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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.
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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

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