VOL. 9, NO. 3, MARCH 2014 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences © 2006-2014 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 215 OPTIMIZATION OF MACHINING PARAMETER FOR TURNING OF EN 16 STEEL USING GREY BASED TAGUCHI METHOD P. Madhava Reddy, P. Vijaya Bhaskara Reddy, Y. Ashok Kumar Reddy and N. Naresh Department of Mechanical Engineering, N.B.K.R.I.S.T, Vidyanagar, Nellore, A.P., India E-Mail: palagirimadhavareddy@gmail.com ABSTRACT This paper presents the optimization of CNC turning parameters for EN 16 steel bar using the Grey Taguchi Method. A plan of experiments based on Taguchi’s L 27 orthogonal array was established and turning experiments were conducted with prefixed cutting parameters for EN 16 steel bar using tungsten carbide tool. The turning parameters are cutting speed, feed rate and depth of cut and the responses are surface finish and material removal rate. Taguchi’s signal-to- noise (S/N) ratio are determined based on their performance characteristics. A grey relational grade is obtained by using S/N ratio. Based on grey relational grade value, optimum levels of parameters have been identified by using response Table and response graph and the significant contributions of controlling parameters are estimated using analysis of variances (ANOVA). Keywords: CNC turning, EN 16 steel, surface roughness, MRR, grey Taguchi method, and ANOVA INTRODUCTION EN 16 is low alloy and high tensile steel and finds its typical applications in manufacturing of automobile, welded construction in structural components, air craft fittings, aerospace components and components for severe chemical environments. Properties of EN16 steel, like low specific heat, good resistance to shock, resistance to wear and excellent ductility. The machining of EN16 steel and its alloys is generally cumbersome owing to several inherent properties of the material. In machining, turning is a most widely used process in which a single point cutting tool removes material from surface of a rotating cylindrical workpiece. Surface roughness (SR) and material removal rate (MRR) have been identified as quality attributes and are assumed to be directly related to performance of mechanical process, productivity and production costs. Both the surface roughness and material removal rate greatly vary with the change of cutting process parameters. It is important to choose the best machining parameters for achieving optimum performance characteristics for machining process. The desired machining parameters are usually selected with the help of referred handbooks, past experience and various trails. However, the selected machining parameters may not be optimal or near optimal machining parameters. Taguchi method can be applied for optimization of process parameters to produce high quality products with lower manufacturing costs [1]. Taguchi’s parameter design is one of the important tools for robust design, which offers a systematic approach for parameters optimization in terms of performance, quality and cost [2- 6]. Taguchi technique had been applied to optimize machining process parameters for turning process of different grade of EN materials like EN-8 and EN-3 with TiN-coated cutting tools [3]. The same methodology had been used by yang et al., [4] to find the optimal cutting parameters, i.e. cutting speed, feed rate and depth of cut for surface roughness in turning operation based on experimental results done on S45C steel bars using tungsten carbide cutting tools. Kopac et al., [5] also used Taguchi orthogonal array for finding optimum cutting parameters, i.e. cutting speed, cutting tool materials, feed rate and depth of cut on surface roughness in machining C15 E4 steel on a lathe. Further, design optimization for quality was carried out by Asilturk et al. [6] to find the optimal cutting parameters in turning process for hardened AISI 4140 steel bars using coated carbide cutting tools by orthogonal array and analysis of variance. Thus, Taguchi methodology can be effectively used to optimize process parameters for single performance characteristic only. However, the optimizations of multiple performance characteristics find more applications and it is also an interesting research program. Many authors [7-10] have been proposed different methods for solving multiple performance characteristic problems. Naveen Sait et al. studied flank wear, crater wear, surface roughness and machining force on turning glass-fibre reinforced plastic (GFRP) pipes using desirability function analysis [7]. Ramanujam et al. [8] also used desirability function analysis for optimizing multiple performance characteristics namely surface roughness and power consumption in turning Al-15%SiC p metal matrix composites. Gopalsamy et al., [9] used grey relational analysis to investigate machinability study of hardened steel and to obtain optimum process parameters. Also applied analysis of variance (ANOVA) to study the performance characteristics of machining process parameters such as cutting speed, feed, depth of cut and width of cut with consideration of multiple responses, i.e. volume of material removed, surface finish, tool wear and tool life. Taguchi method integrated with grey relation theory for solving multi-objective optimization problem has been proposed by Sanjit et al., [10]. They have adopted entropy measurement technique to calculate individual response weights according to their relative priority. The present study aims to achieve an optimum combination of machining process parameters considering two responses namely surface roughness and material removal rate. The traditional Taguchi method focused on