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Wang, Jun et al "Frontmatter"
Computational Intelligence in Manufacturing Handbook
Edited by Jun Wang et al
Boca Raton: CRC Press LLC,2001

©2001 CRC Press LLC

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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
658.5'14—dc21 00-049826
CIP

©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 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 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 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 Andrew Kusiak

Hong Kong Iowa City

©2001 CRC Press LLC

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, 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

.

©2001 CRC Press LLC

Contributors

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

©2001 CRC Press LLC

Yangsheng Xu

The Chinese University
of Hong Kong
China

Lian-Yin Zhai

Nanyang Technological University
Singapore

Y. F. Zhang

National University of Singapore
Singapore

©2001 CRC Press LLC

Table of Contents

PART I Overview

1

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

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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 Manufacturing Process Monitoring and Control

11

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