## 4 Conclusions  
In this study, we aimed to optimize the cutting parameters affecting the turning of AISI 52100 bearing steel with the three-level Box-Behnken experimental design combined with the response surface method. To achieve this aim, the temperature and power consumption values obtained from the FEM analysis based on the cutting speed, depth of cut, and feed rate cutting parameters were analyzed using Design Expert 13. Then, the cutting parameters affecting the temperature and power consumption variables were optimized. Based on the results of the optimization study, the following points were confirmed:  
Figure 9: The experimental setup. * For the test result predictions made with the Box-Behnken experimental design, we obtained values of R\({}^{2}\) (adjusted) = 0.8647 for the temperature and R\({}^{2}\) (adjusted) = 0.8555 for the power consumption. These results indicated that the cutting parameters would be accurately predicted when turning the AISI 52100 bearing steel;
* The temperature and power consumption variables are affected by the cutting speed, depth of cut, and feed rate parameters;
* The feed rate parameter affects both the temperature and power consumption the most;
* The cutting speed parameter has the least effect on both the temperature and power consumption variables;
* The cutting speed should be 162.427 m/min, the depth of cut should be 1.395 (mm), and the feed rate should be 0.247 mm/rev for minimal temperature and power consumption;
* When the results obtained from the RSM were compared in the turning experiments carried out in the real experimental environment, error rates of 3.3% for the temperature and 6.6% for the power consumption emerged. According to these results, it was seen that the quadratic regression equation gave results closer to the real experimental results;
* Since the minimum temperature and power consumption values were obtained with FEM analysis, the Box-Behnken design and real experimental setup are very close to each other. The Box-Behnken experimental design can be used together with an FEM analysis for the optimization of the cutting parameters of AISI 52100 bearing steel. When the literature on the optimization of the cutting parameters of AISI 52100 bearing steel was examined, no study was found that used the RSM and that simultaneously examined the output responses, temperature, and power consumption, as was examined in our study. In [69], in which the CCD and TOPSIS methods were used to determine the parameters that optimize the workpiece surface temperature, it was determined that the parameter that most affected the workpiece surface temperature was the negative rake angle. In another study [70], it was determined that the parameters that affect the cutting temperature the most are the cutting speed and feed rate with the CCD experimental design. In this study, experiments were carried out in a multilayer coated carbide cutting tool insert under a high-velocity pulsing jet minimal cutting fluid application environment. However, in our study, it was determined that the feed rate parameter affected the temperature the most under dry cutting conditions and with a different insert, and the cutting speed variable affected the temperature the least. It was thought that this result was due to the fact that the experiments were carried out in a dry-liquid environment and that the cutting tips were different. In another study [65], which aimed to optimize the cutting forces and used the MLR and genetic algorithm methods, it was seen that the feed rate parameter affected the power consumption the most. This result was similar to the result obtained in our study. When we look at the other cutting parameter optimization studies on AISI 52100 bearing steel in the literature, it can be seen that feed rate generally affects the surface roughness and the depth of cut affects the cutting forces. The first limitation of our study was that only three parameters and three levels were used in the experimental design.