Investigation on sustainable CNC turning of aluminum alloy and development of hybrid optimization

Aluminum alloys like AA5052 are widely used in the aerospace, marine, and automotive industries because they are lightweight, strong, and resistant to corrosion. However, machining these alloys is hard because it wears down tools and makes the surface less smooth. This research examines sustainable CNC turning of AA5052 under Minimum Quantity Lubrication (MQL) conditions utilizing Tungsten Carbide (WC) inserts. Taguchi’s L27 orthogonal array was used to plan the experiments, which looked at spindle speed (800–1000 rpm), feed (0.1–0.14 mm/rev), and depth of cut (0.5–0.7 mm) as variables. Material Removal Rate (MRR) and Surface Roughness (SR) are two examples of performance metrics. The results show that the MRR ranged from 0.6450 to 1.6174 mm3/s, with the highest value being 1.6174 mm3/s at 1000 rpm, 0.14 mm/rev feed, and 0.7 mm depth of cut. On the other hand, surface roughness was between 52.33 and 128.21 μm, and it was lower when the speeds were higher and the feed was lower. ANOVA showed that feed and depth of cut were the main things that affected both MRR (p = 0.000) and SR (p < 0.001). Using Grey Relational Analysis (GRA) for multi-criteria optimization, the best settings were found to be 1000 rpm, 0.1 mm/rev, and 0.7 mm depth of cut. An Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with the JAYA algorithm made very accurate predictions (R2 = 0.9952; MAE = 0.0045). The Pareto-based MO-JAYA optimization also gave trade-off solutions, with SR values as low as 0.681 μm and MRR values of 84.13 mm3/s. The new hybrid optimization model can make better predictions and decisions, which makes it easier to work with AA5052 while having less of an effect on the environment.