OBRABOTKAMETALLOV Vol. 27 No. 2 2025 technology Despite the difficulty in machining nickel-titanium (NiTi) SMA using conventional methods, EDM provides optimal performance when working with this material. However, high tool wear in EDM of NiTi leads to a reduction in the material removal rate. The study presented in [24] was aimed at maximizing MRR and minimizing TWR using the Taguchi method and the utility principle. Experiments were conducted on a die-sinking EDM machine in a liquid dielectric using a Taguchi L36 mixed orthogonal array (22×33). NiTi alloy was used as the workpiece material, and copper was used as the electrode tool. Taguchi analysis revealed that workpiece and tool electrode electrical conductivity, gap current, and pulse-on time are the key factors influencing MRR and TWR. It was found that an MRR of 6.31 mm³/min and a TWR of 0.031 mm³/ min are achieved with the following parameters: workpiece conductivity of 4219 S/m, tool conductivity of 26316 S/m, gap current of 16 A, and pulse-on time of 38 µs [24]. Reference [25] investigates the feasibility of processing nickel-titanium (NiTi) SMAs using EDM with copper, graphite, and tungsten-copper electrodes, and dielectric 358 as the dielectric fluid. The EDM process parameters included three levels of current (6, 12, and 18 A) combined with three values of pulse-on time (200, 400, and 600 µs) at a constant voltage of 3 V and a fixed pulse-off time of 50 µs. The primary objective was to determine the optimal settings to maximize MRR and minimize SR for NiTi shape memory alloys. The surface analysis of the workpiece included the examination of the electrode size and length using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) to assess electrode material adhesion to the workpiece. Analysis of variance (ANOVA) was employed as a statistical method to determine the significance of process parameters. The differences between electrode materials were found to be relatively minor, and overcut was identified as the dominant factor influencing MRR and SR. Surface examination revealed the presence of surface defects in the form of droplets, debris, lumps, microcracks, and holes. Elevated SR values were associated with Cu and W residues from the electrode adhering to the workpiece due to insufficient dielectric rinsing [25]. Study [26] optimized the experimental conditions for surface milling of NiTi SMA under dry cutting conditions. The research aimed to achieve the lowest Ra and the minimal Vb using an uncoated tungsten carbide tool with a nose radius of 0.4 mm or 0.8 mm. Milling experiments were conducted at three cutting speeds (20, 35, and 50 m/min) and three feed rates (0.03, 0.07, and 0.14 mm/tooth) with a fixed axial depth of cut of 0.7 mm. A Taguchi L18 orthogonal array was used as the design of experiment (DOE) method, utilizing Minitab 17 software for data analysis. The analysis of variance (ANOVA) revealed that the cutting tool nose radius is the primary factor determining surface roughness, while the feed rate (fz) has the greatest impact on flank wear (Vb). Confirmation tests verified that the optimal machining parameters accurately predict the results of the laboratory experiments, indicating the success of the optimization process [26]. The optimization of EDM parameters for Cu-based SMA components using machine learning (ML) algorithms is described in [27]. The optimization process focused on varying pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and gap voltage (GV) to minimize tool wear rate (TWR). An empirical design of experiments (DoE) approach utilized a central composite design (CCD) in conjunction with response surface methodology (RSM) to analyze the machining behavior. The study employed both single- and multi-objective optimization using a desirability function approach, as well as genetic algorithms (GA) and teaching-learning-based optimization (TLBO) algorithms [27]. The optimization of process parameters significantly improved the efficiency of the corresponding machining methods. The innovative aspect of the presented research lies in the application of machine learning (ML)-based optimization techniques to the electrical discharge machining (EDM) of Cu-SMAs, opening new perspectives for the aerospace, biomedical, and automotive industries. Based on the results presented in [27], it can be concluded that precisionmachining benefits significantly from the implementation of “smart” materials and data-driven optimization methods. A comprehensive analysis of existing shape memory alloys (SMAs) processing methods was conducted, encompassing both conventional and non-conventional approaches. The review includes research on waterjet machining (WJM), cryogenic machining, wire electrical discharge machining (WEDM), electrical discharge machining (EDM), and electrochemical machining. Key factors determining the performance and limitations of the considered processes are material removal rate (MRR), tool wear rate (TWR), surface
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