Optimization of micro-EDM drilling on titanium alloy (Ti-6AL-4V) using RSM and neural network

Abstract

In modern manufacturing industries, micro machining technology is widely used to machine micro parts for various applications such as in MEMS, die and tool industries, etc. Micro electric discharge machining (Micro-EDM) is widely used in die and tool making. This paper investigates three different input machining parameters such as pulse on time, pulse off time, and servo voltage of micro electric discharge machining performances of tool wear (TW) and Diametrical accuracy (DA) of a hole on titanium alloy (Ti-6Al-4V) using copper micro electrodes of ø 400µm. The experiments ate conducted out with the Box-Behnken design of Response Surface Methodology (RSM). The neural network is used for the optimization of multi response by fitting the regression model. ANOVA is also performed to find the significant contribution of the machining parameter. The predicted optimal machining values with the maximum error of 12.72% for tool wear and 8.78% for diametrical accuracy was achieved on comparing with experimental results.