Altaş, EmreErkan, ÖmerÖzkan, DervişGökkaya, Hasan2023-07-262023-07-2620221059-94951544-1024https://doi.org/10.1007/s11665-022-06769-6https://hdl.handle.net/20.500.12684/12811This study discusses the milling of Nickel-Titanium (NiTi) alloy, one of the innovative and widely used shape memory alloy (SMA). During the face milling operations, the average surface roughness (R-a) was investigated depending on the change in machining parameters, cutting conditions, and cryogenic heat treatment. Experiments were carried out with uncoated and two different coated (PVD, CVD) cutting tools with untreated, shallow (- 80 degrees C) and deep (- 196 degrees C) cryogenic heat treatment. In addition, experiments were carried out using Ethylene Glycol (EG), and boron added Ethylene Glycol (EG+5%BX) cutting fluids as well as dry cutting condition. In the cutting experiments, three different cutting speeds (20-35-50 m/min), three different feeds (0.03-0.07-0.14 mm/tooth), and 0.7 mm fixed cutting depth was used as machining parameters. In the milling mechanism of NiTi shape memory alloys, how the cutting parameters affect the surface quality is discussed in detail. In this context, the cutting parameters were successfully optimized using Taguchi and ANOVA methods. The study is innovative in terms of evaluating the effect of different cutting fluids and cryogenic heat treatment. The results showed that CVD-coated cutting tool, - 196 degrees C cryogenic heat treatment, EG+5%BX cutting fluid, 50 m/min cutting speed, and 0.03 mm/tooth feed are the optimal parameters for the minor surface roughness. In addition, it has been determined that progress is the most influential parameter. On the other hand, ANOVA results showed that the most significant variable on the R-a was feed rate with 42.99%, and then cutting tool type 20.27%, cutting fluid 20.25%, cutting speed 11.68%, and cryogenic heat treatment 1.95%, respectively.en10.1007/s11665-022-06769-6info:eu-repo/semantics/closedAccessAnova; Cryogenic Treatment; Cutting Fluids; Niti Sma Milling; Taguchi OptimizationTransformation Behavior; Smart Materials; Machinability; Microstructure; Performance; DryArticle319731573272-s2.0-85126520974WOS:000770543500003Q2Q3