摘要
后纵臂是汽车底盘的主要结构之一.采用碳纤维复合材料设计后纵臂可以有效减重.然而复合材料的应用也给其优化设计带来了很大的挑战,如复杂的多工况和大量的设计变量.使用Python语言对ABAQUS二次开发,对于各角度铺层占比进行全局遍历得出其有效解与最优解.为了解决多工况下运算时间长的问题,将基于树的算法模型,如XGBoost、DART、随机森林,引入到各铺层角度占比设计的计算中.同时考虑到计算量和计算准确率两者的关系,在新工况计算量在0条和10条的情况下,Tsai-Wu因子的准确率分别可以达到96.3%和98.3%(与失效值1相比).在将已有工况数据量减少一半的情况下,如果提高新工况计算量到40条,准确率可以达到95.0%.为多工况下碳纤维复合材料零件轻量化计算提供了有益的参考.
The rear longitudinal arm is one of the main structures of the automobile chassis. Design of the rear longitudinal arm with carbon fiber reinforced polymer（ CFRP） can reduce its weight effectively.However,the application of composite materials also brings great challenges to the optimization design process,such as complex multiple conditions and a large number of design variables. The secondary development of ABAQUS was conducted with Python to fulfill the global ergodic search for thickness ratios of different ply angles to find the effective range and the optimum solution. In order to reduce the long running time under multi working conditions,the tree-based algorithms,such as XGBoost,DART and random forest,were introduced into the thickness ratio calculation. In view of both the running time and the computation accuracy,for 0 or 10 cases of calculation under the new condition,the accuracy rate of the Tsai-Wu factor can reach 96.3% and 98.3%（ compared with failure value 1）. If the number of cases under new working conditions increases to 40 while existing working conditions decreases by half,the accuracy rate can reach 95.0%. The developed algorithm provides a useful reference for reducing the running time of optimization design of composite parts under multi working conditions.
作者
朱迪
姚远
彭雄奇
ZHU Di;YAO Yuan;PENG Xiongqi(School of Materials Science and Engineering,Shanghai Jiao Tong University,Shanghai 200030,P.R.China)
出处
《应用数学和力学》
CSCD
北大核心
2018年第8期925-934,共10页
Applied Mathematics and Mechanics
关键词
汽车底盘纵臂
碳纤维复合材料
ABAQUS二次开发
基于树的模型
铺层优化
automobile chassis longitudinal arm
carbon fiber reinforced composite
seconda-ry development of ABAQUS
tree based model
ply optimization
