## 2 Materials and Methods
### Machine Learning Models  
In this study, machine learning models were employed to predict cutting forces (Fx, Fy, and Fz) and surface roughness (Ra) during the micro-milling process of AlSi10 Mg aluminum alloy. These models offer a data-driven approach to optimize the machining process, reducing reliance on trial-and-error experimentation and enhancing both process efficiency and accuracy. The dataset used in this study comprises measurements from multiple micro-milling experiments, encompassing various cutting parameters such as feed rate, cutting depth, and spindle speed. The data underwent preprocessing through outlier detection, normalization, and feature selection techniques, ensuring their quality and relevance for model training. Recursive feature elimination (RFE) and mutual information scores were applied to identify the most relevant features for prediction. \begin{table}
\begin{tabular}{c c c c} \hline
**Test Number** & \begin{tabular}{c} **Feed Amount, fz** \\ **(\(\mu\)m/Tooth)** \\ \end{tabular} & \
\begin{tabular}{c} **Milling Speed,** \\ **(\(\mu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Depth of Cut, Doc** \\ **(\(\mu\)m)** \\ \end{tabular} \\ \hline \multirow{4}{*}{1–2–3–4–5–6–7–8–9–10–11–12} & 0.25 & 10,000 & \multirow{4}{*}{50} \\  & 0.5 & 20,000 & \\  & 1 & 30,000 & \\  & 2 & & \\ \hline \multirow{4}{*}{1–2–3–4–5–6–7–8–9–10–11–12} & 0.25 & 10,000 & \multirow{4}{*}{100} \\  & 0.5 & 20,000 & \\ \cline{1-1}  & 1 & 30,000 & \\ \cline{1-1}  & 2 & & \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters applied in the machining testing. Figure 3: Display for measurement of shear forces from valley to peak for Fx, Fy, and Fz. A variety of machine learning models were utilized to model the relationships between cutting parameters and performance metrics (cutting forces and surface roughness). These models, including RFR, GBR, LightGBM, CatBoost, and KNN, were selected for their efficacy in regression tasks and their capacity to handle complex, nonlinear relationships within high-dimensional datasets. These models were specifically chosen for several important reasons. First, efficiency is a key consideration, as deep learning models, despite their power, require substantial computational resources. In contrast, models like GBR and RFR provide high accuracy with relatively lower computational costs, making them more suitable for industrial applications. Second, interpretability is critical, as these models offer valuable insights into feature importance, enabling the identification of cutting parameters that most significantly impact cutting forces and surface roughness. Finally, performance was a determining factor, as ensemble models such as RFR and GBR have consistently demonstrated superior predictive accuracy in prior research, especially in structured datasets with multiple variables, such as those involved in micro-milling. Table 5 provides an overview of the machine learning models employed in this study, including their descriptions, theoretical foundations, and mathematical formulations. The dataset was divided into training (80%) and testing (20%) subsets to evaluate model performance. Hyperparameter optimization was conducted using grid search and cross-validation techniques, focusing on critical parameters such as learning rate, tree depth, and the number of estimators. Python ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)), accessed on 2 June 2025) was employed for model development, leveraging libraries such as Scikit-Learn, XGBoost, and TensorFlow to ensure efficient implementation of the models.