基于机器学习的汽车吸能结构耐撞性智能预测方法OA北大核心CSTPCD

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.