A Modified GTN Model to Predict the Damage Mechanism of Al–Zn–Mg–Cu Alloy Conical Parts During Warm Spinning

Accurately predicting ductile fracture in warm spinning of high-performance thin-walled Al–Zn–Mg–Cu alloy components remains a significant challenge due to the complex thermo-mechanical loading. The classical Gurson-Tvergaard-Needleman (GTN) model is inadequate for capturing material degradation under the low and negative stress triaxiality states prevalent in spinning, particularly the suppression of damage under compression and its acceleration under shear. This work presents a comprehensive solution by developing a physically-based constitutive model that significantly enhances the GTN framework. A key innovation is the introduction of a shear contribution criterion, which precisely governs void behavior under different hydrostatic pressure and enables the accurate description of shear-driven damage evolution. Furthermore, the model incorporates the coupled effects of temperature on void growth and material hardening. The integrated model successfully bridges micro-damage mechanisms with macroscopic failure. Its superior predictive capability is validated through the warm spinning of an Al–Zn–Mg–Cu conical part, where the model accurately predicts the fracture location and a limit thinning rate of 57.44%, with a relative error of 3.23% compared to the experimental value of 59.30%. This work establishes a robust numerical foundation for defect-free process design in warm spinning.