Gast ::
Development and validation of a generalized, AI-based inline void defect detection solution for FSW based on force feedback
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In
Welding in the world 69(2), Seiten/Artikel-Nr.:499-541
2024
Published: 11 December 2024
Online
DOI: 10.18154/RWTH-2024-12022
DOI: 10.1007/s40194-024-01895-2
URL: http://publications.rwth-aachen.de/record/999313/files/999313.pdf
Einrichtungen
Projekte
- DFG project G:(GEPRIS)390621612 - EXC 2023: Internet of Production (IoP) (390621612) (390621612)
- WS-B2.I - Connected Job Shop (X080067-WS-B2.I) (X080067-WS-B2.I)
Thematische Einordnung (Klassifikation)
DDC: 620
OpenAccess: 
PDF
Dokumenttyp
Journal Article
Format
online, print
Sprache
English
Anmerkung
Peer reviewed article
Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85212053036
WOS Core Collection: WOS:001376712700001
Interne Identnummern
RWTH-2024-12022
Datensatz-ID: 999313
Beteiligte Länder
Germany
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; Clarivate Analytics Master Journal List ; DEAL Springer ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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The record appears in these collections: |
PDF