ISBN: 978-93-82062-79-0 Surface Roughness Modeling in Wire Electrical Discharge Machining (WEDM) using Response Surface Methodology M.K. Das 1 , K. Kumar 2 , T.K. Barman 3 and P. Sahoo 4 1,3,4 Department of Mechanical Engineering, Jadavpur University, Kolkata, India 2 Department of Chemical and Polymer Engineering, BIT, Mesra, India e-mail: 1 milanpdas@gmail.com Abstract—The objective of this investigation is to study the surface characteristics of WEDMed surface of EN31 tool steel using a zinc coated brass wire as the tool electrode and to optimize the machining parameters for minimum roughness (centre line average). The machining process parameters selected for this study are discharge current, voltage, pulse on time and pulse off time. The second order response surface model is developed in terms of machining parameters for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. Experiment has been carried out according to the experimental plan based on central composite design (CCD). The significant coefficients are obtained by performing analysis of variance (ANOVA) at 95% confidence level. It is found that parameters, current and pulse on time have most significant effects on the R a . The model selected for optimization has been validated with F- test. Finally, confirmation test is carried out to check the validity of the experimentation. Keywords: WEDM, EN 31 tool steel, ANOVA, Response Surface Methodology (RSM). INTRODUCTION Wire electrical discharge machining (WEDM) is an important non-traditional machining process in which material is eroded from the work piece by a series of discharge spark between the work piece and wire electrode (tool) separated by a thin film of dielectric fluid (deionized water) which is continuously fed to the machining zone to flush away the erode particles. This machining process is widely used in the aerospace and automotive industry. There are many researchers who have tried to study the surface roughness of machined surface in WEDM. An extensive study on WEDM process has been carried out by Trang et. al.(1995) to determine the effects of different machine parameters on the responses viz., MRR and surface roughness using artificial neural network methodology. Similar study has been carried out by Rajkumar and Wang (1993) using a thermal model. Liao et. al. (1997) have performed an experimental study using SKD11 alloy steel as the work piece material and established mathematical models relating the machine performance like MRR, surface roughness and gap width with various machining parameters and then determined the optimal parametric settings for WEDM process applying feasible-direction method of non-linear programming. Kuriakose et. al. (2004) have carried out experiments with titanium 15 alloy (Ti-6Al-4V) and used a data-mining technique to study the effect of various input parameters of WEDM process on the cutting speed and surface roughness. Hewidy et. al. (2005) is developed the mathematical models for correlating the inter relationships of various WEDM machining parameters of Inconel 601 material such as peak current, duty factor, wire tension and water pressure on the metal removal rate, wear ratio and surface roughness using RSM. Ramakrishnan and Karunamoorthy (2006) have described the multi-objective optimization of WEDM process using parametric design of Taguchi methodology. Mahapatra and Patnaik (2006) have developed relationships between various process parameters and responses like MRR, surface roughness and kerf by means of non-linear regression analysis and then employed genetic algorithm to optimize the WEDM process with multiple objectives. Han et. al. (2007) have reported that the surface finish improved by decreasing pulse duration and discharge current in WEDM of alloy steel (Cr12). Kumar et. al. (2012) have presented an investigation on WEDM of pure titanium (grade-2) to investigate the effects of process parameters viz., pulse on time, pulse off time, peak current, spark gap voltage, wire feed and wire