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CAD/CAM Robotics and Factories of the Future

Volume II: Automation of Design, Analysis and Manufacturing

  • Conference proceedings
  • © 1989

Overview

Editors:
  1. Birendra Prasad

    1. Technical System Development, Electronic Data Systems, General Motors, Artificial Intelligence Services, Troy, USA

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  2. S. N. Dwivedi
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  3. K. B. Irani
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Table of contents (4 papers)

  1. Front Matter

    Pages I-XXI
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  2. Computer-Aided Design

    • V. Kumar, M. D. German, S.-J. Lee, E. Atrek, R. Kodali, A. D. Belegundu et al.
    Pages 1-116

  3. Automation in Manufacturing

    • S. K. Taneja, S. P. Rana, N. Singh, Michael D. Oliff, James Davis, Lorenzo Vicens et al.
    Pages 117-164

  4. Applications of Artificial Intelligence

    • Michael E. Grost, Trent Jaeger, Ming C. Leu, Dinesh K. Pai, Qiuming Zhu, A. Kusiak et al.
    Pages 165-229

  5. Expert Systems

    • Sarma R. Vishnubhotla, Gautam Biswas, Michael D. Oliff, T. Cecchin, J. Ragot, D. Sauter et al.
    Pages 231-295

  6. Back Matter

    Pages 297-310
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Keywords

About this book

This volume is about automation - automation in design, automation in manufacturing, and automation in production. Automation is essen­ tial for increased productivity of quality products at reduced costs. That even partial or piecemeal automation of a production facility can deliver dramatic improvements in productivity has been amply demon­ strated in many a real-life situation. Hence, currently, great ef­ forts are being devoted to research and development of general as well special methodologies of and tools for automation. This volume re­ ports on some of these methodologies and tools. In general terms, methodologies for automation can be divided into two groups. There are situations where a process, whether open-loop or closed-loop, is fairly clearly understood. In such a situation, it is possible to create a mathematical model and to prescribe a mathe­ matical procedure to optimize the output. If such mathematical models and procedures are computationally tractable, we call the correspond­ ing automation - algorithmic or parametric programming. There is, however, a second set of situations which include process­ es that are not well understood and the available mathematical models are only approximate and discrete. While there are others for which mathematical procedures are so complex and disjoint that they are computationally intractable. These are the situations for which heuristics are quite suitable for automation. We choose to call such automation, knowledge-based automation or heuristic programming.

Editors and Affiliations

  • Technical System Development, Electronic Data Systems, General Motors, Artificial Intelligence Services, Troy, USA

    Birendra Prasad

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