Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation

Increasing industry acceptance of powder bed metal Additive Manufacturing requires improved real-time detection and classification of anomalies. Many of these anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each lay...

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Vydáno v:	Additive manufacturing Ročník 36; číslo C; s. 101453
Hlavní autoři:	Scime, Luke, Siddel, Derek, Baird, Seth, Paquit, Vincent
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.12.2020 Elsevier
Témata:	Additive manufacturing Convolutional neural network ENGINEERING In-situ anomaly detection Machine learning Semantic segmentation Additive manufacturing Semantic segmentation In-situ anomaly detection Convolutional neural network Machine learning
ISSN:	2214-8604, 2214-7810
On-line přístup:	Získat plný text
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