中国机械工程 ›› 2013, Vol. 24 ›› Issue (18): 2442-2447.

• 机械基础工程 • 上一篇    下一篇

基于粒子群算法的多质量特性下的选装优化方法研究

陈杰;叙述礼   

  1. 南京理工大学,南京,210094
  • 出版日期:2013-09-25 发布日期:2013-09-30
  • 基金资助:
    国家自然科学基金资助重点项目(70931002)
    National Natural Science Foundation of China(No. 70931002)

Optimization of Selective Assembly with Multiple Quality Characteristics Based on MOPSO

Chen Jie;Xu Shuli   

  1. Nanjing University of Science and Technology,Nanjing,210094
  • Online:2013-09-25 Published:2013-09-30
  • Supported by:
    National Natural Science Foundation of China(No. 70931002)

摘要:

提出了一种基于粒子群算法的分组选装的优化方法。针对复杂产品分组装配中多质量特性的特点,构建了以装配间隙波动最小化为目标的优化模型。利用引入共享机制和动态归档机制的多目标粒子群算法对该模型进行求解。最后,通过发动机的活塞和气缸的选择装配实例对所提出方法进行了验证,结果表明,采用该方法可以有效提高产品的装配精度。

关键词: 复杂产品, 选择装配, 多目标粒子群优化算法, 多质量特性

Abstract:

This paper presented a grouping method based on MOPSO for selective assembly. Firstly the properties and quality criteria of selective assembly of components with multi-quality characteristics were analyzed to establish a mathematical model. The objective function of this model was to minimize the clearance variation in selective assembly. Fitness sharing strategy and dynamic archiving strategy were introduced to improve MOPSO what was used to solve the model. Finally, the model was applied to optimize the selective assembly problem of an engine. The results show that the method can improve the accuracy of product assembly effectively.

Key words: complex product, selective assembly, multi-objective particle swarm optimization(MOPSO), multiple quality characteristics

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