Fatigue Performance Prediction of Structural Materials by Multi-scale Modeling and Machine Learning

Structural materials having higher performance in strength, toughness, and fatigue resistance are strongly required. In the conventional materials development, many fatigue tests need to be conducted to validate statistical behavior of fatigue failure. Accordingly the evaluation of fatigue properties with shorter time becomes quite essential. Based on such background, we are developing fatigue prediction methods for wide range of structural materials by multi-scale finite element analysis (FEA) and machine learning in the Materials Integration (MI) system. The multi-scale FEA consists of the following procedures: (i) mechanical and thermal properties are estimated by using commercially available software and database; (ii) temperature field, residual stress and distortion generated during a manufacturing process is calculated on the macroscopic model by thermo-mechanical FEA; (iii) macroscopic stress field under cyclic loading condition is calculated with a hardening constitutive model; (iv) the microscopic stress field is derived by finite element model with the polycrystalline structures and the cycles for a fatigue crack initiation is analyzed by strain energy accumulation on the slip plane; (v) the cycles for fatigue crack propagation is analyzed by extended finite element method (X-FEM) and the total number of cycles to the failure is obtained. The second approach is to use machine learning techniques to obtain empirical prediction formula. The database was prepared from published resources and experiments. Deterministic machine learning techniques such as multivariate linear regression and artificial neural network provided accurate equations to predict fatigue strength from materials and process parameters. Additionally, the concept of model-based machine learning was adopted to incorporate prior knowledge of microstructures and properties, and to account for uncertainty on fatigue life. The results showed that model-based machine learning was a promising tool for predicting fatigue performance in structural materials. The features and limitations of our prediction methods will be discussed.