Defect identification of metal additive manufacturing parts based on laser-induced breakdown spectroscopy and machine learning (Englisch)
- Neue Suche nach: Lin, Jingjun
- Neue Suche nach: Yang, Jiangfei
- Weitere Informationen zu Yang, Jiangfei:
- https://orcid.org/0000-0002-0631-8656
- Neue Suche nach: Huang, Yutao
- Neue Suche nach: Lin, Xiaomei
- Neue Suche nach: Lin, Jingjun
- Neue Suche nach: Yang, Jiangfei
- Weitere Informationen zu Yang, Jiangfei:
- https://orcid.org/0000-0002-0631-8656
- Neue Suche nach: Huang, Yutao
- Neue Suche nach: Lin, Xiaomei
In:
Applied Physics B
;
127
, 12
;
2021
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ISSN:
- Aufsatz (Zeitschrift) / Elektronische Ressource
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Titel:Defect identification of metal additive manufacturing parts based on laser-induced breakdown spectroscopy and machine learning
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Beteiligte:Lin, Jingjun ( Autor:in ) / Yang, Jiangfei ( Autor:in ) / Huang, Yutao ( Autor:in ) / Lin, Xiaomei ( Autor:in )
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Erschienen in:Applied Physics B ; 127, 12
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Verlag:
- Neue Suche nach: Springer Berlin Heidelberg
- Neue Suche nach: Springer
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Erscheinungsort:Berlin
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Erscheinungsdatum:2021
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ISSN:
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ZDBID:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
- Neue Suche nach: 33.18$jOptik / 33.38 / 50.37$jTechnische Optik / 33.18 / 50.37 / 33.38$jQuantenoptik$jnichtlineare Optik
- Weitere Informationen zu Basisklassifikation
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Schlagwörter:
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Klassifikation:
BKL: 33.18$jOptik / 33.38 Quantenoptik, nichtlineare Optik / 50.37$jTechnische Optik / 33.18 Optik / 50.37 Technische Optik / 33.38$jQuantenoptik$jnichtlineare Optik -
Datenquelle:
Inhaltsverzeichnis – Band 127, Ausgabe 12
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