Predictive Model and Analysis of the Resultant Force in the Dry Machining of Mg-4Zn/Si ₃ N ₄ Nanocomposites

The development of lightweight materials for use in aerospace and automotive applications is extremely significant. Magnesium (Mg)-based alloys and composites are good candidate materials from the perspective of low density, good specific strength, and abundance. The Mg-4Zn alloy is one such alloy, which is a lightweight, biocompatible, and eco-friendly Mg-based alloy. In spite of these advantages, there is a strong need and scope to improve its wear resistance and mechanical properties. Mg-4Zn nanocomposites with Si₃N₄ reinforcements (a biocompatible bioceramic) are hypothesized to possess superior properties. Microstructural analysis of the vacuum stir-cast nanocomposites confirms grain refinement and a consequent increase in microhardness with an increase in Si₃N₄ reinforcement wt.%. The addition of Si₃N₄ reinforcement to improve the properties of the Mg-4Zn alloy could introduce challenges in machining. To make products from the nanocomposites, machining them with minimal subsurface defects with minimal energy consumption under sustainable conditions is necessary. The resultant machining force (F_r) is a good indicator of subsurface quality and energy consumption in machining. To investigate the effect of reinforcement wt.% and machining parameters on the resultant machining force, dry turning experiments on the vacuum stir-cast Mg-4Zn/Si₃N₄ nanocomposites were carried out based on the response surface methodology-based Box-Behnken design. It is observed that the regression model for F_r is influenced by the reinforcement wt.%, cutting speed, feed rate, and depth of cut and also their squares and their mutual interactions. Increase in microhardness, variation in porosity, thermal softening, and strain hardening contribute to the variation in F_r. Minimal F_r and hence better subsurface quality and lower energy consumption are obtained at mid values of Si₃N₄ reinforcement wt.% and cutting speed and low values of feed rate and depth of cut. The developed model is an excellent fit, with R² and adjusted R² values of 0.9907 and 0.9799, respectively.