OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 7 2 5 overfitting, meaning the model memorizes training data instead of generalizing. Fig. 3 shows 3–32–16–1 and 3–6–6–1 models remembered the training data too well, but cope poorly with new data. According to results and based on MSE (Table 3), best network configurations are 3–64–64–1 (MSE = 0.0481), 3–20–14–1 (0.0470), and 3–16–16–1 (0.0313). These also have low RMSE (0.2174, 0.2135, 0.1770) and MAE (0.1155, 0.1251, 0.1014). RMSE is interpreted as error in the same units as data. A design with k = 12 experiments was used to test the best models with factors fz ∈ {0.4; 0.5}, γ ∈ {10, 15, 20, 30, 40, 50}, D ∈ {6}, distributed randomly. Table 4 and Fig. 5 present data showing the relationship between the values obtained during the experiment, calculated based on the developed regression model and the predicted BPNN responses. Ta b l e 3 Predictive performance of the neural network Metrics 3-6464-1 3-6432-1 3-3232-1 3-3216-1 3-2014-1 3-1616-1 3-66-1 3-99-1 MSE 0.0481 0.0621 0.0572 0.0685 0.0470 0.0313 0.0415 0.0603 RMSE 0.2174 0.2492 0.2391 0.2617 0.2135 0.1770 0.2037 0.2456 MAE 0.1155 0.1228 0.1656 0.1361 0.1251 0.1014 0.1306 0.1447 R2 0.9904 0.9889 0.9898 0.9878 0.9916 0.9944 0.9926 0.9862 Ta b l e 4 Predicted (Y(Rz)) and experimental (Rz) values for selected configurations, at k = 12 fz γ D Rz Rz(RM) Y(Rz) 3-64-64-1 3-20-14-1 3-16-16-1 0.4 10 6 6.945 7.090 6.680 6.277 6.491 0.4 50 4.610 4.840 4.938 5.025 5.116 0.4 20 6.108 6.528 6.037 6.469 6.303 0.4 40 5.400 5.403 5.357 5.503 5.261 0.5 20 8.341 8.590 7.923 8.372 7.875 0.4 15 6.614 6.809 6.426 6.495 6.330 0.5 10 9.163 9.330 8.272 8.402 8.925 0.4 30 5.826 5.965 6.067 5.987 6.307 0.5 15 8.786 8.960 8.463 8.590 8.072 0.5 40 6.992 7.110 7.035 6.977 7.407 0.5 30 7.694 7.850 7.792 7.443 7.896 0.5 50 6.024 6.370 6.548 6.745 6.541 MSE 0.049 0.136 0.167 0.175 RMSE 0.221 0.369 0.408 0.418 MAE(Rz) 0.195 0.286 0.317 0.384 MAE(Ra) 0.049 0.072 0.081 0.095 R2 0.973 0.924 0.907 0.903
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