OBRABOTKAMETALLOV Vol. 28 No. 1 2026 80 TECHNOLOGY 20. GimadeevM.R., LiA.A.Analysis of automated surface roughness parameter support systems based on dynamic monitoring. Advanced Engineering Research (Rostov-on-Don), 2022, vol. 22 (2), pp. 116–129. DOI: 10.23947/26871653-2022-22-2-116-129. 21. Chen C.H., Jeng S.Y., Lin C.J. Prediction and analysis of the Surface roughness in CNC end milling using neural networks. Application Science, 2022, vol. 12 (1), p. 393. DOI: 10.3390/app12010393. 22. Manjunath K., Tewary S., Khatri N. Surface roughness prediction in milling using long-short term memory modelling. Materials Today: Proceedings, 2022, vol. 64 (3), pp. 1300–1304. DOI: 10.1016/j.matpr.2022.04.126. 23. Chai T., Draxler R.R. Root mean square error (RMSE) or mean absolute error (MAE) arguments against avoiding RMSE in the literature. Geoscientifi c Model Development, 2014, vol. 7 (3), pp. 1247–1250. DOI: 10.5194/ gmd-7-1247-2014. Confl icts of Interest The authors declare no confl ict of interest. © 2026 The Authors. Published by Novosibirsk State Technical University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0).
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