## 4 Discussion
### Tool Wear  
Table 9 provides the results of the analysis of variance (ANOVA) for tool wear, which assessed the contributions and significance of specific machining parameters on tool wear. The table outlines the contributions of three key parameters: feed rate (F) in \(\upmu\)m/tooth, cutting speed (Vc) in \(\upmu\)m, and depth of cut (ap) in \(\upmu\)m. Among these parameters, both feed rate (F) and cutting speed (Vc) made substantial contributions, at 33.18% and 54.02%, respectively, indicating their significant impact on tool wear. This significance was further supported by the low \(p\)-values of 0.001 and 0.000 (both less than 0.05) for feed rate (F) and cutting speed (Vc), respectively, demonstrating their importance in influencing tool wear. In contrast, the depth of cut (ap) was considered non-significant, as its \(p\)-value was relatively high, at 0.466, suggesting that it had a limited effect on tool wear in the conducted experiments. This ANOVA analysis helps identify the primary parameters influencing tool wear, providing valuable insights into the research findings. Tool wear in the milling of titanium alloys typically initiates rapidly, which is primarily attributed to the elevated temperatures encountered and the strong adhesion of the material to the cutting tools, as outlined in previous research [43]. The main effects plots presented in Figure 11 visually illustrate the primary influences on tool flank wear. Notably, a clear  
Figure 10: Standard deviation for burr length.  
and direct correlation emerged between the cutting velocity and the magnitude of tool wear. As cutting velocity increased, there was a simultaneous increase in tool flank wear. This observed phenomenon is attributed to the generation and accumulation of thermal energy during the cutting process, ultimately leading to elevated cutting temperatures. The direct relationship depicted in the main effects plots underscores the significance of cutting velocity as a determining factor in the extent of tool wear during the milling of titanium alloys. The standard deviation for tool wear is displayed in Figure 12.  
Figure 11: Main effects plot for tool flank wear.  
Figure 12: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Source** & **Contribution** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline F (\(\mu\)m/tooth) & 33.18\% & 16.39 & 0.001 & Significant \\ Vc (m/min) & 54.02\% & 26.68 & 0.000 & Significant \\ ap (\(\mu\)m) & 1.66\% & 0.82 & 0.466 & Non-significant \\ Error & 11.14\% & & & \\ \hline Total & 100.00\% & & & \\ \end{tabular}
\end{table}
Table 9: ANOVA for tool wear.  
Elevated cutting temperatures represent a significant contributing factor to the swift onset of tool wear when machining titanium alloys [44]. In addition, previous research has indicated that higher cutting speeds result in a rapid rubbing action between the cutting tool and the workpiece, which generates heat that softens the tool's flank face, consequently leading to increased abrasive wear [45]. Moreover, the increase in cutting speed during milling operations not only amplifies the frequency of tool edge entry into the workpiece but also intensifies the energy of the impact between the cutting edge and the workpiece, underscoring the pivotal role of cutting speed, particularly as the tool nears the end of its operational life [46]. The main effects plot further demonstrates that tool wear decreased with an increase in feed rate. Lower feed rates tended to be dominated by the ploughing mechanism, resulting in elevated tool wear. This phenomenon is closely related to the cutting-edge radius and its impact on shear and ploughing forces, as elucidated in other studies. Below the minimum chip thickness, both ploughing force and specific cutting force exhibited significant increases, culminating in heightened forces during micro-milling. It is also worth noting that tool wear declined when the feed rate transitioned from 0.45 \(\upmu\)m/tooth to 0.90 \(\upmu\)m/tooth.