The wear loss and frictional characteristics of magnesium-based hybrid composites reinforced with boron carbide (B₄C) particles and graphite filler were the main subjects of the investigation. Key parameters, including reinforcement content (0–10 wt%), applied load (5–30 N), sliding speed (0.5–3 m/s), and sliding distance (500–3000 m), were varied. Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. A comparison of model performances revealed that the Gradient Boosting Machine (GBM) provided superior accuracy compared to other machine learning models in predicting both wear loss and the coefficient of friction. Additionally, feature importance analysis indicated that the graphite weight percentage was the most significant influence in predicting the coefficient of friction and wear loss characteristics.

The burr removal and finishing of drilled hole walls typically require multiple post-processing steps. This experimental study introduces a novel single-step drilling approach using modified drill bits for simultaneous burr removal and surface finishing in aluminum 6061-T6. The odified-1 drill, equipped with a deburring micro-insert, achieved superior results, with a chamfer height of −2.829 mm, drilling temperature of 40.28 ◦C, and surface roughness of 0.082 µm under optimal conditions. Multi-objective optimization using the RSM and MOGA-ANN identified the optimal drilling parameters for the Modified-1 drill at 3000 rpm under water lubrication as compared to dry conditions and kerosene. Experimental validation confirmed the high prediction accuracy, with deviations under 6%. These results establish the Modified-1 twist drill bit with a deburring micro-insert as a highly effective tool for burr-free high-quality drilling in a single operation. This innovative drill design presents an efficient, single-step solution for burr elimination, chamfer formation, and surface finishing in drilling operations.