A deep-learning-based in-situ surface anomaly detection methodology for laser directed energy deposition via powder feeding
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Title
A deep-learning-based in-situ surface anomaly detection methodology for laser directed energy deposition via powder feeding
Authors
Keywords
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Journal
Journal of Manufacturing Processes
Volume 81, Issue -, Pages 624-637
Publisher
Elsevier BV
Online
2022-07-19
DOI
10.1016/j.jmapro.2022.06.046
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- (2009) Almir Heralić et al. OPTICS AND LASERS IN ENGINEERING
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