Author:
Denkena Berend,Schumacher Tim,Hein Markus
Abstract
AbstractComplex capital goods such as components from aircraft engines or gas and steam turbines usually have free-form geometry features, individual material deposits, and poor machining accessibility. The recontouring of these components requires increased machine and process flexibility. In sub-project B2, “Dexterous Regeneration Cell”, a dexterous milling repair cell was researched, representing a central component of the real regeneration path in the Collaborative Research Centre (CRC) 871. “Dexterousness” is understood as the ability to carry out a self-optimizing, best possible repair machining. The combination of advanced methods for process design, novel machine tool technologies and adaptive machining functionalities allows the reliable 5-axis recontouring of individual damage cases despite repair-specific variances, influences from upstream processes, and flexibility of the workpiece or tool. In this paper, a force model is created using artificial intelligence to determine variances. Further process knowledge is obtained to minimize shape deviations. Furthermore, the displacement in the recontouring process of turbine blades is carried out with the help of a magnetically guided spindle.
Publisher
Springer International Publishing
Reference32 articles.
1. Altintas, Y. (2011). Manufacturing Automation. Cambridge University Press, Cambridge
2. Boujnah, H. (2019). Kraftsensitiver Spindelschlitten zur online Detektion und Kompensation der Werkzeugabdrängung in der Fräsbearbeitung. Ph.D. Thesis, Leibniz Universität Hannover, Hannover
3. Brecher, C., Wetzel, A., Berners, T., and Epple, A. (2019). Increasing Productivity of Cutting Processes by real-time Compensation of Tool Deflection due to Process Forces. Journal of Machine Engineering. 19, 16–27
4. Charalampous, P. (2020). Prediction of Cutting Forces in Milling Using Machine Learning Algorithms and Finite Element Analysis. Journal of Materials Engineering and Performance, 30(3), 1082–1088
5. Cho, S., Asfour, S., Onar, A., and Kaundinya, N. (2005). Tool breakage detection using support vector machine learning in a milling process. International Journal of Machine Tools and Manufacture, 45, 241–249