An unsupervised machine learning algorithm for in-situ defect-detection in laser powder-bed fusion

This research studies the development of a machine-learning algorithm to detect porosity induced during laser powder bed fusion (LPBF) due to the lack of fusion (LoF) phenomenon. The detection algorithm is based on analyzing in-situ light intensity data emitted from the melt pool during LPBF. A Self...

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Vydané v:	Journal of manufacturing processes Ročník 81; s. 476 - 489
Hlavní autori:	Taherkhani, Katayoon, Eischer, Christopher, Toyserkani, Ehsan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.09.2022
Predmet:	Additive manufacturing Clustering algorithm In-situ sensors Laser powder bed fusion Machine learning Quality assurance Laser powder bed fusion Quality assurance Additive manufacturing Clustering algorithm In-situ sensors Machine learning
ISSN:	1526-6125, 2212-4616
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