## 3 Results and Discussion
### Roughness
While these models are slightly weaker than the one for Rz, they still provide statistically significant fits and can be regarded as useful tools for estimating technological effects. For the Ry parameter,  
Figure 6: Changing surface roughness parameters in relation to eccentricity values. representing the maximum height of a single profile, a significant model with R\({}^{2}\) = 0.4809 was also obtained, although the lack of an intercept term in this case may suggest a simplification of the model or a natural interpretative limitation of this parameter. On the other hand, the last of the analyzed models, which describes the influence of rotational speed n on the Sm parameter--i.e., the mean spacing between adjacent peaks of the surface profile--shows a determination coefficient of R\({}^{2}\) = 0.4829, placing it close to the models based on eccentricity. However, the significantly lower F-statistic value (F = 3.425) may suggest that the explanatory power of this model is limited. It can be assumed that the spatial structure of the surface profile, including the distribution of peaks, is more strongly determined by complex interactions among the technological parameters rather than by rotational speed alone. Therefore, a complete characterization of Sm would likely require the application of multidimensional modeling that accounts for multiple variables and their mutual interactions. The results presented in the normal probability plot indicate that the distribution of residuals in the regression model can be considered consistent with a normal distribution (Figure 7). The data points align closely along the reference line, suggesting no systematic deviations from normality. The deviations of the points from the line are minor and evenly distributed, both in the central region and at the extremes of the residual range. Furthermore, there is no evidence of significant outliers that might distort the residual structure of the model. This pattern indicates that one of the fundamental assumptions of classical regression analysis--the normality of residuals--is satisfactorily met. Consequently, it can be concluded that the parameter estimators obtained from the model comply with the assumptions of the least squares method, and the statistical analysis performed, including the estimation of regression coefficients and significance testing, is based on a sound methodological foundation. The surface roughness profile prior to the application of MAF (Figure 8a) is characterized by significant irregularity, with the presence of deep and sharp depressions. The variation in amplitude indicates high local topographical heterogeneity, suggesting the occurrence of numerous microcracks, defects, and discontinuities. The dominance of valleys, combined with a relatively low proportion of positive peaks (rarely exceeding +2 \(\upmu\)m), indicates profile asymmetry, with a shift in the mean values toward the negative axis. Such morphology may adversely affect the contact properties of the surface, increasing the risk of stress concentration and wear during operation. Figure 7: The normal plot of residuals for Ra. Each point represents the deviation between observed and predicted values; alignment along the straight line indicates normally distributed residuals. After the MAF process, a clear smoothing of the surface profile is observed (Figure 8b). The main amplitude of the surface signal is concentrated within the \(\pm 2\) um range. The profile demonstrates improved symmetry and reduced local variability. Simultaneously, the structure of the profile reveals enhanced uniformity along the measurement length, confirming the high repeatability of the MAF process.