## 4 Conclusions  
This study investigated the effects of cutting parameters on cutting forces and surface roughness in the micro-milling of Al10 MgSi alloy, supported by ML predictive models to enhance process understanding and optimization. Cutting forces analysis revealed that increasing the cutting depth and feed rate significantly raises the tangential (Fx) and radial (Fy) cutting forces due to enhanced friction and material removal rates. Conversely, increasing the spindle speed reduces cutting forces, mainly due to thermal softening effects at the tool-workpiece interface, which is consistent with the thermal dynamics typical in micro-milling processes. Notably, the Fx component dominated the cutting force profile, aligning with the primary direction of material removal and shear deformation. An optimal spindle speed of around 20,000 rpm was identified to balance cutting force reduction and machining efficiency, while a depth of cut of 100 \(\upmu\)m optimized machining time, highlighting the necessary trade-off between tool load and production speed. Machine learning-based predictive modeling demonstrated outstanding accuracy in forecasting cutting force components. Among the evaluated models, CatBoost consistently delivered the best performance, with R\({}^{2}\) values exceeding 0.95 on test datasets for all force components, minimal error rates, and superior generalization capability. This validates CatBoost's suitability for real-time force prediction and adaptive control in micro-milling, a crucial advancement for smart manufacturing environments and Industry 4.0/5.0 implementations. Surface roughness outcomes showed a direct correlation with feed rate and cutting depth, both contributing to increased Ra values due to higher material removal rates and dynamic tool-workpiece interactions. Increased spindle speeds, however, tended to reduce surface roughness, attributed to the formation of built-up edges that smoothen the machined surface. The visual analysis of surface topography highlighted ploughing effects and irregular cutting marks at low parameter settings, underscoring the importance of optimal parameter selection and toolpath strategies to maintain surface integrity. The ML models for surface roughness prediction mirrored those for cutting forces, with CatBoost outperforming others by accurately capturing the complex, nonlinear interactions between process variables and surface quality metrics. This predictive capability enables proactive control strategies for maintaining product quality under varying operational conditions. The practical implications of this research suggest that a balanced selection of cutting depth, feed rate, and spindle speed, informed by robust ML predictions, can significantly improve tool life, machining efficiency, and product quality in micro-milling operations. The integration of ML-based predictive models into CNC control systems and digital twin frameworks represents a promising pathway for advancing precision manufacturing technologies. Future work could expand this approach by incorporating tool wear monitoring, dynamic vibration analysis, and multi-material machining to further enhance the robustness and applicability of predictive models in increasingly complex micro-machining environments. Conceptualization, Z.A.C., K.O., A.E., and G.S. ; methodology, G.S., Z.A.C., K.O., and A.E. ; software, G.S. and A.E. ; validation, G.S., Z.A.C., K.O., and A.E. ; formal analysis, G.S., Z.A.C., K.O., and A.E. ; investigation, G.S., Z.A.C., K.O., and A.E. ; resources, G.S., Z.A.C., and K.O. ; data curation, G.S., Z.A.C., K.O., and A.E. ; writing--original draft preparation, G.S., Z.A.C., K.O., and A.E. ; writing--review and editing, G.S., Z.A.C., K.O., and A.E. ; visualization, G.S., Z.A.C., K.O., and A.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.