  Laser powder bed fusion (LPBF) process can incur defects due to melt pool
instabilities, spattering, temperature increase, and powder spread anomalies.
Identifying defects through in-situ monitoring typically requires collecting,
storing, and analyzing large amounts of data generated. The first goal of this
work is to propose a new approach to accurately map in-situ data to a
three-dimensional (3D) geometry, aiming to reduce the amount of storage. The
second goal of this work is to introduce several new IR features for defect
detection or process model calibration, which include laser scan order, local
preheat temperature, maximum pre-laser scanning temperature, and number of
spatters generated locally and their landing locations. For completeness,
processing of other common IR features, such as interpass temperature, heat
intensity, cooling rates, and melt pool area, are also presented with the
underlying algorithm and Python implementation. A number of different parts are
printed, monitored, and characterized to provide evidence of process defects
and anomalies that different IR features are capable of detecting.
