Abstract Porosity produced in laser metal deposition hampers its application due to the absence of an effective prediction method. Measured thermal images of the melt pool provide a unique opportunity for porosity analytics. Furthermore, a physical model may provide complementary rich data that cannot be measured otherwise. How to leverage both types of data to predict porosity is very challenging. This paper presents a physics-driven deep learning model to predict porosity by integrating both measured and predicted data of the melt pool. The model fidelity is validated with the predicted pore occurrence and size with enhanced interpretability of Ti–6Al–4V thin-wall structures.

中文翻译：

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用于工艺-孔隙率因果关系和孔隙率预测的物理驱动深度学习模型，在激光金属沉积中具有可解释性

摘要 由于缺乏有效的预测方法，激光金属沉积中产生的孔隙阻碍了其应用。测得的熔池热图像为孔隙度分析提供了独特的机会。此外，物理模型可以提供其他方式无法测量的补充丰富数据。如何利用这两种类型的数据来预测孔隙度是非常具有挑战性的。本文提出了一种物理驱动的深度学习模型，通过整合熔池的测量数据和预测数据来预测孔隙度。模型保真度通过预测的孔隙发生和尺寸进行验证，增强了 Ti-6Al-4V 薄壁结构的可解释性。