Machine Learning Based Reconstruction of Process Forces
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-16281-7_3
Reference18 articles.
1. Brecher, C., Wetzel, A., Berners, T., Epple, A.: Increasing productivity of cutting processes by real-time compensation of tool deflection due to process forces. J. Mach. Eng. 19, 16–27 (2019)
2. Wan, M., Zhang, W.H., Qin, G. H., Wang, Z.P.: Strategies for error prediction and error control in peripheral milling of thin-walled workpiece. Int. J. Mach. Tools Manuf. 48(12–13), 1366–1374 (2008)
3. Denkena, B., Bergmann, B., Stoppel, D.: Tool deflection compensation by drive signal-based force reconstruction and process control. Procedia CIRP 104, 571–575 (2021)
4. Denkena, B., Boujnah, H.: Feeling machines for online detection and compensation of tool deflection in milling. CIRP Ann. 67(1), 423–426 (2018)
5. Aslan, D., Altintas, Y.: Prediction of cutting forces in five-axis milling using feed drive current measurements. IEEE/ASME Trans. Mechatron. 23(2), 833–844 (2018)
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