; ;
In
17th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ‘23) / Edited by Roberto Teti Prof.
In
Procedia CIRP 126, Seiten/Artikel-Nr.:360-365
2024
Online
DOI: 10.18154/RWTH-2024-09667
DOI: 10.1016/j.procir.2024.08.376
URL: http://publications.rwth-aachen.de/record/995016/files/995016.pdf
Einrichtungen
Projekte
Inhaltliche Beschreibung (Schlagwörter)
ZSPNTF100 (frei)
Thematische Einordnung (Klassifikation)
DDC: 670
OpenAccess: PDF
Dokumenttyp
Journal Article (Review Article)/Contribution to a conference proceedings
Format
online
Sprache
English
Anmerkung
Peer reviewed article
Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85208593853
Interne Identnummern
RWTH-2024-09667
Datensatz-ID: 995016
Beteiligte Länder
Germany
Journal Article (Review Article)/Contribution to a book/Contribution to a conference proceedings
Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation
17. CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023, IschiaIschia, Italy, 12 Jul 2023 - 14 Jul 2023
Procedia CIRP 126, 360-365 (2024) special issue: "17th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME ‘23) / Edited by Roberto Teti Prof." (9798331306984)
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Conference Presentation
Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation
17. CIRP International Conference on Intelligent Computation in Manufacturing Engineering, ICME 2023, NeapelNeapel, Germany, 12 Jul 2023 - 14 Jul 2023
Files
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EndNote:
XML,
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