中国机械工程 ›› 2015, Vol. 26 ›› Issue (5): 658-663.

• 科学基金 • 上一篇    下一篇

用GAAA优化多阶段装配过程中的夹具布局

谢伟松1;邓铮1;丁伯慧2   

  1. 1.天津大学,天津,300072
    2.天津大学机构理论与装备设计教育部重点实验室,天津,300072
  • 出版日期:2015-03-10 发布日期:2015-03-06
  • 基金资助:
    国家自然科学基金资助项目(51205286, 51275348)
    National Natural Science Foundation of China(No. 51205286, 51275348)

Fixture Layout Optimization in Multi-station Assembly Processes Using GAAA

Xie Weisong1;Deng Zheng1;Ding Bohui2   

  1. 1.Tianjin University,Tianjin,300072
    2.Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education,Tianjin University,Tianjin,300072
  • Online:2015-03-10 Published:2015-03-06
  • Supported by:
    National Natural Science Foundation of China(No. 51205286, 51275348)

摘要:

改进了遗传算法与蚁群算法的融合(GAAA)算法,利用它来解决多阶段装配过程中二维刚性零件的夹具布局优化问题,合理选择定位销的位置使得灵敏度指标最小化。通过改变遗传算法的变异算子,变异长度以及交叉、变异在蚁群算法中发生的位置,提高了GAAA的稳定性和收敛性。以汽车侧边装配为例验证了改进算法的有效性,结果表明改进后的GAAA比基本的GAAA和蚁群算法求得的结果要好,且收敛速度更快,稳定性更好。

关键词: 多阶段装配过程, 状态空间模型, 夹具布局优化, 遗传算法与蚁群算法的融合

Abstract:

GAAA (genetic algorithm-ant colony algorithm) was augmented to solve fixture layout optimization problems of 2D rigid parts in multi-station assembly processes, and the coordinates of two locating pins were properly selected to minimize the sensitivity index. The stability and convergence of GAAA were improved by changing the mutation operator, mutation length of genetic algorithm and the position where crossover and mutation occured in ant colony algorithm. A case about automotive side aperture assembly processes was studied to verify the effectiveness of the augmented GAAA. The results show that the augmented GAAA can generate more accurate results with a faster rate of convergence and a better stability than the basic GAAA and ant colony algorithm.

Key words: multi-station assembly process, state space model, fixture layout optimization, genetic algorithm-ant colony algorithm(GAAA)

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