Sauter, Emil ; Sarikaya, Erkut ; Winter, Marius ; Wegener, Konrad (2021)
In-process Detection of Grinding Burn Using Machine Learning.
In: The International Journal of Advanced Manufacturing Technology, 2021
doi: 10.1007/s00170-021-06896-9
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The improvement of industrial grinding processes is driven by the objective to reduce process time and costs while maintaining required workpiece quality characteristics. One of several limiting factors is grinding burn. Usually applied techniques for workpiece burn are conducted often only for selected parts and can be time consuming. This study presents a new approach for grinding burn detection realized for each ground part under near-production conditions. Based on the in-process measurement of acoustic emission, spindle electric current, and power signals, time-frequency transforms are conducted to derive almost 900 statistical features as an input for machine learning algorithms. Using genetic programming, an optimized combination between feature selector and classifier is determined to detect grinding burn. The application of the approach results in a high classification accuracy of about 99% for the binary problem and more than 98% for the multi-class detection case, respectively.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Sauter, Emil ; Sarikaya, Erkut ; Winter, Marius ; Wegener, Konrad |
| Art des Eintrags: | Bibliographie |
| Titel: | In-process Detection of Grinding Burn Using Machine Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 22 Mai 2021 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | The International Journal of Advanced Manufacturing Technology |
| Jahrgang/Volume einer Zeitschrift: | 2021 |
| DOI: | 10.1007/s00170-021-06896-9 |
| Kurzbeschreibung (Abstract): | The improvement of industrial grinding processes is driven by the objective to reduce process time and costs while maintaining required workpiece quality characteristics. One of several limiting factors is grinding burn. Usually applied techniques for workpiece burn are conducted often only for selected parts and can be time consuming. This study presents a new approach for grinding burn detection realized for each ground part under near-production conditions. Based on the in-process measurement of acoustic emission, spindle electric current, and power signals, time-frequency transforms are conducted to derive almost 900 statistical features as an input for machine learning algorithms. Using genetic programming, an optimized combination between feature selector and classifier is determined to detect grinding burn. The application of the approach results in a high classification accuracy of about 99% for the binary problem and more than 98% for the multi-class detection case, respectively. |
| Freie Schlagworte: | Acoustic emission, Grinding burn, Machine learning, Process monitoring, Time-frequency transform |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > TEC Fertigungstechnologie |
| Hinterlegungsdatum: | 16 Jun 2021 05:53 |
| Letzte Änderung: | 16 Jun 2021 05:53 |
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