INFLUENCES OF MACHINING PARAMETERS DURING DRY TURNING OF AL-SI BASED 1 WT. % SIC NANOPARTICLE REINFORCED NANOCOMPOSITE MATERIAL

The study aims to (1) analyze the role of machining parameters through ANOVA (analysis of variance) and (2) optimize the process parameters through Taguchi–Fuzzy Inference System (Fis) simulation. Dry turning of aluminium alloy based (cast A356 Al - Si alloy) SiC nanoparticle reinforced nanocomposite was performed for the purpose. The A356 aluminium alloy with 1 wt. % SiC nanoparticle nanocomposite was produced by stir casting operation. Dry turning of cast nanocomposite was performed at various speed, feed and depth of cut (DOC) values as per Taguchi L9 orthogonal array. A Tungaloy made coated carbide tool was used for dry turning operation. The ANOVA indicated that depth of cut was the highest contributing parameter in the machining process. Taguchi-Fuzzy inference system simulation also ensured that the depth of cut was the most influential factor in the machining work. This validated the finding of the ANOVA for MRR maximization since this analysis showed significant contribution of DOC for material removal rate (MRR) maximization. Usual trend in variation of surface roughness with respect to increased speed was attainable because of no particle emission and developed material properties of the nanocomposite. This further showed validation of ANOVA result since this analysis indicated that speed was the highest contributing parameter for surface roughness minimization.