China Mechanical Engineering ›› 2021, Vol. 32 ›› Issue (13): 1584-1590,1637.DOI: 10.3969/j.issn.1004-132X.2021.13.010

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Lightweight Optimization Design of Side Collision Safety Parts for BIW Based on Pareto Mining#br#

WANG Dengfeng;LI Shenhua   

  1. State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun,130022
  • Online:2021-07-10 Published:2021-07-16

基于Pareto挖掘的白车身侧碰安全件轻量化优化设计

王登峰;李慎华   

  1. 吉林大学汽车仿真与控制国家重点实验室,长春,130022
  • 通讯作者: 李慎华(通信作者),男,1991年生,博士研究生。研究方向为汽车轻量化设计理论与应用。E-mail:shli18@mails.jlu.edu.cn。
  • 作者简介:王登峰,男,1963年生,教授、博士研究生导师。研究方向为汽车轻量化设计理论与应用。发表论文200余篇。E-mail:caewdf@jlu.edu.cn。
  • 基金资助:
    国家自然科学基金(51975244)

Abstract: To improve the lightweight optimization effectiveness of BIWs, the entropy weight gray correlation analysis method was proposed to mine the optimal solutions in the non-dominated Pareto solution sets. The side impact finite element models of BIW and full vehicle were established, and the validity of the built model was verified by the side collision tests of vehicles. Taking the thicknesses of side impact safety parts as the design variables, considering the basic static-dynamic performance and side impact safety performance of BIW, the radial basis function neural network and Kriging(RBFNN-Kriging) hybrid approximate model combined with non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) was constructed for multi-objective optimization. Finally, entropy weight gray correlation analysis method was proposed for calculating the gray correlation grade of all non-dominated Pareto solutions, and the gray correlation grade was taken as evaluation index to use for multi-objective decision. The optimization decision results show that the mass of side impact safety parts for BIW is reduced by 2.68 kg under the requirements of BIW performance design baseline,the good lightweight effectiveness is achieved.

Key words:  , body-in-white(BIW), lightweight, multi-objective optimization, multi-objective decision, entropy weight gray correlation analysis

摘要: 为提高白车身轻量化优化效果,提出了熵权灰色关联分析法用于挖掘非支配Pareto解集中的最优解。建立了白车身及整车侧碰有限元模型,通过实车侧碰试验验证了所建模型的准确性。以侧碰安全件料厚为设计变量,综合考虑白车身弯扭刚度、振动频率等基本静-动态性能及侧碰安全性能,构建径向基函数神经网络结合Kriging(RBFNN-Kriging)混合近似模型并联合第二代非支配排序遗传算法(NSGA-Ⅱ算法)进行了多目标优化。最后,提出了熵权灰色关联分析法计算所有非支配Pareto解的灰色关联度,并以此为评价指标进行多目标决策。优化决策结果表明:在满足白车身性能设计基线的要求下,白车身侧碰安全件质量减小了2.68 kg,取得了较好的轻量化效果。

关键词: 白车身, 轻量化, 多目标优化, 多目标决策, 熵权灰色关联分析

CLC Number: