A prediction method for the crashworthiness of multi-cell tubes based on machine learning

This study presents a machine learning framework to predict the crashworthiness of multi-cell tubes. Five distinct cross-sectional designs are selected, and various structural configurations are generated by sampling predefined parameters. The training dataset is generated through finite element (FE) simulations. Using the autoencoder, structural features of the voxelized FE simulations are encoded into a one-dimensional latent space. When combined with thickness information, this latent representation provides a comprehensive description of the tube structure. The prediction model in this study is built using a MLP neural network, selected after a comparative analysis of multiple algorithms. The MLP demonstrates strong predictive capability, achieving errors of 14.21% for mean crushing force and 14.49% for peak crushing force. The method based on an autoencoder and MLP enables rapid and accurate prediction of the crashworthiness of multi-cell tubes. Compared to traditional finite element simulations, which require approximately 2.5 hours to evaluate a single sample, the MLP reduces the prediction time to just 0.079 seconds, significantly lowering computational costs and greatly accelerating the structural optimization process.