<![CDATA[Dimensional deviations remain a common issue in CNC milling operations, even with the use of modern machines equipped with enhanced capabilities. Developing a predictive model to determine dimensional deviations based on selected machine parameters, including radial depth of cut, feed rate, axial depth of cut, and cutting speed, is the aim of this study. The goal is to identify the optimal configuration of these parameters to control and restrain dimensional deviations. The research employs response surface methodology to achieve this objective. Experimental data analysis reveals that the predictive model for dimensional deviation is precise, with a correlation coefficient of 0.89, MAPE of 0.938%, RMSE of 5.52%, and an R-Square value of 0.8. The model predicts dimensional deviations within a span of 0.20 µm, and deviations can be limited to 0.1 µm. The study successfully demonstrates the employment of response surface methodology to determine parameter values that mitigate dimensional deviations, potentially eliminating machining process delays. This research may encourage further adoption of similar methodologies in other CNC milling applications.]]>
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