Prediction of flow and dynamic recrystallization behavior based on three machine learning methods for a novel duplex-phase titanium alloy

Abstract

In this work, the vacuum arc-melting was used to prepare the Ti-10V-5Al-2.5Fe-0.1B alloy. Single-pass isothermal compression experiments were carried out on the alloy in the temperature range of 770–920 °C at strain rates of 0.0005–0.5 s⁻¹. The BP model optimized by the bald eagle search algorithm (BES-BP), the BP model optimized by the sparrow search algorithm (SSA-BP), and the BP model optimized by the gray wolf optimization algorithm (GWO-BP) were developed for high-precision prediction of flow stress. The above models were compared by using the mean square correlation coefficient, root mean square error, and average absolute relative error between the predicted and experimental flow stress. The three prediction accuracy parameters have indicated that the BES-BP model has a higher accuracy for flow stress prediction at the known and the new process parameters. A hot processing map based on the dynamic materials model was developed by using the flow stress predicted in the framework of the BES-BP model, and EBSD analysis was performed as well. The results show that the degree of dynamic recrystallization increases with an increase in the power dissipation factor, and the formation of deformation bands is the main cause of instability. The minimum critical stress for inducing dynamic recrystallization of the alloy was found to be 13.13 MPa at 890 °C/0.0005 s⁻¹. Moreover, the power dissipation factor increases with a decrease in critical stress. In addition, microstructure validation data reveal that the dynamic recrystallization model has a high accuracy for critical stress prediction, confirming that the critical stress increases with a decrease in the dynamic recrystallization fraction.