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基于车身装配结构优化的改进图分解算法
引用本文:侯文彬,侯大军,徐金亭,张伟.基于车身装配结构优化的改进图分解算法[J].应用数学和力学,2015,36(5):515-522.
作者姓名:侯文彬  侯大军  徐金亭  张伟
作者单位:工业装备结构分析国家重点实验室(大连理工大学);大连理工大学 运载工程与力学学部 汽车工程学院, 辽宁 大连 116024
基金项目:国家自然科学基金(11272077);教育部留学回国人员科研启动基金
摘    要:在考虑车身制造和装配成本的前提下对车身装配结构优化方法进行了研究,提出一种改进的图分解算法将车身装配结构最优地分解为一组部件.以白车身侧围的装配模型为例,将结构的几何图形转化为与之对应的关系拓扑图,再分割该关系拓扑图为一组工程约束下的单连通不交叉子图集,结合遗传算法中的算子操作,利用有限单元法分析并计算得到产品几何图形的最优分割,采用NSGA-Ⅱ算法并实现该装配体综合性能最优的目标.

关 键 词:车身设计    装配结构    多目标优化    图分解
收稿时间:2014-09-24

A Modified Graph-Partitioning Algorithm for Vehicle Body Assembly Structure Optimization
HOU Wen-bin,HOU Da-jun,XU Jin-ting,ZHANG Wei.A Modified Graph-Partitioning Algorithm for Vehicle Body Assembly Structure Optimization[J].Applied Mathematics and Mechanics,2015,36(5):515-522.
Authors:HOU Wen-bin  HOU Da-jun  XU Jin-ting  ZHANG Wei
Institution:State Key Laboratory of Structural Analysis for Industrial Equipment(Dalian University of Technology); School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian, Liaoling 116024, P.R.China
Abstract:The optimization method for the vehicle body assembly structure was studied in view of the overall performance of manufacture and assembly. A modified graph-partitioning algorithm was proposed to optimally divide the vehicle body assembly structure into a set of components. A side frame model for the typical vehicle body in white was built as an example, in which the structure geometry was transformed to a topological graph at first, then the topological graph was partitioned into a set of simply connected and independent sub-graphs with constraints of engineering meanings. The genetic operators in algorithm NSGA-Ⅱ were combined with the FEM analysis to calculate the optimal partition of the product structure geometry. The results show that the proposed method realizes the objective of optimal comprehensive performance for the vehicle body assembly structure.
Keywords:vehicle body design  assembly structure  multi-objective optimization  graph partition
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