Performance Prediction of Thin-Walled Tube Energy Absorbers Using Machine Learning

This paper studies the behavior and response of triple thin-walled tubes with rectangular cross-sections under axial and dynamic loading. First, a finite element model of the energy absorber is prepared, and the results are validated with available theoretical and experimental studies. Then the effect of different input parameters such as tube-thickness, cross-sectional ratio, slope angle, and material parameters on the performance of thin-walled energy absorbers is studied.The simulation results show that the thickness of the tube has a more significant effect on the absorber's performance than other geometric parameters. The results also show that changing the cross-sectional ratio and the inclination angle of the tube changes the initial peak load more than the average load of the absorber. Comparing the effects of different materials on the performance of absorbers, the results show that steel alloys record the highest average loads and initial peak loads, followed by titanium alloys and then aluminum alloys. This information is then used to develop a machine learning model to predict the performance of the absorbers. Then, the performance of the machine learning developed in this study is evaluated, and it is shown that the developed machine learning can accurately predict the absorber's performance. Finally, a Sobol sensitivity analysis is performed on the machine-learned model and the results are compared with those of parametric study.