A physics-informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy

The exploitation of fatigue life prediction methods based on fatigue indicator parameters revealed the influence of the defect size, position, and morphology on the fatigue life and fatigue behavior of additively manufactured metals. Meanwhile, Data-driven life prediction methods are time-efficient but inexplainable. Current machine learning-based fatigue life prediction methods call for not only the accuracy but also the interpretability and stability of prediction results. Thus, the fusion of physical methods and machine learning methods has been a prevailing research topic in fatigue life prediction. In this study, a novel physics-informed neural network framework is proposed by integrating a fatigue indicator parameter based on defects into the physical constraints term of a loss function. This method outperforms conventional machine learning methods in high-cycle and very high-cycle regimes, exhibiting superior prediction performance and generalization ability. Furthermore, the prediction results can be explained from a physical standpoint, correlating with the applicability range of the introduced physical equation describing defect positions.