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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of manufacturing processes Jg. 81; S. 476 - 489
Hauptverfasser:	Taherkhani, Katayoon, Eischer, Christopher, Toyserkani, Ehsan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.09.2022
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!