Optimization of WEDM Process Parameters for MRR and Surface Roughness using Taguchi-Based Grey Relational Analysis

Introduction

Wire electrical discharge machining (WEDM) is a popular non-conventional machining process. Now a days, WEDM is widely used in the aerospace, automobile and medical industries as well as in virtually all areas of conductive material machining. In this process, 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 eroded particles. As a basic requirement, the machining operation should produce the final product with minimum time and at desired level of surface finish. Machining time is dependent on the material removal rate (MRR) of the process. It can be expressed in mass per unit time as well as volume per unit time. For industrial purpose, it is obvious that MRR should be the maximum from the economic point of view. On the other hand, the surface roughness plays an important role for the tribological operation of any component. It has large impact on the mechanical properties like fatigue behavior, corrosion resistance, creep life etc. It also affects other functional attributes of machine components like friction, wear, light reflection, heat transmission, lubrication, electrical conductivity etc. Surface roughness may depend on various factors like machining parameters, work-piece materials, cutting tool properties, cutting phenomenon etc. Several researchers have attempted to optimize the performance of WEDM process by different approaches. Tarng, Ma, & Chung (1995) have determined the effects of different machine parameters on the responses viz., MRR and surface roughness using artificial neural network (ANN) methodology in WEDM. Similar study has been carried out by Rajkumar and Wang (1993) using a thermal model. Liao, Huang, & Su (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 and Shunmugam (2004) have carried out experiments with titanium 15 alloys (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, EI-Taweel, and EI-Safty (2005) have 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 MRR, wear ratio and surface roughness using response surface methodology (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, Jiang, and Dingwen (2007) have reported that the surface finish is improved by decreasing pulse duration and discharge current in WEDM of alloy steel (Cr12). Das, Kumar, Barman, & Sahoo (2014) have optimized the multi-responses viz. material removal rate and surface roughness in WEDM of EN 31 steel using weighted principal component analysis (WPCA). Kumar, Kumar, and Kumar (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 tension on surface roughness. Datta, and Mahapatra (2010) have utilized RSM coupled with grey-based Taguchi technique to optimize the machining process parameters. Alias, Abdullaha, and Abbas (2012) have focused on the importance of kerf width, MRR, surface roughness (R_a) and surface topography and their strong dependence on the input parameters in WEDM of Titanium Ti-6Al-4V. Das, Kumar, Barman, and Sahoo (2013) have investigated the optimization of an EDM process performed on EN 31 tool steel considering pulse on time, pulse off time, current and voltage as process parameters. They have optimized the multi responses viz. MRR and surface roughness using weighted principal component analysis. Manna and Bhattacharyya (2006) have established mathematical models relating to the machining performance criteria like MRR, surface roughness, spark gap and gap current using the Gauss elimination method for effective machining of Al/SiC-MMC. Rao and Sarcar (2009) have analyzed the effects of process parameters on machining characteristics for CNC WEDM for brass work-pieces and have obtained mathematical relationships between process parameters and responses. Lok and Lee (1997) have compared the machining performance in terms of MRR and surface finish through observations obtained by processing of two advanced ceramics (Sialon and AI203-TiC) under different cutting conditions using WEDM. It is seen that the Wire-Cut EDM process is a viable material processing method for the machining of advanced ceramics. Das, Kumar, Barman, and Sahoo (2014) have studied an approach for the optimization of EDM process (pulse on time, pulse off time, current and voltage) with multiple performance characteristics viz. MRR and surface roughness using grey relational analysis. Spedding and Wang (1997) have studied the optimal combination of process parameters by modeling the process using RSM and ANN for maximum cutting speed, keeping the surface roughness and waviness within the required limits in WEDM of AISI 420. Lin and Wang (2010) have investigated the effects of various machining parameters on MRR and surface roughness in WEDM using Taguchi method-based grey analysis. However, all these studies whether experimental or analytical mostly concentrate on the centre line average roughness (R_a) value for surface quality. But surface generated by any machining is composed of a large number of length scales of super imposed roughness (Sahoo, 2005) that are generally characterized by three different types of parameters, viz. amplitude parameters, spacing parameters and hybrid parameters. Thus, consideration of centre line average roughness alone is not sufficient to describe surface quality. In this paper, five roughness characteristics, viz. centre line average roughness (R_a), root mean square roughness (R_q), skewness (R_sk), kurtosis (R_ku) and mean line peak spacing (R_sm) are considered.