The machinability of graphene-reinforced magnesium-based hybrid nanocomposites produced through the application of powder metallurgy method has not been completely reported. This article presents an experimental investigation on the thrust force, the surface roughness (R_a), and drilled surfaces characteristics in the drilling process of a Mg/SiC/GNPs (magnesium matrix based silicon carbide and graphene nanoplatelets) hybrid magnesium matrix composite. The hybrid composite material was produced through the application of a powder metallurgy method. The experiments were carried out with uncoated, PVD (Physical Vapor Deposition), and CVD (Chemical Vapor Deposition) coated tungsten carbide drill bits at three levels of cutting speeds (30, 40, and 50 m/min), and three different levels of feed rate (0.10, 0.15, and 0.20 mm/rev) under dry machining conditions. Taguchi’s L₂₇ (3³) orthogonal array and S/N ratio were used to optimize the optimal parameters for thrust force and surface roughness. The experimental results indicated that the thrust force and the surface roughness were extremely dependent on a particular type of drill bits, feed rate, and cutting speed. The feed rate parameter is known to have a significant influence on the surface finish.

Incorporating Cu and Zn into Mg as a biomaterial offers a unique opportunity to exploit their antibacterial performance and biodegradability. The main challenge in this area is understanding the ratio and effects of these elements. To achieve this, the present work, based on two separate studies, aims to develop a regression model and apply machine learning (ML) to predict the wear behaviors using the effects of Cu and Zn elements doped into Mg matrix at low ratios on wear and micro and nanostructure properties (Grain size, density, hardness, Crystallite Size, microstrain, dislocation density). The wear behavior of the samples was investigated under 5–20 N loads at a constant sliding speed of 42 mm/s. Auto Sklearn library was used to generate training models that accurately predict the wear loss, friction coefficient, and specific wear rate values. The model showed satisfactory explanatory power and reliability in predicting the volume loss target. It also exhibited remarkable capability in predicting the friction coefficient and specific wear rate targets. The results of sample wear tests (MgZn2 under 15 N) conducted to generate data not included in the dataset showed a high degree of agreement with the ML results. Sensitivity analyses confirmed that Load, Environment, Hardness, and Grain Size are the most influential factors in predicting wear behavior, further validating the model’s reliability and interpretability.