Predicting multiaxial fatigue life of FGH96 superalloy based on machine learning models by considering failure process and loading paths

Wide applications of powder metallurgy superalloys in mechanical engineering imply the great significance of studying their mechanical properties. Fatigue property of superalloys is complicated because of many influencing factors with variability. This means the difficulty to correctly predict a real-valued fatigue life due to the propagation of error. Specially, extreme dispersion of multiaxial fatigue life is an important challenge of fatigue life prediction for superalloys. In this paper, we focus on the difficulty and dispersion by developing machine learning (ML) models for predicting interval-valued fatigue life of FGH96 superalloy under multiaxial loadings. Some data extracted from fatigue failure process of FGH96 superalloy is used to develop ML methods such as back-propagation neural network (BP), support vector regression (SVR) and random forest (RF). Then considering the randomness in the data originating from multiaxial loading paths, measures of sample geometry and mechanical loading, testing system error and others, the low cycle fatigue life of FGH96 superalloy is predicted as an interval with a probability distribution. The obtained results are analyzed and compared with the fatigue experimental observations of FGH96 superalloy under six loading paths. It is found that the multiaxial fatigue failure behavior of FGH96 superalloy can be effectively described, and the developed ML models exhibit some advantages to tackle the difficulty and dispersion in predicting multiaxial fatigue life of alloys.