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Purpose In this research, electro discharge machining (EDM) of Ti-5Al-2.5Sn titanium alloy is performed taking gap voltage, pulse on time, peak current and duty cycle as process parameters. The purpose of this paper is to find out the optimal process parameters setting for getting higher machining efficiency. Design/methodology/approach For experimental design, a face-centered central composite design (FCCCD)-based response surface methodology (RSM) is used. Multi-objective optimization like grey relational analysis (GRA) is adopted to achieve the higher machining efficiency by means of lower radial overcut (ROC), surface roughness (Ra), tool wear rate (TWR) and higher material removal rate (MRR). For the statistical study, analysis of variance (ANOVA) has been carried out. Findings The result shows that gap voltage, peak current and pulse on time are the most efficient parameters for the responses. An optimal parameter setting has been obtained for achieving higher machining efficiency. For validation of the study, confirmation experiment has been performed at optimal parameters setting. Originality/value Optimum parameter level for higher machining performance of Ti-5Al-2.5Sn Titanium alloy has been achieved machined by copper electrode during EDM operation.

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Electro discharge machining (EDM) is most popular non-conventional electro-thermal machining process where electrical energy is used to generate a spark and thermal energy used to remove material from the workpiece. The primary goal of EDM is getting more material removal rate (MRR) with lower tool wear rate (TWR). For this investigation, machining parameters like peak current, pulse on time, gap voltage and duty cycle are considered as process parameter, and material removal rate (MRR) and tool wear rate (TWR) are considered as response. AISI 304 stainless steel and tungsten carbide are used as work material and tool material respectively. Taguchi L27 orthogonal array has been applied for designing the experiment. A hybrid optimization technique like desirability in combination with grey relational analysis (GRA) has been performed to get the optimum level of the control parameter for getting higher MRR and lower TWR. Analysis of variance (ANOVA) is performed for the statistical analysis. These results show that peak current is the most significant parameter for MRR and TWR. The optimal parameter setting for maximum MRR and minimum TWR has obtained by desirability coupled with Grey relational analysis.