## 4 Discussion
### Burr Width (Down Milling)  
In the context of micro-milling titanium alloys, achieving a minimized burr width bears profound implications for the biomedical and aerospace sectors, which formed a focal point in this research investigation. This reduction holds the promise of streamlining production processes by diminishing the necessity for intricate post-processing steps such as deburring and polishing. Beyond mere efficiency gains, the impact of low burr width extends to crucial domains, particularly in biomedical applications, where enhanced precision directly correlates with heightened biocompatibility. Similarly, within aerospace, the resulting smoother surfaces contribute to increased component reliability. The amalgamation of these outcomes underscores the direct and multifaceted contributions of low burr width, advancing not only manufacturing efficiency but also safety and performance within these critical industries. This research underscores the pivotal role of burr width optimization in enhancing the overall efficacy and reliability of micro-milled titanium components in biomedical and aerospace applications. Table 7 presents the ANOVA results for burr width in the context of down milling in micro-machining. The primary factor exerting a notable influence on the variability of burr width was identified as the feed rate (F \(\upmu\)m/tooth), which contributed significantly with a substantial 55.20%. This was supported by a high F-value of 12.19 and a low \(p\)-value of 0.002, marking its statistical significance. This underscores the pivotal role of the feed rate in determining the width of burrs during down milling. In contrast, cutting speed (Vc m/min) exhibited a non-significant impact, contributing 8.94%, with an F-value of 1.97 and a \(p\)-value of 0.185. Similarly, the axial depth of cut (ap \(\upmu\)m) was deemed non-significant, contributing 10.96%, with an F-value of 2.42 and a \(p\)-value of 0.135. The error term accounted for 24.91% of the variability, and the absence of a \(p\)-value indicated its contribution to be non-significant. Figure 7 presents the main effects plot detailing the relationship between down milling burr width and chip load. Notably, a discernible inverse correlation was observed between burr width and the feed per tooth. As the feed per tooth increased, there was a concurrent decrease in burr width, reaching a minimum at a feed rate of 0.65 m/tooth. The trend of decreasing burr width with higher feed rates can be attributed to the influence of different material removal mechanisms. When feed rates were increased, the shearing mechanism became dominant, involving a cutting action that efficiently removed material and led to minimized burr formation. The cutting edges worked more effectively at higher feed rates, resulting in a cleaner cut and less plastic deformation, which is a key factor in burr formation. Conversely, lower feed rates favored the ploughing mechanism, where material is pushed aside rather than cleanly cut. This tended to induce more plastic deformation, contributing to the development of larger burrs. The interaction between the tool and the material became less effective at lower feed rates, promoting the formation of burrs. This observed trend aligns with previous studies, including high-speed micro-milling investigations, where an increase in feed rate led to a notable reduction in burr width. For instance, [PERSON] et al. [2] reported a 4-35% decrease in burr width with an augmented feed rate. These trends align with research conducted on other titanium grades [42]. Cutting speed and depth of cut were insignificant factors for burr width. The standard deviation for burr width is displayed in Figure 8.