The present experimental work was carried out on wire electrical discharge machine (WEDM) over NiTi shape memory alloy for biomedical applications. Improving of machineability of intricate profiles in biomaterial applications is a challenging task. The experiments were performed on WEDM by using a brass wire of 0.25 mm diameter, as tool electrode. A range of 4 to 8 ampere of current, range of 60-120 µs of pulse on time, range of 15-45 µs of pulse off time, range of 11-15 cm²/gm of wire tension and range of 4-8 m/min wire feed were selected as input parameters. The influence of these parameters was observed on surface roughness and kerf width during fabrication of rectangular slots. The discharge craters, voids, microcracks and white layer have been observed in machined surface by scanning electron microscopy (SEM). It was observed that at higher values of discharge energy, the recast layer thickness increases. The higher recast layer found is 15.88 at Ip = 8, Ton = 120, Toff = 30, WT = 11, Wf = 4. The performance of responses was analysed by the response surfacemethodology and artificial neural network modelling. The obtained values of 0.993 and 0.995 from ANN model shows strong correlation between selected parameters. The obtained desirability is 0.957 that presents the developed model and is quite significant for both responses.

The current study performed modeling, analysis, and optimization of electrical discharge machining (EDM) under nano graphene mixed dielectric machining of Inconel 718 using ANFIS and a newly developed multi-objective seagull optimization algorithm. The influence of three major EDM controlling parametersnamely peak current (Ip), pulse on time (Ton), and pulse off time (Toff) have been studied on the output machining characteristics viz. material removal rate (MRR) and cylindricity (CY) deviation for each of the experiments. In this work, EDM performance was enhanced by dispersing nano-graphene powder into EDM Oil as a dielectric medium and improvement from conventional EDM was analysed. The Taguchi L27 orthogonal array was utilized for planning and conducting EDM experiments, while analysis of variance (ANOVA) testsand regression analysis were conducted for examining the influence of input process variables on machining response variables. From results, it was realized that nano graphene mixed dielectric EDM improved the machining performance in comparison to traditional EDM performance. The modeling of response variables in terms of input process variables and optimal process conditions were determined using efficient intelligent methods namely ANFIS model and the newly developed multi-objective seagull optimization algorithm (MOSOA), respectively. It was found that nanographene mixed EDM improved MRR and cylindricity deviation by 13.88% and 25.76% respectively in comparison to conventional EDM without nanographene mixed dielectric. The MOSOA algorithm provides a number of non-dominated pareto solutions and best machining conditions among 32 optimal sets for nanographene mixed EDM was selected as a pulse on time of 12 μs, pulse off time as 7 μs, and peak current at 9 A. Finally, the scanning electron microscopy image also shows the improvement in surface finish of nano graphene mixed dielectric EDM in comparison to traditional EDM.

Shape Memory Alloys (SMAs) have various applications in the field of medical science due to its superior properties such as pseudoplasticity, shape memory effect, biocompatibility, high specific strength, high corrosion resistance, high wear resistance and high anti fatigue property. The machining of TiNi shape memory alloys by traditional processes is very crucial due to poor thermal conductivity, poor surface finish and burr formation. Hence, to overcome these problems the non-conventional machining process viz. water jet machining, electrical discharge machining, laser beam machining, etc. are more suitable to machine SMAs. The appropriate non-conventional machining process EDM/wire EDM, provides better machining and surface characteristics during machining of SMAs. The objective of the current work is to identify the research gap for SMAs during their machining by the EDM and Wire EDM process and to identify the future prospective of SMAs for different applications in the field of biomedical.