Balaji, B.Narendranath, N.2026-02-042023CIRP Journal of Manufacturing Science and Technology, 2023, 47, , pp. 158-16717555817https://doi.org/10.1016/j.cirpj.2023.09.005https://idr.nitk.ac.in/handle/123456789/21571Conventional machining techniques face challenges in processing Ni-Ti-Hf alloys, which exhibit superior properties and are increasingly considered promising materials for high-temperature shape memory actuator applications. Thus, this article focuses on investigating the effect of Wire Electric Discharge Machining (WEDM) input parameters, namely discharge time (T<inf>ON</inf>), pause time (T<inf>OFF</inf>), gap voltage (SV), and wire travel speed (WF), on the surface quality and shape memory properties of these alloys. These parameters were optimized to obtain a better removal rate (MRR) and surface finish quality (Ra) by employing a hybrid approach of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Genetic Algorithm (MOGA). T<inf>ON</inf> emerged as the most influencing parameter for both MRR and Ra, and the sample machined using optimal parameter setting, which had a MRR of 5.287 mm3/min and Ra of 2.335 µm, showed better surface quality with fewer surface defects and irregularities, lower recast layer thickness of 10.057 µm, and better shape memory properties with less than 15 % deviation in their latent heat of transformation values and a less than 5ºC change in their austenite and martensite transformation temperature values, which indicates MOGA was successful in finding a trade-off between the two responses. © 2023 Elsevier LtdEconomic and social effectsElectric discharge machiningElectric dischargesGenetic algorithmsHigh temperature applicationsNickel alloysShape-memory alloySurface defectsSurface roughnessTernary alloysTitanium alloysWireDSCGenetic techniquesHTSMAIdeal solutionsMulti-objectives genetic algorithmsMulti-objectives optimizationNi-ti-hfShape-memory propertiesTechnique for order of preference by similarity to ideal solutionWire electric discharge machiningMultiobjective optimization