Sustainable machining of magnesium alloy (AZ31): investigating tool wear and surface roughness under various conditions

Abstract The present study investigates the influence of various cutting conditions and input parameters on the turning performance of magnesium alloy (AZ31). Machining trials were conducted under dry, minimum quantity lubrication (MQL), and nano minimum quantity lubrication (NMQL) environments to evaluate their effects on tool wear and surface roughness (SR). The experiments were designed using the Taguchi L9 orthogonal array, incorporating three levels and three key input parameters: cutting speed, feed rate, and machining condition. The signal-to-noise (S/N) ratios were analyzed, and analysis of variance (ANOVA) was used to determine the most influential factors affecting the machining responses. The results indicated that cutting speed (with a probability of >96%) and machining environment (with a probability of >99%) were the primary contributors to variations in surface roughness and tool wear. Dry machining exhibited the highest levels of tool wear and surface roughness, while NMQL at a lower cutting speed proved to be the most effective condition for reducing these responses. The incorporation of nano Al 2 O 3 (aluminum oxide nanoparticles) into the machining oil enhanced its physical, chemical, and thermal characteristics, significantly improving cooling and lubrication performance. NMQL demonstrated superior effectiveness compared to other cooling methods.