Artificial Neural Networks for Intelligent Manufacturing

473 Pages·1994·10.08 MB·English

by  Cihan H. Dagli (auth.)

Artificial Neural N etworks for
Intelligent Manufacturing
Intelligent Manufacturing Series
Ser ies Editor: Andrew Kusiak
Department of Industrial Engineering
The University of Iowa, USA
Manufacturing has been issued agreat challenge - the challenge of Artificial
Intelligence (AI). We are witnessing the proliferation of applications of AI in industry,
ranging from finance and marketing to design and manufacturing processes. AI tools
have been incorporated into computer-aided design and shop-floor operations
software, as well as entering use in logistics systems.
The success of AI in manufacturing can be measured by its growing number of
applications, releases of new software products and in the many conferences and new
publications. This series on Intelligent Manufacturing has been established in response
to these developments, and will include books on topics such as:
• design for manufacturing
• concurrent engineering
• process planning
• production planning and scheduling
• programming languages and environments
• design, operations and management of intelligent systems
Some of the titles are more theoretical in nature, while others emphasize an industrial
perspective. Books dealing with the most recent developments will be edited by leaders
in the particular fields. In areas that are more established, books written by recognized
authors are planned.
We are confident that the titles in the series will be appreciated by students entering
the field ofintelligent manufacturing, academics, design and manufacturing managers,
system engineers, analysts and programmers.
Titles available
Object-oriented Software for Manufacturing Systems
Edited by S. Adiga
Integrated Distributed Intelligence Systems in Manufacturing
M. Rao, Q. Wang and 1. Cha
Artificial Neural Networks for Intelligent Manufacturing
Edited By C.R. Dagli
Artificial Neural Networks for
Intelligent Manufacturing
Edited by
Cihan H. Dagli
Associate Professor
Department of Engineering Management
University of Missouri-Rolla
USA
Springer-Science+Business Media, B.V.
First edition 1994
© Springer Science+Business Media Dordrecht 1994
Originally published by Chapman & Hali in 1994
Softcover reprint of the hardcover Ist edition 1994
Typeset in 10/12 pts Times by Thomson Press (India) Ltd, New Delhi
ISBN 978-94-010-4307-6 ISBN 978-94-011-0713-6 (eBook)
DOI 10.1007/978-94-011-0713-6
Apart from any fair dealing for the purposes of research or private study, or
criticism or review, as permitted under the UK Copyright Designs and Patents
Act, 1988, this publication may not be reproduced, stored, or transmitted, in
any form or by any means, without the prior permission in writing of the
publishers, or in the case of reprographic reproduction only in accordance
with the terms of the licences issued by the Copyright Licensing Agency in
the UK, or in accordance with the terms of licences issued by the appropriate
Reproduction Rights Organization outside the UK. Enquiries concerning
reproduction outside the terms stated here should be sent to the publishers
at the London address printed on this page.
The publisher makes no representation, express or implied, with regard to
the accuracy of the information contained in this book and cannot accept any
legal responsibility or liability for any errors or omissions that may be made.
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication data
Artificial neural networks for intelligent manufacturing / edited by
Cihan H. Dagli. - ist ed.
p. cm.
Includes index.
ISBN 0-412-48050-6 (acid-free paper)
1. Neural networks (Computer science) 2. Manufacturing processes.
1. Dagli, Cihan H., 1949-
QA76.87.A7432 1994 93-35422
670'.285'63 - dc20 CIP
~Printed on permanent acid-free text paper, manufactured in accordance
with the proposed ANSljNISO Z 39.48-199X and ANSI Z 39.48-1984
To the memory of my father Kenan Dagli
To my mother Zuhre Dagli
and
My wife Refia and my sons Kenan Cagri and Mehmet Ediz
Contents
Contributors xiii
Preface xv
PART ONE Intelligent manufacturing: Basic concepts and tools
Intelligent manufacturing systems 3
Cihan H. Dagli
1.1 Manufacturing systems and strategies 4
1.2 Hierarchical levels in manufacturing 7
1.3 Characteristics of intelligent 13
manufacturing systems
1.4 Summary 15
References 16
2 Intelligent systems architecture: Design 17
techniques
Deborah Stacey
2.1 Introduction 17
2.2 Knowledge-based systems 18
2.3 Artificial neural networks 24
2.4 Hybrid intelligent systems 30
2.5 Manufacturing systems implementations 34
2.6 Summary 35
References 37
3 Basic artificial neural network architectures 39
Cihan H. Dagli and Pipatpong Poshyanonda
3.1 Basic concepts 39
3.2 Percept ron 43
3.3 Backpropagation 48
3.4 Adaptive resonance theory 56
3.5 Summary 64
References 64
4 Hybrid intelligent systems: Tools for decision 67
making in intelligent manufacturing
Gregory R. Madey, Jay Weinroth and Vijay Shah
4.1 Overview 67
Vlll Contents
4.2 Manufacturing decision-making problems: 68
organization, coordination and executing
levels
4.3 Hybrid intelligent systems developed 71
out of neural networks
4.4 Survey of neural network hybrid 77
intelligent systems
4.5 Case study of the development of a 81
hybrid intelligent system for decision
making in manufacturing
4.6 Summary 86
References 87
PART TWO Neurocomputing for intelligent manufacturing: 91
Organization and coordination level applications
5 Conceptual design problem 93
Ali Bahrami and Cihan H. Dagli
5.1 Characteristics of design problem 93
5.2 Introduction to fuzzy sets and binary 98
relationships between functions and
structures
5.3 Sample problem definition 101
5.4 Fuzzy knowledge representations 102
5.5 Mapping fuzzy functional requirements 105
to design structure by F AM
5.6 Implementation and input/output 106
representations
5.7 Experimental results 108
5.8 Summary 109
References 109
6 Machine-part family formation 111
Cesar O. Malave and Satheesh Ramachandran
6.1 Characteristics of group technology 111
6.2 Neural network approach 117
6.3 Discussion 138
References 141
7 Process planning 143
M adhusudhan Posani and Cihan H. Dagli
7.1 Characteristics of process planning 143
7.2 Sample problem definition 149
7.3 Development of network architecture 150
7.4 Artificial neural network implementation 154
7.5 Performance of the intelligent system 154
architecture
Contents IX
7.6 Summary 156
References 157
8 Scheduling 159
John Y. Cheung
8.1 Characteristics of scheduling problems 159
8.2 The Hopfield net approach 161
8.3 Simulated annealing 174
8.4 Other neural network techniques 182
8.5 Summary 186
References 186
9 Automated assembly systems 195
Cihan H. Dagli and Mahesh Kumar Vellanki
9.1 Automated assembly 196
9.2 Generic assembly cell 198
9.3 Power supply board assembly: 211
A case study
9.4 Summary 227
References 228
10 Manufacturing feature identification 229
Mark R. Henderson
10.1 Characteristics of manufacturing features 229
10.2 Sample problem definition 239
10.3 Development of network architecture 241
10.4 Artificial neural network implementation 247
10.5 Performance of the intelligent system 253
architecture
10.6 Summary 260
Acknowledgements 263
References 263
11 Vision based inspection 265
J oydeep Ghosh
11.1 Introduction 265
11.2 Characteristics of vision based inspection 267
systems
11.3 Representation of 3D objects 269
11.4 Modeling and matching strategies 272
11.5 Artificial neural networks (ANNs) for 273
vision-based inspection
11.6 Viewer-centered object recognition 280
11.7 Direct, object-based ANN approaches 292
11.8 Concluding remarks 293
Acknowledgements 294
References 294
x Contents
12 Performance analysis of artificial neural 299
network methods
Benito Fernandez R.
12.1 Introduction 299
12.2 Artificial neural systems in 300
man ufacturing
12.3 The power of neural networks 301
12.4 Artificial neural network paradigms in 309
manufacturing
12.5 Performance analysis 314
12.6 Benchmarks 321
12.7 Simulation paradox 338
12.8 Performance measures 341
12.9 Decision functions 345
12.10 Metrics from measure 347
12.11 Cluster analysis 348
12.12 ANN paradigm selection in 352
manufacturing
12.13 Tools that increase performance 353
12.14 Summary 363
References 363
PART THREE Neurocomputing for intelligent manufacturing: 369
Execution level applications
13 Process monitoring and control 371
Michel Guillot, Riadh Azouzi and
Marie-Claude Cote
13.1 Introduction to process monitoring 371
and control
13.2 Neural, network models for process 376
monitoring and control
13.3 Neural network approaches to process 378
monitoring
13.4 Neural network approaches to process 380
control
13.5 Implementation cases 384
13.6 Summary 396
References 396
14 Adaptive control in manufacturing 399
Yung Shin
14.1 Characteristics of adaptive control 399
systems
14.2 Sample problem definition 403
14.3 Adaptive neuro-control architecture 406
Contents Xl
14.4 Performance of adaptive neuro-control 410
systems
14.5 Conclusions 411
References 411
15 Fuzzy neural control 413
L.H. Tsoukalas. A. Ikonomopoulos and
R.E. Uhrig
15.1 Problem of fuzzy control 413
15.2 Fuzzy neural architectures 417
15.3 Development of system architecture 422
15.4 Fuzzy neural network implementation 430
and performance
15.5 Summary 432
References 433
16 Neural networks in continuous process 435
diagnostics
N ajwa S. M erchawi and Soundar R. T K umara
16.1 Introduction 436
16.2 Neural networks for diagnostics 436
16.3 Problem description 437
16.4 Knowledge representation for continuous 439
process diagnostics by a neural network
16.5 Example problem: The TMI-2 nuclear 442
reactor
16.6 Implementation and simulation results 447
16.7 Summary and conclusions 460
References 461
Index 463