Fertig, Alexander ; Bauerdick, Christoph ; Weigold, Matthias (2020)
In-Process Quality Monitoring During Turning Based on High Frequency Machine Data.
doi: 10.2139/ssrn.3724115
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The increasing digitalization of production lines enables an increasing availability of large amounts of data across the whole process chain. Machine tools in particular are increasingly being equipped with edge computing solutions to record internal drive signals with high frequency. This fact enables new approaches to monitor and improve cutting processes in machining production. This paper presents a new approach to detect workpiece defects during turning of a control disc for hydraulic pumps. For this purpose, real-time drive data provided by an edge computing solution was recorded and examined which drive signals are influenced through machining of various workpiece defect categories. This correlation analysis provides the base for the subsequent data processing and model creation using approaches of machine learning. The developed system was evaluated by applying it to the next turning process in the considered process chain to ensure the transferability of the new in-process quality monitoring system.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Fertig, Alexander ; Bauerdick, Christoph ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | In-Process Quality Monitoring During Turning Based on High Frequency Machine Data |
| Sprache: | Englisch |
| Publikationsjahr: | November 2020 |
| Verlag: | SSRN |
| Buchtitel: | Proceedings of the Machining Innovations Conference (MIC) 2020 |
| DOI: | 10.2139/ssrn.3724115 |
| Kurzbeschreibung (Abstract): | The increasing digitalization of production lines enables an increasing availability of large amounts of data across the whole process chain. Machine tools in particular are increasingly being equipped with edge computing solutions to record internal drive signals with high frequency. This fact enables new approaches to monitor and improve cutting processes in machining production. This paper presents a new approach to detect workpiece defects during turning of a control disc for hydraulic pumps. For this purpose, real-time drive data provided by an edge computing solution was recorded and examined which drive signals are influenced through machining of various workpiece defect categories. This correlation analysis provides the base for the subsequent data processing and model creation using approaches of machine learning. The developed system was evaluated by applying it to the next turning process in the considered process chain to ensure the transferability of the new in-process quality monitoring system. |
| Freie Schlagworte: | Defect detection, machine learning, Quality Monitoring, Turning |
| 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) > Zerspanungstechnologie (2021 aufgegangen in TEC Fertigungstechnologie) |
| Hinterlegungsdatum: | 16 Dez 2020 06:42 |
| Letzte Änderung: | 16 Dez 2020 06:42 |
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