  Addressing the uncertainty and variability in the quality of 3D printed
metals can further the wide spread use of this technology. Process mapping for
new alloys is crucial for determining optimal process parameters that
consistently produce acceptable printing quality. Process mapping is typically
performed by conventional methods and is used for the design of experiments and
ex situ characterization of printed parts. On the other hand, in situ
approaches are limited because their observable features are limited and they
require complex high-cost setups to obtain temperature measurements to boost
accuracy. Our method relaxes these limitations by incorporating the temporal
features of molten metal dynamics during laser-metal interactions using video
vision transformers and high-speed imaging. Our approach can be used in
existing commercial machines and can provide in situ process maps for efficient
defect and variability quantification. The generalizability of the approach is
demonstrated by performing cross-dataset evaluations on alloys with different
compositions and intrinsic thermofluid properties.
