Abstract
During milling, process forces are acting on the cutting tool, causing tool deflection and subsequently a shape deviation of the workpiece. To compensate these effects, knowledge of the process forces is required. In this work, machine learning (ML) methods are applied to reconstruct process forces from the drive signals of two different milling centers. The results of a linear regression, bagged trees and a stacked LSTM are presented. The approaches show different results depending on the milling center. Only for the LSTM an error lower than 30 N is achieved for both machine tools. Independent of the ML approach, the results strongly depend on the selection of milling processes used for training.
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References
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)
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)
Denkena, B., Bergmann, B., Stoppel, D.: Tool deflection compensation by drive signal-based force reconstruction and process control. Procedia CIRP 104, 571–575 (2021)
Denkena, B., Boujnah, H.: Feeling machines for online detection and compensation of tool deflection in milling. CIRP Ann. 67(1), 423–426 (2018)
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)
Wan, M., Yin, W., Zhang, W.H.: Study on the correction of cutting force measurement with table dynamometer. Procedia CIRP 56, 119–123 (2016)
Totis, G., Adams, O., Sortino, M., Veselovac, D., Klocke, F.: Development of an innovative plate dynamometer for advanced milling and drilling applications. Measurement 49, 164–181 (2014)
Rizal, M., Ghani, J.A., Nuawi, M.Z., Haron, C.H.C.: Development and testing of an integrated rotating dynamometer on tool holder for milling process. Mech. Syst. Signal Process. 52, 559–576 (2015)
Sarhan, A.A.D., Matsubara, A., Sugihara, M., Saraie, H., Ibaraki, S., Kakino, Y.: Monitoring method of cutting force by using additional spindle sensors. JSME Int J., Ser. C 49(2), 307–315 (2006)
Albrecht, A., Park, S.S., Altintas, Y., Pritschow, G.: High frequency bandwidth cutting force measurement in milling using capacitance displacement sensors. Int. J. Mach. Tools Manuf 45(9), 993–1008 (2005)
Yamato, S., Kakinuma, Y.: Precompensation of machine dynamics for cutting force estimation based on disturbance observer. CIRP Ann. 69(1), 333–336 (2020)
Altintas, Y.: Prediction of cutting forces and tool breakage in milling from feed drive current measurements (1992)
Kim, T.Y., Woo, J., Shin, D., Kim, J.: Indirect cutting force measurement in multi-axis simultaneous NC milling processes. Int. J. Mach. Tools Manuf 39(11), 1717–1731 (1999)
Schwenzer, M., Auerbach, T., Miura, K., Döbbeler, B., Bergs, T.: Support vector regression to correct motor current of machine tool drives. J. Intell. Manuf. 31(3), 553–560 (2019). https://doi.org/10.1007/s10845-019-01464-1
Denkena, B., Bergmann, B., Stoppel, D.: Reconstruction of process forces in a five-axis milling center with a LSTM neural network in comparison to a model-based approach. J. Manufact. Mater. Process. 4(3), 62 (2020)
Eesa, A.S., Arabo, W.K.: A normalization methods for backpropagation: a comparative study. Sci. J. Univ. Zakho 5(4), 319–323 (2017)
Meinshausen, N., Ridgeway, G.: Quantile regression forests. J. Mach. Learn. Res. 7(6) 983–999 (2006)
Maas, A.L., Hannun, A. Y., Ng, A.Y. Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of Icml, vol. 30 (1), pp. 3–9 (2012)
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We thank the “Sieglinde Vollmer Stiftung” for funding this research.
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Denkena, B., Klemme, H., Stoppel, D. (2023). Machine Learning Based Reconstruction of Process Forces. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_3
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DOI: https://doi.org/10.1007/978-3-031-16281-7_3
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