Sustainable Nano-assisted Dry Turning of Hybrid Aluminium Matrix Composites: Process Parameter Optimisation via Grey Relational Analysis

The sustainable machining issue has received additional consideration in recent years due to its social, economic, and environmental implications. The paper examines the performance optimisation of green nano-aided dry turning of the hybrid metal matrix aluminium alloy through the lens of Grey Relational Analysis (GRA). By adding a nanoparticle that minimizes a negative environmental impact, the tribological behaviours are enhanced and the cutting efficiency maximized. There are three key turning factors such as, cutting speed, feed rate and depth of cut which were systematically varied to establish the corresponding machining characteristics and sustainability aspect such as carbon emission, energy consumed, cutting force and surface topography. The findings of mean GRG and ANOVA revealed that the parameter that would exert the most impact in achieving sustainable machining is the depth of cut. The optimal set of parameter values that was identified using GRA was the combination of the following parameters, cutting speed of 200 m/min and feed rate of 0.05 mm/rev and depth of 0.2 5 mm. The employment of such parameters, along with the application of the nano-enhanced dry turning conditions led to a 72.84 percent reduction in carbon, 72.82 percent energy, 18.58 percent of cutting power, and 6.83 percent of the surface roughness. There was also an increase in performance by 0.1702 in total GRG. As was observed in this paper, nano-assisted machining strategies and parameter optimisation can play a very crucial role in facilitating sustainable manufacturing of the aluminium-based hybrid composites.