Integrated physics-informed learning and resonance process signature for the prediction of fatigue crack growth for laser-fused alloys

Abstract

Fatigue behaviors of metal components by laser fusion suffer from scattering due to random geometrical defects (e.g., porosity, lack of fusion). Monitoring fatigue crack initiation and growth is critical, especially for laser-fused components with significant inherent fatigue scattering. Conventional statistics-based curve-fitting fatigue models have difficulty incorporating significant scattering in their fatigue life due to the random geometrical defects. A scattering-informed predictive method is needed for laser-fused materials' crack size and growth. Current data-driven machine learning could circumvent the issue of deterministic modeling, but results in a black-box function that lacks interpretability. To address these challenges, this study explores a novel nondimensionalized physics-informed machine learning (PIML) model to predict fatigue crack growth of laser-fused SS-316L by integrating fatigue laws and constraints with small data to ensure a realistic and interpretable prediction. Resonance process signature data were leveraged with Paris's law to train the PIML model without experimental crack growth data. The results show that Paris's law constants can be learned with good similarity to comparable data from the literature, and the crack growth rate can be predicted to compute crack sizes.