Manufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and Adaptation

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) and Ping Lou (Wuhan University of Technology, China)
Release Date: March, 2010|Copyright: © 2010 |Pages: 407
DOI: 10.4018/978-1-60566-864-2
ISBN13: 9781605668642|ISBN10: 1605668648|EISBN13: 9781605668659|ISBN13 Softcover: 9781616922696
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Description & Coverage
Description:

The manufacturing industry has experienced dramatic change over the years with growing advancements, implementations, and applications in technology.

Manufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and Adaptation focuses on the latest innovations for developing, describing, integrating, sharing, and processing intelligent activities in the process of manufacturing in engineering. Containing research from leading international experts, this publication provides readers with scientific foundations, theories, and key technologies of manufacturing intelligence.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Artificial Neural Networks
  • Communication and interaction protocol
  • Computing intelligence
  • Cooperation, negotiation, and behavior coordination
  • Industrial Engineering
  • Intelligent activities in manufacturing
  • Intelligent agents
  • Intelligent technology applications in manufacturing
  • Knowledge acquisition and management
  • Methods of data fusion
  • Modern manufacturing
  • Multi-sensor information fusion
  • Problem solving and interaction mechanism
  • Theories of multi-agent systems
Reviews and Testimonials

Manufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and Adaptation provides readers with the scientific foundations, theories, and key technologies of manufacturing intelligence. Readers will obtain valuable enlightenment for their future research activities.

– Huaiqing Wang, City University of Hong Kong, Hong Kong
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Editor Biographies
Zude Zhou is a professor at the School of Mechanical and Electronic Engineering, Wuhan University of Technology, P. R. China. He is also the President of Wuhan University. He received his Bachelor degree in Electrical Engineering from Huazhong University of Technology, China, in 1970.He attended in a advanced studies at Birmingham, UK, from 1984-1986. He was also a Visiting Professor of the University of Bolton, UK, a Visiting Professor of Hong Kong University, Hong kong, and a Visiting Scientist of National University of Singapore, Singapore. Mr. Zhou specializes in control and application of microcomputer, electromechanical integration technology, intelligent manufacturing, digital manufacturing, network manufacturing. He published more than 300 articles.
Huaiqing Wang is a Professor at the Department of Information Systems, City University of Hong Kong. He is also the Honorary Dean and a Guest Professor of the School of Information Engineering, Wuhan University of Technology, China. He received his PhD in Computer Science from University of Manchester, UK, in 1987. Dr. Wang specializes in research and development of business intelligence systems, intelligent agents and their applications (such as multi-agent supported financial information systems, virtual learning systems, knowledge management systems, conceptual modeling and ontology). He has published more than 40 international refereed journal articles.
Ping Lou is an Associate Professor at the School of Automation, Wuhan University of Technology, P. R. China. She received his PhD in Mechanical Engineering from Huazhong University of Science and Technology, P. R. China, in 2004. Dr. Lou specializes in research and development of intelligent manufacturing and network manufacturing. She has published more than 20 articles.
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Preface

TENTATIVE

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 product 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 scientific 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 manufacturing 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 mimicing 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. Although these two sciences are different, they relate to each other. AI is one of the main foundations of MI; 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 fifteen chapters which include engineering background, foundations, technologies, applications, implementations, case studies, trends of intelligent manufacturing, and prospects for manufacturing intelligence. The second and third parts including thirteen chapters constitute the main part of this book. In these two parts, scientific foundations, key technologies and pragmatic applications of manufacturing intelligence are analyzed. Part I contains one chapter that introduces manufacturing intelligence, the development of intelligent manufacturing, and the features of intelligent activities in the process of manufacturing. Chapters 2 to 10 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 an overview of intelligent agents and multi-agent systems. Chapter 4 presents the principle and techniques of data mining and knowledge discovering. Chapter 5 introduces the principle and applications of neural networks, genetic algorithms, and fuzzy logic in engineering and manufacturing. Chapter 6 presents an overview of information system modeling, including the general processes and strategies, some different modeling approaches and modeling tools. Chapter 7 presents an overview of multi-sensor integration and data fusion theories. Chapter 8 introduces the principle and approaches to group theory, including coding systems for parts, approaches for grouping parts and applications in manufacturing designing and processing. Chapter 9 presents the structure theory of intelligent control, a general architecture of the intelligent controller, and intelligent systems.

Chapters 10 to 14 make up Part III of the book: the applications and case studies for manufacturing intelligence. Chapter 10 presents knowledge-based approaches for designing, beginning with the basic concepts and approaches of conventional computer-aided design (CAD) systems. Chapter 11 presents 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 presents the principles and approaches to intelligent management and decision-making in manufacturing. Chapter 14 presents multi-agent technology and theory used to build the platform frameworks for manufacturing resource sharing.

Part VI contains only one chapter that presents the prospects for manufacturing intelligence and the trends of intelligent manufacturing, including knowledge engineering, bionic manufacturing, holonic manufacturing, and the intelligent 4M System (Modeling, Manufacturing, Manipulation, and Measurement).

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, our 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 overlapping 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, Wu Xiaomei, Liu Hong, Wang Sheng, and Ming Hui compiled the various chapters. I wish to extend my thanks to them for their fruitful work.

The book acquired funding from the International Cooperation Key Project (Multi-agent based digital manufacturing new theory and new method, grant no. 2006DFA73180) from the Scientific and Technology Committee of China, along with all the authors, I would express sincere gratitude.