Conventional 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_ON), pause time (T_OFF), 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_ON 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.

传统的加工技术在加工 Ni-Ti-Hf 合金时面临着挑战，这些合金表现出优异的性能，并且越来越被认为是高温形状记忆执行器应用的有前途的材料。因此，本文重点研究线放电加工 (WEDM) 输入参数，即放电时间 (TON ₎、暂停时间 (TOFF ₎、间隙电压 (SV) 和走丝速度 (WF) 对这些合金的表面质量和形状记忆特性。通过采用与理想解相似的优先顺序技术 (TOPSIS) 和多目标遗传算法 (MOGA) 的混合方法，对这些参数进行优化，以获得更好的去除率 (MRR) 和表面光洁度质量 (Ra)。TON成为对 MRR 和 Ra 影响最大的参数，并且使用最佳参数设置加工的样品，其 MRR 为 5.287 mm ₃ ^/ min，Ra 为 2.335 µm，显示出更好的表面质量，表面缺陷和不规则性更少，重铸层厚度较低，为 10.057 µm，形状记忆性能更好，相变潜热值偏差小于 15%，奥氏体和马氏体相变温度值变化小于 5°C，这表明 MOGA 成功找到了替代方案- 两个响应之间的关闭。