Evaluation of a Learning Tool for In-Situ Monitoring of Metal Additive Manufacturing

Brent Valle; Karel Lietaert; Natania Antler; John Newman; Erik Xiao; Sam Coeck

Working Paper Article Version 1 This version is not peer-reviewed

Version 1 : Received: 17 April 2019 / Approved: 19 April 2019 / Online: 19 April 2019 (11:18:13 CEST)

This paper describes a multi-channel in-situ monitoring system developed to better understand defect formation signatures in metal additive manufacturing. Three high-speed imaging modes coupled with an image computer capable of processing and storing these data streams allowed an examination of defect formations signatures and mechanisms. It was found that defects later detected in X-ray computed tomography (CT) scans were related to regions with anomalous heat signatures and powder bed morphology. Automated defect detection algorithms based on these defect signatures captured 80% of defects greater than 300 µm.

laser powder bed fusion; process monitoring; defect detection; coaxial

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Evaluation of a Learning Tool for In-Situ Monitoring of Metal Additive Manufacturing

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