同济大学学报（自然科学版）2024，Vol.52Issue(z1)：29-38,10.DOI:10.11908/j.issn.0253-374x.24777
基于机器学习的汽车吸能结构耐撞性智能预测方法
Machine Learning Method for Intelligent Prediction of the Crashworthiness of Automotive Energy Absorbing Boxes
摘要
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.关键词
汽车吸能盒/耐碰撞性/有限元分析/机器学习/图卷积神经网络/长短期记忆神经网络Key words
energy-absorbing box/crashworthiness/finite element simulation/machine learning/graph convolution networks/long short-term memory分类
交通工程引用本文复制引用
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(z1):29-38,10.基金项目
国家重点研发计划项目(2022YFE0208000) （2022YFE0208000）
