Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: 10.1016/j.ifacol.2022.04.249
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2022
- Creator
- DOI
-
10.18154/RWTH-2022-04841
- URN
-
urn:nbn:de:101:1-2022051906382497416433
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:33 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Holst, Carsten
- Yavuz, Taha Berk
- Gupta, Pranjul
- Ganser, Philipp
- Bergs, Thomas
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2022
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Untersuchungen zum Verformungs- und Lastabtragungsverhalten einer Kaikonstruktion
Untersuchungen zum Verformungs- und Lastabtragungsverhalten einer Kaikonstruktion
Digital image processing with deep learning for automated cutting tool wear detection
Advanced calculation of the stress distribution in milling tools during cutting under consideration of residual stresses and tool wear
Experimental and Finite Element Analysis of adapted cutting fluid supply on tool temperature and wear progression in Inconel 718 turning
Considering multiple process observables to determine material model parameters for FE-cutting simulations
Considering multiple process observables to determine material model parameters for FE-cutting simulations
Experimental investigation of specific cutting forces and estimation of the heat partitioning under increasing tool wear in machining nickel-based super alloy IN 718
Inverse Determination of the Johnson-Cook Material Model Parameters of Aluminum Al 6060 from Orthogonal Cutting Examinations
Additively manufactured milling tool with focused cutting fluid supply
Analysis of tool wear in face hobbing plunging manufacturing processes
A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear
Untersuchungen zum Verformungs- und Lastabtragungsverhalten einer Kaikonstruktion
Untersuchungen zum Verformungs- und Lastabtragungsverhalten einer Kaikonstruktion
Digital image processing with deep learning for automated cutting tool wear detection
Advanced calculation of the stress distribution in milling tools during cutting under consideration of residual stresses and tool wear
Experimental and Finite Element Analysis of adapted cutting fluid supply on tool temperature and wear progression in Inconel 718 turning
Considering multiple process observables to determine material model parameters for FE-cutting simulations
Considering multiple process observables to determine material model parameters for FE-cutting simulations
Experimental investigation of specific cutting forces and estimation of the heat partitioning under increasing tool wear in machining nickel-based super alloy IN 718
Inverse Determination of the Johnson-Cook Material Model Parameters of Aluminum Al 6060 from Orthogonal Cutting Examinations
Additively manufactured milling tool with focused cutting fluid supply
Analysis of tool wear in face hobbing plunging manufacturing processes