## 3 Results and Discussion
The predicted values obtained from the FEM analysis and quadratic regression model equations for the temperature and power consumption variables are given in Figure 6.  
It can be seen in Figure 6 that the predicted values are compatible with the actual FEM analysis results. This result shows that the developed quadratic regression model can be used to optimize the parameters that are effective for the turnability of AISI 52100 steel. After the ANOVA analysis, the effects of the parameters affecting the turning of the AISI 52100 bearing steel material on the temperature and power consumption variables were examined and the findings are given in Figures 7 and 8. Accordingly, the effect of the parameters affecting the turning of AISI 52100 steel on the temperature can be seen in Figure 7. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Source** & **Sum of Squares** & _df_ & **Mean Square** & _F-Value_ & _p-Value_ & \\ \hline Model & 10,583.57 & 9 & 1175.95 & 10.21 & 0.0099 & significant \\ \(V\) & 1649.96 & 1 & 1649.96 & 14.33 & 0.0128 & significant \\ \(d\) & 3435.00 & 1 & 3435.00 & 29.82 & 0.0028 & significant \\ \(f\) & 388.59 & 1 & 3838.59 & 33.33 & 0.0022 & significant \\ \(Vd\) & 4.02 & 1 & 4.02 & 0.0349 & 0.8591 & \\ \(Vf\) & 987.84 & 1 & 987.84 & 8.58 & 0.0327 & significant \\ \(df\) & 261.49 & 1 & 261.49 & 2.27 & 0.1922 & \\ \(V^{2}\) & 165.76 & 1 & 165.76 & 1.44 & 0.2840 & \\ \(d^{2}\) & 27.81 & 1 & 27.81 & 0.2415 & 0.6440 & \\ \(f^{2}\) & 214.84 & 1 & 214.84 & 1.87 & 0.2302 & \\ Residual & 575.89 & 5 & 115.18 & & & \\ Lack of Fit & 575.70 & 3 & 191.90 & 2010.84 & 0.0005 & significant \\ Pure Error & 0.1909 & 2 & 0.0954 & & & \\ Cor Total & 11,159.47 & 14 & & & & \\ \hline \hline \end{tabular} R\({}^{2}\) = 0.9484, R\({}^{2}\) (adjusted) = 0.8555.  
\end{table} 
Table 10: The ANOVA table for power consumption. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Source** & **Sum of Squares** & _df_ & **Mean Square** & _F-Value_ & _p-Value_ & \\ \hline Model & 44,776.37 & 9 & 4975.15 & 10.94 & 0.0085 & significant \\ \(V\) & 5423.25 & 1 & 5423.25 & 11.93 & 0.0182 & significant \\ \(d\) & 11,083.89 & 1 & 11,083.89 & 24.38 & 0.0043 & significant \\ \(f\) & 24,193.62 & 1 & 24,193.62 & 53.21 & 0.0008 & significant \\ \(Vd\) & 50.91 & 1 & 50.91 & 0.1120 & 0.7515 & \\ \(Vf\) & 2504.55 & 1 & 2504.55 & 5.51 & 0.0658 & \\ \(df\) & 142.19 & 1 & 142.19 & 0.3127 & 0.6001 & \\ \(V^{2}\) & 569.27 & 1 & 569.27 & 1.25 & 0.3140 & \\ \(d^{2}\) & 19.64 & 1 & 19.64 & 0.0432 & 0.8436 & \\ \(f^{2}\) & 911.00 & 1 & 911.00 & 2.00 & 0.2161 & \\ Residual & 2273.33 & 5 & 454.67 & & & \\ Lack of Fit & 1935.42 & 3 & 645.14 & 3.82 & 0.2145 & \\ Pure Error & 337.91 & 2 & 168.95 & & & \\ Cor Total & 47,049.71 & 14 & & & & \\ \hline \hline \end{tabular} R\({}^{2}\) = 0.9517, R\({}^{2}\) (adjusted) = 0.8647.  
\end{table} 
Table 9: The ANOVA table for temperature. Figure 6: The Box-Behnken experimental versus predicted results: (**a**) temperature; (**b**) power consumption. Figure 7: The effects of the parameters affecting the turning of the AISI 52100 bearing steel material on the temperature variable: (**a**) depth of cut-cutting speed; (**b**) feed rate-cutting speed; (**c**) feed rate-depth of cut. In Figure 7, the effects of the factors affecting the turning of the AISI 52100 bearing steel material on the temperature variables are shown. Accordingly, when Figure 7a is examined, it can be seen that the depth of cut parameter has a greater effect on the temperature than the cutting speed parameter, and the temperature continuously increases when the depth of cut increases. In Figure 7b, it can be seen that the feed rate parameter affects the temperature more than the cutting speed parameter, and the temperature increases more as the feed rate increases. The effects of the feed rate and depth of cut parameters on the temperature variables are given in Figure 7c.