## 3 Results and Discussion  
The experimental design and the obtained FEM analysis responses were analyzed using the Design Expert 13 software. As a result of the analysis, the quadratic regression model equations suitable for the optimum cutting parameters, which gave the minimum temperature and minimum power consumption when processing the material, were formed as in Equations (5) and (6). \[ \begin{split} R_{T}&=-266.998+5.7077V+19.101d-692.607f+ 0.285 Vd+\\ &\quad 10.009 Vf\;119.245 df-0.0199V^{2}-9.225d^{2}-1570.758f^{2}\\ \end{split}  \tag{5}\]  
\[ \begin{split} R_{P}&=-190.650+2.989V-26.889d-737.585f+ 0.080 Vd+6.286 Vf+\\ &\quad 161.7054f-0.0107V^{2}+10.978d^{2}-762.804f^{2}\\ \end{split}  \tag{6}\]  
The \(F\) test was used to determine the accuracy of the model and the coefficients in the model related to the temperature and power consumption values obtained from the FEM analysis study, and the analysis of variance (ANOVA) was used to determine the contributions of the cutting parameters affecting the turning to the temperature and power consumption variables. The results of the analysis of variance of the quadratic model are given in Tables 9 and 10. Looking at Table 9, it can be determined that the model was significant (the \(p\)-value is 0.0085 < 0.05) in the analysis of the temperature variables. From this analysis, it can be seen that the model parameters of the cutting speed (\(V\)), depth of cut (\(d\)), and feed rate (\(f\)) are significant and have an effect on the temperature variable. When the significance values are examined, it can be determined that the feed rate (\(f\)) has the greatest effect of the parameters on the temperature. When we look at the pairwise relations of the parameters, there is no significance. The adjusted R\({}^{2}\) (0.8647) shows that the quadratic model can explain 86.47% of the variance in the response. According to Table 10, the model made for the power consumption variable was significant (the \(p\)-value is 0.0099 < 0.05). In this analysis, it can be seen that the model parameters of the cutting speed (\(V\)), depth of cut (\(d\)), feed rate (\(f\)), and cutting speed-feed rate (\(V\)/) are significant and have an effect on the power consumption variable. Considering the significance values of the parameters, it was determined that the feed rate (\(f\)) has the greatest effect of the parameters on the power consumption, as well as on the temperature \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Run No.**} & \multicolumn{3}{c}{**Actual and Coded Level of Variables**} & \multicolumn{3}{c}{**FEM Analysis Response**} \\ \cline{2-6}  & **Cutting Speed** & **Depth of Cut (mm)** & **Feed Rate** & **Temperature** & **Power Consumption** \\  & **(m/min)** & **Cut (mm)** & **(mm/rev)** & **(T, \({}^{\circ}\)C)** & **(P, W)** \\ \hline
1 & \(-1\) (150) & 0 (1) & 1 (0.3) & 310.56 & 42.24 \\
2 & 1 (200) & 0 (1) & 1 (0.3) & 434.642 & 115.19 \\
3 & 1 (200) & 0 (1) & \(-1\) (0.1) & 295.257 & 50.86 \\
4 & \(-1\) (150) & \(-1\) (0.5) & 0 (0.2) & 288.78 & 46.08 \\
5 & 1 (200) & 1 (1.5) & 0 (0.2) & 401.02 & 101.2 \\
6 & 0 (175) & 0 (1) & 0 (0.2) & 341.769 & 76.65 \\
7 & \(-1\) (150) & 0 (1) & \(-1\) (0.1) & 271.266 & 40.77 \\
8 & 0 (175) & 1 (1.5) & 1 (0.3) & 442.699 & 129 \\
9 & 0 (175) & 0 (1) & 0 (0.2) & 367.183 & 76.26 \\
10 & 0 (175) & \(-1\) (0.5) & \(-1\) (0.1) & 245.309 & 30.59 \\
11 & 0 (175) & \(-1\) (0.5) & 1 (0.3) & 364.016 & 69.139 \\
12 & 0 (175) & 0 (1) & 0 (0.2) & 359.215 & 76.87 \\
13 & 1 (200) & \(-1\) (0.5) & 0 (0.2) & 311.755 & 60 \\
14 & 0 (175) & 1 (1.5) & \(-1\) (0.1) & 300.143 & 58.11 \\
15 & \(-1\) (150) & 1 (1.5) & 0 (0.2) & 363.775 & 83.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The Box-Behnken design and FEM analysis responses. variable. The adjusted R\({}^{2}\) (0.8555) shows that the quadratic model can explain 85.55% of the variance in the response.