  Laser powder bed fusion (LPBF) has shown promise for wide range of
applications due to its ability to fabricate freeform geometries and generate a
controlled microstructure. However, components generated by LPBF still possess
sub-optimal mechanical properties due to the defects that are created during
laser-material interactions. In this work, we investigate mechanism of spatter
formation, using a high-fidelity modelling tool that was built to simulate the
multi-physics phenomena in LPBF. The modelling tool have the capability to
capture the 3D resolution of the meltpool and the spatter behavior. To
understand spatter behavior and formation, we reveal its properties at ejection
and evaluate its variation from the meltpool, the source where it is formed.
The dataset of the spatter and the meltpool collected consist of 50 % spatter
and 50 % melt pool samples, with features that include position components,
velocity components, velocity magnitude, temperature, density and pressure. The
relationship between the spatter and the meltpool were evaluated via
correlation analysis and machine learning (ML) algorithms for classification
tasks. Upon screening different ML algorithms on the dataset, a high accuracy
was observed for all the ML models, with ExtraTrees having the highest at 96 %
and KNN having the lowest at 94 %.
