Abstract
In this paper, wire electrical discharge machining of WC-Co composite is studied. Influence of taper angle, peak current, pulse-on time, pulse-off time, wire tension and dielectric flow rate are investigated for material removal rate (MRR) and surface roughness (SR) during intricate machining of a carbide block. In order to optimize MRR and SR simultaneously, grey relational analysis (GRA) is employed along with Taguchi method. Through GRA, grey relational grade is used as a performance index to determine the optimal setting of process parameters for multiple machining characteristics. Analysis of variance (ANOVA) shows that the taper angle and pulse-on time are the most significant parameters affecting the multiple machining characteristics. Confirmatory results, proves the potential of GRA to optimize process parameters successfully for multi-machining characteristics.
Highlights
The cemented carbides such as WC-Co are typically used in tool and die industries because of their excellent hardness and strength
analysis of variance (ANOVA) depicts that three process parameters namely taper angle (A), pulse-on time (C) and Pulse-off time (D) are the most significant parameters affecting the material removal rate (MRR) and surface roughness (SR) under 95% confidence level
Confirmatory experiments were conducted for MRR and SR corresponding to their optimal setting of process parameters to validate the used approach
Summary
The cemented carbides such as WC-Co are typically used in tool and die industries because of their excellent hardness and strength. An extensive experimental study was conducted by Lee and Li (2001) to investigate the effect of machining parameters such as the electrode materials, electrode polarity, open circuit voltage, peak current, pulse duration, pulse interval and flushing on the machining characteristics, such as MRR, surface finish and relative tool wear in EDM of tungsten carbide They observed that the MRR generally decreases with the increase of open circuit voltage. Saha et al (2008) developed a second order multi-variable regression model and a feed forward back-propagation neural network to correlate the input process parameters, such as pulse on time, pulse-off time, peak current and capacitance with the performance measures namely cutting speed and surface roughness while doing WEDM of tungsten carbide-cobalt composite material.
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