Inversion prediction of back propagation neural network in collision analysis of anti-climbing device

Abstract

Targeting to improve the calculation efficiency of the finite element simulation, we introduce the back propagation neural network–based machine learning method to carry out the inversion prediction framework. The inversion collision model is established based on the inversion prediction framework. Then, the prediction results are compared with the finite element simulation results of the anti-climbing device to verify the feasibility of the inversion collision model. The average prediction errors of velocity, displacement, interface force, and internal energy of the anti-climbing device are 3.7%, 4.31%, 3.4%, and 1%, respectively, and the cost time of the inversion collision model is less than 5 min. The results show that the inversion collision model constructed by back propagation neural network can significantly improve the calculation efficiency and greatly reduce the calculation time under the condition of ensuring accuracy. It will provide a new evaluation method and possibility for partially replacing the required experimental and simulation results for the crashworthiness and the safety of the anti-climbing device.

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