## 4 Orthogonal Tests of Microstructure Parameters
### Temperature Effects of Different Microtexturing Parameters on the Front Blade Surface  
The microstructure parameters from Table 3 were imported into finite element simulation software: Abaqus 2019 for simulations, obtaining the front cutting face temperature results shown in Table 7 during milling. These data were processed using range analysis, shown in Table 8, to derive intuitive temperature distribution metrics for the front cutting face. Based on the data from Table 8, a temperature range analysis chart for front-cutting-fac-microstructure tools was generated, as illustrated in Figure 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Number** & **Temperature (°C)** \\ \hline
1 & 226.3 \({}^{\circ}\)C \\
2 & 181.6 \({}^{\circ}\)C \\
3 & 147.3 \({}^{\circ}\)C \\
4 & 192.5 \({}^{\circ}\)C \\
5 & 164.1 \({}^{\circ}\)C \\
6 & 179.5 \({}^{\circ}\)C \\
7 & 172.6 \({}^{\circ}\)C \\
8 & 217.8 \({}^{\circ}\)C \\
9 & 184.6 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The result of milling temperature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **D** & **H** & **L\({}_{1}\)** & **L\({}_{2}\)** & **Result** \\ \hline Experiment 1 & 1 & 1 & 1 & 1 & 226.3 \({}^{\circ}\)C \\ Experiment 2 & 1 & 2 & 2 & 2 & 181.6 \({}^{\circ}\)C \\ Experiment 3 & 1 & 3 & 3 & 3 & 167.3 \({}^{\circ}\)C \\ Experiment 4 & 2 & 1 & 2 & 3 & 192.5 \({}^{\circ}\)C \\ Experiment 5 & 2 & 2 & 3 & 1 & 164.1 \({}^{\circ}\)C \\ Experiment 6 & 2 & 3 & 1 & 2 & 179.5 \({}^{\circ}\)C \\ Experiment 7 & 3 & 3 & 3 & 2 & 172.6 \({}^{\circ}\)C \\ Experiment 8 & 3 & 2 & 1 & 3 & 217.8 \({}^{\circ}\)C \\ Experiment 9 & 3 & 1 & 2 & 1 & 184.6 \({}^{\circ}\)C \\ Average value 1 & 185.067 & 197.133 & 207.867 & 191.667 \\ Average value 2 & 178.700 & 187.833 & 186.233 & 177.900 \\ Average value 3 & 191.667 & 170.467 & 161.333 & 185.867 \\ Variance & 12.967 & 26.666 & 46.534 & 13.767 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Polar visualization analysis of milling temperatures. According to Table 9, the descending order of ranges for the four microstructure parameters is L1 > H > L2 > D. The magnitude of the range reflects the influence degree on temperature, thereby determining the parameter impact sequence on temperature as follows: L1 > H > L2 > D. This indicates that L1 exerts the most significant influence on temperature, while D has a minimal effect. As shown in Table 8 and Figure 7, the temperature of front-cutting-face-microstructure tools exhibits distinct trends with parameter variations: as L1 increases from Level 1 to Level 3, the temperature initially decreases due to optimized L1 reducing the chip friction area while increasing the microstructure's heat dissipation surface area, enhancing heat exchange with external media. When H is elevated from Level 1 to Level 3, temperature decreases as the enlarged microstructure's surface area improves the overall heat dissipation capacity. For D, temperature reduction occurs at Level 1 to Level 2 due to wider grooves improving chip containment and wear resistance, but rises at Level 3 as the excessive groove width forms cutting edges that induce secondary cutting effects, increasing heat generation. With L2 increasing from Level 1 to Level 2, temperature declines as microstructures within the effective cutting zone enhance heat dissipation; however, further elevation to Level 3 causes a temperature rise due to microstructures exceeding the optimal cutting range, reducing efficacy and increasing cutting heat. In conclusion, when selecting a tool's microstructure parameters based on cutting temperature criteria, the optimal groove microstructure parameters are determined as follows: D = 50 \(\upmu\)m, \(H\) = 40 \(\upmu\)m, \(L_{1}\) = 140 \(\upmu\)m, and \(L_{2}\) = 80 \(\upmu\)m.  
When simultaneously evaluating the combined effects of temperature and cutting force on cutting tools, this study utilized Grey Relational Analysis (GRA) to analyze Tables 4, 5, 7 and 8.