Multi-objective optimization of micro-electrical discharge machining of nickel-titanium-based shape memory alloy using MOGA-II
CHALLENGE - Shape memory alloys (SMAs) have received significant attention especially in biomedical and aerospace industries owing to their unique properties. However, they are difficult-to-machine materials. Electrical discharge machining (EDM) can be used to machine difficult to cut materials with good accuracy. However, several challenges and issues related with the process at micro-level continue to exist. One of the aforementioned issues is that the micro-EDM (µEDM) process is extremely slow when compared to other non-conventional processes, such as laser machining, although it offers several other benefits.
SOLUTION - The study considers the analysis and optimization of µEDM by using a multi-objective genetic algorithm (MOGA-II) provided by modeFRONTIER. Drilling of micro-holes is performed by using a tabletop electrical discharge machine. Nickel-Titanium (Ni-Ti) based SMA (a difficult to cut advance material) is used as a specimen. The objective involves determining optimal machining parameters to obtain better material removal rate with good surface finish.
BENEFITS - The results of the study indicate that MOGA-II is an efficient tool to optimize input parameters. Optimum results are obtained with tungsten electrode at low to moderate capacitance values and low discharge voltage.