Intelligent Manufactoring Series – serie

The quest for building systems that can function automatically has attracted a lot of attention over the centuries and created continuous research activities. As users of these systems we have never been satisfied, and demand more from the artifacts that are designed and manufactured. The current trend is to build autonomous systems that can adapt to changes in their environment. While there is a lot to be done before we reach this point, it is not possible to separate manufacturing systems from this trend. The desire to achieve fully automated manufacturing systems is here to stay. Manufacturing systems of the twenty-first century will demand more flexibility in product design, process planning, scheduling and process control. This may well be achieved through integrated software and hardware archi­ tectures that generate current decisions based on information collected from manufacturing systems environment, and execute these decisions by converting them into signals transferred through communication network. Manufacturing technology has not yet reached this state. However, the urge for achieving this goal is transferred into the term 'Intelligent Systems' that we started to use more in late 1980s. Knowledge-based systems, our first efforts in this endeavor, were not sufficient to generate the 'Intelligence' required - our quest still continues. Artificial neural network technology is becoming an integral part of intelligent manufacturing systems and will have a profound impact on the design of autonomous engineering systems over the next few years.

Over the next ten years integrated distributed intelligent systems (IDIS) techniques will be widely used in modern manufacturing and will quicken the development and applications of distributed artificial intelligence (AI). This book describes the concept, methodology, architecture, construction and implementation techniques of IDIS as well as their applications in real-world manufacturing. Its contents spans some high technology areas, including advanced manufacturing technology, advanced process control, AI in engineering applications, advanced industrial automation and distributed AI. In essence, the book presents the state-of-the-art technology in manufacturing systems and the newest development in distributed AI. Applications in both discrete and continuous manufacturing are featured in the text. This text should be useful for teaching graduate and senior undergraduate students, in advanced manufacturing systems and distributed AI courses. It should also assist professorial research staff in tracing the most recent technology in distributed AI, and can be used as a self-instruction tutoring book for retraining engineers in manufacturing and process industries.

This book is aimed at both researchers and practitioners, and provides a collection of expert systems in manufacturing and production engineering along with their knowledge base and rules. We believe that inclusion of the knowledge base and associated rules is essential if practitioners are to derive full benefit from these expert systems. This unique book is the result of our belief and the efforts of our distinguished colleagues who subscribe to this philosophy.A total of 15 different expert systems are included in this book. These expert systems are preceded by an introductory chapter written by Kuo, Preface XVll Mital and Anand. The expert system rules are included on a floppy disk in ASCII and can be easily accessed. These rules and the description of the expert system's structure should assist the usersin customizing these systems.Overall, the expert systems included in this volume cover a fairly wide variety of manufacturing and production engineering topics.

The quest for building systems that can function automatically has attracted a lot of attention over the centuries and created continuous research activities. As users of these systems we have never been satisfied, and demand more from the artifacts that are designed and manufactured. The current trend is to build autonomous systems that can adapt to changes in their environment. While there is a lot to be done before we reach this point, it is not possible to separate manufacturing systems from this trend. The desire to achieve fully automated manufacturing systems is here to stay. Manufacturing systems of the twenty-first century will demand more flexibility in product design, process planning, scheduling and process control. This may well be achieved through integrated software and hardware archi­ tectures that generate current decisions based on information collected from manufacturing systems environment, and execute these decisions by converting them into signals transferred through communication network. Manufacturing technology has not yet reached this state. However, the urge for achieving this goal is transferred into the term 'Intelligent Systems' that we started to use more in late 1980s. Knowledge-based systems, our first efforts in this endeavor, were not sufficient to generate the 'Intelligence' required - our quest still continues. Artificial neural network technology is becoming an integral part of intelligent manufacturing systems and will have a profound impact on the design of autonomous engineering systems over the next few years.