Wang, Jun et al "Frontmatter" Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton: CRC Press LLC,2001 Library of Congress Cataloging-in-Publication Data Wang, Jun Computational intelligence in manufacturing handbook / Jun Wang and Andrew Kusiak p cm — (Mechanical engineering) Includes bibliographical references and index ISBN 0-8493-0592-6 (alk paper) Production management—Data processing Computational intelligence—Industrial applications Manufacturing processes—Automation I Title II Advanced topics in mechanical engineering series TS155.6 W36 2000 658.5'14—dc21 00-049826 CIP This book contains information obtained from authentic and highly regarded sources Reprinted material is quoted with permission, and sources are indicated A wide variety of references are listed Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher All rights reserved Authorization to photocopy items for internal or personal use, or the personal or internal use of specific clients, may be granted by CRC Press LLC, provided that $.50 per page photocopied is paid directly to Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA The fee code for users of the Transactional Reporting Service is ISBN 0-8493-0592-6/01/$0.00+$.50 The fee is subject to change without notice For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale Specific permission must be obtained in writing from CRC Press LLC for such copying Direct all inquiries to CRC Press LLC, 2000 N.W Corporate Blvd., Boca Raton, Florida 33431 Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe © 2001 by CRC Press LLC No claim to original U.S Government works International Standard Book Number 0-8493-0592-6 Library of Congress Card Number 00-049826 Printed in the United States of America Printed on acid-free paper ©2001 CRC Press LLC Preface Computational intelligence involves science-based approaches and technologies for analyzing, designing, and developing intelligent systems The broad usage of this term was formalized by the IEEE Neural Network Council and the IEEE World Congress on Computational Intelligence in Orlando, Florida in the summer of 1994 It represents a union of neural networks, fuzzy systems, evolutionary computation techniques, and other emerging intelligent agents and technologies The past two decades have witnessed the resurgence of studies in neural networks, fuzzy logic, and genetic algorithms in the areas we now call computational intelligence Advances in theory and methodology have overcome many obstacles that previously hindered the computational intelligence research The research has sparked considerable interest among scientists and engineers from many disciplines As evidenced by the appealing results of numerous studies, computational intelligence has gained acceptance and popularity In addition, computational intelligence techniques have been applied to solve numerous problems in a variety of application settings The computational intelligence research opened many new dimensions for scientific discovery and industrial/business applications The desirable features of computationally intelligent systems and their initial successes in applications have inspired renewed interest in practitioners from industry and service organizations The truly interdisciplinary environment of the research and development offers rewarding opportunities for scientific breakthrough and technology innovation The applications of computational intelligence in manufacturing, in particular, play a leading role in the technology development of intelligent manufacturing systems The manufacturing applications of computational intelligence span a wide spectrum including manufacturing system design, manufacturing process planning, manufacturing process monitoring control, product quality control, and equipment fault diagnosis In the past decade, numerous publications have been devoted to manufacturing applications of neural networks, fuzzy logic, and evolutionary computation Despite the large volume of publications, there are few comprehensive books addressing the applications of computational intelligence in manufacturing In an effort to fill the void, this comprehensive handbook was produced to cover various topics on the manufacturing applications of computational intelligence The aim of this handbook is to present the state of the art and highlight the recent advances on the computational intelligence applications in manufacturing As a handbook, it contains a balanced coverage of tutorials and new results This handbook is intended for a wide readership ranging from professors and students in academia to practitioners and researchers in industry and business, including engineers, project managers, and R&D staff, who are affiliated with a number of major professional societies such as IEEE, ASME, SME, IIE, and their counterparts in Europe, Asia, and the rest of the world The book is a source of new information for understanding technical details, assessing research potential, and defining future directions in the applications of computational intelligence in manufacturing ©2001 CRC Press LLC This handbook consists of 19 chapters organized in five parts in terms of levels and areas of applications The contributed chapters are authored by more than 30 leading experts in the fields from top institutions in Asia, Europe, North America, and Oceania Part I contains two chapters that present an overview of the applications of computational intelligence in manufacturing Specifically, Chapter by D T Pham and P T N Pham offers a tutorial on computational intelligence in manufacturing to lead the reader into a broad spectrum of intelligent manufacturing applications Chapter by Wang, Tang, and Roze gives an updated survey of neural network applications in intelligent manufacturing to keep the reader informed of history and new development in the subject of study Part II of the handbook presents five chapters that address the issues in computational intelligence for modeling and design of manufacturing systems In this category, Chapter by Ulieru, Stefanoiu, and Norrie presents a metamorphic framework based on fuzzy logic for intelligent manufacturing Chapter by Suresh discusses the neural network applications in group technology and cellular manufacturing, which has been one of the popular topics investigated by many researchers Chapter by Kazerooni et al discusses an application of fuzzy logic to design flexible manufacturing systems Chapter by Luong et al discusses the use of genetic algorithms in group technology Chapter by Chang and Tsai discusses intelligent design retrieving systems using neural networks Part III contains three chapters and focuses on manufacturing process planning and scheduling using computational intelligence techniques Chapter by Lee, Chiu, and Fang addresses the issues on optimal process planning and sequencing of parallel machining Chapter by Zhang and Nee presents the applications of genetic algorithms and simulated annealing algorithm for process planning Chapter 10 by Cheng and Gen presents the applications of genetic algorithms for production planning and scheduling Part IV of the book is composed of five chapters and is concerned with monitoring and control of manufacturing processes based on neural and fuzzy systems Specifically, Chapter 11 by Lam and Smith presents predictive process models based on cascade neural networks with three diverse manufacturing applications In Chapter 12, Cho discusses issues on monitoring and control of manufacturing process using neural networks In Chapter 13, May gives a full-length discussion on computational intelligence applications in microelectronic manufacturing In Chapter 14, Du and Xu present fuzzy logic approaches to manufacturing process monitoring and diagnosis In Chapter 15, Li discusses the uses of fuzzy neural networks and wavelet techniques for on-line monitoring cutting tool conditions Part V has four chapters that address the issues on quality assurance of manufactured products and fault diagnosis of manufacturing facilities Chapter 16 by Chen discusses an in-process surface roughness recognition system based on neural network and fuzzy logic for end milling operations Chapter 17 by Chinnam presents intelligent quality controllers for on-line selection of parameters of manufacturing systems Chapter 18 by Chang discusses a hybrid neural fuzzy system for statistical process control Finally, Chapter 19 by Khoo and Zhai discusses a diagnosis approach based on rough set and genetic algorithms We would like to express our gratitude to all the contributors of this handbook for their efforts in preparing their chapters In addition, we wish to thank the professionals at CRC Press LLC, which has a tradition of publishing well-known handbooks, for their encouragement and trust Finally, we would like to thank Cindy R Carelli, the CRC Press acquiring editor who coordinated the publication of this handbook, for her assistance and patience throughout this project Jun Wang Hong Kong ©2001 CRC Press LLC Andrew Kusiak Iowa City Editors Jun Wang is an Associate Professor and the Director of Computational Intelligence Lab in the Department of Automation and Computer-Aided Engineering at the Chinese University of Hong Kong Prior to this position, he was an Associate Professor at the University of North Dakota, Grand Forks He received his B.S degree in electrical engineering and his M.S degree in systems engineering from Dalian University of Technology, China and his Ph.D degree in systems engineering from Case Western Reserve University, Cleveland, Ohio Dr Wang’s current research interests include neural networks and their engineering applications He has published more than 60 journal papers, 10 book chapters, edited books, and numerous papers in conference proceedings He serves as an Associate Editor of the IEEE Transactions on Neural Networks Andrew Kusiak is a Professor of Industrial Engineering at the University of Iowa, Iowa City His interests include applications of computational intelligence in product development, manufacturing, and healthcare informatics and technology He has published research papers in journals sponsored by AAAI, ASME, IEEE, IIE, INFORMS, ESOR, IFIP, IFAC, IPE, ISPE, and SME Dr Kusiak speaks frequently at international meetings, conducts professional seminars, and consults for industrial corporations He has served on the editorial boards of 16 journals, has written 15 books and edited various book series, and is the Editor-in-Chief of the Journal of Intelligent Manufacturing ©2001 CRC Press LLC Contributors K Abhary Mitsuo Gen D T Pham University of South Australia Australia Ashikaga Institute of Technology Japan University of Wales Cardiff, U.K F T S Chan A Kazerooni P T N Pham University of Hong Kong China University of Lavisan Iran University of Wales Cardiff, U.K C Alec Chang M Kazerooni Catherine Roze University of Missouri–Columbia U.S.A Toosi University of Technology Iran IBM Global Services U.S.A Shing I Chang Li-Pheng Khoo Alice E Smith Kansas State University U.S.A Nanyang Technological University Singapore Auburn University U.S.A Joseph C Chen Sarah S Y Lam Dan Stefanoiu Iowa State University U.S.A State University of New York at Binghamton U.S.A University of Calgary Canada Runwei Cheng Ashikaga Institute of Technology Japan Ratna Babu Chinnam Wayne State University U.S.A Nan-Chieh Chiu North Carolina State University U.S.A Hyung Suck Cho Yuan-Shin Lee Xiaoli Li State University of New York at Buffalo U.S.A University of Groningen The Netherlands Harbin Institute of Technology China Wai Sum Tang North Carolina State University U.S.A L H S Luong University of South Australia Australia Korea Advanced Institute of Science and Technology South Korea Gary S May R Du A Y C Nee University of Miami U.S.A National University of Singapore Singapore Shu-Cherng Fang Douglas Norrie North Carolina State University U.S.A University of Calgary Canada ©2001 CRC Press LLC Nallan C Suresh Georgia Institute of Technology U.S.A The Chinese University of Hong Kong China Chieh-Yuan Tsai Yuan-Ze University Taiwan Michaela Ulieru University of Calgary Canada Jun Wang The Chinese University of Hong Kong China Yangsheng Xu Lian-Yin Zhai Y F Zhang The Chinese University of Hong Kong China Nanyang Technological University Singapore National University of Singapore Singapore ©2001 CRC Press LLC Table of Contents PART I Overview Computational Intelligence for Manufacturing D T Pham · P T N Pham 1.1 Introduction 1.2 Knowledge-Based Systems 1.3 Fuzzy Logic 1.4 Inductive Learning 1.5 Neural Networks 1.6 Genetic Algorithms 1.7 Some Applications in Engineering and Manufacture 1.8 Conclusion Neural Network Applications in Intelligent Manufacturing: An Updated Survey Jun Wang · Wai Sum Tang · Catherine Roze 2.1 Introduction 2.2 Modeling and Design of Manufacturing Systems 2.3 Modeling, Planning, and Scheduling of Manufacturing Processes 2.4 Monitoring and Control of Manufacturing Processes 2.5 Quality Control, Quality Assurance, and Fault Diagnosis 2.6 Concluding Remarks Holonic Metamorphic Architectures for Manufacturing: Identifying Holonic Structures in Multiagent Systems by Fuzzy Modeling Michaela Ulieru · Dan Stefanoiu · Douglas Norrie 3.1 Introduction 3.2 Agent-Oriented Manufacturing Systems 3.3 The MetaMorph Project 3.4 Holonic Manufacturing Systems 3.5 Holonic Self-Organization of MetaMorph via Dynamic Virtual Clustering 3.6 Automatic Grouping of Agents into Holonic System: Simulation Results 3.7 MAS Self-Organization as a Holonic System: Simulation Results 3.8 Conclusions ©2001 CRC Press LLC PART II Manufacturing System Modeling and Design Neural Network Applications for Group Technology and Cellular Manufacturing Nallan C Suresh 4.1 Introduction 4.2 Artificial Neural Networks 4.3 A Taxonomy of Neural Network Application for GT/CM 4.4 Conclusions Application of Fuzzy Set Theory in Flexible Manufacturing System Design A Kazerooni · K Abhary · L H S Luong · F T S Chan 5.1 Introduction 5.2 A Multi-Criterion Decision-Making Approach for Evaluation of Scheduling Rules 5.3 Justification of Representing Objectives with Fuzzy Sets 5.4 Decision Points and Associated Rules 5.5 A Hierarchical Structure for Evaluation of Scheduling Rules 5.6 A Fuzzy Approach to Operation Selection 5.7 Fuzzy-Based Part Dispatching Rules in FMSs 5.8 Fuzzy Expert System-Based Rules 5.9 Selection of Routing and Part Dispatching Using Membership Functions and Fuzzy Expert System-Based Rules Genetic Algorithms in Manufacturing System Design L H S Luong · M Kazerooni · K Abhary 6.1 Introduction 6.2 The Design of Cellular Manufacturing Systems 6.3 The Concepts of Similarity Coefficients 6.4 A Genetic Algorithm for Finding the Optimum Process Routings for Parts 6.5 A Genetic Algorithm to Cluster Machines into Machine Groups 6.6 A Genetic Algorithm to Cluster Parts into Part Families 6.7 Layout Design 6.8 A Genetic Algorithm for Layout Optimization 6.9 A Case Study 6.10 Conclusion Intelligent Design Retrieving Systems Using Neural Networks C Alec Chang · Chieh-Yuan Tsai 7.1 Introduction 7.2 Characteristics of Intelligent Design Retrieval 7.3 Structure of an Intelligent System 7.4 Performing Fuzzy Association 7.5 Implementation Example ©2001 CRC Press LLC PART III Process Planning and Scheduling Soft Computing for Optimal Planning and Sequencing of Parallel Machining Operations Yuan-Shin Lee · Nan-Chieh Chiu · Shu-Cherng Fang 8.1 Introduction 8.2 A Mixed Integer Program 8.3 A Genetic-Based Algorithm 8.4 Tabu Search for Sequencing Parallel Machining Operations 8.5 Two Reported Examples Solved by the Proposed GA 8.6 Two Reported Examples Solved by the Proposed Tabu Search 8.7 Random Problem Generator and Further Tests 8.8 Conclusion Application of Genetic Algorithms and Simulated Annealing in Process Planning Optimization Y F Zhang · A Y C Nee 9.1 Introduction 9.2 Modeling Process Planning Problems in an Optimization Perspective 9.3 Applying a Genetic Algorithm to the Process Planning Problem 9.4 Applying Simulated Annealing to the Process Planning Problem 9.5 Comparison between the GA and the SA Algorithm 9.6 Conclusions 10 Production Planning and Scheduling Using Genetic Algorithms Runwei Cheng · Mitsuo Gen 10.1 Introduction 10.2 Resource-Constrained Project Scheduling Problem 10.3 Parallel Machine Scheduling Problem 10.4 Job-Shop Scheduling Problem 10.5 Multistage Process Planning 10.6 Part Loading Scheduling Problem PART IV 11 Manufacturing Process Monitoring and Control Neural Network Predictive Process Models: Three Diverse Manufacturing Applications Sarah S Y Lam · Alice E Smith 11.1 Introduction to Neural Network Predictive Process Models 11.2 Ceramic Slip Casting Application 11.3 Abrasive Flow Machining Application 11.4 Chemical Oxidation Application 11.5 Concluding Remarks ©2001 CRC Press LLC 12 Neural Network Applications to Manufacturing Processes: Monitoring and Control Hyung Suck Cho 12.1 Introduction 12.2 Manufacturing Process Monitoring and Control 12.3 Neural Network-Based Monitoring 12.4 Quality Monitoring Applications 12.5 Neural Network-Based Control 12.6 Process Control Applications 12.7 Conclusions 13 Computational Intelligence in Microelectronics Manufacturing Gary S May 13.1 Introduction 13.2 The Role of Computational Intelligence 13.3 Process Modeling 13.4 Optimization 13.5 Process Monitoring and Control 13.6 Process Diagnosis 13.7 Summary 14 Monitoring and Diagnosing Manufacturing Processes Using Fuzzy Set Theory R Du · Yangsheng Xu 14.1 Introduction 14.2 A Brief Description of Fuzzy Set Theory 14.3 Monitoring and Diagnosing Manufacturing Processes Using Fuzzy Sets 14.4 Application Examples 14.5 Conclusions 15 Fuzzy Neural Network and Wavelet for Tool Condition Monitoring Xiaoli Li 15.1 Introduction 15.2 Fuzzy Neural Network 15.3 Wavelet Transforms 15.4 Tool Breakage Monitoring with Wavelet Transforms 15.5 Identification of Tool Wear States Using Fuzzy Method 15.6 Tool Wear Monitoring with Wavelet Transforms and Fuzzy Neural Network ©2001 CRC Press LLC PART V Quality Assurance and Fault Diagnosis 16 Neural Networks and Neural-Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations Joseph C Chen 16.1 Introduction 16.2 Methodologies 16.3 Experimental Setup and Design 16.4 The In-Process Surface Roughness Recognition Systems 16.5 Testing Results and Conclusion 17 Intelligent Quality Controllers for On-Line Parameter Design Ratna Babu Chinnam 17.1 Introduction 17.2 An Overview of Certain Emerging Technologies Relevant to On-Line Parameter Design 17.3 Design of Quality Controllers for On-Line Parameter Design 17.4 Case Study: Plasma Etching Process Modeling and On-Line Parameter Design 17.5 Conclusion 18 A Hybrid Neural Fuzzy System for Statistical Process Control Shing I Chang 18.1 Statistical Process Control 18.2 Neural Network Control Charts 18.3 A Hybrid Neural Fuzzy Control Chart 18.4 Design, Operations, and Guidelines for Using the Proposed Hybrid Neural Fuzzy Control Chart 18.5 Properties of the Proposed Hybrid Neural Fuzzy Control Chart 18.6 Final Remarks 19 RClass*: A Prototype Rough-Set and Genetic Algorithms Enhanced Multi-Concept Classification System for Manufacturing Diagnosis Li-Pheng Khoo · Lian-Yin Zhai 19.1 Introduction 19.2 Basic Notions 19.3 A Prototype Multi-Concept Classification System 19.4 Validation of RClass* 19.5 Application of RClass* to Manufacturing Diagnosis 19.6 Conclusions ©2001 CRC Press LLC ... Associate Editor of the IEEE Transactions on Neural Networks Andrew Kusiak is a Professor of Industrial Engineering at the University of Iowa, Iowa City His interests include applications of computational... the applications of computational intelligence in manufacturing ©2001 CRC Press LLC This handbook consists of 19 chapters organized in five parts in terms of levels and areas of applications The... presents the applications of genetic algorithms for production planning and scheduling Part IV of the book is composed of five chapters and is concerned with monitoring and control of manufacturing processes