LAUSR

Abstract 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. The study considers the analysis and optimization of µEDM by using a multi-objective genetic algorithm (MOGA-II). 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. 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. Conversely, brass electrode yields high MRR at the expense of tool wear and micro-holes quality.