Abstract:The thermal simulation compression experiments were conducted on forged TA16 titanium alloy using the Gleeble-3800 system at the temperature of 730–1030 ℃ and strain rates from 0.1 s–1 to 10 s–1. The true stress-true strain curves of TA16 alloy under these deformation conditions were obtained. Constitutive models for the TA16 alloy were established using three different methods: the Arrhenius model, the Johnson-Cook model, and artificial neural networks (ANN). The model errors were analyzed. The results indicate that the TA16 alloy reaches a dynamic balance between work hardening and softening after yielding at medium and low strain rates. At high strain rates, it initially softens and then enters a balance state, demonstrating good workability. The mean absolute percentage error (MAPE) of the constitutive models for the TA16 alloy using the Arrhenius model, the Johnson-Cook model, and ANN is 11.49%, 23.7%, and 1.69%, respectively. The ANN model shows an order of magnitude higher accuracy compared to the traditional constitutive models. The Arrhenius model exhibits better accuracy at medium and high strain rates and in the medium and low strain range, making it practical for engineering applications. The Johnson-Cook model reflects the trend of high-strain hardening and struggles to describe the dynamic equilibrium state after yielding for the TA16 alloy, resulting in poor model accuracy and making it unsuitable for engineering applications. The ANN model demonstrates extremely high predictive accuracy across the entire range of experimental parameters, and it also maintains good accuracy for data predictions beyond the experimental parameter ranges, providing a highly accurate constitutive model for engineering applications.