## 1 Introduction
In recent years, significant advancements in ML techniques have facilitated highly accurate prediction and optimization in machining processes, especially concerning surface roughness and cutting forces. Numerous studies have effectively integrated ML algorithms such as artificial neural networks (ANN), support vector regression (SVR), random forest (RF), and gradient boosting methods with multi-objective optimization tools like NSGA-II to enhance machining performance. For instance, robust hybrid frameworks combining ML models with NSGA-II have been developed to minimize surface roughness and tool wear in the turning and milling of alloy steels and aluminum alloys [19; 20]. Similarly, ML-based models like ANN and SVR were employed with genetic algorithms for the optimization of surface roughness and cutting forces in the milling of aluminum alloys [21]. The predictive capacity and industrial applicability of such models have also been emphasized in studies highlighting the accuracy, cost-effectiveness, and stability of ML-based systems for forecasting machining parameters across various materials and processes, ranging from conventional metal cutting to specialized applications such as rotary ultrasonic bone drilling [22; 23]. Additionally, ML-aided optimization frameworks have been validated in hard turning scenarios involving advanced coatings and high-strength alloys [24; 25]. Furthermore, hybrid strategies combining ML and simulation techniques, such as finite element analysis and deep reinforcement learning, have been proposed to predict cutting forces and energy consumption accurately [26; 27]. Complementing these, ML modeling under eco-efficient lubrication conditions was explored to predict surface roughness in MQL-assisted turning [28]. Collectively, these studies underscore the potential of ML-integrated optimization approaches as reliable, flexible, and efficient tools for enhancing productivity and sustainability in modern machining operations. However, most existing studies focus on conventional materials or macro-scale processes, with limited exploration of micro-milling performance on additively manufactured (AM) AlSi10 Mg alloys. Additionally, few works compare multiple machine learning models under identical experimental conditions for predicting both cutting forces and surface roughness in micro-machining. This study addresses these gaps by systematically analyzing the machinability of DMLS-fabricated AlSi10 Mg using a wide range of ML models, thereby contributing novel insights into process optimization for the precision manufacturing of AM components. This study investigates the optimum cutting parameters for the micro milling of AlSi10 Mg aluminum alloy produced using the powder bed fusion additive manufacturing process. The samples, AlSi10 Mg aluminum alloy, were produced by the direct metal laser sintering (DMLS) process. To evaluate the impact of machining parameters, various cutting speeds, feed rates, and depths of cut were applied to assess their effects on the efficiency and surface roughness characteristics of the micro-milled components. In addition, machine learning models were applied to predict the cutting forces and surface roughness, providing a data-driven approach to optimize the machining process. This research introduces a novel approach by offering valuable insights into the micro-milling of additively manufactured aluminum alloys, focusing specifically on the DMLS-fabricated samples. It also explores the implications of the findings and identifies potential improvements for the micro-machining of aluminum alloys, particularly those produced via the DMLS process.