## 3 Results and Discussion
### Results on the ANOVA  
The relationship between processing control parameters and manufacturing performance can be shown through statistical analysis. The statistical method of ANOVA was applied to the measured data to determine the impact of the input factors on the overall variation of responses. The STAT EASE Design Expert 13 program was used for response optimization and statistical analysis to establish the design matrix, as revealed in Table 4.  
Figure 4: As-built samples. Figure 5: Optical microscopic (OM) images of cross-sectional multiple tracks of SLM-built samples (**a**) B1-1, (**b**) B1-2, (**c**) B1-3, and (**d**) B1-4. Owing to the analyzed outcomes, the SLM method behavior was enhanced via the optimizing process control conditions. The aim of optimization was to improve the product hardness value, which was made via the SLM technique. Table 5 highlights the optimal values of the processing parameters for hardness performance measurement. Furthermore, the expected outcomes were obtained via the model by engaging varying processing parameters. Process control parameter optimization improved the performance of the SLM process according to the analysis findings. Maximizing the hardness value of the produced samples using SLM technology was the aim of optimization. Table 5 lists the ideal settings for the processing parameters to measure hardness performance. Values of hardness that were anticipated and measured in the experiment confirmed the validity of the numerical optimization. By adjusting the processing parameters, this model allowed for the achievement of the necessary hardness results. The developed model was then tested for adequacy by applying the ANOVA technique. The F-value and the \(p\)-values in Table 6 show that the input parameters, laser power, and scanning speed were significant regarding microhardness. From the outcomes of using this technique, it can be noted that the model was significant, showing \(p<0.05\), and that with an F value 21.0, there was only a 0.01% chance that an F-value this large could occur due to noise. For further illustration of adequacy, the hardness model was established via the regression analysis that was contingent on significant processing parameters. \begin{table} \begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Run Order**} & \multirow{2}{*}{\begin{tabular}{c} **Actual** \\ **Value** \\ \end{tabular} } & \multirow{2}{*}{**Predicted** \\ **Value** \\ \end{tabular}  } & \multirow{2}{*}{**Residual**} & \multirow{2}{*}{**Leverage**} & \multirow{2}{*}{ \begin{tabular}{c} **Internally** \\ **Studentized** \\ **Residuals** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Externally** \\ **Studentized** \\ **Residuals** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Cook’s** \\ **Distance** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Influence** \\ **on Fitted** \\ **Value** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Standard** \\ **Order** \\ **DEFITS** \\ \end{tabular}  } \\ \hline
1 & 107.32 & 101.39 & 5.93 & 0.177 & 0.360 & 0.346 & 0.007 & 0.161 & 11 \\ \hline
2 & 116.27 & 104  
Based on important processing factors, regression analysis was used to build the hardness model. The coefficient of determination (R\({}^{2}\)) for the R\({}_{\rm a}\) model was 0.8402, R\({}^{2}\) adjusted was 0.8003, and predicted R\({}^{2}\) was 0.7168, as shown in Table 7. The predicted R\({}^{2}\) of 0.7168 was in realistic concordance with the adjusted R\({}^{2}\) of 0.8003; meaning the variation was \(<\)0.2. Adeq Precision calculated the signal to noise ratio. A ratio beyond 4 was obtainable. The obtained ratio of 14.287 revealed a satisfactory signal. The developed validity of regression model was further tested by plotting a scatter diagram, as shown in Figure 6, showing the predicted values and actual values of the responses scattered close to the 45\({}^{\circ}\) line, revealing a practically perfect fit of the developed empirical model.