Defects in additively manufactured materials severely limit the performance of parts in practical applications, often exposing them to the risk of fatigue failure. In order to improve the reliability and performance of additively manufactured parts, it becomes crucial to accurately predict the fatigue life of the material. Although traditional semi-empirical formulas can assess the effect of defects on the fatigue performance of parts, they still lack detailed research and consideration of defect morphology features. Therefore, this study proposes a method based on Physics-Informed Neural Networks (PINN). This method improves the predictive capability of the model and enhances its interpretability by extracting the sensitive features of critical defects and embedding known physical knowledge or fracture mechanics methods as loss functions into the training process of the neural network. Additionally, the method effectively captures the complex relationship between defect features and fatigue life, providing a deeper understanding of the model prediction results. The results show that the PINN model considering feature-related knowledge has higher prediction accuracy and reliability, and all predicted fatigue life are narrowed within 2-factor bands, enabling more accurate prediction of fatigue life for SLM 316L stainless steel under different processing conditions.

中文翻译：

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基于缺陷特征预测 SLM 316L 不锈钢疲劳寿命的物理信息神经网络方法


增材制造材料的缺陷严重限制了零件在实际应用中的性能，常常使其面临疲劳失效的风险。为了提高增材制造零件的可靠性和性能，准确预测材料的疲劳寿命变得至关重要。传统的半经验公式虽然可以评估缺陷对零件疲劳性能的影响，但仍缺乏对缺陷形貌特征的详细研究和考虑。因此，本研究提出了一种基于物理信息神经网络（PINN）的方法。该方法通过提取关键缺陷的敏感特征并将已知的物理知识或断裂力学方法作为损失函数嵌入到神经网络的训练过程中，提高了模型的预测能力并增强了其可解释性。此外，该方法有效地捕捉了缺陷特征与疲劳寿命之间的复杂关系，提供了对模型预测结果的更深入的理解。结果表明，考虑特征相关知识的PINN模型具有更高的预测精度和可靠性，所有预测的疲劳寿命都收窄在2因子带内，能够更准确地预测SLM 316L不锈钢在不同加工条件下的疲劳寿命。