Effect of Process Parameters on MRR and Surface Roughness in ECM of EN 31 Tool Steel Using WPCA

1. Introduction

In any machining operation, material removal rate (MRR) and surface roughness are the important parameters considering the economic and tribological points of view. Electrochemical process is a popular machining process in the industry where material removal takes place by the anodic dissolution of the workpiece in an electrolytic cell. Kock et al. (2003) have reported about the application of ultra-short voltage pulses electrochemical reactions which can be used for nanometer accuracy, and allows for high precision machining of electrochemical active materials. Chakradhar and Venu Gopal (2011) have optimized ECM process performed on EN31 steel considering electrolyte concentration, feed rate and applied voltage as process parameters using grey relational analysis. Mithu et al. (2012) have studied the effect of tool diameter, length and applied frequency on the shape and size of the fabricated microholes on MRR. Ruszaj and Zybura-skrabalak (2001) have developed a mathematical model for ECM utilizing a flat ended universal electrode. Senthilkumar et al. (2009) have used response surface methodology (RSM) to study the characteristics of ECM of Al/SiCp composites. The machining parameters chosen for the study are applied voltage, electrolyte concentration, electrolyte flow rate and tool feed rate. Neto et al. (2006) have studied the effects of process parameters on MRR, roughness and over-cut and it is seen that feed rate is the main parameter affecting MRR. Jain and Adhikary (2008) have studied electrochemical spark machining (ECSM) process applied for cutting of quartz using a controlled feed and a wedge edged tool. Bhondwe et al. (2006) have concluded that MRR increases with increase in electrolyte concentration during ECSM. Rao et al. (2008) have discussed effects of tool feed rate, electrolyte flow velocity, and applied voltage on dimensional accuracy, tool life, MRR, and machining cost in ECM. A particle swarm optimization algorithm is presented to find the optimal combination of process parameters. Senthil Kumar et al. (2009) have used both Taguchi technique and Regression analysis to maximize MRR in ECM process of A356/S_iC_p composite. Das et al. (2014) have optimized gas pressure, arc current and torch height with consideration of multiple performance characteristics including MRR and surface roughness using grey Taguchi technique in ECM of EN31 steel and shown that the most significant cutting parameter is electrolyte concentration. Bahre et al. (2013) have attempted to model and optimize the pulse electro chemical machining (PECM) process using RSM. The machining parameters considered in the study are voltage, pulse on time, frequency, feed rate and pressure and the multiple responses are MRR and surface roughness (Ra). Burgera et al. (2012) have investigated the influences of machining parameters on MRR and surface roughness in ECM/PECM of nickel-base single-crystal alloy (LEK94). Rajurkar et al. (1998) have focused on minimizing the material to be removed by predicting minimum machining allowance and improving the degree of localized dissolution. Sharma et al. (2002) and Bilgi et al. (2004) have studied the effect of different feed rates and D.C. voltages on the variation in diameter with the depth of holes. The holes are drilled in a super alloy material by a partially insulated tool. Goswami et al. (2013) have optimized machining process parameters viz. voltage, tool feed and current with consideration of multiple performance characteristics including MRR and surface roughness for ECM of aluminum and mild steel material using Taguchi technique. Sarkar et al. (2006) have studied the influence of operating parameters of ECDM of silicon nitride on MRR, radial overcut and thickness of heat affected zone (HAZ).