汽车工程 ›› 2024, Vol. 46 ›› Issue (7): 1273-1281.doi: 10.19562/j.chinasae.qcgc.2024.07.014

• • 上一篇    

基于数据驱动的车身结构智能设计与分析

曹榕1,华钧伟1,李永成1,郭方俐1,侯文彬1,2()   

  1. 1.大连理工大学汽车工程学院,大连 116024
    2.大连理工大学宁波研究院,宁波 315016
  • 收稿日期:2023-09-24 修回日期:2023-12-29 出版日期:2024-07-25 发布日期:2024-07-22
  • 通讯作者: 侯文彬 E-mail:houwb@dlut.edu.cn
  • 基金资助:
    国家自然科学基金(52072057);宁波市重点研发计划项目(2023Z065)

Intelligent Design and Analysis of Body Structure Based on Data Drive

Rong Cao1,Junwei Hua1,Yongcheng Li1,Fangli Guo1,Wenbin Hou1,2()   

  1. 1.School of Automotive Engineering,Dalian University of Technology,Dalian  116024
    2.Ningbo Research Institute,Dalian University of Technology,Ningbo  315016
  • Received:2023-09-24 Revised:2023-12-29 Online:2024-07-25 Published:2024-07-22
  • Contact: Wenbin Hou E-mail:houwb@dlut.edu.cn

摘要:

概念设计作为汽车设计流程的重要阶段,需要快速地进行方案设计和方案评估。目前一般采用参数化设计和CAE相结合的方法,实现基于分析的车身结构概念设计。随着机器学习和深度学习算法的发展和成熟,智能设计方法将成为车身结构设计主要创新技术。本文使用数据驱动和优化设计相结合的方法,自主研发了车身结构智能设计软件工具(S-iVCD)。首先,基于残差网络和热力图回归算法提取车身结构特征点,实现车身结构概念模型的自动化建模。其次,基于高斯过程采样收集车身结构数据集,采用全连接神经网络模型构建了车身结构网络模型,通过将车身各部件参数输入训练好的网络模型,实时得到车身整体性能的结果。最后,将数据驱动计算与移动渐近线算法结合,快速实现考虑质量、弯曲刚度和扭转刚度的车身结构多目标优化设计。通过与有限元实例对比,计算结果的误差在允许范围内,优化计算时间大为缩短,轻量化率达到了7.4%。由此表明基于数据驱动的车身结构优化设计方法对于汽车概念设计阶段提高效率是有效的。

关键词: 车身结构设计, 轻量化, 数据驱动分析, 全连接神经网络

Abstract:

As an important stage of the automotive design process, conceptual design requires rapid conceptual design and evaluation. The current methods generally use a combination of parametric design and CAE to achieve analysis based conceptual design of car body structures. With the development and maturity of machine learning and deep learning algorithms, intelligent design methods will become the main innovative technology for body structure design. In this article, a combination of data-driven and optimization design method is used to independently develop the vehicle structure intelligent design software tool (S-iVCD). Firstly, based on residual networks and thermal map regression algorithms, feature points of the vehicle body structure are extracted to achieve automated modeling of the conceptual model of the vehicle body structure. Secondly, based on Gaussian process sampling, a body structure dataset is collected and a fully connected neural network model is used to construct the body structure network model. The parameters of various components of the vehicle body can be input into the trained network model to obtain the overall performance results of the vehicle body. Finally, by combining data-driven computing with the moving asymptote algorithm, a multi-objective optimization design of the vehicle body structure that considers mass, bending stiffness, and torsional stiffness is quickly achieved. By comparing with finite element examples, the error of the calculation results is within the allowable range, with the optimization calculation time greatly shortened, and the lightweight rate reaching 7.4%. This indicates that the data-driven body structure optimization design method is effective in improving efficiency in the conceptual design stage of automobiles.

Key words: body structure design, lightweighting, data-driven analysis, fully connected neural networks