Table 1 Appilcation of Virtual Manufacturing in Paint Shop

Area Evaluation of drawing Evaluation of equipments for MH Evaluation of interferences Evaluation of quality of painting Design and evaluation of hanger/skid Evaluation of jigs/fixtures Evaluation of layout of operators Evaluation of equipments Training operators

Effects

Data

Design and operation of plant Virtual engineering, verification of process Design and operation of plant Inspection/quality control

P/PR/R P/R

Virtual engineering, verification of process Virtual engineering, verification of process Verification of process

P/PR/R

Design and operation of plant Visualization of product, process, resource

P/R P/PR/R

P/R P/PR/R

P/PR/R P/R

* P: product, PR: process, R: resource

4 Construction of a Digital Factory for a Paint Shop 4.1 Procedure of the Digital Factory To construct a digital factory, 3D CAD and a simulation model must be implemented. Both modeling activities entail considerable time, cost, and effort. Hence, technological developments are essential for effective measurement and geometric modeling, knowledge-based CAD and simulation, and reusable models. In addition to these technologies, systematic planning and decisions regarding the detailed scope and model maintenance are also very important. Because the construction of a digital factory entails a lot of time and cost, first of all, the definition of the purpose and the construction of a detailed and quantified utilization plan are necessary. Therefore, we consider the level of detail and abstraction of the model depending on the purpose and scope of digital factory construction. After the virtual technology is applied, it is necessary to analyze and verify the effects of the result. A step-by-step strategy is important to apply these technologies in practice. Fig. 5 shows the general procedure of digital factory construction.

4.2 Objectives of a Digital Factory In the case of an automotive paint shop, there are so many robots, jigs, and fixtures. Moreover, painting processes are performed in a paint booth, which is a closed area with harmful objects, such as paints, thinners, etc. Therefore, it is not

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Fig. 5 General procedures to digital factory construction

easy to evaluate machines and equipment in a real work environment. Because of the harmful and closed work environment in a painting shop, a digital factory is required for evaluating various engineering activities. The objectives of a digital factory for automotive companies are listed below. • Planning of the construction and operation of a new plant for new car development • Evaluation of the design data at the early design stage • Evaluation of new machines and equipment, especially interference checks among jigs, fixtures, products, facilities, and robots • Evaluation of new processes during new car production

4.3 Construction of a Digital Factory for a Paint Shop Paint processes consist of detailed handling processes in a real automotive paint shop. These processes are conducted in a paint booth with chemicals. These processes are continuously performed via conveyors, hangers, or carriers; hence, the paint process has a longest production line in the automotive production line. Hence, the carrier/hanger size and conveyor location, besides the paint booth size, are important parameters in a paint shop. In this research, we constructed a digital factory of a paint shop using the proposed construction method, objectives, and considerations. Figs. 6 to 9 show the constructed digital factory for an automotive paint shop. The digital factory of the paint shop constructed in this paper is illustrated in Fig. 6, and Fig. 7 shows the PBS (Paint Body Storage) area in the shop.

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Fig. 6 The digital factory of paint shop

Fig. 7 Painted body storage (PBS) area

Fig. 8 shows an example of overhead and skid conveyors for the transfer of the car body frame along the process line in the paint shop. And Fig. 9 shows the pretreatment process line that is composed of a paint booth and a skid conveyor.

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Fig. 8 Overhead and skid conveyors

Fig. 9 Sealing and under coat line

5 Results of Application We developed an arrangement of machines and equipment, such as the conveyor and the paint booth, followed by a check of interference between facilities and an advance evaluation of the car design data by integrating the 3-dimensional CAD model of the constructed paint digital factory, which included robots, construction

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parts, and facilities. Fig. 10 shows a case of applying the constructed digital paint shop model to the areal analysis of manual workstations in the paint shop pertaining to inspection, heavy repair, etc.

Fig. 10 Evaluation of workstation in paint shop

Interference checking was possible across the frame/booth and conveyor/facility, as shown in Figs. 11 and 12. Figs. 13 depict examples of design and process changes in plant facilities. Hence, it is possible to evaluate the arrangement and size of plant facilities in a virtual environment.

Fig. 11 Interference check between booth and conveyor in Pretreatment line

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Fig. 12 Interference check between booth and conveyor in base coat

Fig. 13 Modification of skid design and skid conveyor arrangement

6 Conclusions In this paper, we have suggested systematic methods, considerations, and expectations as the core basis for applying virtual manufacturing technologies to the engineering activities of an automotive paint shop in new car development. By applying a systematic and efficient method in 3D modeling, the construction of a digital paint shop is more systematic and efficiently performed. Using the digital

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factory of a paint shop, we evaluated the plant layout and the processes. Moreover, we conducted various engineering activities efficiently in practical fields on a virtual-manufacturing basis. By continuous verification using virtual manufacturing technologies, savings in time and cost for many manufacturing preparation activities in new car development processes are enabled in automotive companies. An extended study based on the proposed methodologies in virtual manufacturing is necessary for the entire process of new car development. Acknowledgement. This research was supported by the Brain Korea 21 project sponsored by the Korean Research Foundation. This support is gratefully acknowledged.

References [1] Noh, S.D., Lee, C.H., Hahn, H.S.: Virtual Manufacturing for an Automotive Company(II) - Construction and Operation of a Virtual Body Shop. IE Interfaces 14(2), 127–133 (2001) [2] Noh, S.D., Park, Y.-J.: Manufacturing Preparations in the New Car Development for an Automotive Body Shop by Digital Manufacturing Technologies. Transaction of KSAE 11(6), 118–126 (2003) [3] Lee, J.H.: New product development process of digital manufacturing role and present condition. Mechanical Journal 41(10) (2001) [4] Noh, S.D., Lee, C.H., Hahn, H.S.: Virtual Manufacturing for an Auto-motive Company(I) - Workflow Analysis and Strategic Planning of Manufacturing Prepara-tion Activities Construction and Operation of a Virtual Body Shop. IE Interfaces 14(2), 120–126 (2001) [5] Brown Associates, D.H. Inc., Providing its Worth: Digital Manufacturing’s ROI (1999) http://www.bara.org.uk/info/digital/Digital_Manufacturing_ ROI.pdf (Accessed February 18, 2010) [6] Noh, S.D., Lee, K.I., Han, H.S., Park, Y.-j., Shin, H.-s., Chung, K.H.: Using Virtual Manufacturing Technologies for Continuous Verification of Products, Processes and Resources in the Manufacturing Preparation of Automotive Companies. In: The 35th CIRP-Insternational Seminar on Manufacturing Systems, vol. 12(15), pp. 245–252 (2002) [7] Iwata, K., Onosato, M., Teranoto, K., Osaki, S.: A modeling and simulation Architecture for Virtual Manufacturing Systems. Annals of the CIRP 44(1), 379–383 (1995) [8] Lee, K.I., Noh, S.D.: Virtual Manufacturing System - a Test-bed of Engineering Activities. Annals of the CIRP 46(1), 347–350 (1997) [9] Ulgen, O., Gunal, A.: Handbook of Simulation. John Wiley & Sons, Inc., The United States of America (1998)

Resource Efficiency in Bodywork Parts Production Reimund Neugebauer and Andreas Sterzing*

Abstract. The need for even greater efficiency in handling resources is coming to be seen as a public duty in politics, commerce and research. At the same time this raises the question of what options are open to companies in the manufacturing industries - and, in particular, the OEMs and suppliers to the automotive industry for reducing costs as well as deployment of resources and emissions. In addition to illustrating and analysing the relevance of this topic as far as forming technology is concerned, the following article discusses a selection of approaches that are being adopted in the Fraunhofer Institute for Machine Tools and Forming Technology with a view to reducing the consumption of resources, particularly in the bodywork parts production sector.

1 Background The 21st century is pushing humanity closer to its pre-ordained limits. Whilst the population of the Earth is increasing, the availability of raw materials for industrial enterprises as well as for developing countries is diminishing. Consumerism in developing countries such as China and India, orientated as it is towards the more prosperous nations, calls for a massive increase in gross national product worldwide. As a result, we can expect competition on a global scale for those resources that remain. One aspect that is playing a significant role in this global development and drastically intensifying this situation is humanity’s growing need for mobility. Whereas today some 72 million motor vehicles are produced worldwide, it may be assumed that this figure is poised to increase dramatically over the next few years. Reimund Neugebauer . Andreas Sterzing Fraunhofer Institute for Machine Tools and Forming Technology IWU Reichenhainer Strasse 88 09126 Chemnitz, Germany e-mail: [email protected], [email protected] *

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Hence the need emerges to reduce the quantity of resources consumed at the same time as increasing product output and so to enhance resource productivity. That is, to manufacture as much as possible using a defined quantity of raw materials and energy. The challenge is, therefore, to bring about a drastic reduction in the consumption of resources at the same time as a conspicuous increase in economic growth. The pre-condition for this is technological innovation and longterm investment. Companies that work hard today to give themselves a cost advantage based on efficiency technologies will find themselves in a better position to extend this competitive edge in the future.

2 Relevance for the Manufacture of Bodywork Components Using Forming Techniques The fact that the cost of resources – and in particular the cost of energy – has in most cases previously played only a subordinate role in investment-related decisions is demonstrated by the results of a survey conducted among company decision-makers as part of an EFFPRO (Energy Efficiency in Production) [2] study sponsored by the Federal Institute for Education, Science, Research and Technology (BMBF). More than half of those questioned indicated that these costs had either no effect at all on their scheduled investments or only affected them to a minor extent. In addition, only one third of the companies had even basic facilities at their disposal for assessing resource/energy efficiency or for optimising production processes. However, the dynamic upward trend of prices which has been a feature of the last few years in regard to raw materials and energy is likely to be sustained. Global problems such as overall competition for resources, statutory emissions limits and demographic effects will determine individual company scenarios. Resource/energy efficiency will, in the future, no longer be a matter of merely protecting the environment but primarily a question of operating efficiency. The use of efficiency technologies will thus come to represent a significant pre-requisite for the success of any enterprise in the marketplace and, ultimately, lead to sustained competitive advantages. In motor vehicle production, bodywork represents the component assembly with the greatest cumulative energy expenditure KEAH (Fig. 1). In addition to the supply of the primary materials, other aspects reflected in cumulative energy expenditure include the fabrication of the forming tools and implementation of the actual forming processes. The Fraunhofer Institute for Machine Tools and Forming Technology has set itself the following challenge: by means of innovative solutions, to make a contribution towards reducing the consumption of resources and energy in bodywork manufacture. In addition to the consistent implementation of lightweight construction strategies, a factor which also has a positive effect on the subsequent fuel

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consumption of the vehicles, the development of flexible, re-configurable forming tools as well as the application of resource-saving manufacturing technologies and facilities represents a promising approach.

Fig. 1 Cumulative energy expenditure (KEAH) in relation to the overall vehicle [1]

3 The Need for Action – Augmenting Resource Efficiency in Bodywork Manufacture The conclusions to the above study also included fields of operation as defined by the companies participating in the survey to enable a significant reduction in the resources and energy deployed in the manufacture of bodywork parts. To ensure that positive effects are achieved relatively quickly (i.e. within the next two years), the optimization of individual stages in the process within existing process chains is being viewed as a promising measure. The substitution of individual stages in the process with innovative, resource-saving technologies was identified as a medium-term approach which, it is felt, should produce successes within 2 to 5 years. By way of further measures to increase efficiency, the elimination of complete stages in the process and their replacement with new construction methods as well as resource-saving technology, tooling and system concepts have been specified with a view to achieving shorter process chains. Because the greatest challenges lie in guaranteeing appropriate process capability and stability, implementation and usage in production is only envisaged after a period of 5 years. These solutions will only be implemented if the associated risks as far as the company is concerned can be kept under control. This calls for innovations which, in addition to a high level of resource and energy efficiency, also demonstrate corresponding suitability for volume production as well as a high level of process and application capability. Because under no circumstances must the implementation of these technologies and process chains be allowed to result in shutdowns and consequential loss of production. This forms the basis for further research to be conducted over the next few years. In the following, therefore, examples will be presented showing how, by applying efficiency technologies and facilities, a definite increase in resource and energy efficiency can be achieved in the manufacture of bodywork components based on forming techniques, but at the same time emphasising that consideration must always be given to the process chain as a whole.

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4 Technological Applications 4.1 Material Characterization/Characteristics Determination One important strategy for increasing resource efficiency is to identify and compensate for possible errors at the very beginning of the product development process. This means that careful preparation in relation to the process is more important than ever. Hence the use of FE simulation for designing forming processes acquires a strategic role. However, in order to be able to use simulation as an effective tool, precision results are required which in turn call for precise input values and suitable material models to describe the forming behaviour. A systematic expansion of the characteristics determination process (Fig. 2), a more precise calculation of the flow curves and yield loci, linked with new materials test methods such as, for example, the Maxi-BulgeTest or accurate determination of the start of flow in the tension compression test with high-resolution temperature measurement will lead to a reduction of iteration loops in the simulation and to a significant improvement in the accuracy of simulation calculations. This in turn will have a positive effect on the real tryout process because here too it will be possible to reduce the number of iteration loops. In the bodywork component fabrication sector, temperature-assisted forming processes are gaining more and more importance. So-called ’form hardening’ represents the state of the art and is already being used for a broad spectrum of components. The use of temperature as a process parameter in the processing of magnesium and aluminium alloys using forming technology also provides us with the opportunity of extending the area of application for these materials and thus to make a contribution towards reducing component weights. Here too FE simulations are essential for the design and layout of such processes and, by comparison to traditional sheet metal forming at room temperature, represent the emergence of a new quality factor. Phenomena that may also occur at room temperature but which do not actually have any effect on the forming result as far as the accuracies normally demanded are concerned may be of major significance here. Aspects such as, for example, • • • • • •

flow curves dependent on temperature and expansion rate, temperature- and load-dependent heat transfer coefficients, temperature-dependent heat capacity and heat conductivity capacity, modelling of micro-structural transformations, volume changes in relation to micro-structural transformations and temperature-dependent failure limits of the material

must be taken into account. There is a need for further action in relation to the determination, provision and observance of these temperature-specific input parameters.

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IWU strategy „material testing“ uniaxial tensile test Maxi-BulgeTest

τσΙΙ

II

ϑmax = 1200 °C

τσΙI

tensile-compression test

yield locus diagram

biaxial tensile test

Fig. 2 IWU strategy “extending characteristics determination” [7]

4.2 Efficiency Technologies 4.2.1 Process Monitoring Reducing scrap and avoiding re-working is an important point of departure in optimising the use of energy and resources in bodywork parts manufacture. The current status is characterised by the fact that the quality test is carried out at the end of a press line. Because of the necessary forming and trimming steps it is possible that, when a fault is determined (e.g. necking, cracks), a number of parts may already be affected. One approach that is being used at IWU Chemnitz is the development of suitable process monitoring strategies that enable a rapid response to be made to changes in process conditions. One challenge lies in the identification of appropriate variables that characterise the status of the process. A possible method for identifying appropriate information about the process is by registering the flange movement, which is influenced by a large number of factors. In so doing (and without the need for precise know-ledge of these factors) open or closed loop control techniques as applicable may be deployed in relation to the flange movement by changing the blank holder force [4, 6, 11]. By specifying the process limits for the manufacture of good parts and by logging the trend of progression of the movement of the metal sheet, it is possible to adjust the blank holder force at an early stage should the corresponding tolerance limits be exceeded and hence to significantly reduce the amount of scrap (Fig. 3).

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This strategy has already been used under production conditions using actual components as a model. Taking a longitudinal beam as an example, it proved possible to reduce the scrap rate from 12 % to less than 2 %.

Gutte

Teil part

3200 kN

3400 kN 3600 kN

blankholder force settings Einstellungen Kissenkraft F N FBH [kN]

3200 kN

3000 kN

aus Erf

3400 kN

measuring measuring of flange flange movement

good parts

flange movement ss [mm] Flanscheinzug [mm]

Palette change Cushion force change Flange movement Test part

Fig. 3 Online process monitoring based on flange movement

4.2.2 Use of High-Speed Cutting Processes Depending on the actual forming process, the fabrication of bodywork components using forming technology frequently calls for additional cutting and/or punching operations prior to producing the final component geometry. One of the operations included here is the trimming of formed parts. Another operation is the introduction of perforations or form contours into the component so as to guarantee further processing capability as well as the function of the component. Because of the increasing importance of lightweight materials and, in particular, the growing use of high- and super-strength materials, conventional cutting processes are fast approaching their limits as far as the necessary process forces, achievable quality and realistic service life are concerned. This is leading to the use of laser cutting, e.g. for trimming form-hardened components. In addition to the thermal impact on the material, this approach does not represent an optimum solution from an energy point of view either. Adiabatic separation, as it is known, a technique that has been previously used in particular for separating small diameter cylindrical solid profiles, offers an opportunity to replace laser cutting in the ’form hardening’ process chain, thus achieving a positive influence on the overall energy balance in component manufacture [5, 8]. In addition, the process offers the potential to avoid the need for any re-working because the cut surfaces are produced to a very high quality and are practically burr-free. It is possible, when manufacturing tube-shaped structural components, for this process to replace corresponding sawing operations previously used to provide semi-finished goods and/or for finish machining of the components. In addition to high cut section quality, it is also possible to achieve a considerable increase in the level of material utilization because no chips are produced.

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Thus, based on an in-depth scientific analysis of adiabatic separation, the technical pre-conditions and fundamentals for expanding the field of application of adiabatic separation to bodywork parts manufacture are being created (Fig. 4) at Fraunhofer IWU with a view to providing a framework within which the relevant mechanisms and factors likely to affect the separation result can be identified and evaluated. Resource Efficiency by Innovative Forming/Cutting Processes Ö

adiabatic cutting

vcut Fcut

vcut Fcut

α1

D

- effects - limits - tool/process design

Di Da

α2 vcut Fcut

test tube (1.0037; D = 60 mm, s0 = 2,0 mm)

OBJECTIVE application extension Ö Ö

α2

α1

Limited know-how HSIC

cutting / punching of tubes trimming / punching of flat sheet parts

test sheet part (1.0335, s 0 = 5,0 mm)

Fig. 4 Expansion of the application field for adiabatic separation processes

4.2.3 Shortening the Process Chain by Combining Processes The development of forming strategies for the flexible manufacture of component derivatives also represents one approach to increasing the efficiency of resource deployment in bodywork manufacture. Further successes here may be achieved by the use of a universal blank to serve as a basis for the subsequent production of the derivatives (Fig.5).

Fig. 5 Universal perform

As part of a joint project with various industrial enterprises led by BMW AG the fundamental feasibility of this approach was examined and substantiated, taking a drive console as an example [3, 8]. A traditional deep drawing operation was

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used in the creation of the universal blank. The console derivatives (drive console left, drive console 4-wheel drive) and/or the component-specific areas were then produced with the help of incremental sheet metal forming. So as to be able to manufacture fault-free drive consoles (that is, drive consoles without wrinkles and cracks), extensive systematic investigations were required in order to be able to define appropriate machining strategies and process parameters. The need in so doing to take the whole of the process chain into consideration was evident from the fact that the design of the universal blank has a radical effect on the final forming result. The result of the studies was to demonstrate that, by means of this combination of processes, the process chain for manufacturing the two component variations could be significantly shortened and the number of forming tools required reduced from 5 tools each to 3 tools each per component. 4.2.4 Substitution of Thermal Jointing Operations In bodywork manufacture, the substitution of thermal jointing operations by forming-based jointing processes (e.g. clinching) also offers the potential to significantly reduce the amount of energy used during the production process. The omission of heat means a lower energy requirement and leads to improvements in the achievable form and dimensional accuracy of the components. Taking thick sheet clinching (e.g. as used in the construction of commercial vehicles) as an example, the potential for reducing energy consumption by comparison to a thermally assisted connection (e.g. MAG welding) was determined analytically in the basic investigations. Taken as the reference value here was the value of the joint energy obtained (energy input per unit of strength) which, in the medium to long term, may also be applied as a significant parameter for the energetic evaluation of industrial jointing processes. Calculations show that the amount of energy under static load required to achieve a corresponding level of strength using clinching instead of the thermal process is only around one third (Fig. 6). thermally

mechanically

energy per strength 150 J/kN

3

50 J/kN

:

1

:

1,7

flange dimensions

1 need for research

- identification of application areas - integration in process chains

Fig. 6 Saving energy by process substitution [2]

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The dynamic load on joints represents an additional aspect. Since motor vehicle structures are designed to cope with cyclic strength and because clinched as opposed to welded joints demonstrate a significantly better response to cyclic load, it may be assumed that the joint energy obtained from clinched joints as opposed to thermal processes can be reduced still further. In this phase of the investigations it should be pointed out that, for these initial considerations, possible further aspects such as e.g. accessibility have for the present been disregarded.

4.3 Resource Efficiency in Tool Making 4.3.1 Flexible Geometry Tool Principle “System Module” The development and implementation of modular tool systems for the flexible manufacture of bodywork parts represents a further approach to energy and resource efficiency in automotive production. At the same time, the objective is to manufacture a range of tooling components by utilising and re-assembling tooling components. Using this approach it will be possible to significantly reduce the consumption of materials and energy for manufacturing forming tools and this procedure is particularly suitable for the manufacture of internal structural parts. The above-mentioned joint project was carried out in collaboration with various industrial enterprises under the leadership of BMW AG [3, 8]. In the project, the process capability of a “system module“ (Fig. 7) for manufacturing a range of seat crossmembers using forming technology underwent a theoretical assessment. Its potential in terms of re-configurability and recyclability of existing tooling components for the manufacture of derivatives in the “seat crossmembers” family of components was evaluated. At the same time it was established that 80% of the components in the basic structure as well as up to 50% of the tool active parts can be re-used for the manufacture of crossmember derivatives. The prototype of a

part-specific, tool-related

unified part geometry Fig. 7 System module

not toolrelated

basis tool

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tooling system of this kind is currently undergoing extensive tests at the Fraunhofer IWU in Chemnitz with a view to providing evidence of the effects as regards recyclability and sustainability, including in practical application. 4.3.2 Use of Active Media The use of active media and energies (e.g. elastomers, fluids, gases, electromagnetic fields ...) in forming processes offers, by comparison to traditional forming, advantages in terms of • the achievement of high levels of deformation, • guaranteeing a high form and dimensional accuracy (reduction of springback behaviour), • guaranteeing high levels of component rigidity and • the integration of additional forming, cutting and jointing operations into the main forming process. In addition, and especially in conjunction with the manufacture of forming tools, an opportunity is provided to significantly increase resource efficiency because the active media used assume the functions of tool active parts, thus enabling savings to be made in relation to parts of this kind. In active media-based sheet metal forming, the medium assumes, for example, the function of a forming punch which means that the design of the forming tool can be considerably simplified and the amount of material that needs to be used as well as the processing expenditure can be significantly reduced.

basis: uniform part geometry

PART I

PART II

Fig. 8 Flexible part manufacturing using gas generators

One innovative example of the use of active media is the use of gas generators that are responsible for triggering airbags and, in conjunction with this, generate corresponding volumes of gas within very short time units. By way of follow-up to this basic idea, investigations are underway at the IWU in Chemnitz to establish whether this technology can be used both for cutting and for forming processes (Fig. 8).

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In gas pressure cutting, the gas takes over the function of the cutting punch. Based on an appropriate tool principle and the way in which the gas is conveyed to the cutting zone, perforating and trimming operations are possible outside the main effective direction of the press which have hitherto only been possible using expensive tooling and tapered slide valve technology. In addition to the positive effects in reducing the complexity of the tooling technology required, it is also possible by integrating gas pressure cutting into forming operations to achieve not only a considerable shortening in process chains but also to guarantee a high quality cut surface due to the effects of high speed [3, 9, 10].

4.4 Efficiency Facilities Even the use of low energy forming machines in bodywork parts production will lead to an increase in efficiency and hence resource efficiency. One approach consists in the extensive avoidance of “lost energy“ through the deployment of closed resource cycles (Fig. 9). One example of this is the development of energy-saving die cushions for forming presses. The present-day cushion systems are mainly based on the principle of displacement. Functioning as an actuator here is a hydraulic valve that acts as hydraulic resistance. The total output of the displacement device is converted into dissipated energy at the valve and this leads to heating of the fluid, thus making additional cooling necessary. As part of a project with BoschRexroth, a principle for an energy-saving die cushion has been developed which considerably reduces lost energy [2, 5]. Here a variable displacement pump is used as an actuator and this can also operate as the motor. The principle is based on the fact that the output of the displacement device minus the transformer losses is converted into mechanical energy at the pump shaft. This mechanical energy can either be stored (flywheel) or fed back as effective power into the electricity network via the motor (in generator operation). Solution Approach Leistungseintrag input

recovered Rückgewonnene Energie energy

Leistungs-

energy verlustloss main drive Hauptantrieb

Leistungsperformance loss verlust Energierückenergy recovering gewinnung

forming Umformwork arbeit Leistungsenergy verlust loss Kissen

cushion

Energy Savings up to 65 % (in comparison with conventional cushions)

Fig. 9 Production of closed resource cycles –energy-saving die cushions

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The recouped power can then be absorbed simultaneously by the main drive of the forming machine and this will ultimately reduce the amount of power taken from the network. Current experimental studies have indicated that up to 65% of power input can be recouped for certain load ranges.

5 Summary The technological applications show that a large number of options exist for increasing resource efficiency in bodywork manufacture. Using efficiency technologies means that there is potential not just for reducing the amount of energy consumed by the motor vehicle in drive operation. In addition, an opportunity also emerges for reducing material deployment on the one hand and the amount of energy required for the production of components on the other. However, an essential pre-condition is the need to consider the process chain as a whole and to take account of all interactions between the individual components. • the implementation of new construction methods involving the use of innovative lightweight construction materials and semi-finished goods so as to guarantee optimum material utilization, • consistent shortening of process chains on the basis of resource-optimised technologies and process combinations, • guaranteeing maximum process reliability so as to reduce scrap and reworking or • the use of highly flexible and reconfigurable low energy production systems and facilities. References [1] Hoffmann, C.: Kumulierter Energieaufwand und optimierte Nutzungsdauer von Personenkraftwagen. IfE Schriftenreihe. Heft 31. TU München (1996) [2] Neugebauer, R. (ed.): Energieeffizienz in der Produktion. Untersuchung zum Handlungs- und Forschungsbedarf. Abschlussbericht (gefördert durch das BMBF). Fraunhofer-Gesellschaft (2008) [3] Neugebauer, R., Sterzing, A.: Ressourceneffiziente Umformtechnik. In: Proceedings of the 5th Chemnitz Car Body Colloquium, Chemnitz, Germany, November 11-12, pp. 81–95 (2008) [4] Neugebauer, R., Bräunlich, H., Scheffler, S.: Process Monitoring and Closed Loop Controlled Process. In: Proceedings of the International Conference and Exhibition on Design and Production of Machines and Die/Molds, Cesme, TurKey, June 21-23, pp. 279–287 (2007) [5] Neugebauer, R., Bräunlich, H., Kräusel, V.: Umformen und Schneiden mit Hochgeschwindigkeit – Impuls für die ressourceneffiziente Karosserieteilbearbeitung. In: Proceedings of the 5th Chemnitz Car Body Colloquium, Chemnitz, Germany, November 11-12, pp. 205–213 (2008)

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[6] Neugebauer, R., Bräunlich, H., Scheffler, S.: Process Monitoring and Closed Loop Controlled Process. In: Proceedings of the 1st International Lower Silesia-Saxony Conference AutoMetForm, Wroclaw, Poland, May 6-9, pp. 21–41 (2008) [7] Neugebauer, R., Sterzing, A., Müller, R., et al.: New Approaches for the Characterization of Sheet Materials – A Precondition for the Use of New Sheet Materials and Forming Processes. In: Proceedings of the 9th International Conference on Technology of Plasticity, Gyeongju, Korea, September 7-11, p. 106 (2008) [8] Neugebauer, R., Kräusel, V., Weigel, P., et al.: Adiabatic Cutting – Use of High Speed for Resource-Efficient Manufacturing. In: Proceedings of the 7th Internation Conference on Industrial Tools and Material Processing Technologies, Ljubljana, Slovenia, October 4-7, pp. 97–100 (2009) [9] Neugebauer, R., Sterzing, A., Bräunlich, H., et al.: Resource Efficiency in Tool and Die Making – Chances for Competitiveness. In: Proceedings of the7th Internation Conference on Industrial Tools and Material Processing Technologies, Ljubljana, Slovenia, October 4-7, pp. 3–8 (2009) [10] Neutz, J., Ebeling, H., Hill, W., et al.: Application of Pyrotechnic Gas Generators in Sheet Metal Forming Technologies. In: Proceedings of the 9th International Symposium and Exhibition on Sophisticated Car Occupant Safety Systems, Karlsruhe, Germany, December 1-3, pp. 17/1–17/15 (2008) [11] Roscher, H.J., Neugebauer, R., Wolf, K., et al.: Control of Sheet Metal Forming Processes with Piezoactuators in Smart Structures. In: Proceedings of the International Conference Smart Structures and Materials and NDE for Health Monitoring and Diagnostics, San Diego, USA, February 27-28 (2009) 2006.paper 61710E

Self-Tracking Order Release for Changing Bottleneck Resources Matthias H¨usig

Abstract. In this paper a self-tracking order release strategy for job shop production with well-defined routes is presented. The release strategy is a combination of different known methods. The changing machine loads, caused by the different products manufactured in the job shop, are compensated. Additionally the constraint of the due date of each individual order is kept. Balanced load on all machines is achieved by controlling the sequence of released order and the release times. For the adjustment of the controller only the average WIP (Work in Process) of each machine has to be set. The strategy is tested with two plant models implemented as Petri net simulations.

1 Introduction Consider a flexible job shop where a multiplicity of distinct orders is processed using machines on different routes. Each order is produced within a defined sequence of process steps. The sequence of the process steps is stored in the process plan. With this the orders are routed through the job shop on one defined way. If several orders are in a buffer in front of a machine, a sequencing rule decides which order is produced next. In this job shop every machine buffer releases the orders with the FIFO (First-in-First-Out) principle. A machine can process only one order mutually exclusive at a time. This situation is shown in Fig. 1 where two kinds of orders (A and B) are produced in one plant. The process plans guide both kinds of orders on different routes through this plant. Machine 1 accomplishes the first production step for both kinds of orders; the second is accomplished by machine 2 and 3, respectively. Orders with different routes lead to a dynamic variation of buffer loads of subsequent machine buffers in the job shop. In the given example the buffer loads of machine 2 and 3 changes with a delay after release of job A and B, respectively. Matthias H¨usig Institute of Automation, Hamburg University of Technology, 21073 Hamburg, Germany e-mail: [email protected]

254

M. H¨usig released orders A, B

plant

buffer 1

machine 1

material flow

buffer 2

buffer 3

machine 2

machine 3

finished order A

finished order B

Fig. 1 Material flow diagram of the plant. After the release of orders A and B they are processed on machine 1 first. Then the orders of the type A are processed on machine 2 and orders of the type B are processed on machine 3.

This delay is made up of the processing time in machine 1 and a queue time in the buffer of the particular machine. For this example, it is difficult to control the loads of buffer 2 and 3 because of different delays at machine 1. In this paper a production control method is described which achieves two targets — it reduces dynamic load fluctuation in machine buffer and keeps to the due dates as far as possible, with low work in process (WIP) in the whole plant.

2 State of the Art The normal CONWIP (Constant Work in Process) [5] release rule leads to a constant WIP in the whole job shop. However different types of orders lead to different loads of the machines. This happens even if the occurrence of both kinds of orders is uniformly distributed over time. Workload control is used to get a high efficiency of the whole production. Realignment of orders depending on the algorithm controlling the workload can lead to violations of the due dates of the individual orders. The calculation of the optimal release sequence achieving both goals need a lot of calculation time and can be done via mixed integer linear programming (MILP) [2, 9]. This calculation method is not part of this paper. In this paper, only methods usable for online dispatching are inspected. Online dispatchers detect shifting bottlenecks and control the workload of all machines in the job shop. This should work also for cyclic behavior with multiple uses of certain machines during production. Workload control and order release strategies are an ongoing subject of scientific research [8, 11, 7]. The strategies balance different and conflicting objectives in production control. For each objective there exist different release strategies. The workload of the machines can be controlled e.g. by WIPLOAD [12], CONWIP [5],

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255

CONLOAD [10] or other workload balancing control mechanisms like starvation avoidance [6]. With the sequence of order releases the lateness of orders and other production objectives can be controlled. Two of these are the balanced workload of the machines and the due date of the single order. All related production rules try to minimize the throughput time and try to achieve high machine utilization. In research not only workload control is a topic but also the detection of the current bottleneck resource [11, 3]. Single parts of the controller algorithm presented in this paper are known, but the different combination of rules leads to new effects like adaption of stock levels and machine load.

3 Scheduling Algorithm In production planning several conditions have to be considered by the rules of the controller. Two most important conditions are the workload and the due date. The order release has an influence on both values. The algorithm is divided into two parts. In the first part, the decision is made which order will be released next. In the second part, the release time is determined. The automatic selection and release is used for self tracking the load of the machines over time with respect to the due date.

3.1 WIP Stretch Definition Normal load balancing algorithms as described in [13] and [1] use the WIP of a single machine controlling the order release. If the WIP of the bottleneck machine exceeds a lower limit, orders using this machine will be released next. A production line behaves like a time delay system. It is difficult to calculate the delay of each order arriving at a certain machine exactly because orders have different production times and particularly different idle times in the buffers of the machines. This is the reason why the WIP of a production facility is stretched to all facilities before. This approach is similar to the workload control of Land and Gaalman [7]. After order release the workload of all machines is increased. With this procedure the future load of machines in the job shop can be estimated. The predicted load of the machine increases depending on the process step. It is devaluated by dividing the WIP by the number of process step. The predicted workload for orders guided on routes through the production are calculated before release, by summing up all machine WIP stretches of machines used on this route. Table 1 shows an example of the calculation for the increase of the WIP stretch of the machines used during production. The online calculation of the WIP stretch of each machine can be done in a similar way. Released orders increase only the WIP of the first machine, but the WIP stretch increases for every machine used during production. The WIP stretch of the machine increases by a Δ of the WIP of a particular job i at the process step k of route j divided by the process step:

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Table 1 Example for WIP stretch calculation used work stations

process step

real workload

Δ WIP stretch

2 1 3

1 2 3

100 80 90

100\1 = 100 80\2 = 40 90\3 = 30

W IPi,k (1) k If an order is released the WIP stretch increases for all following machines according to (1). This being the case the WIP stretch has to be reduced by the same value if an order finishes a process step at a machine. The new WIP stretch of a ma chine W IPstretch (i, j) (2) has to be updated by order release and by finishing a prok cess steps at a machine. It is calculated by adding and subtracting respectively the Δ W IPstretchk (i, j).

Δ W IPstretchk (i, j) =

 (i, j) = W IPstretchk (i, j) ± Δ W IPstretchk (i, j) W IPstretch k

(2)

To calculate the expected average WIP stretch (W IPstretch j ) for a route j all WIP stretches of the machines are summed up and divided by the number of process steps N of route j. N( j) W IPstretchk (i, j) ∑ (3) W IPstretch j = k=1 N( j) The number of the process step is included to accommodate the different length of the routes.

3.2 Sequencing Rule Order release and in particular the sequencing use the average WIP stretch of each order from (3) to chose the next release candidate. The average WIP stretch of an order captures the predicted workload for all machines which will be used during production. The scheduling algorithm should release orders which pass through machines with low workload and procrastinating orders which pass through machines with high workload. If the orders in the plant are processed on different machines, or on the same machines on other routes, the load of the machines in the shop can be balanced. A high average WIP stretch of one particular type of order shows that machines which will be used suffer under high workload. In contrast to this low average WIP stretch shows that the machines have a low workload and with this the order will have a short throughput time. On this way the scheduling algorithm avoids starvation and overload of the machines as far as possible. Up to here, the algorithm considers only the workload of the machines. To consider the due date of a single order the algorithm is enlarged to keep the due date.

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The average WIP stretch is combined with a time factor (T F). To calculate the time factor the remaining time for production of the job must be calculated first. The urgency of a job is represented with the release buffer time (RBT ). This is the time per process step N( j) being left for production; this is the throughput time corresponding with the sum of the WIP stretches. With the current time τ and the due date DD the release buffer time for job i on route j can be calculated as follows: N( j)

RBTi =

DDi − τ − ∑k=1 W IPstretchk (i, j) N( j)

(4)

The RBT can assign positive and negative values depending on the delay of the order. For the release rule a time factor is needed which represents the possibility to suspend the release of an order to get a better load balance in the whole plant. The algorithm should prefer orders with a near due date and ignore the due date if there is enough time left for workload balancing. This is gained by taking the logarithm of the RBT . For delayed orders the RBT reaches negative values, but the domain of the natural logarithm is only the positive numerical range. Therefore two cases are separated:  ln(RBTi + 1) for RBTi > 0 T Fi = (5) RBTi else. To get a continuous curve the argument of the logarithm is incremented by 1 for the case of positive RBT . The final scoring factor (SF) for the release decision is a multiplication of W IPstretch and T F. For all orders left in the stock SF is calculated. The order i with the smallest value SF is released next. SFi = min(W IPstretchi × T Fi ) i

(6)

This factor includes the predicted load and a time factor for the particular order. The order with the smallest SF leads to a good combination of load balance and due date adherence for the present plant workload situation.

3.3 Release Time Both the sequence, as well as the release time has an influence on the workload of the machines. When orders are released too early, this leads to a heavy workload, and then to less planning flexibility. For late releases, bottleneck resources may suffer from disruptions of material flow. Due to the different routes of orders produced in the job shop they do not have the same influence on the load of each machine, however for this reason starvation of a machine with little load cannot be avoided. The release time is calculated with only the load of the local bottleneck resource in mind. This tends to result in a high workload but not a good efficiency. For the following calculations the bottleneck resource m for order i must be detected. The

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bottleneck is the resource with the maximum WIP stretches of all k resources used by order i: W IPstretchm (i, j) = max(W IPstretchk (i, j)) k

(7)

For the bottleneck resource the execution time (ET ) is calculated. The WIP stretch of the local bottleneck will be the same over a long time if we use the execution time as release time. To control the WIP stretch to the given set point the release time can be shortened or enlarged. If the WIP stretch is lower than the given set point the release time must be shorter. To reach the set point of the WIP the release time is scaled by a quotient of WIP stretch at the bottleneck divided by set point WIP stretch (W IPsp ). The release time is computed by adding the scaled execution time by the present time τ : RT = τ + ET

W IPstretchm (i, j) W IPsp

(8)

The load of different bottleneck resources is controlled by selecting the next order depending on the T Fi and scaling of the release time (RT ) automatically. After the release time is expired the order is released and the WIP stretch will be updated. The algorithm is restarted and the smallest average WIP stretch of the remaining orders is calculated.

4 Scheduling Example In order to evaluate the scheduling algorithm it is tested with the plant described in Fig. 1. A production of 500 job orders is simulated using a Petri net based simulator similar to [4]. Each order has some attributes storing the lot size, due date and route number, respectively. The distribution of the attributes ’due date’ and ’lot size’ is uniform in between defined limits (Tab.2). The routes are uniformly distributed among both kinds of orders A and B, respectively.

Table 2 Attributes for the orders in the scheduling Example attribute

min

max

due date [in days]

1

5

lot size in one job

1000

3000

In Fig. 2a the overall production time is plotted with respect to the WIP of the whole plant. It shows two curves representing different dispatching rules; the dotted curve represents the CONWIP rule and the solid line the WIP stretch rule. Each curve arises from a series of simulations with an increasing WIP in the whole plant.

Self-Tracking Order Release for Changing Bottleneck Resources

259

overall production time

11000

CONWIP WIP stretch 9000

7000

percent delayed orders

a)

0

10

20 30 average work in process

40

60 40 20 0 0 b)

10

20 30 average work in process

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Fig. 2 a) Overall processing time for all orders with respect to WIP of the whole plant b) Percent of delayed orders with respect to WIP of the whole plant

The dotted curve shows a declining overall production time approaching a lower limit. For good machine utilization the WIP can be increased. High WIP leads to low overall processing times and a good adherence to due dates. Contrary to the CONWIP rule the WIP stretch rule leads to a shorter overall processing time for all load levels. The solid line of the WIP stretch rule approaches very fast the minimum value of the overall production time. This means optimal performance can be achieved even with little WIP, if the WIP stretch algorithm is used. If the orders are scheduled with the CONWIP rule the WIP of this method must be more than 8 times higher to get near equal overall processing time. In Fig. 2b the resulting numbers of delayed orders are plotted with respect to WIP. It can be seen that even for high WIP it is not possible to hold the due date for all orders using the CONWIP rule. Contrary to the CONWIP release rule the WIP stretch rule can hold the due date for all orders already with low WIP. In addition to Fig. 2, each individual machine load over time is plotted in Fig. 3 in order to understand the different behavior of the plant. The average WIP of the whole plant is equal to 12, for the simulation data in which both methods are compared. In the diagrams in Fig. 3, the WIP of the machines are shown resulting from the different release strategies. On the on hand with the index 1 the CONWIP controlled, and on the other hand with index 2 the WIP stretch controlled ones. Fig. 3a) shows the overall WIP with respect to time. It can be seen, that the WIP for the CONWIP rule is exactly 12. This is due to the fact that WIP is limited to a maximum load to make it comparable to the WIP stretch rule. The WIP stretch rule tunes itself with the correct parameter to this average WIP. It is obvious that the overall processing time is shorter for the WIP stretch rule than for the CONWIP rule. Fig. 3b)–d)

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show the WIP with respect to time for the buffer in front of each machine. Machine 1 is well utilized with both scheduling algorithms, because there is no break of the material flow. But this does not apply for machine 2 and 3, respectively. They run idle because of loss of required jobs Fig. 3c1)–d1). If machine 2 runs idle machine 3 has heavy load and vice versa. The curves of the WIP run in opposite direction, seldom both machines work at the same time. The CONWIP release strategy leads to a good load for machine 1. Machine 2 and 3 suffer under altering load utilization. If a break at the material flow appears the productivity decreases. The WIP of machine 2 and 3 has got a very dynamic character controlled with the CONWIP rule. The WIP increases and decreases on a short time scale. These fluctuations cannot be prevented with higher load. In contrast, the WIP stretch rule smoothes the WIP for machine 2 and 3, respectively. It leads to constant workload, short overall production time and a good adherence to due date Fig. 3c2)–d2).

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5 Industrial Scheduling Application For further verification the scheduling rule is also tested with an industrial application which is more realistic. The job shop consists of 4 work groups (WG) each with two machines. Jobs produced in this plant are routed through the shop on 7 different routes. The decision which machine in a work group is used for a job is defined by the attributes ’material’, ’color’ or ’size’ of the order. Orders have two additional attributes which store the due date and the production quantity. The order pool consists of 500 different types. New orders reach the order pool statistically. Orders are released with the described online release rules dependent on the orders in the present order pool. This industrial application shows similar behavior (Fig. 4) as the model from section 4. The overall production time decreases with a growing WIP in the plant similar to the scheduling example and approaches a lower limit (Fig. 4a). For a high WIP both methods approach to the same limit. In Fig. 5 the WIP of all machines

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is plotted with respect to time. In this simulation the average workload is 12 orders for both release rules. The overall production time is far shorter for the order release using the WIP stretch rule than order release using the CONWIP rule, resulting from a balanced WIP of all machines in the plant. The upper curve simulated with the CONWIP rule show altering WIP in short intervals for all machines. Break of material flow appears also with the WIP stretch rule, but not as often as it appears with the CONWIP rule.

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Additionally the algorithm is tested for robustness against sudden machine breakdown. As an example a breakdown of machine 1 for 480 time steps is simulated (Fig. 6). The WIP is increased just a little after breakdown for a short time. The breakdown has only less influence on the inventory of each machine as shown in Fig. 6. After breakdown the average WIP stretch for orders using machine 1 increases. Hence such orders will be restrained and will not be released.

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6 Conclusion The presented order release strategy can easily be implemented for complex job shop problems with altering routes. The algorithm leads to a short over all processing time, and a good adherence to due dates. A low WIP gives the planner of the plant more flexibility. It can also be used for cyclic job shop problems with multiple use of the same machine. As a consequence of the load balancing principle the algorithm has a smooth response of manufacturing disturbances and machine breakdown.

References 1. Bechte, W.: Controlling Manufacturing Lead Time and Workin-Progress Inventory by Means of Load Oriented Order Release. In: American Production and Inventory Control Society, Conferences Proceedings (1982) 2. Brucker, P.: Scheduling Algorithms, 4th edn. Springer, Berlin (2004) 3. Cheng, H.-C., Chiang, T.-C., Fu, L.-C.: Multiobjective job shop scheduling using memetic algorithm and shifting bottleneck procedure. In: IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched (2009), doi:10.1109/SCIS.2009.4927009 4. von Drathen, A.: Compact modeling of manufacturing systems with petri nets. In: IEEE International Conference on Systems, Man and Cybernetics ISIC, pp. 3487–3492 (2007), doi:10.1109/ICSMC.2007.4413580 5. Enns, S.T., Rogers, P.: Clarifying CONWIP versus push system behavior using simulation. In: Simulation Conference, WSC 2008, pp. 1867–1872 (Winter 2008), doi:10.1109/WSC.2008.4736277 6. Glassey, C.R., Resende, M.G.C.: Closed-loop job release control for VLSI circuit manufacturing. IEEE Transactions on Semiconductor Manufacturing (1988), doi:10.1109/66.4371 7. Land, M., Gaalman, G.: Towards simple and robust workload norms. In: Proceedings of Workshop on Production Planning and Control, pp. 66–96 (1996) 8. Land, M., Gaalman, G.: Workload control concepts in job shops A critical assessment. International Journal of Production Economics 46(7), 535–548 (1996) 9. Leung, J.Y.-T.: Handbook of Scheduling, Algorithms, models, and performance analysis. Chapman & Hall/CRC, Boca Raton (2004) 10. Rose, O.: CONLOAD-a new lot release rule for semiconductor wafer fabs. In: Proceedings of the 31st conference on Winter simulation, pp. 850–855 (1999), doi:0.1109/WSC.1999.823297 11. Roser, C., Nakano, M., Tanaka, M.: Shifting bottleneck detection. In: Simulation Conference (2002), doi:10.1109/WSC.2002.1166360 12. Qi, C., Sivakumar, A.I.: Job release based on WIPLOAD control in semiconductor wafer fabrication. In: 8th Electronics Packaging Technology Conference, EPTC 2006, pp. 665–670 (2006), doi:10.1109/EPTC.2006.342793 13. Wiendahl, H.-P.: Load-Oriented Manufacturing Control. Springer, Berlin (1995)

Integrated Operational Techniques for Robotic Batch Manufacturing Systems Satoshi Hoshino, Hiroya Seki, Yuji Naka, and Jun Ota

Abstract. This paper focuses on a batch manufacturing system with multiple industrial robots. Inappropriate coordination of the robots might cause a bottleneck. In addition, a bottleneck is a constraint that dominates the entire system performance, that is, the productivity. Therefore, for an efficient system, these robots are required to operate appropriately while relating to each other. This is a challenge in this study. We propose the following operational techniques: route planning approaches and operation dispatching rules on the basis of task-assignment that will reduce the effect of a bottleneck. Furthermore, reactive cooperation, so that the robots respond to a fluctuating heavy workload caused by the shifting bottleneck, is an essential operational technique. Throughout the simulation experiments, each combination of the operational techniques is examined; finally, the integrated operational techniques are shown.

1 Introduction Generally, in manufacturing systems for high-mix low-volume production, a number of different machines are operating. In order to improve the throughput of the whole of the operation in the system, it is not enough to use each of the machines efficiently, but an efficient coordination technology, based on the careful consideration of the operations of all the machines, is required. A pipeless batch manufacturing plant in chemical process industries is an applicable environment of a flexible batch manufacturing system from the standpoint of the reasonable and multi-product production of chemical products, such as Satoshi Hoshino · Hiroya Seki · Yuji Naka Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503 Japan e-mail: [email protected] Jun Ota Research into Artifacts, Center for Engineering (RACE), The University of Tokyo

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lubricants, adhesives, pharmaceuticals, paints, and inks, in addition to adaptation to a fast-changing market. The reason for this trend is that materials are transported by movable vessels and production processes are conducted at a number of fixed process stations. Thus, compared to a general batch manufacturing plant, which consists of a pipe network, a pipeless batch plant is able to prevent material contamination when materials or products are switched from batch to batch. Furthermore, each recipe for a production process is different from others; herewith, a multi-product production in one plant is made possible. However, since advanced coordination technology for the equipment is required, only a few plants have started operations so far. With regard to this issue, we focus on a robotic batch manufacturing system. Each industrial robot is able to perform a task agilely according to its control law and to respond to the changing circumstances flexibly by sharing information via communication. In this regard, let us notice that a manufacturing system is usually located in a closed plant facility; thus, a heavy workload for a robot that arises from a bottleneck at a place in the system affects productivity as a whole. Moreover, the bottleneck may shift to another place in the system even if it is corrected [1]. Therefore, for an efficient system, robots are required to operate appropriately while relating to each other and tracking the shifting bottleneck. We propose the following operational techniques: route planning approaches and operation dispatching rules on the basis of task-assignment that will reduce the effect of a bottleneck. Furthermore, reactive cooperation, so that the robots respond to a fluctuating heavy workload caused by the shifting bottleneck, is an essential operational technique. Throughout the simulation experiments, each combination of the operational techniques is examined; finally, the integrated operational techniques are shown.

2 Previous and Related Works Many previous studies that focused on pipeless batch manufacturing systems have addressed a production scheduling problem. The scheduling problem has been formulated mainly with the use of the Mixed Integer Linear Programming, MILP [2, 3, 4]. However, a main weakness of the MILP approach is that, as the complexity of a plant increases, the scheduling problem becomes very hard to formulate properly [5]. Huang et al., using constraint satisfaction techniques, have proposed an integrated scheduling methodology in consideration of the behavior of movable vessels [6, 7]. In industrial robotics, Yang et al. have proposed a robotic system that assists production in flexible manufacturing environments [8]. In the system, off-line robots work exclusively to support on-line robots. These previous and related works, however, have been based on the following assumptions: (I) fewer movable vessels with large capacity (≥10 [m3 ]); (II) fixed transport time of a vessel between process stations; (III) fixed operation (process) time at a station; (IV) a specific process equipment installed in every station; and (V) two categorized robots, directly operating and indirectly operating ones. Hence,

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(I’) it has been difficult to control the vessels agilely and flexibly; (II’) and (III’) as the case may be, an expected production volume according to the scheduling result is not achieved in the event that the vessel speed or the operation time at a process station fluctuates due to a disturbance [9]; (IV’) low system reconfigurability and multi-productivity result; and (V’) low resource utilization takes place due to these assumptions. In view of the above reasons, (I’) ∼ (V’) , in this paper, (I”) instead of a vessel, we use a large number of robots with small volume, namely, a material-handling robot with high mobility; (II”) and (III”) we take into account the actual robot’s behavior including uncertainty, i.e., variable moving and operation times; (IV”) instead of specific processing equipment, we use fewer movable robots, namely, materialprocessing robots, and the robots have various equipment; and (V”) both types of the robots perform tasks directly.

3 Challenges In this paper, there are two types of robots performing their own tasks, which are material handling and processing. These robots must cooperatively execute the assigned task. In other words, even if one robot’s efficiency improves, the other experiences a bottleneck and, as a result, a heavy workload. This phenomenon is a so-called shifting bottleneck, as described in Sect. 1. Of course, this workload sometimes occurs due to a bottleneck resulting from the given tasks and layout structure. Furthermore, since the heavy workload for the robot caused by the shifting bottleneck affects the overall system productivity, it is necessary to balance the fluctuating workload among the robots. To tackle the challenge, we propose the following operational techniques for the robots and synthesize the most efficient ones as integrated operational techniques. 1) Route planning for the material-handling robot; 2) operation dispatching for the material-processing robot; 3) task assignment to the robots; and 4) reactive cooperation among the material-processing robots depending on the situation.

4 Robotic Batch Manufacturing System In this decade, most new automated material-handling systems have usually been designed with a spine- or perimeter-type of configuration that formed a material flow loop within a plant facility [10]. Several layouts that have single-loop and cyclic structures have been reported [11, 12]. For this reason, we adopt a cyclic layout structure, as shown in Fig. 1. This layout is based on the well-known cyclic operations. In the system, the Material-Handling Robot (MHR) moves to transport materials and a product, and the Material-Processing Robot (MPR) moves among stations and conducts production processes, such as coupling, feed, blending, separation, discharge, and cleaning, at the stations.

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In order for the MHR to move agilely, three types of lanes, i.e., main (one-way), passing (one-way), and intermediate (two-way), are provided. Process stations are placed on the main lanes. Materials circulate through the process stations with the MHR; then, a final product is produced. Inside the main lanes, four bi-directional lanes for the MPRs are provided. Each MPR basically works at its own station (e.g., MPR 1 works at stations 1 ∼ 4). In this paper, assuming that each robot has a radio communication device, the robots are allowed to share and exchange information via distributed blackboards installed on them. This is a sign board model [13]. Using this communication model, the MHR is allowed to move on the lanes flexibly while selecting lanes and changing a suitable route to a destination, and the MPR is allowed to move to its own stations or other MPRs’ stations to support them depending on the circumstances. Each of the process stations, 1 ∼ 12, has a different property of a specific operation. At each station, it is impossible for an MPR to conduct a production process with two or more MHRs at the same time. Since the MPR provides varied equipment, such as a coupler, stirrer, reactive and separation meters, and a scrubber, the MPR is able to conduct all the production processes depending on a station. As for the discharging of a final product and cleaning of the MHR, these are the requisite processes in one batch; therefore, exclusive stations are set up for each of them. In

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the system, the MHR moves from the cleaning station to the discharging station in the clockwise direction through any of the stations, 1 ∼ 12.

5 Operational Techniques for the Robots 5.1 Route Planning for the MHR In order for the MHR to move adequately on the three types of lanes, we apply the following three route-planning approaches to the MHR: (a) shortest-path routing; (b) dynamic routing looking ahead to one station; and (c) dynamic routing looking ahead to all the stations to a destination. As for approaches (b) and (c), the breadth-first search method with an objective function regarding the distance to the destination is applied. Hence, the MHR does not detour any more than is necessary. In addition, these two approaches are repeatedly applied each time the MHR passes through a station and intersection on the planned route. Fig. 2 shows that MHR 1 is planning a route to the destination (goal station) from the current position (start station). At the start station, if MHR 1 plans a route to the goal station with the use of the shortest-path routing (a), it has to stop on the planned route due to impeditive robots, such as MHR 2 and MHR 3 (see Fig. 2(a)). On the other hand, by applying planning approach (b) to the MHR, MHR 1 determines whether an MHR is present at the next station via communication and then changes the lane to detour MHR 2 (see Fig. 2(b)). However, as shown in Fig. 2(c), MHR 1 needs to plan a route once again through the passing lane due to an obstacle, MHR 3. To avoid this waste of time, MHR 1 selects a route to the destination, as shown in Fig. 2(d), by planning a route with the use of planning approach (c) on the basis of the situation of all stations with regard to the destination.

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5.2 Operation Dispatching for the MPR MPRs 1 ∼ 3 work for the production processes at four stations, i.e., 1 ∼ 4, 5 ∼ 8, and 9 ∼ 12, respectively. Therefore, when multiple MHRs arrive at different stations (e.g., the 1st, 2nd, 3rd, and 4th stations) at the same time, it is required to appropriately dispatch the MPR (e.g., MPR 1) to the operations in order to improve the robot’s operating efficiency. For this purpose, we focus on the operation execution sequence among the MHRs and MPR; the MPR determines the next operation partner (MHR) and station on the basis of the following three dispatching rules: (a’) First-In First-Out (FIFO); (b’) nearest-neighbor; and (c’) minimization of the total moving distance using the full-search method. This execution sequence is repeatedly determined each time a task is finished according to the state of other stations if there is an MHR stopping at a station for the task. Fig. 3 shows a case in which, while an MPR is conducting the production processes to MHR 2 at a station, MHRs arrived at all other stations in the following sequence: MHR 4, MHR 1, and MHR 3. In this case, the MPR reciprocates to the right and left unnecessarily if rule (a’) is applied. On the other hand, the operations are performed smoothly in the following order: MHR 2 → MHR 3 → MHR 4 → MHR 1 by using rule (b’) and MHR 2 → MHR 1 → MHR 3 → MHR 4 by using rule (c’). These dispatching rules are, similarly, applied to MPR 4.

5.3 Task Assignment to the MHRs After an MHR is washed at the cleaning station, the next production recipe is assigned as a task to the MHR. In a material-handling system, task assignment policies for Automated Guided Vehicles (AGVs) have been proposed [14]; additionally, these assignment policies have been applied to a previous pipeless batch plant [15]. However, in the proposed heuristic rules, only the next destination of the AGV has been considered. In other words, a “single task [16]” has been assumed so far. In contrast, it is impossible to address an MHR equipped with a vessel and its content (materials or product) separately in the robotic batch manufacturing system. Moreover, each product is produced on the basis of its own recipe, which consists of a series of processes. All of the processes for one product are carried out by the same (one) MHR. Namely, this is a “multi task [16]” problem. Therefore, a suitable production task, in consideration of all destinations, needs to be selected from other tasks and then assigned to an MHR. For this purpose, we propose the following Material tank MHR 1

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objective function; a task (Taskk ) is assigned to the MHR based on the function. The objective function denotes that a task with the lowest similarity to the execution state of all the tasks in the system is assigned to the MHR. By doing this, a heavy workload due to the bottleneck that arises from the given tasks is made as uniform as possible. minimize ∑ ∑ Taskn,k (ExeTaskn − Taskn,k ), k

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where k is a reference task number and n represents a station number. ExeTaskn represents the total number of MHRs that are being and going to be processed at stations n following the production recipes. As for Taskn,k , a binary variable, 0 or 1, is given whether station n of the k-th reference task is a destination in the recipe for a product. In this regard, however, if a task is selected and assigned to an MHR by referring to all other unexecuted tasks on the basis of the objective function, tasks with fewer processes are gathered in the first half of the production activity, while tasks that have many processes remain in the second half. For this problem, we propose the following three assignment policies: (a”) in sequence (k = 1, 2, 3, · · · ); (b”) the whole task reference (∀k ∈ K); and (c”) a partial task reference (∀k ∈ K p ). By applying (c”), a batch is assigned to an MHR by referring to a limited number of unexecuted batches within a given range K p . Policies (b”) and (c”) are applied on the basis of the proposed objective function.

6 Simulation Experiment 6.1 Experimental Condition From Sect. 5.1, Sect. 5.2, and Sect. 5.3, in total, 3 × 3 × 3 = 27 combinations of the operational techniques are simulated. As a case study, the total number of tasks, K, is 200, and the process time at stations 1 ∼ 12 is determined to be 30 ∼ 80 [s] with a uniform distribution in a random manner. In the production recipe, a 0-1 binary variable is given by one-third and two-thirds for each station. If a 1 is given to a station, the MHR goes to the station. At the discharging and cleaning stations, 10 ∼ 40 [s] and 20 ∼ 80 [s] with a uniform distribution are required in a random manner. With regard to the assignment policy (c”) described in Sect. 5.3, the partial task reference range is 10 tasks (i.e., K p = 10).

6.2 Impact Evaluation of Each Operational Technique The system operation time with the use of the route planning, operation dispatching, and task assignment is shown in Fig. 4. This operation time is equal to the system throughput by dividing the time by the number of tasks, 200. In order to compare and evaluate the impact of each of the three techniques, (a) ∼ (c), (a’) ∼ (c’), and (a”) ∼ (c”), on the operating efficiency, for instance, the averaged operation time

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obtained through the simulations with fixed route planning (a), (b), or (c) and nine other combinatorial techniques ((a’) ∼ (c’) × (a”) ∼ (c”)) are shown in Fig. 4(a). We can see that the route planning approaches, (b) and (c), and task assignment policy, (c”), resulted in an efficient system regardless of the number of MHRs (see Fig. 4(a) and Fig. 4(c)). This result indicates that the synthesized operational techniques reduced the effect of the bottleneck and increased the production volume. Here, it must be noted that operational techniques with task assignment policy (b”) resulted in the most inefficient system (see Fig. 4(c)) due to the heavy workload caused by the given tasks. Moreover, while the operation time decreased as the number of MHRs increased from 5 to 10, the results of the operation time with 10 and 15 MHRs were almost the same. This is because that the fleet size was the factor that decides the throughput before the bottleneck occurred. To discuss the detail of this result, we analyze the robots utilization ratio.

6.3 Bottleneck Analysis In Fig. 5, as the robots utilization ratio, the sojourn time ratio of the MHRs (5, 10, and 15) at each station and the operation time ratio of the MPRs (1 ∼ 4) are shown on the basis of the combination of the operational techniques that achieved the given 200 tasks in the shortest time, namely, the best system. From the results of Fig. 5(a), Fig. 5(b), and Fig. 5(c), it is noticeable that the sojourn time ratio at the discharging and cleaning stations increases as the number of MHRs increases from 5 to 10 and then to 15. For this reason, the system throughput at the stations, 1 ∼ 12, increased as a result of the use of efficient operational techniques, and the MHRs often arrived at the discharging and cleaning stations in which no passing lane is provided. On the other hand, from a comparison of the results shown in Fig. 5(d), Fig. 5(e), and Fig. 5(f), it is evident that each MPR operated almost evenly. That is to say, in spite of the fact that all MPRs fully operated at their two or four stations, a bottleneck occurred at two stations due to the layout structure; eventually, MPR 4 had a heavy workload.

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(e) Operating ratio (10)

MPR 3 25 [%]

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Fig. 5 Robots Utilization Ratio in the Best System ((·) indicates the number of MHRs)

In order to improve the bottleneck, it is general practice to add an MPR to the discharge or cleaning station. However, as discussed in Sect. 1, this is an insufficient approach to the shifting bottleneck. As a result, this approach probably induces a new bottleneck in another place. Therefore, it is obvious that reactive cooperation among the MPRs over their own process stations is necessary for the shifting bottleneck. In other words, if there is an MPR that has a heavy workload, other MPRs will support it by performing its task in order to balance the workload as called for by the particular situation.

7 Additional Operation Technique: Reactive Cooperation 7.1 Workload Balancing Tewolde et al. have proposed a distributed workload-balancing algorithm to assign tasks to robots evenly [17]. They, however, have assumed a single task under a static environment, and the proposed algorithm was thus executed only at the beginning of the operation. This is insufficient for a multi-task and shifting bottleneck. For such an environment, we have shown the effectiveness of reactive robot behavior [18]. Therefore, we focus on this robot’s reactivity for workload balancing, and we then propose the following reactive cooperation technique among adjacent MPRs. The detailed algorithm of the proposed technique is listed in Algorithm 1.

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Algorithm 1. ReactiveCooperation (MHR, MPR) 1: if f lagOperationMPRi = false then 2: if xMPRi = xSMPRi then 3: if xMHR = xSMPRi−1 then 4: if f lagOperationMPRi−2 = true then 5: Set NTMPRi ← Cooperation 6: else 7: if xT − xMPRi < xT − xMPRi−2 then 8: Set NTMPRi ← Cooperation 9: end if 10: end if 11: else if xMHR = xSMPRi+1 then 12: if f lagOperationMPRi+2 = true then 13: Set NTMPRi ← Cooperation 14: else 15: if xT − xMPRi < xT − xMPRi+2 then 16: Set NTMPRi ← Cooperation 17: end if 18: end if 19: end if 20: else 21: if xMHR = xSMPRi then 22: Set NTMPRi ← Maintain cooperation 23: else 24: Set NTMPRi ← Return to xSMPRi 25: end if 26: end if 27: else 28: if xMHR = xSMPRi then 29: Set NTMPRi ← Maintain cooperation 30: else 31: Set NTMPRi ← Return to xSMPRi 32: end if 33: end if

The MPR decides the cooperation to support other MPRs on the basis of the information of the MHR and other MPRs, MHR and MPR. MPRi represents the host MPR that has its own process stations, denoted as SMPRi . x shows a position; thus, xMHR , xMPRi , and xSMPRi represent the positions of the MHR, MPR, and its stations, respectively. xT is the position of the target station. Note that MPRi−1 and MPRi+1 are the MPRs adjacent to MPRi and SMPRi+1 and SMPRi−1 are their own process stations. f lagOperationMPR shows whether an MPR is operating (true represents operating, and f alse represents free). NTMPR indicates the next task of an MPR. If the MPR, MPRi , is free at its own station and an MHR arrived at the adjacent MPR’s station, xSMPRi−1 , the MPR begins to move to the station to support MPRi−1 if the other adjacent MPR, MPRi−2 , to MPRi−1 is operating. In this regard, we

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presuppose that the adjacent MPR, MPRi−1 , is also operating. If MPRi−2 is also free, a closer MPR to the target position begins the cooperation. In the same way, reactive cooperation among the MPRs, MPRi , MPRi+1 , and MPRi+2 , takes place if an MHR arrived at the adjacent MPR’s station, xSMPRi+1 . If MPRi is already at the adjacent MPR’s station, it stays at the station to support MPRi−1 or MPRi+1 as long as an MHR does not arrive at xSMPRi . If the MHR arrived at xSMPRi , MPRi returns to its own station. On the other hand, in a case in which MPRi is already operating at another MPR’s station, MPRi continues operating at xSMPRi−1 or xSMPRi+1 to support MPRi−1 or MPRi+1 if no MHR arrives at xSMPRi . If an MHR arrived at xSMPRi , MPRi returns to its own station after the current cooperative task. The MPR does not perform the reactive cooperation if it is operating at its own station.

7.2 Simulation Result Including Reactive Cooperation Table 1 shows the simulation result for 5, 10, and 15 MHRs. Under the “non-reactive cooperation,” the results in Sect. 6 are listed, and the other ones under “reactive cooperation” show the results including the reactive cooperation technique. In the columns labeled “best” and “worst,” the shortest and longest operation times are respectively described. Under the best and worst operation times, the combinations of the applied operational techniques are shown. From this result, we can see that the best and worst times with 5 MHRs obtained by using the reactive cooperation technique were 0.15 and 0.14 [h] longer than the results without the technique. The reason for this result is that no bottleneck occurred in this system; accordingly, reactive cooperation for workload balancing was not necessary. Therefore, the integrated operational techniques, when a small number of MHRs is used, are (b), (c’), and (c”) without reactive cooperation. On the other hand, the best and worst times are improved (10 MHRs: 0.27 and 0.68 [h], 15 MHRs: 0.66 and 0.87 [h]) as the number of MHRs increases by using the reactive cooperation technique. Furthermore, the best operation time with 15 MHRs is reduced by 0.43 [h] from the result with 10 MHRs. These results indicate that workload balancing for a shifting bottleneck was appropriately performed. The

Table 1 Simulation Result on the Operation Time

# of MHRs 5 10 15

Total operation time [h] Non-reactive cooperation Reactive cooperation Best Worst Best Worst 9.23 11.18 9.38 11.32 (b)(c’)(c”) (a)(b’/c’)(b”) (b)(b’/c’)(a”) (a)(b’/c’)(b”) 7.16 8.62 6.89 7.94 (b)(c’)(c”) (a)(a’)(b”) (c)(c’)(c”) (a)(a’)(b”) 7.12 8.27 6.46 7.4 (c)(b’)(c”) (a)(c’)(b”) (c)(c’)(c”) (a)(b’)(a”)

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Discharging & Cleaning stations 26 [%] Stations 9 to 12 25 [%]

Stations 1 to 4 24 [%] Stations 5 to 8 25 [%]

(a) Sojourn ratio (5)

Discharging & Cleaning stations 26 [%]

Stations 9 to 12 26 [%]

Stations 1 to 4 24 [%]

Stations 5 to 8 24 [%]

(b) Sojourn ratio (10)

Discharging & Cleaning stations 25 [%] Stations 9 to 12 27 [%]

Stations 1 to 4 23 [%]

Stations 5 to 8 25 [%]

(c) Sojourn ratio (15)

MPR 1

MPR 4

MPR 1

MPR 4

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25 [%]

24 [%]

24 [%]

25 [%]

23 [%]

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26 [%]

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(d) Operating ratio (5)

26 [%]

(e) Operating ratio (10)

(f) Operating ratio (15)

Fig. 6 Robots Utilization Ratio with the Use of the Operational Techniques Including the Reactive Cooperation in the Best System ((·) indicates the number of MHRs)

integrated operational techniques for more MHRs are, therefore, (c), (c’), and (c”) with reactive cooperation. For the analysis of the bottleneck, Fig. 6 shows the robots utilization ratio with the reactive cooperation technique on the basis of the best result in Table 1. In a comparison of the results of Fig. 6(a) and Fig. 6(d) with the results of Fig. 5(a) and Fig. 5(d), the robots utilization ratio at each station is almost the same and even (about 25 [%]). For more MHRs, 10 and 15, workload balancing was performed sufficiently well with the use of the reactive cooperation technique; eventually, the shifting bottleneck around the cleaning and discharging stations (see Fig. 5(b) and Fig. 5(c)) was successfully improved, as can be seen in Fig. 6(b) and Fig. 6(c). Moreover, from the results of Fig. 6(e) and Fig. 6(f), the operation ratio of MPR 4 was reduced from 28 (see Fig. 5(e) and Fig. 5(f)) to 24 and 23 [%]. This is because the adjacent MPRs, 1 and 3, supported MPR 4; then, MPR 2 supported MPR 1 and MPR 3; eventually, heavy workloads among the MPRs were made uniform.

8 Conclusions In this paper, we focused on a robotic batch manufacturing system with a cyclic layout. In order for the robots, MHR and MPR, to operate appropriately while relating to each other, we proposed operational techniques, such as route planning for the

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MHR and operation dispatching for the MPR on the basis of task-assignment to the robots to reduce the effect of the bottleneck and increase the production volume, in addition to reactive cooperation technique among the MPRs for workload balancing. From the simulation results, we showed that these techniques could effectively improve the shifting bottleneck and evenly spread a heavy workload. Finally, integrated operational techniques depending on the number of MHRs were shown.

References 1. Roser, C., Nakano, M., Tanaka, M.: Comparison of bottleneck detection methods for AGV systems. In: Proc. of Winter Simulation Conf., pp. 1192–1198 (2003) 2. Bok, J.W., Park, S.: Continuous-time modeling for short-term scheduling of multipurpose pipeless plants. Industrial & Engineering Chemistry Research 37(9), 3652–3659 (1998) 3. Lee, K.H., Chung, S., Lee, H.K., Lee, I.B.: Continuous time formulation of shortterm scheduling for pipeless batch plants. J. of Chemical Engineering of Japan 34(10), 1267–1278 (2001) 4. Realff, M.J., Shah, N., Pantelides, C.C.: Simultaneous design, layout and scheduling of pipeless batch plants. Computers and Chemical Engineering 20(6), 869–883 (1996) 5. Huang, W., Chen, B.: Scheduling of batch plants: Constraint-based approach and performance investigation. Int. J. of Production Economics 105(2), 425–444 (2007) 6. Huang, W., Chung, P.W.H.: Scheduling of pipeless batch plants using constraint satisfaction techniques. Computers and Chemical Engineering 24(2), 377–383 (2000) 7. Huang, W., Chung, P.W.H.: Integrating routing and scheduling for pipeless plants in different layouts. Computers and Chemical Engineering 29(5), 1069–1081 (2005) 8. Yang, H.Z., Yamafuji, K., Arita, K., Ohara, N.: Development of a robotic system which assists unmanned production based on cooperation between off-line robots and on-line robots: Concept, analysis and related technology. Int. J. of Advanced Manufacturing Technology 15(6), 432–437 (1999) 9. Gonzalez, R., Realff, M.J.: Operation of pipeless batch plants - I. MILP schedules. Computers and Chemical Engineering 22(7), 841–855 (1998) 10. Peters, B.A., Yang, T.: Integrated facility layout and material handling system design in semiconductor fabrication facilities. IEEE Trans on Semiconductor Manufacturing 10(3), 360–369 (1997) 11. Sinriech, D., Tanchoco, J.M.A.: The segmented bidirectional single-loop topology for material flow systems. IIE Transactions 28(1), 40–54 (1996) 12. Guzman, M.C.D., Prabhu, N., Tanchoco, J.M.A.: Complexity of the AGV shortest path and single-loop guide path layout problems. Int. J of Production Research 35(8), 2083–2092 (1997) 13. Wang, J.: On sign-board based inter-robot communication in distributed robotic systems. In: Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 1045–1050 (1994) 14. Egbelu, P.J., Tanchoco, J.M.A.: Characterization of automatic guided vehicle dispatching rules. Int. J. of Production Research 22(3), 359–374 (1984) 15. Gonzalez, R., Realff, M.J.: Operation of pipeless batch plants – II. Vessel dispatch rules. Computers and Chemical Engineering 22(7), 857–866 (1998)

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16. Gerkey, B.P., Mataric, M.J.: A formal analysis and taxonomy of task allocation in multirobot systems. Int. J. of Robotics Research 23(9), 939–954 (2004) 17. Tewolde, G.S., Wu, C., Wang, Y., Sheng, W.: Distributed multi-robot work load partition in manufacturing automation. In: Proc. of IEEE Int. Conf. on Automation Science and Engineering, pp. 504–509 (2008) 18. Hoshino, S., Seki, H., Naka, Y.: Development of a flexible and agile multi-robot manufacturing system. In: 17th IFAC World Congress, pp. 15786–15791 (2008)

A Mathematical Model for Cyclic Scheduling with Assembly Tasks and Work-In-Process Minimization Mohamed Amin Ben Amar, Hervé Camus, and Ouajdi Korbaa*

Abstract. In this paper, we deal with the cyclic scheduling problem. More precisely, we consider the cyclic job shop with assembly tasks. Such a problem is made of several jobs, each job consisting of tasks (assembly/disassembly tasks and transformation tasks) being assigned to machines in a cyclic way. This kind of scheduling problem is well fitted to medium and large production demands, since the cyclic behavior can avoid the scheduling of the whole tasks by considering only a small temporal window (cycle). Thus, cyclic scheduling is a heuristic to solve the scheduling problems whose complexity is NP-hard in the general case. Many methods have been proposed to solve the cyclic scheduling problem. Among them, we focus on the mathematical programming approach. We will propose here a mathematical model for cyclic scheduling with assembly tasks and Work-In-Process minimization, and we illustrate this approach with an example from literature.

1 Introduction Cyclic scheduling problems take place in different application areas such as compiler design, automated manufacturing systems, digital signal processing, railway scheduling, timetabling, etc. We will focus here on the automated manufacturing systems. In this domain, the production consists of cyclic jobs assigned to Mohamed Amin Ben Amar LI3 laboratory, ISG, University of Tunis, Tunisia e-mail: [email protected]

*

Hervé Camus LAGIS laboratory, EC Lille, Villeneuve d'Ascq, France e-mail: [email protected] Ouajdi Korbaa LI3 laboratory (ISG, Tunis), ISITCom Hammam Sousse, Tunisia e-mail: [email protected]

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machines. Each job consists of assembly/disassembly tasks and transformations tasks. The assembly tasks show the synchronization between operations, and the disassembly promote parallelism. This problem has to be optimized with regard to several criteria like throughput and Work-in-Process (WIP - the number of parts in the system). The WIP, which is an economic criterion, represents the intermediate stock. In the cyclic context, the throughput criteria will be replaced by minimizing the Cycle Time (CT). These two criteria are antagonistic. On one hand, to maximize the throughput, we have to use enough parts (WIP) to feed the bottleneck machine. On the other hand, with a few number of WIP (one for example) the resources will be pending for parts (in particle, the bottleneck machine(s)) and the throughput will not be optimized. Hence to take into account these two criteria, we will follow the resolution developed by Camus [4]. It consists of two phased approach. The planning step, which determine the optimal cycle time, and the scheduling step, which consists on minimizing the WIP while respecting the optimal cycle time (as a hard constraint). This approach ensures the existence of a scheduling, since a sufficient number of WIP allows to saturate the bottleneck1 machine(s). We focus here on the scheduling of operations in a precalculated cycle time, and we do not look for the best production ratios to be produced during a cycle time (an issue largely studied by Chrétienne [5] and Hanen [11]). In fact, we suppose that we know exactly what to produce during a cycle, which allows us to determine the optimal cycle time based on the workload of the critical resource. We are interested here in the cyclic scheduling problem with assembly tasks and Work-In-Process minimization. The remainder of this paper is organized as follows. In section 2, we will introduce systems with assembly/disassembly tasks, and we will propose a cyclic approach to solve these problems. In addition, we will define the WIP in these systems. In section 3, we will describe the mathematical model. In section 4, we will use an illustrative example in the literature to explain our approach. To conclude, we propose several perspectives to extend our work.

2 Cyclic Scheduling Problem with Assembly/Disassembly Tasks 2.1 Systems with Assembly/Disassembly Tasks The production lines of manufacturing system often include assembly/disassembly tasks. This can be accounted for the nature of the aimed output, which requires to

1

Machine with the maximum workload, i.e. machine m ∈ M for which the quantity

∑

( m , dij )∈OG

(d ij ) is the greatest.

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Disassembly task 


















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Stage_1









































































Branches 
































































Stage_2





































Assembly task Stage_3 






















Fig. 1 Assembly/Disassembly problem

assemble and/or disassemble several parts ([13, 17, 18, 19, 20, 21]). There are also many systems with only disassembly tasks, for example disassembly lines used in recycling ([8, 10, 15]). We can also find a system with only assembly tasks (like [16]). In this context, the system must include the suitable resources able to perform these tasks. In these systems (“Fig.1,” [20]) there are three categories of operations: • Transformation tasks: affects the state of the pieces without adding extra parts in the system. • Assembly tasks: consists of assembling at least two parts to produce a new one. In this case, the number of parts in the system will decrease. • Disassembly tasks: consists of disassembling one piece to produce, at least, two parts. In this case, the number of parts in the system will increase. It follows that the precedence constraints of operations can be multiple. Which means that, with assembly, one task can have two or more predecessors. However, with disassembly one task can have two or more successors. Hence, we have to deal with non-linear job, which means that a job can include many branches.

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We will consider here systems that contain only one disassembly operation and one assembly operation. The disassembly operation comes before the assembly one. Between these two tasks we will find at least two branches (on Stage_2 – “Fig.1”). Thus, the disassembly and the assembly tasks will delimit the different stages of the system (“Fig.1”). This choice can be viewed as a primary model type that takes into account the most important constraint which is encountered in assembly/disassembly systems: the synchronization of tasks. This choice can be justified by the need of simplification to start studying this scheduling problem. However, in future works, we will consider other types of models within extended constraints (like systems with several assembly and disassembly tasks and/or with imbricated stages).

2.2 Cyclic Behavior We propose to schedule a production plan, i.e. to determine the sequences of operations on resources and the time of lunching of each task. The schedule must be within the Cycle Time, and every cycle we produce one piece. The Scheduling problems are well known to be highly combinatorial. It has been shown that project planning problems are of polynomial complexity and that cyclic scheduling problems are NP-complete. Taking into account transformation tasks and assembly/disassembly one, makes the first problem NP-hard in most cases and keeps the second one in the NP-complete class. Hence, the use of heuristics is generally recommended. The scheduling of cyclic production system can be a possible solution to global scheduling. The answer to the total demand will be given by the repetition of a sequence known as cyclic scheduling. However, the optimal scheduling of a cycle does not guarantee the optimality of the total production, since the “the sum of optimal sub-paths is not necessarily an optimal path” [1]. That's why the cyclic behavior is still a heuristic. We can evaluate the performance of the cyclic scheduling by comparing the total time production with a lower bound computed from workflow analysis and the workload of the bottleneck machine. In this work, we suppose that the production and the optimal cycle time have been fixed in the planning step from workflow analysis and performance evaluation using Petri nets (Camus [4], Korbaa [14]).

2.3 Work-In-Process The aim of minimizing the WIP of a system is mainly due to the minimization of costs (intermediate stock, pallets design, and manufacturing). In factories, WIP levels between machines have capacity limits. This is mainly due to the limited physical space available to store the parts temporarily and the limits of the transport system. Also, if the number of WIP increases, it can produce a deadlock by overloading the system.

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To understand the WIP in systems with assembly/disassembly tasks, we suggest to present this concept in linear jobs systems with (system within which each task has only one predecessor and one successor operation i.e. without assembly/disassembly tasks). In linear Job systems, the WIP represents the number of products in a system. Since there are no assembly/disassembly tasks, then every part in the system is bound to a single product. Moreover, many studies [3, 6, 7, 12, 14] ...) consider that parts remain clamped to their transport resource (for example pallet) during their entire journey in the system. Hence, minimizing WIP or the number of pallets is the same. However, the number of parts in systems with non-linear production line does not match the number of products. In fact, if we consider that we have to produce a “chair,” we have to assemble 6 parts: 4 legs, the back and the seating. This means that, after assembly, the number of parts changes from 6 to 1. Hence, the previous definition of WIP has to be reviewed. In this context, Fournier [9], has proposed a definition for the WIP. He supposes that all parts generated after a disassembly task or disappeared after assembly (i.e. parts which belong to the same product), represent only one WIP. Hence, if we consider a cyclic production system, and we suppose that there are several parts in the cyclic window which belong to x products, then, there is x WIP in the system. However, this definition does not consider the number of pallets in the system. In fact, we can find two schedules that present two WIPs, for example, the first schedule needs 10 pallets while the second requires 15 pallets. With this definition, we can not choose the first schedule (which needs less pallets) compared to the second one. Another point of view concerning the definition of the WIP is proposed by Trouillet [19]. He considers that the WIP in a system is equal to the maximum number of parts present in a cyclic window. In this paper, we will consider the definition of Trouillet, which aims to minimizing the maximum number of parts in the system. Moreover, we will consider that parts remain clamped to their transport resource (for example pallet) during their entire journey in the system.

3 A Mathematical Model for Cyclic Scheduling Problem with Assembly Tasks 3.1 Job Shop We use the following notations to define a job shop F. Machines: The set M = {m1, m2,…, m|M|} defines the set of machines of F. These resources are renewable and not shared by any operations. This means that they are reusable once they have finished the execution of a task and can only process one task at a time.

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Operation: We define an operation of F using the machine m ∈ M as a pair (m,d) ∈ M × N* where d is called the duration of the corresponding operation. We denote by O∞ ≡ M × N* the set of possible (machine, duration) pairs. Job: We define a job g of the job shop F as a sequence of operations. Among these operations, we can find Assembly/Disassembly tasks. We denote by:

OG: set of all operations of the problem. Ei: Number of stages of the Job i. bi,j: Number of branches in stage j of the job i.

Eki , j : Number of operations in the branch k of the stage j of the job i. We denote operations by oki ,,lj , while i, j, k and l stands respectively for: the job, the stage, the branch and the index of the operation for the corresponding branch. s(i,j,k,l) = (I,J,K,L) where oKI ,,JL represent(s) the successor(s) of oki ,,lj . Job Shop: We define a job shop as a set of jobs G = {g1, g2, …, g|G|}. We denote by G the cardinal number of G, and we order the jobs of the job Shop by the formal parameter i∈a1, G b.

3.2 Cyclic Scheduling Problem The goal of cyclic scheduling is to schedule the cyclic pattern, within which each operation of the job shop is scheduled in a time range called cycle time. The optimal cycle time is defined as the sum of durations of tasks associated with the bottleneck machine(s). Since this optimal cycle time is reachable (we have to use enough parts: a WIP for each tasks in the system), classical scheduling problems consist in minimizing the number of pieces in the system for a given cycle time, equal to * Cmax = max ( ∑ (dij )). m∈M ( m, d )∈OG ij

* We will work here with a Cycle Time (CT) which is equal to Cmax . * CT = Cmax

3.3 Mixed Integer Programming Model We define below, “Fig.2,” a mixed integer programming model corresponding to the cyclic scheduling problem defined in Sect.3.2.

A Mathematical Model for Cyclic Scheduling with Assembly Tasks Minimize

∑

oki ,,lj ∈O G

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( nb − 1) + (α si ,( ij,, kj ,,kl ,l ) + β si ,(ij,, kj ,,kl ,l ) ) s.t.: (1)

•∀ i ∈ a1,G b, ∀j ∈ a1,Ei b, ∀ k ∈ ced1,bi , j fhg , ∀ l ∈ cde1,E ki , j fgh ⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ i, j ⎨t k ,l ⎪ ⎪t ki ,,lj ⎪ ⎪t i , j ⎪ k ,l ⎪t i , j ⎩ k ,l

* t ki ,, lj ∈ cde 0, C max − 1fgh

(2)

∈

{0,1}

(3)

β si ,(ij,, kj ,,kl , l ) ∈

{0,1}

α si ,(ij,, kj ,,kl , l )

sI , sJ − t sK , sL

−

i , j , k ,l B.α sI , sJ , sK , sL

(4)

≥ 1− B −

d ki ,,lj

(5)

i , j , k ,l i, j sI , sJ − t sK , sL − B .α sI , sJ , sK , sL ≤ − d k ,l sI , sJ − t sK , sL

−

(6)

≥

* C max

+1− B −

i , j , k ,l − B.β sI , sJ , sK , sL ≤ i, j I , J •∀ m ∈ M , ∀ok ,l , o K , L ∈ O G m s.t. ⎧ δ Ii ,, Jj ,,kK, l, L ∈

* C max

−

i , j , k ,l B.β sI , sJ , sK , sL

sI , sJ − t sK , sL

d ki ,,lj

d ki ,,lj

(7) (8)

( i , j , k , l ) ≤ ( I , J , K , L ),

{0,1}

⎪ ⎪ i, j i , j , k ,l i, j I ,J * * ⎨t k ,l − t K , L + C max .δ I , J , K , L ≤ − d k , l + C max ⎪ I ,J i , j , k ,l * I ,J ⎪ t K , L − t ki ,,lj − C ma x .δ I , J , K , L ≤ − d K , L ⎩ * −1 with B ∈ N , B ≥ 2.C max

(9) (10) (11)

Fig. 2 Mixed Integer Programming Model

* • Variable tki ,,lj ∈ cde 0, Cmax − 1fgh corresponds to the activation date of the operation

oki ,,lj within the considered cycle; • Variable δ Ii ,, Jj ,,kK,l, L ∈ {0,1} is the binary variable corresponding to the order between operations performed on the same machine, such that δ Ii,, Jj ,,kK,l, L = 1 if tki ,,lj < tKI ,,JL and 0 otherwise. “Fig.3” presents a scheduling of the illustrative ex1,2,2,2 = 1, since: ample used below (“Fig.6”). In this schedule, we have δ1,2,2,1 1,2 t1,2 2,1 < t2,2 . , k ,l i , j , k ,l • Variables α sIi, j, sJ , sK , sL and β sI , sJ , sK , sL correspond to binary variables used to

compute the WIP: sI , sJ i, j , k ,l -- α sIi , j, sJ , sK , sL = 1 if osK , sL is executed before the completion time of ok ,l , where sI , sJ i, j osK , sL stands for a successor of operation ok ,l in the job; i, j , k ,l -- β sIi , ,jsJ , sK , sL = 1 if ok ,l overlaps two cycles and completes after the activation time sI , sJ of osK , sL on the next cycle;

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Fig. 3 Variable δ (delta)

Fig. 4 Binary variables used to compute the WIP (α and β)

“Fig.4” presents a scheduling of a cyclic linear job. This job consists of 3 tasks: 1,1 1,1 o1,1 0,1 , o0,2 and o0,3 : 1,1 1,1 1,1 1,1 1,1 o1,1 0,1 is followed by o0,2 , o0,2 is followed by o0,3 and o0,3 is followed by o0,1 .

We presents in “Fig.4” a scheduling to illustrate the case when we have , k ,l = 1 and β sIi , ,jsJ , sK , sL = 1 .

i , j , k ,l α sI , sJ , sK , sL

More explanations for α and β can be found in [2], since these two variables keep the same meaning for systems with or without assembly/disassembly tasks. , k ,l • B ∈ N is a constant used to constrain the discrimination variables α sIi, j, sJ , sK , sL , k ,l and β sIi, ,jsJ , sK , sL in a linear way. It has to be “big enough” (lower bound: * 2.Cmax − 1 ) in order to make the inequalities (5) to (8) valid. This lower bound

was computed as follow:

A Mathematical Model for Cyclic Scheduling with Assembly Tasks

In tki ,,lj

order sI , sJ − tsK , sL

to

consider

“(6)”

i , j , k ,l − B.α sI , sJ , sK , sL

as a valid inequality, we must have: , k ,l and if we consider α sIi, j, sJ , sK , sL = 1, then:

− d ki ,,lj . ,

≤

287

i, j i, j i, j sI , sJ * * tki ,,lj − tsK , sL + d k ,l ≤ B . In addition, we know that tk ,l ≤ Cmax − 1 and d k ,l ≤ Cmax , then

B must respect the following inequality: * 2.Cmax −1 ≤ B

• Remaining inequalities (5) to (8), (10) and (11) constrain the previous variables according to their meanings. • Finally, the objective function “(1),” corresponds to the minimization of the WIP of the considered scheduling. It consists of two parts: a constant plus decision variables (α and β). Note that, if we consider only decision variables in the objective function (1), the mathematical model will compute, only, the WIP needed for one path and the extra WIP required by the other branches. Hence, to know the WIP of the schedule, we have to add the WIP needed to perform the rest of the branches. Indeed, in “Fig.5” we notice that, after a disassembly task, the system generates (nb–1) new WIP, nb stands for the number of branches in the second stage (Stage_2). For example, before firing transition t1, the system presents one WIP, then, after firing t1, the system presents 3 WIP, which means that we need 2 = (3 –1) extra WIP to process the rest of branches in the second stage. l

m

o n

q

r

¬













s







p





¯

i























¯

i



Fig. 5 Variations of the Numbers of Tokens

In addition to this mathematical model, we define two other properties. Firstly, we consider that the first operation of the first job (operation o1,1 0,1 ) starts at time 0 1,1 (which means that t0,1 = 0 ). Indeed, the steady state can be observed at different

dates and always presents the same WIP. This is due to the cyclic behavior of the production. Hence, generality is maintained by considering that the cycle begins when the execution of operation o1,1 0,1 starts. Secondly, the total WIP is made of the WIP needed to achieve each job separately. Let Ti be the total duration of the ith possible path from the first operation to the last one. If Ti is great to CT, then this sequence of operations is longer than a cycle and has to be cut into several cycles. This is done by introducing several parts of this sequence of operations: WIP. This number has to be at least equal to the integer superior or equal to Ti by CT. WIPmin =

⎡ Ti ⎤ ⎢ ⎥ i: Possible operations sequences ⎢ CT ⎥

∑

(12)

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Concerning the optimality of the solutions, this model reflects exactly the constraints that can be found in a scheduling problem, namely the constraints of precedence between operations and the constraints of resource sharing. Therefore, this model ensures the optimality of the solutions. On the other hand, we work with an exact approach and the resolution is done by a linear program solver (CPLEX). Note that the mathematical model can deal, either, with problems with linear jobs [2] or with Assembly/Disassembly tasks.

4 Illustrative Example In this section we will use the example “Fig.1” of Disassembly/Assembly system, in order to illustrate the approach to compute optimal WIP with the mathematical model. The original example “Fig.6” was used by Trouillet in [20]. There are two main differences between “Fig.1” and “Fig.6”: • Transition t7 in “Fig.6” will be considered as the first operation in “Fig.1.” In fact, this is possible since the problem is cyclic. • In “Fig.6,” all the transitions are fired once except t1 and t2 which are fired twice. This property is replaced by the use of two successive operations for each transitions t1 and t2. Indeed, this choice is justified by the fact that we use the same resource M3 for these two transitions. Hence, necessarily, transitions t1 and t2 will be performed on M3 sequentially. Here, we look to work with ordinary Petri net for reasons of understanding and readability for our model. The system contains 3 stages. The second stage contains 2 branches.

p1

p2 M3

t1

(3)

t2

(3)

p3 (1)

¾

p4 M5

t3

(1)

¾

p5

t4 p6

(3)

t5

M2

p7 (1)

t6

M4

p8 (1)

t7

Fig. 6 Illustrative example used by Trouillet in [20]

M1

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289

There are five resources denoted by M1, M2, M3, M4, M5. The Cycle Time (CT) is equal to 12, which is the workload of M3 (bottleneck resource). To compute a lower bound for the WIP, we know the optimal cycle time CT and the total duration of each possible path from the first task to the last one, through the different branches. For our example, we have two paths: 1,2 1,2 1,2 1,3 1,3 • o1,1 0,1 , o1,1 , o1,2 , o1,3 , o0,1 , o0,3 . 1,2 1,2 1,2 1,3 1,3 • o1,1 0,1 , o2,1 , o2,2 , o2,3 , o0,1 , o0,3 .

If we suppose that we will process only the first path, then we will need at least 12 t.u., which means, at least, one part, i.e. one WIP. Then, the second path needs at least one WIP, as well. ⎡ 12 ⎤ ⎡ 12 ⎤ The WIP lower bound is equal to: ⎢ ⎥ + ⎢ ⎥ = 2 . ⎢ 12 ⎥ ⎢ 12 ⎥

Fig. 7 Scheduling on resources

“Fig.7” represents the computed schedule of tasks on the resources using linear program solver CPLEX 9.0 on an Intel Pentium 4 at 2.8 GHz and 1Go RAM, under Windows XP. The resolution takes about 1s. “Fig.8” represents the same schedule, but, here, we focus on the number of pallets used in the system. “Fig.8” shows that the schedule requires 3 pallets. Hence the optimal number of WIP is equal to 3. This level of WIP was found by the mathematical model through variables α and β:

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Fig. 8 Scheduling from the part’s point of view 1,1,0,1 1,1,0,1 1,2,1,1 1,2,1,2 1,2,2,1 1,2,2,2 1,2,2,3 1,2,1,3 α1,2,1,1 + α1,2,2,1 + α1,2,1,2 + α1,2,1,3 + α1,2,2,2 + α1,2,2,3 + α1,3,0,1 + α1,3,0,1 + 1,3,0,1 1,3,0,2 1,1,0,1 1,1,0,1 1,2,1,1 1,2,1,2 1,2,2,1 1,2,2,2 α1,3,0,2 + α1,1,0,1 + β1,2,1,1 + β1,2,2,1 + β1,2,1,2 + β1,2,1,3 + β1,2,2,2 + β1,2,2,3 + 1,2,2,3 1,2,1,3 1,3,0,1 1,3,0,2 β1,3,0,1 + β1,3,0,1 + β1,3,0,2 + β1,1,0,1 =2 1,2,1,2 1,3,0,2 All the variables here are null except: α1,2,1,3 = α1,1,0,1 = 1. We can verify these 1,2 1,2 1,2 1,3 1,3 1,1 two values from “Fig.8,” while t1,2 + d1,2 > t1,3 and t0,2 + d 0,2 > t0,1 .

The mathematical approach computes the WIP needed for one path and the extra WIP required by the other branches. Hence, to find out the WIP needed for the whole schedule, we must add (nb–1) pallets (Sect.3.3) to the WIP level found by the resolution of the mathematical model. In this case, the WIP computed using mathematical model is equal to 2 and we have two branches in the second stage. Hence, the WIP of the schedule is equal to 2 + (2 – 1) = 3. We notice here that the optimal WIP computed with our approach is equal to 3 and that the lower bound of the WIP is equal to 2. In fact, this theoretical value cannot be reached. Indeed, we mentioned that we have two possible paths from the first task to the last one. If we consider that we will perform each path separately, which means that we consider that machine M3 will be available at any time. With this relaxation, we need 12 t.u. to perform each path apart, which means 2 WIP. However, “Fig.1” shows that M3 is shared by the two paths. Hence, there will be, necessarily, an extra time while processing one of these two paths, which means that there will be a need for at least one more WIP. Then, the level of WIP found by our approach (3) is thus optimal.

5 Conclusion This paper deals with cyclic scheduling problems with assembly/disassembly tasks and Work-In-Process minimization. The main contribution here is to propose a mathematical model of the scheduling issue of such systems. First, we have presented systems with assembly/disassembly tasks and we have shown the interest of using cyclic scheduling approach to solve these problems. Secondly, we have clearly defined the concept of WIP in these systems.

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Afterwards, we have proposed a mathematical model which deals with the specificity of assembly systems, i.e. synchronization of multiple tasks. Then, we have used an illustrative example that has been previously used by Trouillet [20] to explain our model and our resolution method. This study shows that one can solve optimally problems with assembly/disassembly tasks. We have shown that we can find an optimal scheduling (cycle time) for assembly/disassembly systems like Fournier in [9]. However, our approach allows, in addition, to find the optimal WIP in the system. Future works will consider extended assembly/disassembly systems: with several tasks and imbricated stages. In addition, we can add extra constraints to the problems like working with a limited WIP. Moreover, we aim to substitute the CPLEX solver for an algorithm especially fitted to the mathematical model, in order to improve the resolution time.

References [1] Bellmann, R.: Dynamic Programming. Princeton University Press, Princeton (1965) [2] Ben Amar, M.A., Bourdeaud’huy, T., Korbaa, O.: Cyclic Scheduling MIP Implementation: Cutting Tetchniques. In: ICPR 2007, Valparaiso, Chili (2007) [3] Bourdeaud’huy, T., Korbaa, O.: A Mathematical Model For Cyclic Scheduling With Work-In-Progress Minimization. In: INCOM 2006, Saint Etienne, France (2006) [4] Camus, H., Ohl, H., Korbaa, O., Gentina, J.-C.: Cyclic Schedules in Flexible Manufacturing Systems with Flexibilities in operating sequences. In: Proceedings of the 17th International Conference on Application and Theory of Petri Nets (ICATPN), Osaka, Japan, pp. 97–116 (1996) [5] Chretienne, P., Coffman, E.G., Lenstra, J.K., Liu, Z.: Scheduling: Theory and its applications, ch. 3, pp. 33–64. John Wiley & Sons, Chichester (1997) [6] Driss, O.B., Korbaa, O., Ghedira, K., Yim, P.: A distributed transient inter-production scheduling for flexible manufacturing systems. Journal Europeen des Systemes Automatises, JESA 2007 41(1) (2007) [7] Dupas, R., Cavory, G., Goncalves, G.: Optimising the throughput of a manufacturing production line using a genetic algorithm. In: Real-World Applications GECCO 1999, p. 1775 (1999) [8] Field, F.R., Clark, J.P.: Recycling of USA automobile materials: a conundrum for advanced materials. ATA 1991 44(8/9), 541–555 (1991) [9] Fournier, O., Lopez, P., Lan Sun Luk J.D.: Cyclic scheduling following the social behavior of ant colonies. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 2002, vol. 3, pp. 450–454 (2002) [10] Gupta, S.M., McLean, C.R.: Disassembly of products. Computers and Industrial Engineering 31(1-2), 225–228 (1996) [11] Hanen, C., Munier Kordon, A.: Periodic schedules for linear precedence constraints. Discrete Applied Mathematics 157(2), 280–291 (2009) [12] Hsu, T., Korbaa, O., Dupas, R., Goncalves, G.: Cyclic scheduling for F.M.S.: Modelling and evolutionary solving approach. European Journal of Operational Research, EJOR 191(2), 463–483 (2008) [13] Mo, J., Zhang, Q., Gadh, R.: Virtual Disassembly. International Journal of CAD/CAM 2(1), 29–37 (2002)

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[14] Korbaa, O., Camus, H., Gentina, J.-C.: A New Cyclic Scheduling Algorithm for Flexible Manufacturing Systems. International Journal of Flexible Manufacturing Systems (IJFMS) 14(2), 173–187 (2002) [15] Lambert, A.J.D.: Disassembly sequencing: a survey. International Journal of Production Research 41, 3721–3759 (2003) [16] Mane, A., Nahavandi, S., Zhang, J.: Sequencing production on an assembly line using goal chasing and user defined algorithm. In: Winter Simulation Conference (WSC 2002), vol. 2, pp. 1269–1273 (2002) [17] Mascle, C., Balasoiu, B.A.: Disassembly-assembly sequencing using feature-based life-cycle model. In: Proceedings of 2001 IEEE International Symposium on Assembly and Task Planning, pp. 31–36 (2001) [18] Sundaram, S., Remmler, I., Amato, N.M.: Disassembly sequencing using a motion planning approach. In: Proceedings of 2001 IEEE International Conference on Robotics and Automation, pp. 1475–1480 (2001) [19] Trouillet, B., Benasser, A., Gentina, J.-C.: Transformation of the Cyclic Scheduling Problem of a Large Class of FMS into the Search of an Optimized Initial Marking of a Linearizable Weighted T-System. In: Sixth International Workshop on Discrete Event Systems (WODES 2002), p. 83 (2002) [20] Trouillet, B., Dupas, R., Goncalves, G., Hsu, T.: Two approaches to the cyclic scheduling with assembly. In: 12th IFAC Symposium on Information Control Problems in Manufacturing, INCOM 2006, Saint-Etienne, France (2006) [21] Trouillet, B., Korbaa, O., Gentina, J.-C.: Formal Approach for FMS Cyclic Scheduling. IEEE SMC Transactions, Part C 37(1), 126–137 (2007)

Author Index

Aiyama, Yasumichi

21

Baek, KyeongKeun 141 Baptiste, Pierre 157 Bargiel, Sylwester 99 Bautista, Joaqu´ın 211 Ben Amar, Mohamed Amin Camus, Herv´e 279 Chica, Manuel 211 Choi, Byung-Wook 185 Choi, Kyung-Hyun 187 Cl´ecy, C´edric 99 ´ Cord´ on, Oscar 211 Craiovan, D. 113 Damas, Sergio Franke, J.

Gorecki, Christophe

99

Hasegawa, Yuji 35 Heikkil¨ a, Riku 127 Heikkil¨ a, Tapio 171 Hoshino, Satoshi 265 Hwang, Cheol-woong 227 H¨ usig, Matthias 253

J¨ arvenp¨ aa ¨, Eeva Jeong, Seon Hwa

279 Latremouille-Viau, Julie Lee, Eon 227 Lee, Sukhan 97, 141 Lutz, Philippe 99

157

Maeda, Yusuke 85 Maida, Kazuhiro 35 Mascle, Christian 157

211

113

Inui, Masatomo

Kim, Gun Yeon 227 Kim, Hyeonnam 227 Korbaa, Ouajdi 279 Koskinen, Jukka 171 Kubota, Toru 21 Kyung, Jin-Ho 5

35 127 227

Kim, Daesik 141 Kim, Dong-Soo 187

Naka, Yuji 265 Neugebauer, Reimund Noh, Sang Do 227

239

Oh, Jong-Kyu 141 Ota, Jun 265 Park, Chanhun 5 Park, Dong IL 5 Park, Kyoungtaik 5 Park, Yang Ho 227 Penalba, Francesc 53 Pulkkinen, Topi 171 Rabenorosoa, Kanty 99 Roa, M´ aximo A. 69 Rosell, Jan 53

294 Seki, Hiroya 265 Shin, Hyunshik 227 Sterzing, Andreas 239 Su, Yang Bong 187 Su´ arez, Ra´ ul 1, 53, 69

Author Index Thanh, Tran Trung Tuokko, Reijo 127 Ushioda, Tatsuya Youn, Sangil

227

187

85