Predicting buckling of carbon fiber composite cylindrical shells based on backpropagation neural network improved by sparrow search algorithm

The buckling load of carbon fiber composite cylindrical shells (CF-CCSs) was predicted using a backpropagation neural network improved by the sparrow search algorithm (SSA-BPNN). Firstly, two CF-CCSs, each with an inner diameter of 100 mm, were manufactured and tested. The buckling behavior of CF-CCSs was analyzed by finite element and experiment. Subsequently, the effects of ply angle and length–diameter ratio on buckling load of CF-CCSs were analyzed, and the dataset of the neural network was generated using the finite element method. On this basis, the SSA-BPNN model for predicting buckling load of CF-CCS was established. The results show that the maximum and average errors of the SSABPNN to the test data are 6.88% and 2.24%, respectively. The buckling load prediction for CF-CCSs based on SSA-BPNN has satisfactory generalizability and can be used to analyze buckling loads on cylindrical shells of carbon fiber composites.