## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Workpiece Name and**} & \multirow{2}{*}{\begin{tabular}{c} **Workpiece** \\ **Electrical** \\ **Conductivity** \\ **(S/m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Gap** \\ **Current** \\ **(A)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Gap** \\ **Voltage** \\ **(V)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Pulse On** \\ **Time (\(\mu\)s)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Pulse Off** \\ **Time (\(\mu\)s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Removal** \\ **Rate** \\ **(mm\({}^{3}\)/min)** \\ \end{tabular} } \\ \hline NiTi Untreated & 3268 & 8 & 40 & 13 & 5 & 2.09 \\ NiTi Untreated & 3268 & 12 & 55 & 26 & 7 & 4.56 \\ NiTi Untreated & 3268 & 16 & 70 & 38 & 9 & 7.11 \\ NiTi Treated & 4219 & 8 & 40 & 26 & 7 & 3.96 \\ NiTi Treated & 4219 & 12 & 55 & 38 & 9 & 6.5 \\ NiTi Treated & 4219 & 16 & 70 & 13 & 5 & 4.16 \\ NiCu & 5515 & 8 & 55 & 13 & 9 & 2.76 \\ Untreated NiCu & 5515 & 12 & 70 & 26 & 5 & 3.33 \\ NiCu & 5515 & 16 & 40 & 38 & 7 & 9 \\ Untreated NiCu Treated & 5625 & 8 & 70 & 38 & 7 & 3.1 \\ NiCu Treated & 5625 & 12 & 40 & 13 & 9 & 5.98 \\ NiCu Treated & 5625 & 16 & 55 & 26 & 5 & 6.26 \\ BeCu & 5645 & 8 & 55 & 38 & 5 & 3.41 \\ Untreated BeCu & 5645 & 12 & 70 & 13 & 7 & 3.08 \\ BeCu & 5645 & 16 & 40 & 26 & 9 & 9.08 \\ Untreated BeCu Treated & 5902 & 8 & 70 & 26 & 9 & 2.8 \\ BeCu Treated & 5902 & 12 & 40 & 38 & 5 & 6.7 \\ BeCu Treated & 5902 & 16 & 55 & 13 & 7 & 6.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental Layout with trial conditions and observed value. In this study the experimental layout was considered as per Taguchi method. There are five input parameters namely workpiece electrical conductivity with six level and other parameters at three levels viz, gap current, gap voltage, pulse on time and pulse off time. The nearest orthogonal array that will satisfies the criterion of selecting the orthogonal array is mixed L\({}_{18}\) (\(6^{1}\times 3^{4}\)) orthogonal array having seventeen degrees of freedom. The experimental results obtained as per Taguchi Experimental layout was used in Machine learning as dataset for prediction of material removal rate based on regressor and classification based approaches. The present study utilized four supervised machine learning regression-based algorithms i.e., Random Forest, Decision Tree, Gradient Boosting and Artificial Neural Network developed by Python programming for predicting the Material Removal Rate. In the present study, three supervised machine learning classification-based algorithms i.e., Decision Tree algorithm, Random Forest Algorithm, and AdaBoost algorithms are employed for classification of Material Removal Rate. If Material Removal Rate is below 5 mm\({}^{3}\)/min then it is labeled as '0' and vice versa. Figure 1a represents the flowchart for implementing the Supervised Machine Learning algorithms on the dataset. The dataset was split into two parts i.e., the training set and testing set out of which 80% of dataset points were used for training purposes and 20% of dataset points were used for testing purposes. In the ANN model, the number of epochs employed was 2000. Figure 1b shows the feature importance graph. It is observed from the graph that one of the experimental input parameters i.e., Pulse off Time and Workpiece Electrical Conductivity were not contributing to the output parameter. So, in the present study, the main contributing input parameters are Gap Current, Gap Voltage, and Pulse On time, while Material Removal Rate is the output parameter. Figure 1c shows the heatmap plot of the experimental dataset. A heatmap uses a warm-to-cool color combination to graphically represent visitor behavior data as hot and cold regions. Light color is the location with the maximum visitor contact, and warm colors point to those areas, dark colors denote those areas with the least visitor interaction. Figure 1: _Cont._