Heat Transfer Coefficient Prediction and Simulation of γ-TiAl Alloy Machining with Supercritical CO₂ Minimum Quantity Lubrication

Addressing the challenges of temperature control and the inadequate precision of conventional heat transfer models in γ-TiAl alloy machining, this study establishes a temperature-pressure dependent heat transfer coefficient prediction model that incorporates the variable thermophysical properties of supercritical CO₂（scCO₂）.Through the implementation of a modified Martin correlation incorporating density and viscosity ratio corrections, we achieved precise calculations of local heat transfer coefficients during the cutting process. A two-dimensional finite element model coupling thermal-mechanical effects was developed on the ABAQUS platform to systematically investigate the influence of cutting speeds on temperature field evolution and material removal mechanisms. Our findings demonstrate that the proposed heat transfer coefficient calculation methodology effectively predicts the cooling performance of scCO₂, providing theoretical foundations for optimizing high-efficiency machining processes of γ-TiAl alloys.