Computational intelligence in manufacturing handbook

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

Pham, D. T. et al "Computational Intelligence for Manufacturing"

Computational Intelligence in Manufacturing Handbook

Edited by Jun Wang et al

Boca Raton: CRC Press LLC,2001

©2001 CRC Press LLC

1

Computational

Intelligence for

Manufacturing

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

1.1 Introduction

Computational intelligence refers to intelligence artificially realised through computation. Artificial intel￾ligence emerged as a computer science discipline in the mid-1950s. Since then, it has produced a number

of powerful tools, some of which are used in engineering to solve difficult problems normally requiring

human intelligence. Five of these tools are reviewed in this chapter with examples of applications in

engineering and manufacturing: knowledge-based systems, fuzzy logic, inductive learning, neural net￾works, and genetic algorithms. All of these tools have been in existence for more than 30 years and have

found many practical applications.

1.2 Knowledge-Based Systems

Knowledge-based systems, or expert systems, are computer programs embodying knowledge about a

narrow domain for solving problems related to that domain. An expert system usually comprises two

main elements, a knowledge base and an inference mechanism. The knowledge base contains domain

knowledge which may be expressed as any combination of “If-Then” rules, factual statements (or asser￾tions), frames, objects, procedures, and cases. The inference mechanism is that part of an expert system

which manipulates the stored knowledge to produce solutions to problems. Knowledge manipulation

methods include the use of inheritance and constraints (in a frame-based or object-oriented expert

system), the retrieval and adaptation of case examples (in a case-based expert system), and the application

of inference rules such as modus ponens (If A Then B; A Therefore B) and modus tollens (If A Then B;

Not B Therefore Not A) according to “forward chaining” or “backward chaining” control procedures and

“depth-first” or “breadth-first” search strategies (in a rule-based expert system).

With forward chaining or data-driven inferencing, the system tries to match available facts with the If

portion of the If-Then rules in the knowledge base. When matching rules are found, one of them is

D. T. Pham

University of Wales

P. T. N. Pham

University of Wales