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Optimized process planning by generative simulated annealing

Published online by Cambridge University Press:  27 February 2009

K.N. Brown  and
J. Cagan
K.N. Brown
Affiliation:
Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, U.K.
J. Cagan
Affiliation:
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.
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Abstract

Manufacturing process planning is a difficult problem with a prohibitively large search space. It is normally tackled by decomposing goal objects into features, and then sequencing features to obtain a plan. This paper investigates an alternative approach. The capabilities of a manufacturing process are represented by a formal language of shape, in which sentences correspond to manufacturable objects. The language is interpreted to describe process plans corresponding to the shape generation, complete with cost estimates. A macro layer that describes single operations of the machine is implemented on top of the formal language. The space it describes is searched by the generative simulated annealing algorithm, a stochastic search technique based on simulated annealing. Plans that are close to the optimum are generated in reasonable time.

Keywords

Process PlanningSimulated AnnealingShape GrammarSemanticsOptimization

Information

Type
Articles
Information
AI EDAM , Volume 11 , Issue 3 , June 1997 , pp. 219 - 235
Copyright
Copyright © Cambridge University Press 1997

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