Abstract
Electrical discharge machining (EDM) is now a widely adopted subtractive manufacturing process to shape titanium and its alloys to obtain desired profile and surface integrity. Predicting and optimizing the process behavior is the need of today’s manufacturing paradigm. Many new and better optimization algorithms have came into existence in the recent past to extract best out of any manufacturing processes. In this paper, EDM has been carried out on Ti–6Al–4 V alloy by varying important electrical parameters to estimate the two of the major performances i.e. material removal rate (MRR) and tool wear rate (TWR). Second-order regression equation using statistical analysis has been obtained for both the performances. Single objective optimization has been done using four evolutionary optimization algorithms. All the four algorithms have been compared for their performances in the present research environment.
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