Multi Response Optimization during Machining of Magnesium Matrix Nanocomposite Reinforced with TiN Nanoparticulates

The present investigation is all about multiple process parameters optimization in the milling of TiN nanoparticulate reinforced magnesium matrix composite, which was carried out using Taguchi based Grey Relational Analysis (GRA).The parameters considered during the end milling process were the weight fraction of TiN in the composite, feed rate and depth of cut.Spindle speed was maintained constant at 1000 rpm.The performance parameters considered in the study include cutting force, material removal rate and surface roughness of the machined surface.The milling process was carried out in normal atmospheric conditions and at room temperature.An end mill cutter made of polycrystalline diamond (PCD) coated carbide was used in the work.L9 orthogonal array was used in the design of experiments and optimization process.The results of the optimization revealed that the combination of process parameters is A3B2C1, corresponding to 5-wt.%TiN, 10 mm/min feed rate and 0.15 mm depth of cut respectively, ranked top with respect to Grey Relational Grade (GRG), which was an indicator of optimization for the machinability of Mg-TiN nanocomposite.The predicted combination of optimized process parameters was confirmed theoretically and experimentally again and found that the combination of process parameters was A1B1C1, corresponding to 1.5 wt.% TiN, 5 mm/min feed rate and 0.15 mm depth of cut respectively for being achieved minimum cutting forces, maximum material removal rate and least surface roughness on the machined surface.The main effect and interaction effect plots confirmed the fact that the effect of process parameters on machinability performance was appreciably significant in the order: depth of cut, feed rate and weight fraction of TiN nanoparticles and complemented with normal probability, surface, and residual plots.