Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L

Despite the many benefits of additive manufacturing, the final quality of the fabricated parts remains a barrier to the wide adoption of this technique in industry. Predicting the quality of parts using advanced machine learning techniques may improve the repeatability of results and make additive m...

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Hlavní autoři:	Hind Abdulla, Maher Maalouf, Imad Barsoum, Heungjo An
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2022-04-01
Edice:	Applied Sciences
Témata:	additive manufacturing selective laser melting porosity 316L stainless steel kernel ridge regression support vector regression
On-line přístup:	https://www.mdpi.com/2076-3417/12/9/4252
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