Low cycle fatigue life prediction of titanium alloy using genetic algorithm-optimized BP artificial neural network

This paper proposes a novel approach for estimating titanium alloy structural parts' low cycle fatigue (LCF) life based on the continuous damage mechanics (CDM) model. Using a genetic algorithm-optimized back -propaga-tion artificial neural network (GABP-ANN), the LCF life of titanium alloy structural parts is accurately predicted. Firstly, experimental data and finite element simulation data are combined to constitute a fatigue life database, and the relative error is taken as the training target for the BP neural network. The relationship is established between the relative error and the initial parameters (such as hidden layer nodes and learning rate). Secondly, the GABP-ANN model improves the shortage of the BP model at randomly selected initial training parameters and further enhances the predictive performance. Then, the accuracy and stability of five titanium alloy struc-tural parts under two machine learning (ML) models are analyzed. The experimental values, finite element model (FEM) simulated values, and predicted values of the two ML models are compared and analyzed. It is found that the GABP-ANN model has a significant advantage in predicting the fatigue life of titanium alloy structural parts. Finally, it is verified that the proposed ML models have better data learning ability. The above results indicate that the GABP-ANN technique provides a highly accurate and stable method for predicting the LCF life of tita-nium alloy structural parts.

Automated summary

This paper proposes a novel approach for estimating the low cycle fatigue (LCF) life of titanium alloy structural parts based on the continuous damage mechanics (CDM) model. The genetic algorithm-optimized back-propagation artificial neural network (GABP-ANN) accurately predicts the LCF life of titanium alloy structural parts.