OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 7 No. 2 2025 (R²) shows what proportion of the variance of the dependent variable is explained by the independent variables of the model: 2 2 1 2 1 . n i i i n i i i Y Y R Y Y where n is the number of data; Yi is the observed values; Ŷ is the predicted values; Ȳ is the mean value of Y. Despite its usefulness, R² has limitations: it does not account for the number of predictors and can be biased by outliers. MAE is a measure of the absolute error | | Y Y between predicted and actual values: 1 0 1 . n i i i MAE Y Y n MAE is less sensitive to large errors than MSE and RMSE because it uses absolute error values. MSE and RMSE are characterized by the mean square error and its square root, respectively: 1 2 0 1 , n i i MSE Y i Y n 1 2 0 1 . n i i RMSE Y i Y n MSE is sensitive to large errors because squared differences increase with large deviations. Since RMSE is measured in the same units as the data itself, it is easier to interpret than MSE. However, like MSE, RMSE is also sensitive to large errors. Analysis of these metrics is critical for a comprehensive assessment of predictive performance [15, 16]. When comparing these metrics, special attention is paid to MSE, which has the advantage of detecting and accounting large errors, making it useful in machine learning tasks where minimizing large deviations is important.Additionally, MSE is smooth and differentiable, simplifying gradient computation in optimization methods such as gradient descent. Therefore, MSE is often a more suitable choice for accuracy assessment. Furthermore, the coefficient of determination R², with values close to 1, is considered most favorable. A preliminary data analysis is also performed before using machine learning models. This includes checking for normality and identifying/removing outliers that can significantly affect model accuracy. Model optimization is an important step for effective solutions. Configuring hyperparameters ensures the best performance estimated from validation datasets within the selected algorithm. Hyperparameters play a significant role in controlling the learning process and significantly affect predictive accuracy. Proper setting of hyperparameters also helps reduce overfitting and underfitting, improving accuracy. Dropout is a method to prevent overfitting by randomly excluding neurons during training, preventing coadaptation [14]. The aim of this work is to develop a predictive neural network model for assessing surface roughness when milling stainless steel with a ball-end tool. To achieve this aim, the following tasks were addressed: – study of predicting surface roughness parameter Rz when milling with a ball-end tool, including optimizing ANN architecture, selecting number of layers, and tuning model parameters to improve prediction accuracy. – analysis of the influence of various input parameters, including tool tilt angle, on roughness prediction accuracy, and development of an approach to minimize input data without loss of model effectiveness, as well as study of model applicability with limited training sets. – final testing of the developed model, assessment of accuracy using MSE, RMSE, MAE, and R² metrics, and evaluation of effectiveness through comparison of predicted and experimental data.
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