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Sensorvergleich zur ML-Verschleiß-prognose beim Drehen : Bewertung der Informationsqualität verschiedener Sensoren zur Vorhersage des Werkzeugverschleißes beim Längsdrehen unter Einsatz von Maschinellem Lernen
Other Titles
Sensor comparison for ML-based Wear Prediction in turning - evaluation of the information quality of various sensors for predicting tool wear in longitudinal turning using machine learning
Publikationstyp
Journal Article
Date Issued
2026-05-25
Sprache
German
Volume
121
Issue
5
Start Page
385
End Page
390
Citation
Zeitschrift für Wirtschaftlichen Fabrikbetrieb 121 (5): 385-390 (2026)
Publisher DOI
Scopus ID
Suitable sensors are required for data-driven wear predictions. The objective was to quantify the predictive power of five sensors for predicting the wear mark width VBmax using deep learning models for the turning process. The models based on the cutting force components achieved the highest prediction accuracy (MAE = 9.68 µm, R<sup>2</sup> = 0.93). Acoustic emission, temperature, and tool vibrations were in a close mid-range with MAE ~ 15 µm. The workpiece surface exhibited the lowest information content with an MAE of 23.47 µm.
Subjects
Coated Tools
Deep Neural Networks
External Longitudinal Turning
Machine Learning
Tool Wear
DDC Class
600: Technology