© 2014 IJIRT | Volume 1 Issue 11 | ISSN: 2349-6002 IJIRT 101697 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 440 APPLICATION OF TAGUCHI METHOD FOR OPTIMIZING SURFACE ROUGHNESS IN CNC TURNING OF EN 8 STEEL N. Sathiya Narayanan, M. Ganesan, V. Prem Kumar, P. Vijayakumar, N. Baskar Department of Mechanical Engineering, Saranathan College of Engineering, Trichy, Tamil Nadu, INDIA Pincode- 620012 Abstract--Turning operation is the basic metal removal process; during this process heat is generated between the work piece and cutting tool which affects the surface finish of the work piece. The advantage of using this CNC turning process is to reduce the cost and also enhance the quality of the finished component. In this experimental work conducted on EN8 material using CNC Lathe with SINUMERIK 802D Control System with variable cutting speed of 60 rpm, 80 rpm and 100 rpm based on the L9 orthogonal array. The turning parameters such as spindle speed, feed rate and depth of cut was selected and investigated at three different levels to study the effect on surface roughness. The surface roughness of the work piece is measured using TR 1900 SURFACE ROUGHNESS TESTER. The optimum level of turning parameters was determined by using Taguchi design of experiments. The statistical methods of signal to noise ratio (S/N) and analysis of variance (ANOVA) were applied to investigate the effects of turning process parameters on surface roughness. Confirmation test with the optimal levels of cutting parameters shows that the optimized value of SR falls within 95% confidence level Index Terms- Turning Process, Design of Experiments, EN 8 Steel, Surface roughness. I.INTRODUCTION The turning process parameters such as spindle speeds, feed rates and depth of cuts are the main factors that affect the surface roughness. The main objective of this experimental work is to find the optimum machining parameters for better surface quality of the work pieces. Shreemoy Kumar Nayak et al (2014) investigated the influence of machining parameters namely cutting speed, feed and depth of cut on turning of AISI 304 stainless steel using ISO P30 grade uncoated cemented carbide insert and adopted L27 orthogonal array to measure the characteristics of machinability such as material removal rate (MRR), Cutting force (Fc) and surface roughness (Ra). The machining parameters are optimized using gray relational analysis. Ali R. Yildiz (2013) used evolutionary based optimization technique of artificial bee colony algorithm for selecting the optimal cutting parameters in multi- pass turning operations and compared with previously published results. Doriana M. D’Addona et al (2013) determined the optimal cutting parameters during turning process using genetic algorithm for reducing the production cost and time. L.B. Abhang et al (2012) carried out the turning process in EN31 steel alloy using tungsten carbide inserts by varying the cutting parameters namely feed rate, depth of cut, and lubricant temperature to observe the effects on surface finish. Khaider Bouacha et al (2014) conducted an experimental study of hard turning of AISI 52100 bearing steel, with CBN tool by using response surface methodology (RSM) to find the relationship between process parameters and performance characteristics. The results show that the cutting speed exhibits maximum influence on abrasive tool wear and depth of cut affects strongly the cutting forces. Arshad Noor Siddiquee et al(2014) focused on optimizing deep drilling parameters based on Taguchi method for minimizing surface roughness by conducting experiments on CNC lathe machine using solid carbide cutting tool on material AISI 321 austenitic stainless steel and determined the machining parameter which significantly affects the surface roughness and also the percentage contribution of individual parameters. Murat Sarıkaya et al (2014) used design of experiments to study the effect of turning parameters such as cooling condition, cutting speed, feed rate and depth of cut on arithmetic average roughness (Ra) and average maximum height of the profile (Rz) by turning of AISI 1050 steel. The mathematical model for surface roughness is created using response surface