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Development and validation of a generalized, AI-based inline void defect detection solution for FSW based on force feedback

; ;

In
Welding in the world 69(2), Seiten/Artikel-Nr.:499-541

ImpressumBerlin ; Heidelberg : Springer

Umfang1-16

ISSN1878-6669

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

  1. Lehrstuhl und Institut für Schweißtechnik und Fügetechnik (417610)

Projekte

  1. DFG project G:(GEPRIS)390621612 - EXC 2023: Internet of Production (IoP) (390621612) (390621612)
  2. WS-B2.I - Connected Job Shop (X080067-WS-B2.I) (X080067-WS-B2.I)

Thematische Einordnung (Klassifikation)
DDC: 620

OpenAccess:
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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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Medline ; Creative Commons Attribution CC BY 4.0 ; OpenAccess ; 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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417610
 Record created 2024-12-16, last modified 2025-09-11
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