Cutting fluid has been widely used to enhance the heat dissipation of cutting systems. However, whether cutting fluid can fully play its role is closely correlated with its thermophysical characteristics, such as viscosity, surface tension, etc. In this work, to study the effect of the thermophysical characteristics of cutting fluid on cutting performance, three green vegetable oils (semi-synthetic fluid (L1), rapeseed oil (L2), canola oil (L3)) were selected as cutting fluids of the MQL system, and differences in cutting performance were compared and analyzed under varied lubrication environments. Firstly, the thermophysical characteristics of the vegetable oils were determined by experimental methods. Afterwards, parameters, including tool wear, cutting force, and temperature, as well as the quality of machined workpieces, were selected to evaluate cutting performance, and essential reasons for the difference in cutting performance under varied lubrication environments were clarified. The results demonstrated that the cutting force, cutting temperature, and tool wear produced in the three MQL environments were lower than those in the dry cutting environment, while only the L1 and L2 MQL environments exhibited higher machined surface quality than the dry cutting environment. Moreover, obvious differences in cutting performance under the three MQL environments were also observed due to the different thermophysical characteristics of the three vegetable oils. The best cutting performance was achieved when L2 was used as the MQL cutting fluid. The efforts of this study will give an important reference for the choosing of green cutting fluid in the cutting process of difficult-to-cut materials and be of great significance for accelerating the development of green processing.

In the context of sustainable manufacturing practices, minimum quantity lubrication (MQL) has been extensively employed in machining operations involving hard-to-cut materials. While substantial experimental and numerical investigations on MQL-assisted machining have been conducted, existing simulation approaches remain inadequate for modeling the dynamic flow field variations inherent to MQL processes, significantly compromising the predictive reliability of current models. This study introduced an innovative bidirectional iterative coupling framework integrating finite element (FE) analysis and computational fluid dynamics (CFD) to enhance simulation accuracy. Since fluid flow characteristics critically influence tribological and thermal management at the tool–workpiece interface during machining, CFD simulations were initially performed to evaluate how MQL parameters govern fluid flow behavior. Subsequently, an integrated FE-CFD modeling approach was developed to simulate Ti-6Al-4V alloy turning under MQL conditions with varying feed rates. The novel methodology involved transferring thermal flux data from FE simulations to CFD’s heat source domain, followed by incorporating CFD-derived convective heat transfer coefficients back into FE computations. This repetitive feedback process continued until the thermal exchange parameters reached convergence. Validation experiments demonstrated that the proposed method achieved improved alignment between the simulated and experimental results for both cutting temperature profiles and principal force components across different feed conditions, confirming the enhanced predictive capability of this coupled simulation strategy.