Prediction of Rubber Fatigue Life Using an Assimilation-based Learning Approach and Incremental Crack Propagation Model

Abstract

Accurately and efficiently predicting the fatigue life of rubber materials has been a long-standing challenge due to limited understanding of the fatigue mechanism. In this study, a variational assimilation-based machine learning method assisted with incremental crack propagation model is proposed to predict the fatigue life of rubber materials. Firstly, according to the fracture mechanics theory, a new rubber fatigue life prediction model based on incremental crack propagation and sparse experimental data is established, which owns higher accuracy than the classical crack energy density model. Further, a rubber fatigue life solver coupled incremental crack propagation model and nonlinear finite element method is introduced to generate a dense fatigue life dataset of rubber materials with high accuracy. Finally, the artificial neural network model is trained, cross-validated, and tested using the dense dataset, and the three-dimensional variational assimilation model is employed to merge the predicted values of artificial neural network with experimental data. By comparing against the experimental data, the effectiveness of the proposed method was verified; thereby, we offer an accurate and efficient approach to predict the rubber fatigue life.