Material removal by turning is the basic and the most important process in metal cutting. Product quality mainly depends on the process used and the parameters which affect them. The important turning parameters which affect the quality of product are cutting speed, feed rate, depth of cut, cutting tool used, cutting fluid and the material of the workpiece. The study involves the identification of the optimized process parameters in CNC turning of AISI 15b25 alloy steel. Three important parameters, namely, cutting speed, feed and depth of cut, have been considered as machining input parameters, and the output parameters to be optimized are Material Removal Rate (MRR) and surface roughness (Ra). Response Surface Methodologies (RSM) of the design e-Expert have been implemented for the optimization of process parameters. The optimal values of the surface finish and MRR are found to be 2.29 ľa and 1327.93 mm3/min respectively.

Surface roughness (Ra) and Material Removal Rate (MRR) are the most important response parameters in manufacturing process, and optimization depends on different input parameters. During turning operation, the factors which affect the MRR and the Ra are spindle speed, feed rate, depth of cut, tool nose radius, etc. The change in the factors will produce a significant effect on the output. Prasad et al. (1997) reported the development of an optimization module for determining process parameters for turning operations as part of a PC-based generative CAPP system with minimization of the production time as an objective. Paulo and Francisco (2005) presented an optimization study of Ra in turning Fiber Reinforced Plastic (FRP) using polycrystalline diamond cutting tool and found that the Ra value increases with the increase in feed rate, reducing the cutting velocity. Thamizhmanii et al. (2007) focused on the analysis of optimum cutting conditions for the optimized Ra in turning SCM 440 alloy steel by Taguchi method. It has been concluded that the depth of cut has a significant role in achieving lower Ra, followed by feed rate. Kirby (2006) analyzed the experiments for optimizing Ra generated by a CNC turning operation using the Taguchi parameter design. The study produced a verified combination of controlled factors and a predictive equation for determining Ra with a given set of parameters. Dilbag and Venkateswara Rao (2007) conducted experiments for predicting the effect of cutting condition and tool geometry on Ra in the finished hard turning of bearing steel (AISI 52100). Mixed ceramic inserts made up of aluminum oxide and titanium carbonitride having different nose radius and different rake angles were used as a cutting tool. The study showed that the feed rate is the dominant factor, followed by nose radius and cutting velocity. Kaladhar et al. (2010) dealt with the optimization of AISI 202 unaustenitic stainless steel using CVD coated cemented carbide tools. During the experiment, the process parameters such as speed, feed, depth of cut and nose radius have been considered to explore their effect on Ra of the workpiece. The experiments have been conducted using full factorial on CNC lathe. From the analysis, the feed rate has been observed as the most significant factor that influences Ra.