## 2 Experimental Approach
### Machine Learning Methods  
In this study, it was tried to predict the Almen intensity values of different steels by using residual stress, surface roughness, and surface hardness values. Artificial neural network (ANN), deep neural network (DNN), AdaBoost Regressor (ABR), Decision Tree Regressor (DTR), Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), and XGBoost Regression (XGBR) methods, popular machine learning methods, were used for this purpose. The artificial neural networks (ANNs) known as deep neural networks (DNNs) include several layers between the input and output layers [30]. Before being sent to the next layer, each layer's collection of neurons processes and transforms the incoming data. In order to learn hierarchical representations of the input data, DNNs are built with each layer learning increasingly complicated and abstract features [31]. DNNs have a wide range of applications, but training them requires a lot of labeled data and it is computationally expensive [32]. The training of significantly deeper and larger DNNs became possible through recent improvements in hardware and deep learning methods, resulting in state-of-the-art performance on a variety of applications [33; 34]. The human nervous system served as inspiration for ANNs, which are machine learning algorithms. They consist of input, output, and hidden layers, with the weights being changed during training to improve \begin{table}
\begin{tabular}{c c c} \hline \hline
**Applied Process** & \multicolumn{2}{c}{**Process Parameters**} \\ \hline SP & \multicolumn{2}{c}{Almen Intensity} \\  & 12–14 A & 18–20 A & 24–26 A \\ \hline VSP & Shot diameter (mm) & Time (s) \\  & 2 & 30 \\  & 2 & 60 \\  & 4 & 30 \\  & 4 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The study’s process parameters. accuracy [35]. The error between the projected and expected output is utilized to adjust weights by using forward propagation and back propagation [36]. Regression techniques are also utilized to solve prediction problems. The Decision Tree Regressor (DTR) is a machine learning technique for regression issues that generates a tree-like model of decisions and possible outcomes that may be used to forecast a continuous numerical output [37]. Additionally, a tool for regression problems called the Support Vector Regressor (SVR) finds a hyperplane in a high-dimensional space that optimizes the difference between expected and actual output values [38]. SVR is an effective machine learning technique for performing regression tasks, especially when dealing with high-dimensional data and detecting intricate nonlinear correlations [39]. Random Forest Regressor (RFR), another machine learning approach for regression, mixes different decision trees to produce a potent predictor of the target variable. It can capture nonlinear relationships, handle numerical and categorical data, and yield a measure of feature relevance [40]. It can, however, be computationally costly and delicate to noisy data [41]. Furthermore, boosting techniques also address the regression issues. A machine learning approach called Gradient Boosting Regressor (GBR) is used for regression tasks. Multiple weak learners (simple models) are combined to produce a powerful predictor of the target variable [42]. Different weak learners, such as decision trees, linear models, or neural networks, can be used with GBR. With only a small number of weak learners, it can attain excellent accuracy and is especially helpful for addressing nonlinear interactions [43]. However, it can be computationally expensive and sensitive to erratic data. An enhanced form of GBR is called XGBoost (Extreme Gradient Boosting) Regression [44], a technique for group learning that constructs several decision trees to correct errors made by the prior tree. Its capability to manage big datasets with high dimensionality and intricate interactions between characteristics and target variables makes it strong and well-liked.