# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion
### Effect of Parameter on Corner Error  
When the experimental data collection was completed, the analysis of variance using RSM was performed to investigate the influence of the independent variable on the performance variable. The analysis of variance (ANOVA) results of the quadratic model for 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) corners are given in Tables 3-5. These developed models are statistically significant which are indicated by the value of F and \(p\). The larger the value of F and the smaller the value of \(p\), the more significant [27; 28]. The F-value was found to be 33.331 (30\({}^{\circ}\)), 7.95 (60\({}^{\circ}\)), and 5.15 (90\({}^{\circ}\)), representing that these models are statistically significant. The large F-values may occur due to the noise and only 0.01% possibility. The \(p\)-value was observed to be less than 0.0500, indicating that these models are significant. For 30\({}^{\circ}\) corner error, the model terms ON, WT, and the second order of OFF\({}^{2}\), WT\({}^{2}\) were found to be highly significant, while OFF, OV, SV, WA, and the second order of ON\({}^{2}\), OV\({}^{2}\), SV\({}^{2}\) were significant factors. In the case of the 60\({}^{\circ}\) corner, the model terms ON, OFF, WA, and the second order of \begin{table}
\begin{tabular}{c c c c c c} \hline
**OV** & **ON** & **OFF** & **SV** & **WT** & **WA** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 2: _Cont._SV\({}^{2}\) were found to be highly significant, while OV, SV, two-level interaction ON OV, and the second order of OFF\({}^{2}\), WT\({}^{2}\) were the significant model terms. Meanwhile, for the case of the 90\({}^{\circ}\) corner, the model terms ON and WT were found to be highly significant, while OFF, OV, SV, WA, and the second order of SV\({}^{2}\) were significant factors. The significant parameters and their interaction effect on the output response corner error and the machining time can be seen through the Pareto chart as shown in Figure 3. In addition to _p_-value, the coefficient of determination R\({}^{2}\), adjusted R\({}^{2}\) (\(R_{adj}^{2}\)), and predicted R\({}^{2}\) (\(R_{pred}^{2}\)) were used to evaluate the developed model. The value of R\({}^{2}\) for the 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) corners were 0.9719, 0.9644, and 0.9539, respectively. For a model to be adequate, R\({}^{2}\) value should not be less than 0.75 [29]. Although the larger the R\({}^{2}\) value is, the more accurate the model is, the R\({}^{2}\) will be overestimated due to the influence of the sample. Therefore, the \(R_{adj}^{2}\) value needed to be considered to avoid the overestimation. The value of \(R_{adj}^{2}\) for the 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) corners were 0.9558, 0.9427, and 0.9385, respectively. The small deviation between R\({}^{2}\) and \(R_{adj}^{2}\) indicates the adequacy and fitness of the model. Meanwhile, the \(R_{pred}^{2}\) value for the 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) corners were 0.9340, 0.9225, and 0.9196, respectively. It can be seen that the predicted R\({}^{2}\) value was in close agreement with the adjusted R\({}^{2}\).