TY - GEN
T1 - Performance Prediction of Thin-Walled Tube Energy Absorbers Using Machine Learning
AU - Ghasemi, Mostafa
AU - Silani, Mohammad
AU - Yaghoubi, Vahid
AU - Concli, Franco
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Finite element method
KW - Machine learning
KW - Thin-walled energy absorbers
UR - http://www.scopus.com/inward/record.url?scp=85136999760&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-14317-5_8
DO - 10.1007/978-3-031-14317-5_8
M3 - Conference contribution
AN - SCOPUS:85136999760
SN - 9783031143168
T3 - Lecture Notes in Networks and Systems
SP - 87
EP - 99
BT - Managing and Implementing the Digital Transformation - Proceedings of the 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022
A2 - Matt, Dominik T.
A2 - Vidoni, Renato
A2 - Rauch, Erwin
A2 - Dallasega, Patrick
A2 - Matt, Dominik T.
PB - Springer
T2 - 1st International Symposium on Industrial Engineering and Automation, ISIEA 2022
Y2 - 21 June 2022 through 22 June 2022
ER -