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The challenge in modern industry is to manufacture low cost, high quality products in terms of work piece dimensional accuracy, surface finish, high production rate, less wear on the cutting tools and also in short time period with less environmental impact. Machining plays an important role in manufacturing products. Machining by turning in general involves the process of material removal from the outer surface of cylindrical or conical components. Turning is one of the most fundamental and most widely used material removal processes in a real manufacturing environment amongst various cutting processes (Zerti et al., 2019). The quality of manufactured products is determined by its surface quality or surface roughness which is produced due to mechanism of the material removal process. Surface roughness is characteristic evidence on a machined surface in the form of finely spaced micro irregularities left by the cutting tool (Davim, 2008).
Surface roughness in general is characterized by amplitude, spacing and hybrid parameters (Sahoo, 2005). Amplitude parameters include center line average roughness (Ra), root mean square roughness (Rq), etc. Spacing parameters are characterized by mean line peak spacing (Rsm), high spot count, etc. Hybrid parameters include root mean square wavelength of profile, root mean square slope, etc. The effect of turning process parameters on surface roughness has been investigated extensively through the use of design of experiments (DOE) and statistical techniques (Haque et al., 2017). The most widely used optimization technique over the years has been the Taguchi coupled grey relational analysis (GRA) and response surface methodology (RSM) due to their simplicity, cost-effectivity and quick outcomes (Zerti et al., 2018; Lin, 2004; Bouacha et al., 2010; Nian et al., 1999; Asiltürk & Neşeli, 2012; Yang & Tarng, 1998; Nalbant et al., 2007; Saidi et al., 2019; Alok & Das, 2019). Asiltürk & Neşeli (2012) investigated the effect of influence of cutting speed, feed rate and depth of cut on surface roughness of AISI 304 austentitic stainless steel using Taguchi based RSM. Optimal Ra was achieved for 50 m/min cutting speed, 0.15 mm/rev feed rate and 1.5 mm depth of cut. Yang and Tarng (1998) investigated the effect of cutting speed, feed rate and depth of cut on Ra of S45C steel bars using Taguchi’s signal to noise (S/N) ratio and analysis of variance (ANOVA). Optimal Ra was achieved at 135 m/min cutting speed, 0.08 mm/rev feed rate and 1.1 mm depth of cut and feed rate was seen to have the highest contribution in controlling the Ra. The OA, Taguchi analysis and ANOVA was used to predict optimal parametric condition to achieve minimum surface roughness of AISI 1030 steel in turning (Nalbant et al., 2007). An insert (TiN coated) radius of 1.2 mm, feed rate of 0.15 mm/rev and depth of cut 0.5 mm results in improved Ra. ANOVA results indicated that insert radius and feed rate were the most influential parameters. Likewise Taguchi based GRA, RSM and ANOVA was used study the effect of turning parameters on roughness of different grades of steel, metal matrix composites, brass, copper, titanium alloy (Ti-6Al-4V), etc. (Lin, 2004; Bouacha et al., 2010; Saidi et al., 2019; Alok & Das, 2019; Palanikumar & Karthikeyan, 2006; Li & Laghari, 2019; Sahoo et al., 2008; Liang et al., 2019).