Automated flank wear segmentation and measurement with deep learning image processing
- Alternative title
-
Automatisierte Segmentation und Messung von Freiflächenverschleiß mit Deep Learning Bildverarbeitug
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
RWTH Aachen University, Dissertation, 2024
- Keyword
-
Werkzeugverschleiß
Spanende Bearbeitung
Hartmetallwerkzeug
Bildsegmentierung
Spanwerkzeug
Prozessmodell
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2025
- Creator
- Contributor
- DOI
-
10.18154/RWTH-2025-01423
- URN
-
urn:nbn:de:101:1-2503090043309.999805638969
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:25 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
- Bergs, Thomas
- Dix, Martin
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2025
Other Objects (12)
Acoustic emission signal source separation for a flank wear estimation of drilling tools
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
Analysis of tool wear in face hobbing plunging manufacturing processes
Analyse der Wirkmechanismen beim laserunterstützten Drehen von Siliziumnitridkeramik
Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation
Investigation of the influence of asymmetrical profiles on the wear behavior in gear hobbing
Material removal and grinding wheel wear mechanisms during grinding of polycrystalline cubic boron nitride
Machine Learning for Tool Wear Classification in Milling Based on Force and Current Sensors
Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement
Autoencoder based Wear Assessment in Sheet Metal Forming
Acoustic emission signal source separation for a flank wear estimation of drilling tools
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
Analysis of tool wear in face hobbing plunging manufacturing processes
Analyse der Wirkmechanismen beim laserunterstützten Drehen von Siliziumnitridkeramik
Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation
Investigation of the influence of asymmetrical profiles on the wear behavior in gear hobbing
Material removal and grinding wheel wear mechanisms during grinding of polycrystalline cubic boron nitride
Machine Learning for Tool Wear Classification in Milling Based on Force and Current Sensors
Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement
Autoencoder based Wear Assessment in Sheet Metal Forming
Acoustic emission signal source separation for a flank wear estimation of drilling tools
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
Analysis of tool wear in face hobbing plunging manufacturing processes
Analyse der Wirkmechanismen beim laserunterstützten Drehen von Siliziumnitridkeramik
Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation
Investigation of the influence of asymmetrical profiles on the wear behavior in gear hobbing
Material removal and grinding wheel wear mechanisms during grinding of polycrystalline cubic boron nitride
Machine Learning for Tool Wear Classification in Milling Based on Force and Current Sensors
Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement