基于深度学习的商用车前轴结构拓扑优化应用  

Application of Deep Learning in Topological Optimization for Structure of Commercial Vehicle Front Axle

作　　者：张东东[1,2,3] 张乐迪 李亮亮 姬晨阳 赵礼辉 ZHANG Dongdong;ZHANG Ledi;LI Liangliang;JI Chenyang;ZHAO Lihui(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;CMIF Key Lab for Automotive Strength&Reliability Evaluation,Shanghai 200093,China;Shanghai Technology Service Platform of Reliability Evaluation for New Energy Vehicle,Shanghai 200093,China;Zhucheng Yihe Alxes Company Technical Center,Zhucheng Shandong 262200,China)

机构地区：[1]上海理工大学机械工程学院,上海200093 [2]机械工业汽车机械零部件强度与可靠性评价重点实验室,上海200093 [3]上海市新能源汽车可靠性评价专业技术服务平台,上海200093 [4]义和车桥有限公司技术中心,山东诸城262200

出　　处：《机械设计与研究》2024年第4期104-110,共7页Machine Design And Research

摘　　要：汽车轻量化是促进节能减排最重要的手段之一。前轴作为商用车的簧下零部件,轻量化设计的节能减排效果尤为明显,对提升整车的市场竞争力具有重要意义。以商用车前轴为对象,利用深度学习技术进行拓扑优化,实现轻量化设计。首先,确定前轴轻量化设计目标,并借助Optistruct平台进行拓扑优化可行性分析;然后,基于Unet网络,构建适用于多工况输入的Res-CB-Unet卷积神经网络;接着,考虑前轴载荷、尺寸变化,以静力学分析结果构建网络输入数据集,以对应的拓扑构型构建标签数据集;最后,采用Adam算法训练得到用于预测前轴拓扑构型的网络模型。算例模型评估结果表明,提出的方法能够快速有效地生成前轴的拓扑构型,可获得比传统拓扑结果更优的构型,计算效率显著提升。该方法可为汽车典型结构的快速轻量化设计提供技术支持和实施路径。Promoting energy efficiency and emission reduction is crucial in the automotive industry,and lightweight design is a key strategy.Commercial vehicles benefit significantly from the lightweight designof the front axle,employedas a critical suspension component.This paper focuses on deep learning for topological optimization to achieve lightweight design for front axles.The approach involves establishing lightweight design objectives and performing a feasibility analysis using topological optimization through the Optistruct platform.A Res-CB-Unet convolutional neural network,based on the Unet architecture,is then constructed.The network is trained using input parameters derived from static analysis results of the initial front axle structure,considering factors such as load and dimensional variations.A corresponding dataset of labeled topological configurations for the front axle is generated.The results of this study demonstrate that the proposed method rapidly and effectively predicts the front axle's topological configuration,potentially outperforming traditional methods,thus significantly enhancing computational efficiency.This approach provides valuable technical support and a practical pathway for efficient lightweight designof typical automotive structures.

关 键 词：前轴 轻量化设计 深度学习 拓扑优化 Res-CB-Unet网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] U463[自动化与计算机技术—控制科学与工程]