Optimization of Process Parameters for Precise Corner Accuracy in Wire Electric Discharge Machining of AISI D2 Tool Steel

Abstract

Abstract This research is carried out to explore the effect of machining (WEDM) parameters on the corners cutting in D2 tool steel including the determination of optimum input values such as pulse on time, pulse off time, discharge gap, wire speed, and wire tension, for excellent machining performance. In the present study Taguchi technique, which uses L16 orthogonal array is utilized for the design of experiments and optimization, signal-to-noise (S/N) ratio, and main effects plots, analysis is used to identify significant factors and their optimal values according to its level to obtain superior machining characteristics. Five process control parameters, namely, pulse on, pulse off time, discharge gap, wire feed, and wire tension are analyzed for four response parameters viz kerf width, corner error of top and bottom side of the machined profile, and surface roughness. Regression models are developed for all the WEDM responses, and main effects plots and analysis of variance (ANOVA) are used to analyze the notable factors influencing the kerf width, corner error top and bottom sides, and surface roughness. Pulse-on and pulse-off time, discharge gap, and wire speed have been identified as the most influential factors for kerf width, corner error, and surface roughness. It is observed that the predicted values of all the performance measures from regression models are within 95% confidence intervals compared to experimental results of all the response variables, so the predicted regression equations of all response values are useful to estimate the machining performance of WEDM processes. The WEDM machined surface of selected workpieces is analyzed with the help of an optical microscope, and scanning electron microscope (SEM), coupled with energy dispersive X-ray spectroscopy (EDX), elemental composition, resolidified region, micro-cracks, craters, heat-affected zone, and white layer, are observed on the machined surface at different magnification.