OBRABOTKAMETALLOV Vol. 27 No. 2 2025 139 EQUIPMENT. INSTRUMENTS References 1. SuslovA.G., Medvedev D.M., Petreshin D.I., Fedonin O.N. Sistema avtomatizirovannogo tekhnologicheskogo upravleniya iznosostoikost’yu detalei mashin pri obrabotke rezaniem [System for automated wear-resistance technological control of machinery at cutting]. Naukoemkie tekhnologii v mashinostroenii = Science intensive technologies in mechanical engineering, 2018, no. 5 (83), pp. 40–44. DOI: 10.30987/article_5ad8d291cdd cd8.06334386. 2. CaiY., Starly B., Cohen P., LeeY.S. Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Procedia Manufacturing, 2017, vol. 10, pp. 1031–1042. DOI: 10.1016/j. promfg.2017.07.094. Prediction of surface roughness in milling with a ball end tool using an artifi cial neural network Mikhail Gimadeev a, *, Vadim Stelmakov b, Aleksandr Nikitenko c, Maksim Uliskov d Pacifi c National University, 136 Tihookeanskaya St., Khabarovsk, 680035, Russian Federation a https://orcid.org/0000-0001-6685-519X, 009063@togudv.ru; b https://orcid.org/0000-0003-2763-1956, 009062@togudv.ru; c https://orcid.org/0000-0003-4729-5558, 005392@togudv.ru; d https://orcid.org/0009-0001-9858-423X, 2016104779@togudv.ru Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2025 vol. 27 no. 2 pp. 126–141 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2025-27.2-126-141 ART I CLE I NFO Article history: Received: 30 January 2025 Revised: 01 March 2025 Accepted: 27 March 2025 Available online: 15 June 2025 Keywords: Milling Roughness Regression analysis Artifi cial intelligence Neural network Standard error Funding This work has funded by the Ministry of science and higher education of Russian Federation (project № FEME– 2024–0010). ABSTRACT Introduction. Milling stainless steel with a ball-end tool is a complex technological process that requires precise control of processing parameters to ensure high surface quality. In this regard, it is an urgent task to develop methods for predicting roughness parameters, such as Rz. The aim of this work is to develop a predictive neural network model that can estimate surface roughness when milling stainless steel using a ball-end tool. Method and methodology. The main focus is on error backpropagation and gradient descent methods, as well as hyperparameter tuning, which are necessary to prevent overfi tting and underfi tting of the model. Experimental studies include the analysis of both controlled variables, such as feed per tooth, angle of inclination and diameter of the tool, and uncontrolled, including coolant supply and tool wear. Results and discussions. The use of coolant for milling austenitic steel has reduced the roughness parameters Rz by an average of 14%. A strong negative correlation has been established between the dimensional wear of the tool and the parameter Rz (−0.95). At the same time, wear in the range of 2…4 μm aff ects an increase in the Rz parameter by 21% compared to the minimum values. The data obtained were used to train eight confi gurations of artifi cial neural networks, which were used to predict roughness using the Rz parameter. The results show that the 3-16-16-1 network confi guration showed the lowest MSE (0.0313), followed by 3-20-14-1 (0.0470) and 3-64-64-1 (0.0481), respectively. In addition, these confi gurations also demonstrated the lowest average absolute error values, which demonstrate the average of the absolute diff erences between predicted and observed values (0.1014; 0.1251 and 0.1155, respectively), and the coeffi cient of determination, which is a statistical measure indicating the proportion of data variability explained by the model (0.9944; 0.9916; 0.9904). A comparison of the experimental data with the predictions of various models allowed us to determine the average value of the absolute diff erences for the models according to the parameter Ra ≈ 0.074. The study suggests approaches to training neural network models for accurate prediction of roughness parameters, which makes a signifi cant contribution to the methods of modeling machining processes. For citation: Gimadeev M.R., Stelmakov V.A., Nikitenko A.V., Uliskov M.V. Prediction of surface roughness in milling with a ball end tool using an artifi cial neural network. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2025, vol. 27, no. 2, pp. 126–141. DOI: 10.17212/1994-6309-2025-27.2-126-141. (In Russian). ______ * Corresponding author Gimadeev Mikhail R., Ph.D. (Engineering), Associate Professor Pacifi c National University, 136 Tihookeanskaya st., 680035, Khabarovsk, Russian Federation Tel.: +7 924 216-31-39, e-mail: 009063@togudv.ru
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