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An increasing number of laser powder bed fusion machines use off-axisinfrared cameras to improve online monitoring and data-driven controlcapabilities. However, there is still a severe lack of algorithmic solutions toproperly process the infrared images from these cameras that has led to severalkey limitations: a lack of online monitoring capabilities for the laser tracks,insufficient pre-processing of the infrared images for data-driven methods, andlarge memory requirements for storing the infrared images. To address theselimitations, we study over 30 segmentation algorithms that segment eachinfrared image into a foreground and background. By evaluating each algorithmbased on its segmentation accuracy, computational speed, and spatter detectioncharacteristics, we identify promising algorithmic solutions. The identifiedalgorithms can be readily applied to the laser powder bed fusion machines toaddress each of the above limitations and thus, significantly improve processcontrol.

Towards Online Monitoring and Data-driven Control: A Study of Segmentation Algorithms for Laser Powder Bed Fusion Processes