International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 268-273 © Research India Publications. http://www.ripublication.com 268 Effect of Process Parameters on MRR and Surface Roughness in Turning Operation using Taguchi Method Piyush Pant*, Kunwar Laiq Ahmad Khan 1 , Rupesh Chalisgaonkar 1 and Shobhit Gupta 1 Department of Mechanical Engineering, KIET Group of Institutions, Ghaziabad, India ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Abstract In present study, effect of process parameters in turning of mild steel has ben investigated. The process parameters speed, feed and depth of cut are considered. Material removal rate and surface roughness are taken as the response variables. Taguchi method is employed and signal to noise ratio is calculated using L9 orthogonal array. The combination of optimum levels of process parameters to achieve simultaneous maximization of material removal rate and minimization of surface roughness, is found out and also checked experimentally. Keywords: Process Parameters; Material Removal Rate; Surface Roughness; Taguchi Method -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- I. Introduction In current scenario, machining industries have a challenging task to obtain high quality with respect to dimensional accuracy, surface finish, low wear, machining economy. Surface roughness of the machined workpiece is the most important criteria to judge the quality of machining. The literature survey has revealed that several researchers have attempted to calculate the optimum cutting conditions in a turning operation. Now a day’s more attention is given to accuracy and surface roughness. Surface roughness is the most important criteria in determining the machinability of the material .Surface roughness and dimensional accuracy are the major factors needed to predict the machining performances of any machining operation. Optimization of machining parameters increases the utility for machining parameters increases the utility for machining economics and also increases the product quality to greater extent. II. Literature Review Kothiyal et al (2013) performed optimization of parameter using Taguchi methodology and ANOVA. The L9 Orthogonal array is used in MINITAB 15 which shows the percentage contribution of each influencing factor on MRR. The material used for experiment is (100 × 34 × 20 mm) blocks of aluminium cast heat-treatable alloy. Chandrasekaran et al (2013) studied the machinability of AISI 410 on CNC lathe for SR using taguchi method. The effect and optimization of machining parameters on SR is investigated. L27 0rthogonal array, analysis of variance was used in this investigation. The experiment was conducted on CNC lathe. Work material of Ø32 mm and length 60 mm was used. Joshi et al (2012) investigated the SR response on CNC milling by Taguchi technique. Analysis of variance was used in this investigation. The material used for the experiment is (100 × 34 × 20 mm) 5 blocks of aluminium cast heat-treatable alloy. The output characteristic, surface finish is analysed by software Minitab 15 and ANOVA is formed, which shows the percentage contribution of each influencing factor on surface roughness. Zhang et al (2012) investigated the Taguchi design application to optimize surface quality in a CNC face milling operation. An orthogonal array of L9 was used and ANOVA analyses were carried out to identify the significant factors affecting surface roughness. CNC Mill : Fadal VMC-40 vertical machining centre was used for this experiment and 19. 1×38. 1×76. 2 mm aluminum blocks as a work piece. The experimental results indicate that in this study the effects of spindle speed and feed rate on surface were larger than depth of cut for milling operation. Oenafdos et al (2011) used neural network modelling approach for the prediction of surface roughness in CNC face milling. Taguchi design of experiments method is used and MATLAB version 5. 3. 0. 10183 (Rll) program is used to create, train and test the ANNs. Gologlu et al (2011) Studied about pocket milling which is often encountered in plastic mould manufacture. The implementation and selection of cutting path strategies with appropiate cutting parameters have significant effect on surface roughness. The aim of this tudy is to investigate optimum cutting speed of DIN 1.2738 mould steel using high- speed steel end mills. Reddy et al (2010) optimized the parameters for surface roughness using response surface methodology and genetic algorithm. The work piece material used for the present investigation is P20 mould steel of flat work pieces of 100mm x100mm x10 mm. Pre-hardened steel (p20) is a widely used material in the production of moulds/dies due to less wear resistance and usedforlarge components. The experiments were conducted using Taguchi's L50 orthogonal array using design of experiments by considering the machining parameters such as nose radius, cutting speed, feed, axial depth of cut and radial depth of cut. Kromanis et al (2010) developed a technique to predict a surface roughness of part to be machined. 3D surface parameters gave more precise picture of the surface, therefore it is possible to evaluate more precisely the surface parameters according to technological parameters. In result of the study, the mathematical model of end-milling is achieved and qualitative analysis is maintained. Achieved model could help technologists to understand more completely the process of forming surface roughness. Routara et al (2010) performed the