## 4 Artificial Neural Network  
ANNs originally developed by [PERSON] and [PERSON] [47] are based on the behaviour and structure of the human brain. The basic computational units of ANN are known as neurons (nodes), which are connected through weights, and are responsible for computing the results within the defined range. To achieve the desired results, the data need to be divided into two groups, i.e., training and testing datasets. Training data usually consist of 70% or 80% of the dataset, which act as patterns and the ANN establishes a non-linear connection between them. A second dataset, known as the testing data, adopts the recognized patterns to evaluate the generalization potential of the system. It is worth noting that the developed network is unfamiliar with the testing data [48]. Therefore, ANNs attempt to learn the hidden pattern from the training dataset and then applies it to the testing dataset, to examine the generalisation ability of the system. A multilayer perceptron (MLP) neural network was employed to establish the relationship between the input and output parameters using a single hidden layer. MLP is the most commonly used network, among the diverse types of ANNs used by various researchers [49; 50]. The MLP consists of three layers, namely input, hidden and output layers. [PERSON] [51] comprehensively proved that, for a regression problem, a single hidden layer is adequate to map a relationship between input and response variables. Additionally, a single hidden layer is adequate to map a relationship between input and response variables as it provides satisfactory results in the approximation of nonlinear problems [52]. Further, the increase in the number of nodes causes the model to become complicated with many different parameters [53]. The proposed scheme for the prediction of \(R_{a}\) using ANN is given in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Source** & **DF** & **Seq SS** & **Adj SS** & **Adj MS** & **F-Value** & **\(p\)-Value** & **Contribution** \\ \hline Model & 18 & 7.70713 & 7.70713 & 0.42817 & 191.81 & 0 & 99.77\% \\ Linear & 6 & 7.52638 & 7.52638 & 1.2544 & 561.93 & 0 & 97.43\% \\ \(n\) & 2 & 5.04159 & 5.04159 & 2.52079 & 1129.24 & 0 & 65.26\% \\ \(f\) & 2 & 2.23015 & 2.23015 & 1.11508 & 499.52 & 0 & 28.87\% \\ \(Dt\) & 2 & 0.25465 & 0.25465 & 0.12732 & 57.04 & 0 & 3.30\% \\
2-Way Interactions & 12 & 0.18075 & 0.18075 & 0.01506 & 6.75 & 0.006 & 2.34\% \\ \(n\times f\) & 4 & 0.102 & 0.102 & 0.0255 & 11.42 & 0.002 & 1.32\% \\ \(n\times Dt\) & 4 & 0.06481 & 0.06481 & 0.0162 & 7.26 & 0.009 & 0.84\% \\ \(f\times Dt\) & 4 & 0.01394 & 0.01394 & 0.00349 & 1.56 & 0.274 & 0.18\% \\ Error & 8 & 0.01786 & 0.01786 & 0.00223 & - & - & 0.23\% \\ Total & 26 & 7.72499 & - & - & - & - & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANOVA for surface roughness. It is noteworthy that data normalisation is important before feeding it to any machine learning model so that each variable receives the same attention [49]. Therefore, for this study, all parameters were normalised between \(-1\) to \(1\) using the following relationship. \[X_{n}=2\frac{X-X_{min}}{X_{max}-X_{min}}-1 \tag{1}\]  
where \(X_{max}\) and \(X_{min}\) represent the maximum and minimum values of the parameters, respectively. Figure 7: Proposed scheme for predicting the surface roughness. The tangent-sigmoid activation function is applied between the input and hidden layers given mathematically as [48]:  
\[\tanh(x)=\frac{2}{1+e^{-2x}}-1 \tag{2}\]  
The hit and trial method was utilised to find the optimal number of neurons in the hidden layer.