On-Machine Flank Wear Analysis in Cutting Tools Using Artificial Intelligence
Karimi, Ehsan
1; Schindler, Florian Alexander 1; Schulze, Volker 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)
1; Schindler, Florian Alexander 1; Schulze, Volker 11 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)
Abstract (englisch):
This study introduces an in-line on-machine monitoring system for
cutting tools, focusing on flank wear analysis using artificial intelligence and
computer vision techniques. By integrating a smart camera capable of executing
AI algorithms, this system aims to improve machining efficiency by accurately
detecting tool wear during the milling process. This approach supports the goals of
Industry 4.0 by reducing machine downtime and maintenance costs, maximizing
tool life, and contributing to the advancement of smart manufacturing.
cutting tools, focusing on flank wear analysis using artificial intelligence and
computer vision techniques. By integrating a smart camera capable of executing
AI algorithms, this system aims to improve machining efficiency by accurately
detecting tool wear during the milling process. This approach supports the goals of
Industry 4.0 by reducing machine downtime and maintenance costs, maximizing
tool life, and contributing to the advancement of smart manufacturing.
| Zugehörige Institution(en) am KIT | Institut für Produktionstechnik (WBK) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsmonat/-jahr | 07.2025 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-3-031-86893-1 ISSN: 2194-0525 KITopen-ID: 1000192585 |
| Erschienen in | Production at the Leading Edge of Technology – Proceedings of the 14th Congress of the German Academic Association for Production Technology (WGP), Chemnitz University of Technology, December 2024. Ed.: W.-G. Drossel |
| Veranstaltung | 14th Congress of the German Academic Association for Production Technology (2024), Chemnitz, Deutschland, 02.12.2024 – 04.12.2024 |
| Verlag | Springer Nature Switzerland |
| Seiten | 30–38 |
| Serie | Lecture Notes in Production Engineering |
| Vorab online veröffentlicht am | 22.07.2025 |
| Schlagwörter | Tool Wear, In-Line Monitoring, Artificial Intelligence, Milling |
| Nachgewiesen in | OpenAlex Scopus |