• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2016, Vol. 52 ›› Issue (1): 130-138.doi: 10.3901/JME.2016.01.130

• 制造科学与技术 • 上一篇    下一篇

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基于混合粒子群算法的复杂机械产品装配质量控制阈优化方法

王小巧,  刘明周,  葛茂根,  马靖,  刘从虎   

  1. 合肥工业大学机械与汽车工程学院  合肥  230009
  • 收稿日期:2015-01-08 修回日期:2015-08-13 出版日期:2016-01-05 发布日期:2016-01-05
  • 通讯作者: 王小巧,男,1989年出生,博士研究生。主要研究方向为装配质量控制、制造过程监测与控制。 E-mail:wxq20061087@163.com
  • 基金资助:
    国家重点基础研究发展计划资助项目(973计划,2011CB013406)

Online Control Threshold Optimization for Complex Mechanical Products Assembly Process Based on Hybrid Genetic Particle Swarm Optimization

WANG Xiaoqiao,  LIU Mingzhou,  GE Maogen,  MA Jing,  LIU Conghu   

  1. School of Mechanical and automotive Engineering, Hefei University of Technology, Hefei 230009
  • Received:2015-01-08 Revised:2015-08-13 Online:2016-01-05 Published:2016-01-05

摘要: 为提高复杂机械产品的装配精度和服役安全性,提出一种多载荷作用下的基于混合粒子群算法的复杂机械产品装配过程质量控制点控制阈优化方法。分析复杂机械产品装配及服役的特点,定义关键质量控制点的多载荷影响因子;综合考虑装配质量损失成本和装配时间调整成本,建立基于多载荷影响因子的关键质量控制点装配损失-控制阈函数,揭示了质量控制点总损失与控制阈的关系;在保证装配精度的情况下,以总成本最小化为目标,构建多载荷影响因子的装配质量控制阈优化模型,并给出了基于混合粒子群算法的模型求解过程。最后以某型发动机的缸盖拧紧过程为例,将基于多载荷影响因子的模型求解出的产品装配方案结果与应用该方法之前装配结果做了对比分析,结果表明该方法能够显著提高装配质量及服役安全性能,为复杂机械产品装配质量控制阈优化提供了一种方法。

关键词: 多载荷影响因子, 混合粒子群算法, 控制阈优化, 装配损失-控制阈函数, 装配质量

Abstract: To improve assembly precision and service safety performance for complex mechanical products, an online control threshold optimization approach based on hybrid genetic particle swarm optimization is put forward. First, the characteristics of complex mechanical product assembly and service are analyzed, and the key quality control points’ multiple load influence factor is defined. Assembly quality loss cost and assembly time cost are considered, the assembly cost-control threshold function based on multiple load influence factor is established to describe the relationship between the assembly tolerance and the cost. In the case of ensuring the accuracy of the assembly, the online control threshold optimization model is established. And the process of hybrid genetic particle swarm optimization model for gaining the minimum assembly cost is given. Finally, an example of online tolerance optimization model in the assembly process of the engine cylinder head is given. The results show that the method can significantly improve the quality of assembly and service safety performance by comparing analysis the present method and the previous’, and  a practical method for complex mechanical product assembly quality online control is provided.

Key words: assembly cost-control threshold function, assembly quality, hybrid particle swarm optimization, multiple load influence factor, online control threshold optimization

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