Development of a macro–micro coupled constitutive model with hierarchical parameter identification for γ-TiAl alloy machining

γ-TiAl alloys are widely used in aerospace engine components due to their low density and excellent high-temperature performance; however, their high brittleness and poor machinability lead to strongly coupled thermo-mechanical conditions in the cutting zone, involving large strains, high strain rates, and elevated temperatures, which demand accurate constitutive descriptions. This study develops a macro–micro coupled constitutive model based on the Johnson–Cook framework: microscale mechanisms including dislocation multiplication/annihilation competition and dynamic recrystallization (DRX) softening are incorporated, while macroscale coupled strain–strain-rate–temperature effects are introduced. To address the large number of parameters and calibration difficulty, a hierarchical identification framework is proposed by segmenting experimental data by deformation regimes, successively solving parameter subsets, and using the obtained solutions as physically meaningful initial values for a final global optimization over the full dataset. Model parameters are jointly calibrated using split Hopkinson pressure bar (SHPB) data and flow-stress estimates derived from orthogonal cutting experiments. Validation results show that, compared with the classical Johnson–Cook model, the proposed model keeps flow-stress prediction errors generally within 20% under machining-relevant high strain rates and elevated temperatures, and better captures the variations of cutting forces with respect to cutting parameters across the tested range.