The miniaturization of smart materials has become a new trend in the modern manufacturing industry due to its enormous application in the aerospace, biomedical, and automobile sectors. Nickel–titanium (NiTi)-based binary shape memory alloys (SMAs) are one of the smart materials with certain supreme features like shape memory effect, pseudo-elasticity, high ductility, strong corrosion-resistance, and elevated wear resistance. For this, several micro-machining processes have been developed to machine NiTi SMAs. This paper summarizes all of the conventional and non-conventional micro-machining processes employed to machine NiTi SMAs. In this review process, the surface integrity, dimensional accuracy of the machined surface, cutting force and tool wear analysis during conventional and non-conventional micro-machining of NiTi SMA are evaluated mostly with the aid of input process variables like cutting speed, depth of cut, width of cut, types of coolants, tool coating, discharge voltage, capacitance, laser fluence, pulse duration, scan speed, electrolysis concentration and gap voltage. The optimization of process parameters using different methods during conventional and non-conventional micro-machining of NiTi SMAs is also analyzed. The problems faced during conventional micro-machining of NiTi SMAs are overcome by non-conventional micro-machining processes as discussed. The present study aims to recognize potential developments in the improvement of the micro-machinability of NiTi SMAs.

Micro-electrical discharge machining (µ-EDM) is one the most favourable process to machine difficult to cut materials in micro-scale with higher accuracy and precision. In this paper, two multi-response optimisation tools called Taguchi’s grey relation analysis (GRA) and response surface methodology (RSM)-based desirability function are used to evaluate optimised micro-machining parameters for minimal overcut and circularity error during µ-EDM of nickel-titanium (NiTi) shape memory alloy (SMA). Taguchi’s GRA revealed that electrode material has the strongest influence on both the responses followed by capacitance and discharge voltage. ANOVA analysis established that electrode material has the highest percentage contribution of 40.15% on both the responses. The optimisation result obtained by Taguchi’s GRA approach is validated by the RSM-desirability function-based technique. The final optimisation result (capacitance 155 pF, electrode material tungsten, discharge voltage 80 V) is a very close agreement with the literature model (capacitance 355 pF, electrode material tungsten, discharge voltage 80 V), with reference to the overcut and circularity error for the improvement of the dimensional accuracy of the micro-holes. Further, the response surface plots disclosed the variation of overcut and circularity error with the process parameters at various levels in a descriptive manner.

Nickel-titanium (NiTi) shape memory alloy (SMA) is one of the smart materials which has a vast application in the aerospace and biomedical industry due to its shape memory effect (SME), strong corrosion and wear resistance. Thus, the present study investigates the influence of process variables like gap voltage (V), powder concentration (PC) and pulse on time (t_on) towards the material removal rate (MRR), surface roughness (SR) and micro-hardness (MH) amid graphene nano powder added micro-electrical discharge milling (µ-ED milling) of NiTi SMA. ANOVA analysis has been carried out to determine the % distribution of individual machining parameters towards all responses. The addition of graphene nano particles to the dielectric oil considerably enhanced the MRR, MH, and decreased the SR of the milled micro-channel. Taguchi’s grey relational analysis (GRA) and grey-desirability approach have been applied for multi-response optimisation to find the maximum MRR, MH, and minimum SR. It is discovered that the grey-desirability method improves all the responses. The Field emission scanning electron microscopy (FESEM) micro-graphs confirm defect-free mirror-like surface finish at the optimal grey-desirability setting. The X-ray diffraction (XRD) analysis discovered the formation of oxides and hardened phases in the machined surface. The application of graphene enhanced the recast layer thickness and reduced the indentation depth at subsurface region. The nano indentation test validates the rise in nano hardness and elastic modulus at the grey desirability optimal setting compared to the initial machining condition.