Fertig, Alexander ; Bauerdick, Christoph ; Weigold, Matthias (2020)
In-Process Quality Monitoring During Turning Based on High Frequency Machine Data.
doi: 10.2139/ssrn.3724115
Conference or Workshop Item, Bibliographie
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2020 |
| Creators: | Fertig, Alexander ; Bauerdick, Christoph ; Weigold, Matthias |
| Type of entry: | Bibliographie |
| Title: | In-Process Quality Monitoring During Turning Based on High Frequency Machine Data |
| Language: | English |
| Date: | November 2020 |
| Publisher: | SSRN |
| Book Title: | Proceedings of the Machining Innovations Conference (MIC) 2020 |
| DOI: | 10.2139/ssrn.3724115 |
| 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. |
| Uncontrolled Keywords: | Defect detection, machine learning, Quality Monitoring, Turning |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Machining Technology (2021 merged in TEC Fertigungstechnologie) |
| Date Deposited: | 16 Dec 2020 06:42 |
| Last Modified: | 16 Dec 2020 06:42 |
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