摘要
建立了某轿车车门的有限元模型,利用灵敏度分析筛选出对模态和刚度性能比较敏感的关键钣件厚度作为设计变量,采用拉丁超立方试验设计进行样本设计,使用径向基函数(RBF)神经网络模型拟合车门质量、垂向刚度、侧向刚度及一阶模态频率响应的近似模型。以车门质量最小和侧向刚度最大为目标,以车门垂向刚度和一阶模态频率为约束,应用多目标遗传算法进行寻优,最终在满足模态和垂向刚度性能要求的前提下,侧向刚度提高了2.0%,车门质量减轻了1.7%,实现了车门的多目标优化设计。
The finite element model of a vehicle door was established.The method of sensitivity analysis was used to screen out key coin thickness which is sensitive to model and stiffness as the design variables.Latin hypercube experimental design method is used to design sample test.Radial basis function(RBF)neural network model is used to fit quality of the door,vertical stiffness,the lateral stiffness and first-order modal frequency response approximation model.With the minimum mass and maximum lateral stiffness of the door as the targets,and the vertical stiffness and first-order modal frequency as the constraints,the multi-objective genetic algorithm was applied for optimization.Finally,on the premise of satisfying the performance requirements of modal and vertical stiffness,the lateral stiffness was increased by 1.7%and the door mass was reduced by 2%.The multi-objective optimization of the door is realized.
作者
王凯迪
李迪
冷杨松
李孟迪
徐家川
姜宁
WANG Kaidi;LI Di;LENG Yangsong;LI Mengdi;XU Jiachuan;JIANG Ning(School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo 255049,China;School of Automotive Engineering,Linyi Vocational University of Science and Technology,Linyi 276000,China)
出处
《山东理工大学学报（自然科学版）》
CAS
2021年第2期77-82,共6页
Journal of Shandong University of Technology:Natural Science Edition
关键词
车门
多目标优化
径向基函数神经网络模型
灵敏度分析
拉丁超立方试验设计
vehicle door
multi-objective optimization
RBF neural network model
sensitivity analysis
Latin hypercube experimental design
