A hybrid physics informed predictive scheme for predicting low-cycle fatigue life and reliability of aerospace materials under multiaxial loading conditions

Engineering components such as engine blades, turbofans, external parts, etc., are often subjected to complex loads in the serving environment. Fatigue failure of components under multiaxial loading will occur, causing a severe influence on operational safety. Centered on low-cycle fatigue under multiaxial loading conditions, we have developed a novel fatigue life prediction framework, which utilizes the physics-guided machine learning approach as a surrogate model for fatigue life prediction. We conducted preliminary experiments to obtain the material's mechanical properties and established reliable finite element analysis (FEA) models based on these properties. Subsequently, we generated high-confidence datasets using the FEA models. By leveraging the strengths of both deep learning methods and LightGBM, we proposed a fusion surrogate model called DL-LGBM-DRS. The DL-LGBM-DRS can efficiently and accurately predict low-cycle fatigue life under various multiaxial loading conditions. Lastly, we defined a new fatigue life degradation relationship, KBM-N, using Brown-Miller parameters and fitted probabilistic fatigue life degradation curves based on the KBM-N relations.