Optimization Of Process Parameters Of Wirecut Electric Discharge Machine Processed En 31 Steel By Grey Relational Analysis

Abstract

 WIRE EDM has become an important non-traditional machining process as it provides an effective solution for producing components made of difficult to machine materials like titanium, zirconium etc. and intricate shapes which are not possible by conventional machining methods. Due to large number of process parameters and responses lots of researchers have attempted to model this process. This paper reviews the research trends in WEDM on relation between different process parameters like pulse on time(TON), pulse off time(TOFF), servo voltage(sv) on different process responses like Material Removal Rate, Surface Roughness. In addition, the paper highlights different modelling and optimization methods. The final part of the paper includes some recommendations about the trends for future wire Edm researches.

The main objectives of this study investigate and evaluate the effect of different input process parameters (pulse on time, pulse off time, servo voltage) on material removal rate, surface roughness as response parameters have been considered for each Experiment. Experimentation was planned as per Taguchi’s L27 Orthogonal array during machining of EN 31 work material which is High Carbon High Chromium Die Steel (HCHCR). Brass wire electrode with 0.25mm Diameter was used as tool in the Experiments. The results are analyzed using analysis of variance (ANOVA) method. This parametric analysis (ANOVA) shows the percentage contribution of parameters individually. Grey relational analysis are applied to determine the suitable selection of machining parameters for wire cut EDM process. A grey relational grade obtained from the grey relational analysis is used to optimize the process parameters. By analyzing the Grey relational grade we find the optimum parameters. Confirmation test has been conducted to optimize the process parameter. Further mathematical models are developed using Box-Behnken design of experiments of response surface methodology to optimize the process parameters using state of art optimization techniques for future studies.