Abstract
Monitoring the actual wear of a tool enables a tool to be used to the end of its life, despite tool life variations. However, such monitoring currently requires an extensive teach-in on the monitored machine. This article describes an approach for tool wear monitoring that omits the machine-specific teach-in phase. Instead, the teach-in is based on data that was previously recorded on other machines. Further, a demonstrator for monitoring flank wear width during milling is presented.
| Titel in Übersetzung | Tool Wear Monitoring Using Process Data of Multiple Machine Tools by Means of Machine Learning |
|---|---|
| Originalsprache | Deutsch |
| Seiten (von - bis) | 298-301 |
| Seitenumfang | 4 |
| Fachzeitschrift | Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF) (online) |
| Jahrgang | 118 |
| Ausgabenummer | 5 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 1 Mai 2023 |
Schlagwörter
- Federated Learning
- Machine Tools
- Milling
- Monitoring
- Tool Wear
- Transfer Learning
ASJC Scopus Sachgebiete
- Allgemeiner Maschinenbau
- Strategie und Management
- Managementlehre und Operations Resarch
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