Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-23T06:32:54.814Z Has data issue: false hasContentIssue false
  • English
  • Français

Integration of qualitative and quantitative reasoning in iterative parametric mechanical design

Published online by Cambridge University Press:  27 February 2009

Von-Wun Soo  and
Tse-Ching Wang
Von-Wun Soo
Affiliation:
Department of Computer Science, National Tsing-Hua University, Hsin-Chu, Taiwan, Republic of China, 30043.
Tse-Ching Wang
Affiliation:
Department of Computer Science, National Tsing-Hua University, Hsin-Chu, Taiwan, Republic of China, 30043.
Get access Rights & Permissions [Opens in a new window]

Abstract

A framework IPD (Iterative Parametric Design) is proposed to assist the iterative parametric mechanical design process. To effectively find a set of satisfiable values for the design parameters the key is to find good heuristics to adjust or tune the parametric values resulting from previous design iterations. We propose that heuristics can come from two aspects by both qualitative and quantitative reasoning. Qualitative reasoning, based on confluences, provides global control over the feasible directions of variable adjustments, while quantitative reasoning, based on the dependency network and perturbation analysis, can be used to propose actual quantity of local variable adjustments. We used the design of a helical compression spring as an example to illustrate the performance of IPD system. We show that IPD can often find a solution faster than those without guidance of qualitative and quantitative reasoning.

Type
Research Article
Information
AI EDAM , Volume 6 , Issue 2 , May 1992 , pp. 95 - 109
Copyright
Copyright © Cambridge University Press 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akman, V., Hagen, P. J. W. and Tomiyama, T. 1990. A fundamental and theoretical framework for an intelligent CAD system. Computer-Aided Design, 22, 352–367.CrossRefGoogle Scholar
Chung, J. C. H. and Schussel, M. D. 1989. Comparison of variational and parametric design. International Operations,13, 22–36.Google Scholar
Davis, E. 1987. Constraint propagation with interval labels. Artificial Intelligence, 32, 281–331.CrossRefGoogle Scholar
DeKleer, J. and Brown, J. S. 1984. A qualitative physics based on confluences. Artificial Intelligence, 24, 7–84.CrossRefGoogle Scholar
Dixon, J. R. 1986. Artificial intelligence and design: a mechanical engineering view. Proceedings of AAAI, 872–877.Google Scholar
Dixon, J. R., Howe, A. E., Cohen, P. R. and Simmons, M. K. 1987. Dominic I: progress towards domain independence in design by iterative redesign. Engineering with Computers, Vol. 2, 137–145.CrossRefGoogle Scholar
Forbus, K. D. 1984. Qualitative process theory. Artificial Intelligence, 24, 85–186.CrossRefGoogle Scholar
Friedland, P. 1979. Knowledge-based hierarchical planning in molecular genetics. Ph.D. thesis, Stanford University.Google Scholar
Mackworth, A. 1977. Consistency in networks of relations. Artificial Intelligence, 8, 99–118.CrossRefGoogle Scholar
Maier, K. W. 1961. A new approach to compression spring design in Spring Design and Application, McGraw-Hill Inc., pp. 2–8.Google Scholar
Marcus, S., Stout, J. and McDermott, J. 1988. VT: an expert elevator designer that uses knowledge-based backtracking. AI Magazine, Spring, 99–118.Google Scholar
Murtagh, N. and Shimurai, M. 1990. Parametric engineering design using constraint-based reasoning. Proceedings of AAAI, pp. 505–510.Google Scholar
Orelup, M. F., Dixon, J. R., Cohen, P. R. and Simmons, M. K. 1988. Dominic II: meta-level control in iterative redesign. Proceedings of AAAI, pp. 25–30.Google Scholar
Sriram, D., Stephanpoulos, G., Logcher, R., Gossard, D., Nicholas, G., Serrano, D. and Navinchandra, D. 1989. Knowledge-based system applications in engineering design: research at MIT. AI Magazine, Fall, 79–96.Google Scholar
Stefik, M. J. 1980. Planning with constraints. Ph.D. thesis, Stanford University.Google Scholar
Sussman, G. J. and Steele, G. L. 1980. CONSTRAINTS–a language for expressing almost hierarchical descriptions. Artificial Intelligence, 14, 1–39.CrossRefGoogle Scholar
Ward, A. C. and Seering, W. P. 1989. Quantitative inference in a mechanical design COMPILER, Design Theory and Methodology, ASME, 89–97.Google Scholar