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

Date
2022
Authors
Nettekoven, Alexander
Fish, Scott
Beaman, Joseph
Topcu, Ufuk
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Abstract

An increasing number of laser powder bed fusion machines use off-axis infrared cameras to improve online moni- toring and data-driven control capabilities. However, there is still a severe lack of algorithmic solutions to properly process the infrared images from these cameras, which has led to several key limitations: a lack of online moni- toring 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 iden- tify 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.

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