## 3 Results and Discussion
### Effects of Cutting Parameters on Surface Roughness in Micro-Milling  
The milling process is one of the most important performance indicators in terms of surface roughness and machining accuracy. The machining data obtained from this process is analyzed using variance analysis (ANOVA) and utilized to optimize machining parameters. With the increasing use of aluminum alloys, especially in sectors such as defense, automotive, and aviation, the milling process has become one of the most widely preferred methods for processing these materials. Figure 10 illustrates the surface roughness results of all Taguchi experimental designs. These findings are not only statistically significant but also practically important, as they guide optimal parameter selection in precision-dependent industries such as aerospace and biomedical manufacturing. Additionally, Figures 11 and 12 illustrate the distribution of surface roughness (Ra) values based on the main effects and signal-to-noise (SN) ratio graphs within the framework of the Taguchi design method. The SN ratio is a statistical measure used to evaluate the robustness of different parameter combinations; a higher SN ratio indicates more consistent and lower-variance results. According to the analysis, surface roughness increases as both the depth of cut and feed rate rise. Moreover, when the cutting speed exceeds 15,000 rpm, surface quality deteriorates significantly due to accelerated tool wear from elevated temperatures and cutting forces. Increased material deformation and machining-induced vibrations also contribute to this decline in surface finish. Figure 10: Experimental results for surface roughness. Such trends highlight the necessity for real-time process monitoring systems in high-speed micro-milling applications to proactively mitigate surface degradation. In view of the experimental data obtained from the Taguchi trial sets, the Al10 MgSi sample with a 100 \(\upmu\)m depth of cut, a cutting speed of 20,000 rpm, and a feed rate of 2 \(\upmu\)m/tooth exhibited the highest average Ra value of 3.03 \(\upmu\)m. To facilitate a more detailed examination of bivariate relationships, contour diagrams are additionally provided, as shown in Figure 13.  
In Figure 14, the surface roughness values (Ra) for cutting depths of 50 \(\upmu\)m and 100 \(\upmu\)m are compared on the same graph. This figure illustrates how surface roughness varies as a function of feed rate and spindle speed for different depths of cut. For the material machined at a 50 \(\upmu\)m cutting depth, Ra values increased proportionally with the feed rate, and were particularly pronounced at a spindle speed of 30,000 rpm. Similarly, for a 100 \(\upmu\)m cutting depth, the areal surface roughness (Sa) values showed a marked increase with increasing feed rate. This relationship is associated with the higher material removal rate: As feed rate increases, the volume of material removed per unit time also rises, which causes more pronounced fluctuations in the surface profile and leads to higher roughness values [50; 51]. Figure 11: Main effects plot for surface roughness. Figure 12: SN ratios corresponding to surface roughness values. In contrast, higher spindle speeds tended to reduce surface roughness values. This effect is attributed to the formation of built-up edges along the tool's cutting edges, which can smooth the machined surface [52]. These insights are particularly valuable for industrial applications, where they can be used to define adaptive control strategies within CNC systems to maintain optimal surface integrity under variable machining conditions. Figures 15 and 16 present 3D surface topography images obtained from a profilometer for cutting depths of 50 \(\upmu\)m and 100 \(\upmu\)m, respectively. These figures clearly illustrate the surface irregularities under the lowest parameter settings (fz = 0.25 \(\upmu\)m/tooth and n = 10,000 rpm), where ploughing effects dominate.