In industrial applications, particularly in aero, marine and medical industries, titanium has received great attention as a useful material and electrical discharge machining as its machining process. Selection of optimal machining parameters in a multi-objective environment is essential for specific workpiece and tool material combination, which is the concern of industries to improve the overall productivity at less cost. In this article, optimization of critical electrical discharge machining parameters such as pulse current, on time of pulse, off time of pulse and tool geometry depending on the responses such as titanium machining rate, graphite wear rate, surface roughness and deviation between entry and exit while machining titanium grade 5 alloy with graphite tool electrode at negative polarity is presented. Taguchi’s L27 orthogonal array was used to design the experiment with interaction between factors. The weighing method was used to integrate different objectives into one performance. The optimal combination of process parameters was found statistically using signal-to-noise ratios. Significance was checked by analysis of variance. Optimum parameters were found to be pulse current 15 A, on time of pulse 50 µs, off time of pulse 200 µs and cylindrical tool geometry. Resultant percentage improvements in different responses were presented.
In this study, the effect of electrical parameters such as current, pulse-on time, pulse-off time, their interaction, tool geometry, and cross-sectional area over the important machining responses such as metal removal rate, tool wear rate, surface roughness, and entry-exit deviation were studied while machining titanium grade 5 alloy with graphite tool electrode at negative polarity in sink electric discharge machining process. The experiment was designed with Taguchi's L27 orthogonal array with interaction between factors. The analysis of variance was used to check the significance of parameters effect. The data obtained were analyzed with signal-to-noise ratio analysis. Residual analysis was performed to check the fitness, and a regression model had been created and validated to relate the experiment factors with the responses. The model was found to be good after experimental validation. The ranking of influence of the factors over the responses was also presented.
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