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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.. The applicatio

Wang, Jun et al "Frontmatter"

Computational Intelligence in Manufacturing Handbook

Edited by Jun Wang et al

Boca Raton: CRC Press LLC,2001

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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)

1 Production management—Data processing 2 Computational intelligence—Industrial applications 3 Manufacturing processes—Automation I Title II Advanced topics in mechanical engineering series

TS155.6 W36 2000

CIP

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 meth-odology 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 com-putationally 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 appli-cations 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 direc-tions in the applicadirec-tions of computational intelligence in manufacturing

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 1 by D T Pham and P T N Pham offers a tutorial on compu-tational intelligence in manufacturing to lead the reader into a broad spectrum of intelligent manufac-turing applications Chapter 2 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 3 by Ulieru, Stefanoiu, and Norrie presents a metamorphic framework based on fuzzy logic for intelligent manufacturing Chapter

4 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 5 by Kazerooni et

al discusses an application of fuzzy logic to design flexible manufacturing systems Chapter 6 by Luong

et al discusses the use of genetic algorithms in group technology Chapter 7 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 8 by Lee, Chiu, and Fang addresses the issues on optimal process planning and sequencing of parallel machining Chapter 9 by Zhang and Nee presents the appli-cations 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 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, 2 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 health-care 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 interna-tional 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

K Abhary

University of South Australia Australia

F T S Chan

University of Hong Kong China

C Alec Chang

University of Missouri–Columbia U.S.A

Shing I Chang

Kansas State University U.S.A

Joseph C Chen

Iowa State University U.S.A.

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

Korea Advanced Institute

of Science and Technology South Korea

R Du

University of Miami U.S.A

Shu-Cherng Fang

North Carolina State University U.S.A

Mitsuo Gen

Ashikaga Institute of Technology Japan

A Kazerooni

University of Lavisan Iran

M Kazerooni

Toosi University of Technology Iran

Li-Pheng Khoo

Nanyang Technological University Singapore

Sarah S Y Lam

State University of New York

at Binghamton U.S.A

Yuan-Shin Lee

North Carolina State University U.S.A

Xiaoli Li

Harbin Institute of Technology China

L H S Luong

University of South Australia Australia

Gary S May

Georgia Institute of Technology U.S.A

A Y C Nee

National University of Singapore Singapore

Douglas Norrie

University of Calgary Canada

D T Pham

University of Wales Cardiff, U.K

P T N Pham

University of Wales Cardiff, U.K

Catherine Roze

IBM Global Services U.S.A

Alice E Smith

Auburn University U.S.A

Dan Stefanoiu

University of Calgary Canada

Nallan C Suresh

State University of New York

at Buffalo U.S.A

University of Groningen The Netherlands

Wai Sum Tang

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

The Chinese University

of Hong Kong

China

Lian-Yin Zhai

Nanyang Technological University Singapore

Y F Zhang

National University of Singapore Singapore

Table of Contents

PART I Overview

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

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 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

PART II Manufacturing System Modeling and Design

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

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

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

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

PART III Process Planning and Scheduling

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

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

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 Manufacturing Process Monitoring and Control

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