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Feature extraction from large CAD databases using genetic algorithm
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DOI：10.1016/j.cad.2004.08.002.png)
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摘要
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Syntactic recognition, Graph based method, expert systems and knowledge-based approach are the common feature recognition techniques available today. This work discusses a relatively newer concept of introduction of Genetic Algorithm for Features Recognition (GAFR) from large CAD databases, which is significant in view of the growing product complexity across all manufacturing domains. Genetic Algorithm is applied in a random search process in the CAD data using population initialisation; offspring feature creation via crossover, evolution and extinction of the offspring sub-solutions and finally selection of the best alternatives. This method is cheaper than traditional hybrid and heuristics based direct search approaches. Case study is presented with simulation results. (C) 2004 Elsevier Ltd. All rights reserved.
Keyword:
feature recognition
crossover
fitness function
pocket feature
homologising
FEV representation
offspring
hybrid approach
solution path
search time
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