Namlu, Ramazan HakkıNamlu,R.H.Lotfi,B.Lotfi, BahramKiliç,S.E.Mechanical EngineeringDepartment of Mechanical Engineering2024-09-102024-09-10202402212-827110.1016/j.procir.2024.05.0182-s2.0-85196837483https://doi.org/10.1016/j.procir.2024.05.018https://hdl.handle.net/20.500.14411/7397Inconel 718 is a widely used superalloy in the aerospace industry, owing to its exceptional creep and corrosion resistance, as well as its ability to retain strength at elevated temperatures. However, its machinability presents challenges due to its low thermal conductivity and high work hardening rate during conventional machining, resulting in inadequate surface quality. To address this issue, a recent technique known as Ultrasonic Vibration-Assisted Machining (UVAM) has emerged. UVAM involves applying high-frequency, low-amplitude vibrations to the cutting tool or workpiece. Additionally, Minimum Quantity Lubrication (MQL) has been considered as an alternative cooling technique to enhance machining performance. Optimizing the performance of UVAM can be achieved by employing various vibration axes. Additionally, the effectiveness of MQL can be enhanced through the utilization of nanofluids. This study investigates the combined application of multi-axis UVAM and Al2O3-CuO added Hybrid Nanofluid MQL (HNMQL) during the milling of Inconel 718. The evaluation parameters include surface roughness, topography, burr formations, and cutting forces. The results demonstrate that the simultaneous use of multi-axis UVAM and HNMQL significantly improves the machining performance of Inconel 718. This combination leads to better surface quality and overall process efficiency, offering promising prospects for the aerospace industry and other applications involving difficult-to-cut materials. © 2024 The Authors. Published by Elsevier B.V.eninfo:eu-repo/semantics/closedAccessInconel 718Minimum Quantity LubricationSurface QualityUltrasonic Vibration-Assisted MachiningMulti-axial ultrasonic vibration-assisted machining of Inconel 718 using Al2O3-CuO hybrid nanofluid MQLConference ObjectN/AQ21238994