  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), which
instead of directly using machine setting parameters to predict porosity levels
of printed parts, it 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, 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.
