Ensemble Machine Learning for Predicting Machining Responses of LB-PBF AlSi10Mg Across Distinct Cutting Environments with CVD Cutter

The efficiencies of additive manufacturing (AM) over conventional processes have enabled the rapid production of aluminum (Al) alloys with AM. Because laser beam powder bed fusion (LB-PBF) parts do not offer the surface quality and geometrical accuracy for direct use, the functional surfaces of LB-PBF parts are usually machined by subtractive machining. The machinability of LB-PBF AlSi10Mg was studied in dry, MQL (used corn oil), and cryo-LN2 cutting environments across distinct speed–feed combinations using CVD-AlTiN-coated carbide inserts, and surface integrity and tool life were quantified in terms of surface roughness (Ra) and flank wear (Vb), respectively. The lowest Ra (0.98–1.107 μm) was obtained with cryo-LN2, followed by MQL and dry cutting environments, because the trends observed were consistent with the surface mechanisms observed in 3D topography and bearing curves. Similarly, the tool wear results mirrored the Ra results, lowest with LN2 (0.087–0.110 mm), due to improved thermal management, reduced adhesion and abrasion, and shorter contact length. Cryo-LN2 provided the best surface finish and tool life among all tested environments. To enable data-driven prediction, the limited dataset was augmented using SMOTE, and machine learning (ML) models were trained to predict Ra and Vb. CatBoost was found to yield the best Ra predictions (R2 = 0.9090), while Random Forest and XGBoost yielded the best Vb predictions (R2 ≈ 0.878).