灰色关联分析
计算机科学
托普西斯
替代模型
数学优化
多目标优化
遗传算法
最优化问题
参数统计
数学
算法
机器学习
统计
运筹学
作者
Shuai Zhang,Hao Song,Kefang Cai,Liyou Xu
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 67413-67436
被引量:6
标识
DOI:10.1109/access.2022.3185412
摘要
In order to improve the lightweight level, crash safety performance and optimization design efficiency of body-in-white (BIW), this article proposes a lightweight multi-objective optimization design method for mixed-material body. The implicit parametric model of the BIW is created by using SFE-CONCEPT software, and the validity and correctness of the model are verified by tests. Material, shape and dimension parameters are introduced as design variables for design of experiments (DOEs), and 26 important design variables are screened out by combining contribution analysis with nonlinear main effects analysis. The approximate model method is used to fit the Kriging surrogate model and the RBF surrogate model, and it is found that the RBF surrogate model can better reflect the relationship between nonlinear crash performance and optimization variables. A hybrid method combined entropy weighted grey relational analysis (EGRA) with modified non-dominated sorting genetic algorithm (MNSGA-II) is proposed to carry out the lightweight multi-objective optimization of BIW in front crash and side impact, which improves the population diversity of multi-objective optimization problems and quantifies the comprehensive performance of each scheme. Comparing and analyzing the optimization platform recommending scheme, the technique for order preference by similarity to an ideal solution (TOPSIS) method preferring scheme and the EGRA method optimum scheme, it is found that EGRA method can obtain the optimal compromise scheme, and the performance improvement of the BIW are more obvious and the improvement rates are also more balanced. The results verify the feasibility of the ranking method, avoid the blindness of optimal solution selection, and establish an objective evaluation method of multi-objective optimization results. The optimization results show that the improvement rates of the BIW lightweight coefficient, the average value of the maximum acceleration of the B-pillars on both sides during the front crash, and the maximum intrusion displacement of the B-pillar chest during the side impact have reached 11.5%, 6.5%, and 6.8%, respectively. Other performance response improvement rates are also above 3.3%, the lightweight and crash safety performance are significantly improved.
科研通智能强力驱动
Strongly Powered by AbleSci AI