ML Models for Intelligent Tool Replacement

Enthalten in
Zeitschrift für Wirtschaftlichen Fabrikbetrieb 
Volume
118
Issue
11
Start Page
752
End Page
757
Citation
Zeitschrift für wirtschaftlichen Fabrikbetrieb 118 (11): 752-757 (2023-11)
Publisher DOI
10.1515/zwf-2023-1155
Scopus ID
2-s2.0-85180153828
The wear limits of milling tools should not be exceeded with regard to the workpiece quality. Current approaches of relying on fixed service lifetimes for tool replacement overlook the crucial factor of varying wear rates and fail to determine the optimal times for replacement. This article presents an approach that uses ML algorithms in the decision-making process, which enables a tool replacement while considering varying wear rates within a defined range of cutting parameters. © 2023 Walter de Gruyter GmbH, Berlin/Boston, Germany
Schlagworte
Werkzeugverschleiß
Standzeit
Prozessüberwachung
Maschinelles Lernen
Fräsen
Sensorik
Tool Wear
Tool Service Life
Process Monitoring
Machine Learning
Milling
Sensors
MLE@TUHH
DDC Class
600: Technology
620: Engineering