  An increasing number of laser powder bed fusion machines use off-axis
infrared cameras to improve online monitoring and data-driven control
capabilities. However, there is still a severe lack of algorithmic solutions to
properly process the infrared images from these cameras that has led to several
key limitations: a lack of online monitoring capabilities for the laser tracks,
insufficient pre-processing of the infrared images for data-driven methods, and
large memory requirements for storing the infrared images. To address these
limitations, we study over 30 segmentation algorithms that segment each
infrared image into a foreground and background. By evaluating each algorithm
based on its segmentation accuracy, computational speed, and spatter detection
characteristics, we identify promising algorithmic solutions. The identified
algorithms can be readily applied to the laser powder bed fusion machines to
address each of the above limitations and thus, significantly improve process
control.
