Predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy using backpropagation neural network optimized by the improved dung beetle algorithm

Abstract

AbstractIn this study, we propose an innovative approach that enhances the performance of the backpropagation (BP) neural network in predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy by improving the dung beetle optimization (DBO) algorithm with the maximin Latin hypercube design (MLHD) strategy. To address the challenges posed by complex geometric components under different temperature conditions, this research employs finite element simulation to expand the limited experimental dataset and utilizes these data to further guide and optimize the MLHD_DBO_BP model. Test results indicate that the proposed MLHD_DBO_BP model significantly outperforms the traditional finite element method (FEM) and other neural network models in terms of fatigue life prediction performance. This research demonstrates the effectiveness of machine learning models that combine experimental and simulation data in predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy.