Assembly operation optimization based on hybrid particle swarm optimization and genetic algorithm

被引：0

作者：

Xing, Yan-Feng ^{[1
]}

Wang, Yan-Song ^{[1
]}

机构：

[1] Automotive Engineering College, Shanghai University of Engineering Science, Shanghai 201620, China

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2012年 / 18卷 / 04期

关键词：

Assembly - Particle swarm optimization (PSO) - Directed graphs - Quality control;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

To improve the auto-body assembly dimensional quality by assembly technology optimization, aiming at the geometric feasible assembly sequences for the auto-body, the precedence relationships and assembly control characteristics quantity between parts were described by multi-attribute directed graph to eliminate the unfeasible engineering assembly sequences. With assembly dimensional quality as the objective function, the hybrid particle swarm optimization and genetic algorithm was proposed to optimize the assembly operations between parts. The optimal assembly sequence was obtained through assembly variation propagating based on linear assembly variation analysis model. The optimization of assembly control characteristics was illustrated by auto-body side assembly. The results indicated that the selection of assembly control characteristics was affected by different assembly sequences, thus the final product assembly variation was affected.

引用

页码：747 / 753