Data-driven approach to very high cycle fatigue life prediction

The research on life prediction for mechanical structures in very high cycle fatigue regime is pivotal to improve structure service, but it can be costly and time-consuming to collect fatigue data. In response, the data-driven approach of machine learning emerged as a solution to data insufficiency. In this work, after extracting a small dataset of GCr15 bearing steel subjected to very high cycle fatigue tests from open literature, the Z-parameter model was applied to obtain extended datasets to establish models driven by support vector machine, artificial neural network, and Z-parameter based physics-informed neural network, respectively. With training on extended datasets and the original data as test set, fatigue life prediction for GCr15 steel was carried out and evaluated between these models. Results showed that the physics-informed neural network calibrated by Z-parameter model trained on a larger dataset featured more accurate and reliable prediction than other models did, which demonstrated effectiveness of Z-parameter in data extension and model construction as priori physics knowledge for a data-driven approach. Looking into the future, Z-parameter model deserves more attention to its employment in life prediction for more engineering materials and structures serving in the very high cycle fatigue regime.