Crack Length Prediction of Ultra-High Temperature Ceramics Based on SSA-BP Algorithm
The use of ultra-high temperature ceramic components in aerospace applications often presents difficulties in detection and failure within a certain range after cracks are produced in the component.This paper proposes a method based on the Sparrow Search Algorithm(SSA)to predict the crack length of ultra-high temperature ceramic components using traditional BP(back propagation)neural networks,which has a strong dependence on the connection weights and thresholds,resulting in slow convergence,easy to fall into the local optimum and poor stability.This paper presents a method for predicting crack length based on the SSA optimization of BP neural networks.This paper uses the parameters related to the crack length of ultra-high temperature ceramics derived from ABAQUS finite element analysis software as the base data set as the input to the model.The initial weights and thresholds of the BP neural network are optimized by using SSA to obtain better fitting results.The results show the feasibility of using SSA-BP neural for prediction.
ultra-high temperature ceramicscrack length predictionSSA-BPnumerical simulation
万方数据
维普
