Fertig, Alexander ; Grau, Lukas ; Altmannsberger, Marius ; Weigold, Matthias (2021)
Tool Condition Monitoring and Tool Defect Detection for End Mills Based on High-Frequency Machine Tool Data.
In: MM Science Journal, 2021 (5)
doi: 10.17973/MMSJ.2021_ 11_ 2021174
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
In the context of increasing digitalization, machine tools have a decisive impact on the manufacturing of technically sophisticated products. The resulting large amount of available data opens up new opportunities for process monitoring and optimization. In this paper, a new in-process tool condition monitoring (TCM) approach for end mills is developed. Besides in-process wear determination, the presented approach also enables the early detection of tool manufacturing defects on end mills. By applying machine learning algorithms, high prediction accuracies can be achieved. The results allow the implementation of an in-process TCM system based on internal machine tool data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Fertig, Alexander ; Grau, Lukas ; Altmannsberger, Marius ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Tool Condition Monitoring and Tool Defect Detection for End Mills Based on High-Frequency Machine Tool Data |
| Sprache: | Englisch |
| Publikationsjahr: | 26 Oktober 2021 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | MM Science Journal |
| Jahrgang/Volume einer Zeitschrift: | 2021 |
| (Heft-)Nummer: | 5 |
| DOI: | 10.17973/MMSJ.2021_ 11_ 2021174 |
| Kurzbeschreibung (Abstract): | In the context of increasing digitalization, machine tools have a decisive impact on the manufacturing of technically sophisticated products. The resulting large amount of available data opens up new opportunities for process monitoring and optimization. In this paper, a new in-process tool condition monitoring (TCM) approach for end mills is developed. Besides in-process wear determination, the presented approach also enables the early detection of tool manufacturing defects on end mills. By applying machine learning algorithms, high prediction accuracies can be achieved. The results allow the implementation of an in-process TCM system based on internal machine tool data. |
| Freie Schlagworte: | Defect Classification, Internal Machine Tool Data, Machine Learning, Tool Condition Monitoring |
| Zusätzliche Informationen: | Special Issue: HSM 2021, 16th International Conference on High Speed Machining, October 26-27, 2021, Darmstadt, Germany |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) |
| Hinterlegungsdatum: | 10 Nov 2021 07:26 |
| Letzte Änderung: | 10 Nov 2021 13:47 |
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