OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 2 2025 Fig. 5. Predicted and Experimental values for selected classifications When evaluating Table 4 and Fig. 5, one can conclude that the models under consideration showed predicted values (Y(Rz)) close to the actual ones (Rz). The coefficient of determination R2 was 0.973 for the regression model and 0.924, 0.907, 0.903, respectively, for the considered configurations. For all configurations, MAE ≈ 0.2955 μm means that, on average, model predictions deviate from actual Rz values by 0.2955 μm. The Rz parameter correlates strongly with Ra (correlation coefficient 0.91) [21–23]. Statistical processing shows the relationship: Ra = (Rz – 0.391) / 4.022. Comparison of experimental data with model predictions shows MAE for Ra ≈ 0.049 μm, which is negligible in surface roughness context, indicating close agreement between observations and true mean values. Thus, errors found do not significantly affect result accuracy, confirming data compliance. Conclusion This paper discusses the development of an artificial neural network model to predict surface roughness when milling with a ball-end tool. The tuning process of ANN architecture, especially the selection of number of layers and neurons, is described to improve prediction accuracy. The concept of parameter selection based on the significance of the contribution to the accuracy of surface roughness prediction Rz is considered to reduce the input factors to the minimum possible. The results show that it is possible to obtain accurate predictions of surface roughness even when taking into account a small number of input parameters with relatively small training sets. Selecting the correct network configuration and input parameters is important to ensure accurate prediction. In addition, the study highlights the importance of considering the inclination angle of the ballend tool (10° to 50°) in training the ANN models, and the increase in the angle affects the decrease in the magnitude of the roughness parameters. The final tests conducted to check the adequacy of the proposed model showed that the model works well with reasonable accuracy under the given set of parameters. In conclusion, it can be said that this study contributes significantly to machining process modeling by milling.
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