## 3 Results and Discussion  
The central composite design (CCD) and the experimental results are presented in Table 6. Analysis of variance (ANOVA) is a statistical technique employed to evaluate the impact of input variables on the output responses of a technical process with a 95% confidence level. In other words, a \(p\)-value of less than 0.05 indicates that the model is statistically significant. Another essential statistic is the coefficient of correlation (R\({}^{2}\)), which measures the model's degree of fit. Notably, the simulation findings and empirically measured cutting temperature show similar patterns, with only a 2% difference, indicating comparable results. As shown in Table 6, the simulated temperature values are all higher than the experimental measurement values. This discrepancy arises because the infrared thermal imager used in the experiments captures surface temperatures, whereas the simulation estimates the internal cutting-zone temperature (at the interface between the tool and the chip). This is because the internal zones naturally experience higher temperatures than those visible externally. The average local rake angle and cutting temperature results will be further analyzed in the following figures. Figure 5 presents the cutting process in the hard-turning method. The simulation results demonstrated that the main cutting edge does not participate in the cutting action. The cutting process occurs exclusively at the tool-nose radius. Moreover, the chip makes contact only with the chamfered face of the cutting edge. The data obtained from the further experiments results could reveal more information. Table 7 shows the ANOVA results for the average local rake angle. The results indicate that the most significant factor is the inclination angle, accounting for 65.84%, followed by the rake angle, contributing 31.43%. The cutting-edge angle only contributes 0.98% to the average local rake angle. Therefore, optimizing the hard-turning process should concentrate on modifying the inclination angle and rake angle. The cutting-edge angle could be ignored as its effect is minor. The main effect plots for the average local rake angle are presented in Figure 6. Increasing the negative rake and inclination angles leads to a greater negative local rake angle of the cutting-edge elements at the tool-nose radius engaged in the cutting process. \begin{table}
\begin{tabular}{c c c c} \hline
**Thermo-Mechanical Properties** & **AISI H13** & **Ceramic Tool** & **TiN Coating** \\ \hline Young’s Modulus (GPa) & 211 & 145 & - \\ Poisson ratio & 0.28 & 0.22 & 0.25 \\ Density (kg/m\({}^{3}\)) & 7800 & 3500 & 4650 \\ Thermal conductivity (W/m\({}^{.5}\)C) & 37 & 24 & 23.5 \\ Hardness (HV) & 544 & - & 2300 \\ \hline \end{tabular}
\end{table} 
Table 5: Thermo-mechanical characteristics of the tool and the workpiece. Figure 7 presents the temperature distributions at the chip-tool interface, as determined through 3D FEM simulation. The cutting temperature is highest at the contact area between the chip and the tool, consistent with the findings of [PERSON] et al. [33]. The cutting temperature could reach a range of 800-900 \({}^{\circ}\)C, which is close to the experimental results shown in Table 5. This is due to the heat generated in the primary shear-deformation zone being transferred to the chip and the heat produced by friction between the chip and the chamfered face. Moreover, as shown in Figure 5, the contact type is only a point at the small cutting depth in the hard-turning process rather than the facial type at the higher cutting depth in the conventional machining process. Table 8 displays the ANOVA for experimental cutting temperature. The results show that inclination angle is the dominant contributor to cutting temperature, accounting for 40.75% of total variability, whereas the rake angle has a modest influence, which accounts for 32.39% of the total variability. The cutting-edge angle has a lower level of contribution (7.03%), and all interaction effects are insignificant.