The Computer Integrated Manufacturing Laboratory is a constituent laboratory of the Institute for Systems Research at the University of Maryland.

Design Classification and Hybrid Variant-Generative Process Planning

This material is based upon work supported by the National Science Foundation under Grant Number 9713718. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Principal Investigators:

• Dana S. Nau
  Dept. of Computer Science and Institute for Systems Research
  University of Maryland
  College Park, MD 20742
• Jeffrey W. Herrmann
  Dept. of Mechanical Engineering and Institute for Systems Research
  University of Maryland
  College Park, MD 20742
• William C. Regli
  Drexel University
  Department of Mathematics and Computer Science
  3141 Chestnut Street
  Philadelphia, PA 19104

Developing successful generative process planners for complex machined parts is a difficult challenge. Although researchers have developed generative techniques for process selection, they have been less successful developing generative techniques for selecting the fixtures needed to complete the process plan. To address this problem, we are developing a new hybrid approach to process planning.

We believe that, in most cases, a generative planner is a better approach for creating a preliminary process plan. A variant approach is a very useful technique, however, for completing the process plan and adding the necessary details (like fixturing).

This research is developing a new hybrid approach that uses a successful generative process planning approach and adds a variant fixture planning approach. The fixture planning approach must identify designs and process plans that have fixtures that can hold the new design. We have developed an approach for defining a usefulness measure that explicitly reflects fixture usefulness. A specific example shows how one can use this approach to measure the usefulness of setups.

See also the web page describing our Design for Production research.

• A. Elinson, J.W. Herrmann, I. Minis, D. Nau, G. Singh, "Toward hybrid variant/generative process planning," Design for Manufacturing Symposium, ASME Design Engineering Technical Conference, Sacramento, California, September 14-17, 1997.
• S. Balasubramanian, A. Elinson, J.W. Herrmann, and D. Nau, Fixture-based usefulness measures for hybrid process planning, 1998 ASME Design for Manufacturing Conference, Atlanta, Georgia, September 13-16, 1998. (Note this is a PDF file.)
• S. Balasubramanian, A. Elinson, J.W. Herrmann, and D. Nau, Measuring the Usefulness of Fixtures for Hybrid Process Planning, 1999 NSF Grantee's Conference, January, 1999.
• Sundar Balasubramanian and Jeffrey W. Herrmann, Using Neural Networks to Generate Design Similarity Measures, Technical Report 99-38, Institute for Systems Research, University of Maryland, College Park, 1999.
• Jeffrey W. Herrmann and Mandar Chincholkar, Reducing Manufacturing Cycle Time during Product Design, Technical Report 99-54, Institute for Systems Research, University of Maryland, College Park, 1999.
• D.S. Nau, J.W. Herrmann, and W.C. Regli, Design Classification and Hybrid Variant-Generative Process Planning, 2000 NSF Design and Manufacturing Research Conference, Vancouver, Canada, January, 2000. (PDF file)
• Herrmann, J.W., S. Balasubramanian, and G. Singh, Defining specialized design similarity measures, International Journal of Production Research, Volume 38, Number 15, pages 3603-3621, 2000.
• Herrmann, J.W., and M.M. Chincholkar, Reducing Throughput Time during Product Design Journal of Manufacturing Systems, Volume 20, Number 6, pages 416-428, 2001/2002.

Last updated on September 5, 2002, by Jeffrey W. Herrmann.