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.

翻译：激光熔融金属部件的疲劳行为因随机几何缺陷（如孔隙、未熔合）而存在显著分散性。监测疲劳裂纹萌生与扩展至关重要，尤其对于具有显著固有疲劳分散性的激光熔融部件。传统基于统计的曲线拟合疲劳模型难以纳入由随机几何缺陷引起的显著分散性，从而影响其疲劳寿命预测。针对激光熔融材料，需要一种考虑分散性的裂纹尺寸与扩展预测方法。当前数据驱动的机器学习虽能规避确定性建模的局限，但会生成缺乏可解释性的黑箱函数。为应对这些挑战，本研究探索了一种新颖的无量纲化物理信息机器学习模型，通过将疲劳定律与约束条件与小样本数据相结合，以预测激光熔融SS-316L的疲劳裂纹扩展，确保预测结果既符合实际又具备可解释性。研究利用共振过程特征数据与帕里斯定律训练PIML模型，无需实验裂纹扩展数据。结果表明，该模型能够学习到与文献可比数据高度相似的帕里斯定律常数，并可预测裂纹扩展速率以计算裂纹尺寸。