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基于机器学习的汽车吸能结构耐撞性智能预测方法
作者:
作者单位:

1.同济大学 汽车学院，上海 201804;2.同济大学 软件学院，上海 201804

作者简介:

贺宏伟（1999—），男，硕士研究生，主要研究方向为汽车轻量化及机器学习在汽车上的应用。E-mail：2233517@tongji.edu.cn

通讯作者:

余海燕（1976—），女，教授，博士生导师，工学博士，主要研究方向为汽车轻量化。E-mail：yuhaiyan@tongji.edu.cn

中图分类号:

U463;TP181

基金项目:

国家重点研发计划项目（2022YFE0208000）


Machine Learning Method for Intelligent Prediction of the Crashworthiness of Automotive Energy Absorbing Boxes
Author:
Affiliation:

1.School of Automotive Studies， Tongji University， Shanghai 201804， China;2.School of Software， Tongji University， Shanghai 201804， China

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    摘要:

    汽车零部件正向设计中，为快速预测所设计的吸能结构的碰撞吸能特性，以吸能盒为研究对象，通过有限元压溃变形仿真生成数据集，训练得到一种新的可识别几何结构和记忆时序特征的预测模型。模型通过基于图的编码器进行几何结构识别，采用长短期记忆网络和图卷积神经网络处理时序数据，并输出预测结果。对比表明：吸能盒压溃形态预测结果与有限元仿真结果一致，压溃变形量的预测精度可达95.33%，最大吸能值的预测精度可达99.98%。预测模型相较于有限元计算，其计算效率分别提高了174.5倍和210.5倍，可以快速准确地预测吸能盒的碰撞性能。

    Abstract:

    This study aims to achieve intelligent prediction of collision energy absorption characteristics of new structures in forward design of automotive parts. An energy-absorbing box is taken as the research object to generate training data sets by finite element crush deformation simulation. A graph-based encoder is adopted for geometric structure recognition. Long and short-term memory networks and graph convolutional neural networks were used to process adjacent temporal data. The novel neural network prediction system proposed can recognize geometric structures and memorize temporal data. The comparison between the model prediction results and simulation results shows that the predicted crush pattern of the energy-absorbing box is consistent with the finite element simulation results， and the prediction accuracy of the model for the crush deformation amount can reach up to 95.33%， while the prediction accuracy of the maximum energy absorption value can reach 99.98%. Compared with the finite element calculations， computational efficiency is 174.5 times and 210.5 times higher respectively， which manifested that the system can accurately and quickly predict the crash performance of the energy-absorbing box.

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贺宏伟,余海燕,高泽,饶卫雄.基于机器学习的汽车吸能结构耐撞性智能预测方法[J].同济大学学报(自然科学版),2024,52(S1):29~38

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  • 收稿日期:2023-12-01
  • 在线发布日期: 2024-11-20
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