Feature extraction from large CAD databases using genetic algorithm

被引：23
|
作者：
Pal, P
Tigga, AM
Kumar, A
机构：
[1] Tata Technol, Jamshedpur 831010, Bihar, India
[2] Natl Inst Technol, Dept Prod Engn & Management, Jamshedpur 831014, Bihar, India
来源：
COMPUTER-AIDED DESIGN | 2005年 / 37卷 / 05期
关键词：
feature recognition; crossover; fitness function; pocket feature; homologising; FEV representation; offspring; hybrid approach; solution path; search time;
D O I：
10.1016/j.cad.2004.08.002
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
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页码：545 / 558
页数：14
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