Manufacturing Intelligence for Industrial Engineering

Manufacturing

Intelligence for

Industrial Engineering:

Methods for System

Self-Organization, Learning,

and Adaptation

Zude Zhou

Wuhan University of Technology, China

Huaiqing Wang

City University of Hong Kong, Hong Kong

Ping Lou

Wuhan University of Technology, China

Hershey • New York

EnginEEring sciEncE rEfErEncE

Director of Editorial Content: Kristin Klinger

Director of Book Publications: Julia Mosemann

Acquisitions Editor: Lindsay Johnston

Development Editor: Joel Gamon

Publishing Assistant: Deanna Zombro

Typesetter: Michael Brehm

Production Editor: Jamie Snavely

Cover Design: Lisa Tosheff

Printed at: Yurchak Printing Inc.

Published in the United States of America by

Engineering Science Reference (an imprint of IGI Global)

701 E. Chocolate Avenue

Hershey PA 17033

Tel: 717-533-8845

Fax: 717-533-8661

E-mail: [email protected]

Web site: http://www.igi-global.com/reference

Copyright © 2010 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in

any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher.

Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or

companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.

Library of Congress Cataloging-in-Publication Data

Zhou, Zude, 1946-

Manufacturing intelligence for industrial engineering : methods for system self-organization, learning, and adaptation / by

Zude Zhou, Huaiqing Wang, and Ping Lou.

p. cm.

Includes bibliographical references and index.

Summary: "This book focuses on the latest innovations in the process of manufacturing in engineering"--Provided by publisher.

ISBN 978-1-60566-864-2 (hardcover) -- ISBN 978-1-60566-865-9 (ebook) 1. Technological innovations. 2. Industrial

engineering. 3. Artificial intelligence. I. Wang, Huaiqing. II. Lou, Ping. III. Title. T173.8.Z486 2010

670.285--dc22

2009034472

British Cataloguing in Publication Data

A Cataloguing in Publication record for this book is available from the British Library.

All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the

authors, but not necessarily of the publisher.

Foreword .............................................................................................................................................vii

Preface .................................................................................................................................................. ix

Chapter 1

Intelligent Manufacturing and Manufacturing Intelligence .................................................................... 1

Introduction ............................................................................................................................................. 1

Manufacturing Activities ......................................................................................................................... 2

Artificial Intelligence and Manufacturing Intelligence ........................................................................... 3

Intelligent Manufacturing ....................................................................................................................... 4

Summary ............................................................................................................................................... 11

References ............................................................................................................................................. 11

Chapter 2

Knowledge-Based Systems ................................................................................................................... 13

Introduction ........................................................................................................................................... 13

The Process of Building KBS-Knowledge Engineering ........................................................................ 16

KBS Evaluation ..................................................................................................................................... 31

Applications of KBS in Intelligent Manufacturing ................................................................................ 34

Case Study ............................................................................................................................................. 36

Summary ............................................................................................................................................... 44

References ............................................................................................................................................. 44

Chapter 3

Intelligent Agents and Multi-Agent Systems ........................................................................................ 47

Intelligent Agents .................................................................................................................................. 47

Basic Theories of Multi-Agent Systems ................................................................................................. 52

Communication and Interaction Protocol in MAS ............................................................................... 59

Cooperation and Behavior Coordination ............................................................................................. 64

Applications of Agent in Intelligent Manufacturing ............................................................................. 70

Case Study ............................................................................................................................................. 74

Summary ............................................................................................................................................... 81

References ............................................................................................................................................. 81

Table of Contents

Chapter 4

Data Mining and Knowledge Discovery ............................................................................................... 84

Introduction ........................................................................................................................................... 84

Basic Analysis ....................................................................................................................................... 92

Methods and Tools for DMKD .............................................................................................................. 96

Application of DM and KD in Manufacturing Systems ...................................................................... 102

Case Study ........................................................................................................................................... 105

Summary ............................................................................................................................................. 109

References ........................................................................................................................................... 110

Chapter 5

Computational Intelligence ................................................................................................................. 111

Introduction ......................................................................................................................................... 111

Artificial Neural Networks .................................................................................................................. 113

Fuzzy System ....................................................................................................................................... 120

Evolutionary Computation .................................................................................................................. 125

Case Study ........................................................................................................................................... 130

Summary ............................................................................................................................................. 134

References ........................................................................................................................................... 134

Chapter 6

Business Process Modeling and Information Systems Modeling ....................................................... 137

Introduction ......................................................................................................................................... 137

Modeling Techniques .......................................................................................................................... 142

Case Study: Conceptual Modeling of Collaborative Manufacturing for Customized Products ......... 150

Summary ............................................................................................................................................. 156

References ........................................................................................................................................... 156

Chapter 7

Sensor Integration and Data Fusion Theory ....................................................................................... 160

Introduction ......................................................................................................................................... 160

Data Fusion ........................................................................................................................................ 167

The Methods of Data Fusion ............................................................................................................... 172

Applications of Multi-Sensor Information Fusion .............................................................................. 174

Case Study ........................................................................................................................................... 181

Summary ............................................................................................................................................. 186

References ........................................................................................................................................... 186

Chapter 8

Group Technology ............................................................................................................................... 189

Introduction ......................................................................................................................................... 189

Part Family Formation: Coding and Classification Systems ............................................................. 192

Group Technology in Intelligent Manufacturing ................................................................................ 209

Summary ............................................................................................................................................. 211

References ........................................................................................................................................... 211

Chapter 9

Intelligent Control Theory and Technologies ..................................................................................... 214

Introduction ......................................................................................................................................... 214

Foundations of Intelligent Control ..................................................................................................... 215

Models for Intelligent Controllers ...................................................................................................... 219

Intelligent Control Technologies ......................................................................................................... 221

Intelligent Control Systems ................................................................................................................. 226

Challenges of Intelligent Control Technologies .................................................................................. 236

Neural Network Based Robotic Control: A Case Study ...................................................................... 237

Summary ............................................................................................................................................. 242

References ........................................................................................................................................... 242

Chapter 10

Intelligent Product Design: Intelligent CAD ...................................................................................... 245

Introduction ......................................................................................................................................... 245

Research and Application of ICAD ..................................................................................................... 251

Technique and Research Methods of ICAD ........................................................................................ 256

Case Study ........................................................................................................................................... 264

Summary ............................................................................................................................................. 270

References ........................................................................................................................................... 271

Chapter 11

Intelligent Process Planning: Intelligent CAPP .................................................................................. 273

Introduction ......................................................................................................................................... 273

Application of GA to Computer-Aided Process Planning ................................................................... 277

The Implementation of ANN in CAPP System ................................................................................... 281

The Use of Case-Based Reasoning in CAPP ...................................................................................... 286

Multi-Agent-Based CAPP ................................................................................................................... 289

Case Study ........................................................................................................................................... 296

Summary ............................................................................................................................................. 299

References ........................................................................................................................................... 299

Chapter 12

Intelligent Diagnosis and Maintenance ............................................................................................... 301

Introduction ......................................................................................................................................... 301

Diagnosis Techniques ......................................................................................................................... 304

Remote Intelligent Diagnosis and Maintenance System ..................................................................... 312

Multi-Agent-Based Intelligent Diagnosis System ............................................................................... 316

Case Study ........................................................................................................................................... 319

Future of Intelligent Diagnosis ........................................................................................................... 324

Summary ............................................................................................................................................. 325

References ........................................................................................................................................... 327

Chapter 13

Intelligent Management Information System ..................................................................................... 229

Introduction ......................................................................................................................................... 229

IMIS Methodologies ............................................................................................................................ 330

Case Study I: Multi-Agent IDSS Based on Blackboard ...................................................................... 337

Case Study II: Intelligent Reconfigurable ERP System ...................................................................... 339

Summary ............................................................................................................................................. 355

References ........................................................................................................................................... 355

Chapter 14

Trend and Prospect of Manufacturing Intelligence ............................................................................. 357

Introduction ......................................................................................................................................... 357

Driving Forces and Challenges of the Manufacturing Industry ......................................................... 359

Reviews on Forementioned MI Technologies...................................................................................... 367

MI vs Conventional Technologies in manufacturing .......................................................................... 371

Prospect of Manufacturing Intelligence ............................................................................................. 377

Summary ............................................................................................................................................. 383

References ........................................................................................................................................... 384

About the Authors ............................................................................................................................. 388

Index ................................................................................................................................................... 390

vii

Foreword

Manufacturing engineering has come a long way, from the “black art” in the 1800s to the first scientific

analysis of machining operations by F.W. Taylor in early 1900s (On the Art of Cutting Metals, 1906).

In the early 1950s, computers were developed to take control of machine tools and NC machines were

born, and later, CNC machines. The 60s and 70s saw a rapid proliferation of software and hardware

development in support of manufacturing operations in the form of design, analysis, planning, process￾ing, measurement, dispatch and distribution. The late M Eugene Merchant, then Director of Research

Planning of Cincinnati Milacron Inc., made an exciting Delphi-type technological forecast of the future

of production engineering at the General Assembly of CIRP in Warsaw, 1971. Five years later, he made

another report on the “Future Trends in Manufacturing – Towards the Year 2000” in the 1976 CIRP GA

in Paris. He reported that between then (1976) and the year 2000, the overall future trend in manufac￾turing will be towards the implementation of the computer-integrated automatic factories. More than

30 years had since whisked past, manufacturing technologies had indeed progressed even more rapidly

than Dr Merchant’s prediction then.

Manufacturing operations have changed from programmed operations to programmable operations.

In the last two decades, many manufacturing operations and processes have become near autonomous,

i.e. they possess sufficient intelligence to diagnose, optimize, decide and correct any actions with mini￾mum human interaction. Some systems can acquire and learn from past cases and become increasingly

more “learned” through usage. Machine tools which are Internet-enabled can be continuously monitored

by their manufacturers and their “state-of-heath” is exactly known and predictable to enable the reduc￾tion of breakdown time and to ensure timely maintenance. Computer-integrated Manufacturing (CIM)

has evolved to become Computer-Human Integrated Manufacturing (CHIM). Seamless integration of

human and computer intelligence is another measure to capture the perfect complementation between

man and machine.

It is with great pleasure to witness this new book ‘Manufacturing Intelligence for Industrial Engineer￾ing: Methods for System Self-Organization, Learning and Adaption’ by Zude Zhou, Qinghuai Wang and

Ping Lou. It is a timely capture of the state-of-the-art development of intelligent manufacturing processes,

covering a vast amount of materials from design, planning, diagnosis, information control, agents, and

many enabling platforms and supporting theories. I have, beyond doubt, that this contribution will be

invaluable to researchers as well graduate students in the field of manufacturing engineering.

I sincerely congratulate the authors on having produced this splendid new book

A. Y. C. Nee, DEng, PhD

National University of Singapore

Regional Editor IJAMT

Regional Editor IJMTM

viii

A. Y. C. Nee received his PhD from the Victoria University of Manchester in 1973 and Doctor of Engineering (DEng) degree

from UMIST in 2002. He joined then University of Singapore as a faculty member in 1974. He has held various administrative

positions including Head of Department of Mechanical Engineering from 1993 to 1996, Dean of Faculty of Engineering from

1995 to 1998, other appointments include: Director of Office of Quality Management, Dean of Admissions, CEO of Design

Technology Institute, Co-Director Singapore-MIT Alliance, Deputy Executive Director, then NSTB SERC, Director of Office of

Research. Prof Nee received his National Day Award in Public Administration—PPA(P) in 2007. Professor Nee is well known

in the field of manufacturing engineering. His research focuses on computer-aided design of fixtures, molds and dies, distrib￾uted manufacturing systems, AI and augmented reality applications in manufacturing. He was selected a Fellow of the Society

of Manufacturing Engineers with citation in 1990, and a Fellow of the International Academy for Production Engineering

(CIRP) in the same year. He was elected as Vice-President (Elect) at the CIRP recent senate meeting in August 2009, and will

be Vice President in August 2010 and President of CIRP from August 2011. He has published over 250 papers in international

refereed journals, 5 authored and 5 edited books. Professor Nee is regional editor of International Journal of Machine Tools

and Manufacture, and International Journal of Advanced Manufacturing Technology. In addition, he is editorial board member

and associate editor of another 20 refereed journals. He is also Chairman of an NUS spin-off company—Manusoft Technolo￾gies Pte Ltd established in 1997.

ix

Preface

The environment of the manufacturing industry has changed impressively during this half century. New

theories and technologies in the field of computers, networks, distributed computation, and artificial

intelligence are extensively used in the manufacturing area. Integration and intelligence have become

the developing trends of future manufacturing systems. These inform the concept of manufacturing

change from the narrow sense of fabrication technique to the broad sense of extensive manufacture,

that is, from the transformation of raw materials into finished goods, to the whole process of the prod￾uct life cycle involving product design, fabrication, planning, managing, and distribution. Intelligent

manufacturing will become one the most promising manufacturing technologies in the next generation

of manufacturing industries.

Manufacturing Intelligence (MI), as a new discipline of manufacturing engineering, focuses on sci￾entific foundations and key technologies for developing, describing, integrating, sharing, and processing

intelligent activities in the process of manufacturing. It mainly covers intelligent-control theory and

technology for manufacturing equipment, intelligent management and decision making for the manu￾facturing process, intelligent processing of manufacturing information, representation and reasoning of

manufacturing knowledge, as well as intelligent surveillance and diagnosis for manufacturing equipment

and systems.

Clearly, MI is different from Artificial Intelligence (AI). AI is one aspect of theoretical research led

by the requirements of mimicking human intelligence. It mainly focuses on exploring the mechanism of

the process of human intelligent activities and emphasizes general theories, which highlight explorations

of theory, as well as having serious logicality and reasoning. By contrast, MI mainly studies the mimicry

of human intelligence to solve issues with intelligent computers (including software and hardware), and

is a type of foundational research led by the requirements of applications in the manufacturing field.

Although these two disciplines are different, they are related each other. AI is one of the main founda￾tions of MI and the development of MI and the solution to the issues unsolved by AI will accelerate the

development of AI.

This book consists of four parts with fourteen chapters which include engineering background, founda￾tions, technologies, applications, implementations, case studies, trends of intelligent manufacturing, and

prospects for manufacturing intelligence. Part I contains one chapters, viz. chapter 1, which introduces

manufacturing intelligence, the development of intelligent manufacturing, and the features of intelligent

activities in the process of manufacturing. Part II and Part III including twelve chapters constitute the

main part of this book. In these two parts, scientific foundations, key technologies and pragmatic appli￾cations of manufacturing intelligence are analyzed. Among them, chapters 2 to 8 composing the Part II

offer an extensive presentation of the engineering scientific foundations in manufacturing intelligence.

Chapter 2 describes knowledge-based systems which mainly details general approaches for knowledge

representation, acquirement, and general techniques for searching and reasoning. Chapter 3 presents

x

an overview of intelligent agents and multi-agent systems. Chapter 4 contains the principle and tech￾niques of data mining and knowledge discovering. Chapter 5 introduces the principle and applications

of computational intelligence in engineering and manufacturing, including neural networks, genetic

algorithms, and fuzzy logic. Chapter 6 has an overview of information system modeling, including the

general processes and strategies, some different modeling approaches and modeling tools. Chapter 7

includes an overview of multi-sensor integration and data fusion theories. Chapter 8 introduces the prin￾ciple and approaches to group theory, including coding systems for parts, approaches for grouping parts

and applications in manufacturing designing and processing. Chapters 9 to 13 make up Part III of the

book: the applications and case studies for manufacturing intelligence. Chapter 9 presents the structure

theory of intelligent control, a general architecture of the intelligent controller, and intelligent systems.

Chapter 10 contains knowledge-based approaches for designing, beginning with the basic concepts and

approaches of conventional computer-aided design (CAD) systems. Chapter 11 includes an overview of

computer-aided process planning, including concepts and enabling technologies, and the architecture and

decision-making process of intelligent computer-aided process planning is also presented. Chapter 12

presents an overview of remote monitoring and intelligent diagnosis. Chapter 13 consists of the principles

and approaches to intelligent management and decision-making in manufacturing. Like Part I, Part VI

also contains only one chapter, viz. chapter 14. In chapter 14, first the summarization of the theories,

technologies, and applications in the aforementioned chapters is presented, and then these intelligent

manufacturing technologies compare to the traditional manufacturing technologies. Last the prospects

for manufacturing intelligence and the trends of intelligent manufacturing in the future are discussed..

This book is intended primarily for senior undergraduate and graduate students in mechanical, electro￾mechanical and industrial engineering programs. Its integrated treatment of the subject makes it a suitable

reference for practicing engineers and other professionals who are interested in pursuing research and

development in this field. For professors and students, this book may be used for teaching as well as

self-study. It gives them an up-to-date, in-depth source of material on manufacturing intelligence. For

researchers, the publication helps them better understand the field as a whole. They will obtain valuable

enlightenment for their future research activities.

The book also provides readers with the scientific foundations, theories, and key technologies of

manufacturing intelligence. Hence, readers may use this publication achieve two different but overlap￾ping goals. Firstly, it may help readers to understand manufacturing intelligence in a deeper and more

comprehensive way. Furthermore, throughout this book numerous references to literature sources are

provided, enabling interested readers to further pursue specific aspects of manufacturing intelligence.

Xue Ligong, Jiang Xuemei, Zhang Xiaomei, Liu Hong, Wang Sheng, Ai Qingsong and Ming Hui

compiled the various chapters. I wish to extend my thanks to them for their fruitful work.

The book is supported by the International Cooperation Key Project (Multi-agent based digital manu￾facturing new theory and new method, grant no. 2006DFA73180) from the Scientific and Technology

Committee of China.

1

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Chapter 1

Intelligent Manufacturing and

Manufacturing Intelligence

Manufacturing is a prime generator of wealth and is

critical in establishing a sound basis for economic

growth. Manufacturing is also the cornerstone of

all economic activities, and efforts to continuously

advance manufacturing technology are therefore

vital to a richer and more stable future. Intelligent

Manufacturing (IM), believed to be the next gen￾eration advanced manufacturing paradigm is exten￾sively investigated by industry and academia. In this

chapter, we firstly recall the course of manufacturing

development and summarize the characteristics of

the four revolutions in this course. Subsequently,

the broad sense of ‘manufacturing’ is articulated

and the characteristics of manufacturing activities

in the operation of manufacturing processes are

depicted. The differences and relationships between

Artificial Intelligence (AI) and Manufacturing

Intelligence (MI) are then presented. The back￾ground of intelligent manufacturing, the attributes

of intelligent manufacturing technology and the

future development of intelligent manufacturing

system are described. Lastly, a summary of this

chapter is given.

INTRODUCTION

Manufacturing has played, and continues to play,

a vital role in the world economy. In recent years,

manufacturing has undergone profound changes

because of the development of science and tech￾nology, the requirements of global manufacturing

and the changing manufacturing environment.

Changes have to be made in order to satisfy the

increasingly changing and diversified demands of

customers. These changes are bringing manufactur￾ing from a resource-based centralized paradigm to

a knowledge-intensive, innovation-based, adaptive,

digital and networked one. Integration and intelli￾gence are two vital factors of modern manufactur￾ing. Looking at the history and the present state of

manufacturing, it is clear that there have been four

revolutions according to the four stages of manufac￾turing industrial development (Wang, 2005). These

are the age of craftsmanship, the age of machines

and hard automation, the age of information and

flexible automation, and the age of knowledge and

intelligent automation.

In the age of craftsmanship, all manufacturing

DOI: 10.4018/978-1-60566-864-2.ch001 activities from raw materials to finished products

2

Intelligent Manufacturing and Manufacturing Intelligence

were entirely performed by physical labor, in

which a person with hand tools was used to make

objects. The quality of products relied very much

on individual skills. As technology progressed,

such as animal, wind and water power were gradu￾ally employed, and more sophisticated tools were

developed, but the basic structure of craft-based

production remained unchanged.

The industrial revolution that dates back 200

years brought manufacturing to the age of ma￾chines and hard automation, and machinery has

played an increasingly prominent role since then.

In the early days, the mechanization of manual

procedures was the first step towards automation.

In the later stage of the period of mechanization,

the total process of production was analyzed and

subdivided into a number of simpler production

functions as products were becoming increas￾ingly complex. Workers were carefully but rather

narrowly trained to operate their own tools and

specialized machines. Such a manufacturing pat￾tern is well suited to mass production.

In the age of information and flexible automa￾tion, information processed by computers and

automatic control has led to significant changes

in manufacturing patterns and technologies. At the

early stage of this period, a great deal of emphasis

was placed on the development and application of

‘hardware’ and on the search for hard automation.

In the later stage of this period, information and

flexible automation have been the primary focus

of development. In order to improve production

efficiency, considerable effort has been placed on

the development and application of new manu￾facturing techniques in programmable equipment

(hardware: such as NC, CNC, and robotics,) and

the computer-aided systems (software: such as

CAD, CAPP, CAE, CAM, and so forth).

In today’s information age, manufacturing

is forced to be more intelligent in order to have

the power to process escalating manufacturing

information and to satisfy dynamical marketing

demands. With the rapid development of Arti￾ficial Intelligence (AI) and the quick improve￾ment various related enabling technologies, the

manufacturing age of knowledge and intelligent

automation has already begun. The main focus in

this age will be on manufacturing flexibility and

adaptability in the various aspects of manufac￾turing, such as automatic and intelligent design,

production planning and control, configura￾tion management, intelligent decision support,

automatic and intelligent failure detection and

maintenance, and so on. Furthermore, Manufac￾turing systems’ self-organization, self-learning

and adaptation according to the conditions of

outside environments will also be of paramount

importance. One of the developing trends of future

manufacturing systems will be intelligent and

knowledge-intensive systems, which focus on the

integration of knowledge available from various

manufacturing domains and the combination of

human-computer intelligence.

MANUFACTURING ACTIVITIES

The word ‘manufacturing’ includes much more

than the basic fabrication techniques. It involves

diverse activities from raw materials to finished

products during the processes of manufactur￾ing, including marketing prediction, procure￾ment, design, plan, fabrication, distribution, as

well as recycling and services. Customer needs

or marketing predictions are the beginning of

manufacturing activities. Products are designed

according to customer needs. Product design is a

complicated process, involving conceptual design,

configuration, parametrization, and manufactur￾able analysis. Product design is normally made

accessible to a planning subsystem, including

process planning, scheduling and manufacturing

resource planning, which transforms product

design into production plan and manufacturing

resource plan. After the plans are developed,

the product can then be manufactured. With the

rapid development of manufacturing intelligence

and information technology, the process of pro-

3

Intelligent Manufacturing and Manufacturing Intelligence

duction could usually be made autonomous or

semi-autonomous. To ensure that the process

is under control, it is necessary to monitor the

process and obtain status information about the

different processes and product variables. If there

happen to be faults, the functions of diagnosis

and maintenance start working to make instigate

recovery. The principal functionalities of produc￾tion are process planning, scheduling, material

resource planning, monitoring, diagnosis, control,

inspection, and assembly. Each subsystem, such

as design, planning, production, and so forth

functions effectively as the fundamental objec￾tive of optimizing the running of manufacturing

systems as a whole. For manufacturing systems

to run optimally, however, they are not only

dependent on the integration of each subsystem,

but also the cooperation and coordination of

each subsystem. In order to ensure that different

subsystems cooperate and coordinate, there is a

system-level subsystem whose functions are to

develop system organization, control strategies,

and cooperative mechanism.

ARTIFICIAL INTELLIGENCE AND

MANFUACTURING INTELLIGENCE

Artificial intelligence (AI), an expression coined

by Professor John McCarthy from Stanford Uni￾versity in 1956, is a branch of computer science

concerned with making computers behave like

humans by modeling human thoughts on comput￾ers. Computational Intelligence (CI), as a new

development paradigm of intelligent systems, has

resulted from a synergy among Neural Networks

(NN), Fuzzy Sets (FS), and Genetic Algorithms

(GA) (Engelbrecht, 2007). CI, as one important

branch of AI, is an effective complement to AI

and also an important component of AI. With the

development and application of AI intelligence

can be built into machines and manufacturing

processes. In general, AI can be divided into two

categories: the first is Symbol Reasoning (SR)

including Expert Systems (ES), Knowledge-Based

Systems (KBS), Case-Based Reasoning (CBR),

and the second is CI including NN, FS, and GA.

Combining symbolic reasoning with computa￾tional intelligence, we can take full advantage

of the ‘intelligence’ provided by computational

method and also the logical reasoning power based

on knowledge of symbolic reasoning. Hence, a

powerful intelligent system can be developed

via the combination of SR and CI. When AI, as

the core technique of IM, is applied to different

manufacturing stages, such as design, planning,

operation, quality control, maintenance, market￾ing and distribution, and so forth, and we regard

this as manufacturing intelligence (MI). AI is an

indispensable and greatly important fundamental

element of MI, and MI is an application of AI in

manufacturing. However all techniques, besides

AI, which can help improve the intelligence level

of manufacturing, are also important components

of MI. They include conventional optimization

methods, cluster analysis tools, fractal theory, and

so on. MI is an extension of AI in the manufactur￾ing domain and also a comprehensive application

of diverse non-intelligent optimization techniques

in improving automation and intelligence of

manufacturing (see Figure 1). Fractal Theory, for

instance, provides us with the mathematical tools

to analyze the geometrical complexity of natural

and artificial objects (Castillo & Melin, 2003);

Cluster Analysis provides us with the mathemati￾cal tools to classify data according to similarity

(Mirkin, 2005); conventional optimization ap￾proaches provide us with mathematical tools for

optimization problems (Saravanan, 2006).

MI, as a fundament of intelligent manufactur￾ing, is a combination of diverse AI techniques, such

as reasoning, learning, self-improvement, goal￾seeking, self-maintenance, problem-solving and

adaptability, and other available non-intelligent

techniques in manufacturing, which exhibit ca￾pabilities when applied to solve manufacturing

engineering problems in design, planning, produc￾tion, process modeling, monitoring, inspection,

4

Intelligent Manufacturing and Manufacturing Intelligence

diagnosis, maintenance, assembly, system model￾ing, control and integration. With the development

of related techniques, especially of AI, MI also

continuously develops and improves to satisfy the

requirements of manufacturing development. In

the early stage of development many manufac￾turing systems improve their intelligence level

through the application of knowledge-based ES,

and the trait of this stage is symbolic reasoning.

Because most ES are closed intelligent systems

with weak self-learning abilities, they can only

deal with single domain knowledge in a standalone

manner, and normally do not communicate with

the outside environment. Their capability to handle

complex numerical calculation is very limited.

However, manufacturing is a complicated process,

which needs more than just logical reasoning based

on a certain domain, and it also needs capabilities

to optimize, self-learn, self-organize, and adapt

so as to optimize and improve various functions

during the process of manufacturing. AI com￾bined with CI and other techniques is employed

in manufacturing to overcome the drawbacks of

ES. These new techniques with the attributes of

flexibility, generality and precision combined with

the knowledge-based symbolic reasoning of ES

will continuously improve the intelligence level

of manufacturing and provide optimal solutions

with the development of MI.

An alternative view of human intelligence

has emerged in 1990s, and it is regarded as the

capability of a system to interact with its environ￾ment without clearly defined goals, to learn from

this interaction and, in an incremental fashion,

to both articulate and achieve its goals (Reti &

Kumara, 1997). Multi-agent technology represents

a promising approach to designing an intelligent

system as a cluster of intelligent agents, which

have the power to deal with distributed problems.

A multi-agent system is a group of loosely con￾nected agents that are autonomous and indepen￾dent. These agents are capable of communicating

with each other, processing received messages and

making decisions, and learning from experiences

collectively. The overall system performance is

not globally optimized but develops through the

dynamic interaction of agents. The novelty of this

approach is in replacing hierarchical architectures

with network configurations in which nodes with

advanced communication capacities are capable

of negotiating how to achieve specified goals

without any centralized control. Another similar

term is called ‘holon’. A holon is an autonomous,

cooperative, and sometimes intelligent entity;

it can be made up of other holons. A system of

holons cooperating to achieve a goal or objective

forms a holarchy; the holarchy defines the basic

rules for cooperating among holons and therefore

limits their autonomy. A holonic Manufacturing

System (HMS) is a holarchy that integrates the

entire range of manufacturing activities from raw

material purchasing to finished product. HMS is

a kind of effective system for IMS.

INTELLIGENT MANUFACTURING

The journey from small-sized production to

mass-production, and until to the current mass￾cutomization, combined with the advent and

Figure 1. Manufacturing intelligence