## 4 Orthogonal Tests of Microstructure Parameters
### Effect of Temperature on the Back Face of the Cutter with Different Microtexturing Parameters  
Compared to the impact of back-cutting-face microstructures on milling force, microstructure effects on temperature are more significant. Simulation data derived from experiments, processed via range analysis, yield intuitive temperature analysis results for back-cutting faces in Table 13. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Factor**} & **Average** & **Average Correlation** & **Grey** \\  & **Correlation** & **Coefficient** & **Comprehensive** \\  & **Coefficient (Force)** & **(Temperature)** & **Correlation Degree** \\ \hline D & 0.782 & 0.694 & 0.738 \\ H & 0.821 & 0.733 & 0.777 \\ L\({}_{1}\) & 0.698 & 0.719 & 0.743 \\ L\({}_{2}\) & 0.635 & 0.621 & 0.678 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Grey Relational Analysis (GRA) results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **D** & **H** & **L\({}_{1}\)** & **L\({}_{2}\)** \\ \hline Experiment 1 & 40 & 30 & 120 & 90 \\ Experiment 2 & 40 & 40 & 140 & 110 \\ Experiment 3 & 40 & 50 & 160 & 130 \\ Experiment 4 & 50 & 30 & 140 & 130 \\ Experiment 5 & 50 & 40 & 160 & 90 \\ Experiment 6 & 50 & 50 & 120 & 110 \\ Experiment 7 & 60 & 30 & 160 & 110 \\ Experiment 8 & 60 & 40 & 120 & 130 \\ Experiment 9 & 60 & 50 & 140 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Visual analysis of milling temperature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Cutting Force** & **Temperature** \\ \hline Experiment 1 & 425.6 N & 216.4 \({}^{\circ}\)C \\ Experiment 2 & 426.7 N & 221.2 \({}^{\circ}\)C \\ Experiment 3 & 411.3 N & 222.7 \({}^{\circ}\)C \\ Experiment 4 & 420.7 N & 231.1 \({}^{\circ}\)C \\ Experiment 5 & 426.9 N & 199.0 \({}^{\circ}\)C \\ Experiment 6 & 418.6 N & 211.3 \({}^{\circ}\)C \\ Experiment 7 & 417.8 N & 206.8 \({}^{\circ}\)C \\ Experiment 8 & 421.6 N & 215.4 \({}^{\circ}\)C \\ Experiment 9 & 412.7 N & 185.7 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Temperature and cutting force results. Using range analysis on the range values of the data, the temperature effect curve of microstructures is derived and visualized in Figure 8.  
During the tool simulation process, the tool temperature gradually increases. As L\({}_{1}\) increases, the tool temperature initially shows a significant downward trend. This is attributed to the reduced friction area between chips due to optimized microstructure spacing, while the total microstructure area increases, enhancing heat dissipation efficiency at the cutting edge. When H increases, the tool temperature decreases markedly, as the inner surface area of the microstructures expands, improving the tool's heat dissipation capacity. An increase in D further reduces tool temperature by enlarging chip containment volume and enhancing anti-friction performance. Conversely, as L\({}_{2}\) increases, the tool temperature rises because the effective cutting section of the microstructures diminishes, leading to greater heat accumulation. Considering both tool temperature and cutting force, based on Grey Relational Analysis (GRA) from Tables 11-13, the optimal back microstructure parameters for the cutting tool are determined as D = 40 \(\upmu\)m (groove width), H = 50 \(\upmu\)m (groove depth), L\({}_{1}\) = 140 \(\upmu\)m (spacing), and L\({}_{2}\) = 90 \(\upmu\)m (density), as shown in Table 14. This parameter combination demonstrated the highest comprehensive relational degree (D: 0.667, H: 0.683, L\({}_{1}\): 0.768,