To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machine setting parameters, such as laser angle and part pose. However, these setting-based models are machine dependent; hence, they often do not transfer to analysis of porosity for a different machine. To address the first problem, a physics-informed, data-driven model (PIM) is used, which instead of directly using machine setting parameters to predict porosity levels of printed parts, first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure. Then, these physical, machine-independent effects are used to predict porosity levels according to “pass,” “flag,” and “fail” categories instead of focusing on quantitative pore size prediction. With six learning methods’ evaluation, PIM proved to achieve good performances with prediction error of 10\(\sim \)26%. Finally, pore-encouraging influence and pore-suppressing influence were analyzed for quality analysis.

为了控制零件质量，分析孔隙产生机理至关重要，为将来的孔隙率控制奠定理论基础。当前的孔隙度分析模型使用机器设置参数，例如激光角度和零件姿态。但是，这些基于设置的模型取决于机器。因此，它们通常不会转移到另一台机器的孔隙率分析中。为了解决第一个问题，使用了一种基于物理的数据驱动模型（PIM），该模型不是直接使用机器设置参数来预测印刷零件的孔隙度，而是先将机器设置解释为物理效应，例如激光能量密度。和激光辐射压力。然后，这些与机械无关的物理效应将根据“通过”，“标记”，”和“失败”类别，而不是专注于定量孔径预测。通过六种学习方法的评估，PIM被证明具有良好的性能，预测误差为10\（\ sim \） 26％。最后，分析了鼓励毛孔的作用和抑制毛孔的作用，以进行质量分析。