## 3 Results  
### The Results and Analysis for Tool Wear  
The experiment utilized an L\({}^{9}\) (3\({}^{4}\)) orthogonal design to examine the effects of various milling parameters on the wear width, \(b\), of the rake face. To ensure data accuracy and reliability, the wear width, \(b\), was measured independently three times under each experimental condition. This methodology not only aids in minimizing random errors but also provides a more accurate representation of actual wear conditions. Ultimately, we calculated the average of these three measurements, with the results from the orthogonal experiments presented in Table 4.  
Figure 4: The flowchart of genetic algorithm. The signal-to-noise ratio S/N (\(\eta\)) for the tool wear condition presented in Table 4 was analyzed using range analysis and variance analysis, with the results detailed in Tables 5 and 6. The range analysis indicates that the influence of milling parameters on the signal-to-noise ratio S/N (\(\eta\)) of tool wear, follows this order: spindle speed \(n>\) feed rate \(v_{\text{f}}>\) axial milling depth \(a_{\text{p}}\). According to Equation (5), the smaller the tool wear, the higher its signal-to-noise ratio. As depicted in Figure 5, to minimize the wear width of the rake face while ensuring compliance with milling force and tool life requirements, the optimal milling parameters are identified as \(n=\) 12,000 r/min, \(v_{\text{f}}=\) 1000 mm/min, and \(a_{\text{p}}=\) 3 mm. During the process of data analysis, the F-test approach with statistical significance was employed scientifically and rationally, with the significance level fixed at \(\alpha=0.05\), in an attempt to form objective and reasonable judgments through calculating and comparing the relevant data and, thus, guaranteeing that the conclusions reached are highly reliable. The analysis of variance results reveal that F (\(n\)) > F (\(v_{\text{f}}\)) > F critical value > F (\(a_{\text{p}}\)), suggesting that there exists a significant relationship among the spindle speed \(n\), the feed rate \(v_{\text{f}}\), and the signal-to-noise ratio S/N (\(\eta\)) of tool wear. The degree of significant correlation is that the spindle speed is greater than the feed rate. This outcome is in accordance with the conclusion of the range analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**No**} & \multicolumn{3}{c}{**Parameters**} & \multicolumn{2}{c}{**Results**} \\ \cline{2-6}  & \(n\) **(r/min)** & \(v_{\text{f}}\) **(mm/min)** & \(a_{\text{p}}\) **(mm)** & \(b\) **(mm)** & **S/N (\(\eta\))** \\ \hline
1 & 12,000 & 1000 & 3 & 0.0445 & 13.5067 \\
2 & 12,000 & 1500 & 4 & 0.0611 & 12.1325 \\
3 & 12,000 & 2000 & 5 & 0.0702 & 11.5428 \\
4 & 15,000 & 1000 & 4 & 0.0582 & 12.3582 \\
5 & 15,000 & 1500 & 5 & 0.0781 & 11.0679 \\
6 & 15,000 & 2000 & 3 & 0.0842 & 10.7417 \\
7 & 18,000 & 1000 & 5 & 0.0782 & 11.0735 \\
8 & 18,000 & 1500 & 3 & 0.0872 & 10.5899 \\
9 & 18,000 & 2000 & 4 & 0.1023 & 9.8970 \\ \hline \end{tabular}
\end{table} 
Table 4: Orthogonal Experimental Results of tool wear. Figure 5: The effect curve of milling parameters on the signal-to-noise ratio S/N (\(\eta\)). The data of Table 4 were fitted through Equation (1), and the resulting outputs are displayed in Table 7. It can be discerned from the table that the coefficient of determination, R\({}^{2}\), is 0.989. The determination, R\({}^{2}\), is a highly significant indicator in statistics and data analysis, gauging the predictive ability of the independent variable for the dependent variable. The value of R\({}^{2}\) spans from 0 to 1, and the nearer it is to 1, the more precise the model's prediction. Therefore, it can be inferred that the model has satisfactory fitting effects.