OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 2 5 Prediction of surface roughness in milling with a ball end tool using an artificial neural network Mikhail Gimadeev a, *, Vadim Stelmakov b, Aleksandr Nikitenko c, Maksim Uliskov d Pacific 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 Artificial 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 overfitting and underfitting 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 affects an increase in the Rz parameter by 21% compared to the minimum values. The data obtained were used to train eight configurations of artificial neural networks, which were used to predict roughness using the Rz parameter. The results show that the 3-16-16-1 network configuration 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 configurations also demonstrated the lowest average absolute error values, which demonstrate the average of the absolute differences between predicted and observed values (0.1014; 0.1251 and 0.1155, respectively), and the coefficient 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 differences 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 significant 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 artificial 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 Pacific National University, 136 Tihookeanskaya st., 680035, Khabarovsk, Russian Federation Tel.: +7 924 216-31-39, e-mail: 009063@togudv.ru Introduction The quality of the machined surface plays a decisive role in ensuring the operational properties of machine parts [1]. Surface roughness (Rz and Ra) often serves as one of the main metrics for assessing the surface condition in the machining process [2]. Modeling methods for predicting Rz can be divided into three categories: experimental models, analytical models, and artificial intelligence (AI)-based models [3, 4]. In recent years, AI-driven models have become widely used among researchers to predict characteristics related to machining processes [5], and the use of artificial neural networks (ANN) is considered by the authors to predict surface roughness, tool wear, and other parameters in machining [6].
RkJQdWJsaXNoZXIy MTk0ODM1