Comprehensive evaluation of energy absorption characteristics of Al‐CFRP hybrid tube under radial impacts

Abstract This paper aimed to investigate the energy absorption characteristics and damage mechanism of carbon fiber reinforced plastics/aluminum (Al‐CFRP) under radial impact. In this study, the damage mode of the hybrid tube is analyzed by falling hammer impact experiments, and the correctness of the finite element model is verified by comparing the experimental and numerical simulation results. Then, the damage mechanism of the hybrid tube under impact loading and the effects of the increase of the fiber angle, the wall thickness of Aluminum, and the number of outer CFRP layers on the impact resistance were analyzed according to the model. The comprehensive performance model of the hybrid tube was established by using the complex scale comprehensive evaluation method, and the genetic algorithm was used to optimize the hybrid tube and determine its optimal performance under radial impact load. The results show that the Al‐CFRP hybrid tube's overall stiffness and deformation resistance under radial impact is improved with the increase of the fiber angle, the wall thickness of the Al tube, and the number of outer CFRP layers. Combined with the parametric analysis of the numerical simulation results, a comprehensive performance evaluation model based on the complex proportionality assessment method (COPRAS) was developed and a genetic algorithm (GA) under back propagation (BP) neural network prediction was used. The optimal hybrid tube form with the best overall performance under radial impact was obtained using GA with BP neural network prediction. Highlights The energy absorption characteristics of hybrid tubes under radial impact are investigated A comprehensive performance evaluation model of hybrid pipe based on the complex proportional assessment method ( COPRAS ) is developed The failure process and energy absorption mechanism of the hybrid pipe under radial impact are analyzed A genetic algorithm ( GA ) with backpropagation ( BP ) neural network prediction was used to obtain the hybrid pipe form with the best overall performance under radial impact