A data-assisted physics-informed neural network (DA-PINN) for fretting fatigue lifetime prediction

In this study, we present for the first time the application of physics-informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of fretting fatigue. We propose a data-assisted PINN (DA-PINN) for predicting fretting fatigue crack initiation lifetime. Unlike traditional PINN that solves partial differential equations for specific problems, DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy. The DA-PINN method, employed in this study, consists of two main steps. First, damage parameters are obtained from the finite element method by using critical plane method, which generates a data set used to train an artificial neural network (ANN) for predicting damage parameters in other cases. Second, the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models, which predict fretting fatigue lifetime. The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed. Additionally, the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.