Jiang, Y.; Drescher, B.; Wittstamm, M.; Hu, C.; Clemens, F. et al.: Tool Wear Prediction Upgrade Kit for Legacy CNC Milling Machines in the Shop Floor. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. Hannover : publish-Ing., 2022, S. 131-140. DOI: https://doi.org/10.15488/12150
Zusammenfassung: | |
The operation of CNC milling is expensive because of the cost-intensive use of cutting tools. The wear and tear of CNC tools influence the tool lifetime. Today’s machines are not capable of accurately estimating the tool abrasion during the machining process. Therefore, manufacturers rely on reactive maintenance, a tool change after breakage, or a preventive maintenance approach, a tool change according to predefined tool specifications. In either case, maintenance costs are high due to a loss of machine utilization or premature tool change. To find the optimal point of tool change, it is necessary to monitor CNC process parameters during machining and use advanced data analytics to predict the tool abrasion. However, data science expertise is limited in small-medium sized manufacturing companies. The long operating life of machines often does not justify investments in new machines before the end of operating life. The publication describes a cost-efficient approach to upgrade legacy CNC machines with a Tool Wear Prediction Upgrade Kit. A practical solution is presented with a holistic hardware/software setup, including edge device, and multiple sensors. The prediction of tool wear is based on machine learning. The user interface visualizes the machine condition for the maintenance personnel in the shop floor. The approach is conceptualized and discussed based on industry requirements. Future work is outlined. | |
Lizenzbestimmungen: | CC BY 3.0 DE |
Publikationstyp: | BookPart |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2022 |
Die Publikation erscheint in Sammlung(en): | Proceedings CPSL 2022 Proceedings CPSL 2022 |
Pos. | Land | Downloads | ||
---|---|---|---|---|
Anzahl | Proz. | |||
1 | Hong Kong | 217 | 30,14% | |
2 | Germany | 181 | 25,14% | |
3 | United States | 102 | 14,17% | |
4 | Japan | 23 | 3,19% | |
5 | India | 22 | 3,06% | |
6 | Czech Republic | 16 | 2,22% | |
7 | Brazil | 13 | 1,81% | |
8 | Turkey | 12 | 1,67% | |
9 | Russian Federation | 12 | 1,67% | |
10 | United Kingdom | 11 | 1,53% | |
andere | 111 | 15,42% |
Hinweis
Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.