Analysis, predictive modelling and multi-response optimization in electrical discharge machining of Al-22%SiC metal matrix composite for minimization of surface roughness and hole overcut

Due to the widespread engineering applications of metal matrix composites especially in automotive, aerospace, military, and electricity industries; the achievement of desired shape and contour of the machined end product with intricate geometry and dimensions that are very challenging task. This experimental investigation deals with electrical discharge machining of newly engineered metal matrix composite of aluminum reinforced with 22 wt.% of silicon carbide particles (Al-22%SiC MMC) using a brass electrode to analyze the machined part quality concerning surface roughness and overcut. Forty-six sets of experimental trials are conducted by considering five machining parameters (discharge current, gap voltage, pulse-on-time, pulse-off-time and flushing pressure) based on Box-Behnken's design of experiments (BBDOEs). This article demonstrates the methodology for predictive modeling and multi-response optimization of machining accuracy and surface quality to enhance the hole quality in Al-SiC based MMC, employing response surface methodology (RSM) and desirability function approach (DFA). Finally, a novel approach has been proposed for economic analysis which estimated the total machining cost per part of rupees 211.08 during EDM of Al-SiC MMC under optimum machining conditions. Thereafter, under the influence of discharge current several observations are performed on machined surface morphology and hole characteristics by scanning electron microscope to establish the process. The result shows that discharge current has the significant contribution (38.16% for Ra, 37.12% in case of OC) in degradation of surface finish as well as the dimensional deviation of hole diameter, especially overcut. The machining data generated for the Al-SiC MMC will be useful for the industry