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Artificial Neural Networks for Intelligent Manufacturing

  • Cihan H. Dagli

Part of the Intelligent Manufacturing Series book series (IMS)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Intelligent manufacturing: Basic concepts and tools

    1. Front Matter
      Pages 1-1
    2. Cihan H. Dagli
      Pages 3-16
    3. Cihan H. Dagli, Pipatpong Poshyanonda
      Pages 39-65
    4. Gregory R. Madey, Jay Weinroth, Vijay Shah
      Pages 67-90
  3. Neurocomputing for intelligent manufacturing: Organization and coordination level applications

    1. Front Matter
      Pages 91-91
    2. Ali Bahrami, Cihan H. Dagli
      Pages 93-110
    3. César O. Malavé, Satheesh Ramachandran
      Pages 111-142
    4. Madhusudhan Posani, Cihan H. Dagli
      Pages 143-157
    5. John Y. Cheung
      Pages 159-193
    6. Cihan H. Dagli, Mahesh Kumar Vellanki
      Pages 195-228
    7. Mark R. Henderson
      Pages 229-264
    8. Joydeep Ghosh
      Pages 265-297
  4. Neurocomputing for intelligent manufacturing: Execution level applications

    1. Front Matter
      Pages 369-369
    2. Michel Guillot, Riadh Azouzi, Marie-Claude Cote
      Pages 371-397
    3. Yung C. Shin
      Pages 399-411
    4. L. H. Tsoukalas, A. Ikonomopoulos, R. E. Uhrig
      Pages 413-434
    5. Najwa S. Merchawi, Soundar R. T. Kumara
      Pages 435-461
  5. Back Matter
    Pages 463-469

About this book

Introduction

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.

Keywords

adaptive control artificial neural network control fuzzy intelligent systems manufacturing neural networks

Editors and affiliations

  • Cihan H. Dagli
    • 1
  1. 1.Department of Engineering ManagementUniversity of Missouri-RollaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-011-0713-6
  • Copyright Information Springer Science+Business Media B.V. 1994
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-4307-6
  • Online ISBN 978-94-011-0713-6
  • Buy this book on publisher's site