@misc{nielsen_automatic_melt_2022, 
author={Nielsen, M.,Gloy, J.,Lott, D.,Sun, T.,Müller, M.,Staron, P.},
title={Automatic melt pool recognition in X-ray radiography images from laser-molten Al alloy},
year={2022},
howpublished = {journal article},
doi = {https://doi.org/10.1016/j.jmrt.2022.10.121},
abstract = {Size and shape of the melt pool play an important role in the microstructure formation in materials additively manufactured by laser powder bed fusion (LPBF) techniques. It is an enormous challenge to determine them automatically in radiography image series taken during LPBF when the melt pool has a very low contrast to the surrounding base material. In this work, an approach solving this problem for Al alloys is presented. The melt pool is detected by a combination of different image processing methods and boundary conditions. The method developed in this work is demonstrated on high-speed radiography images taken at a synchrotron beamline during an in-situ LPBF experiment using an Al-Si alloy.},
note = {Online available at: \url{https://doi.org/10.1016/j.jmrt.2022.10.121} (DOI). Nielsen, M.; Gloy, J.; Lott, D.; Sun, T.; Müller, M.; Staron, P.: Automatic melt pool recognition in X-ray radiography images from laser-molten Al alloy. Journal of Materials Research and Technology : JMRT. 2022. vol. 21, 3502-3513. DOI: 10.1016/j.jmrt.2022.10.121}}