Thermal deformation behavior investigation of Ti–10V–5Al-2.5fe-0.1B titanium alloy based on phenomenological constitutive models and a machine learning method

The two-phase titanium alloy Ti–10 V–5Al-2.5Fe-0.1 B was taken as the experimental material, and thermal compression experiments were carried out at a deformation temperature of 770–920 °C and a strain rate of 0.0005–0.5 s−1. An Arrhenius model, a modified Johnson-Cook model, and an improved BP neural network model based on the sparrow search algorithm (SSA-BP) model were established to predict the high temperature rheological stress of the alloy. A comparison of the prediction accuracy of the three models was made. When the partial random data in the rheological curves was used for model building and relatively independent data were used for predicting the rheological stress, the SSA-BP model had higher prediction accuracy, which exhibits the highest mean square correlation coefficient (R2) value of 0.9992 and the lowest root mean square error (RMSE) and average absolute relative error (AARE) values of 1.3031, and 2.0947 %, respectively. The ability of three models to predict the rheological stress for the new process parameters was verified. Results show that the SSA-BP model still has better prediction ability, which exhibits the highest mean square correlation coefficient (R2) value of 0.9720 and the lowest root mean square error (RMSE) and average absolute relative error (AARE) values of 5.0099, and 6.0382 %, respectively. The predicted values of SSA-BP for the rheological stress were used to construct the hot processing map. Results show that the trend of the power dissipation factor (η) value from the hot processing map predicted by SSA-BP can well agree with the microstructure evolution of the alloy.