Version 1
: Received: 17 April 2019 / Approved: 19 April 2019 / Online: 19 April 2019 (11:18:13 CEST)
How to cite:
Valle, B.; Lietaert, K.; Antler, N.; Newman, J.; Xiao, E.; Coeck, S. Evaluation of a Learning Tool for In-Situ Monitoring of Metal Additive Manufacturing. Preprints2019, 2019040217
Valle, B.; Lietaert, K.; Antler, N.; Newman, J.; Xiao, E.; Coeck, S. Evaluation of a Learning Tool for In-Situ Monitoring of Metal Additive Manufacturing. Preprints 2019, 2019040217
Valle, B.; Lietaert, K.; Antler, N.; Newman, J.; Xiao, E.; Coeck, S. Evaluation of a Learning Tool for In-Situ Monitoring of Metal Additive Manufacturing. Preprints2019, 2019040217
APA Style
Valle, B., Lietaert, K., Antler, N., Newman, J., Xiao, E., & Coeck, S. (2019). Evaluation of a Learning Tool for In-Situ Monitoring of Metal Additive Manufacturing. Preprints. https://doi.org/
Chicago/Turabian Style
Valle, B., Erik Xiao and Sam Coeck. 2019 "Evaluation of a Learning Tool for In-Situ Monitoring of Metal Additive Manufacturing" Preprints. https://doi.org/
Abstract
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.
Keywords
laser powder bed fusion; process monitoring; defect detection; coaxial
Subject
Computer Science and Mathematics, Software
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
