Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti-6Al-2Zr-1Mo-1V alloy by artificial neural network

The isothermal compressions of as-cast Ti-6Al-2Zr-1Mo-1V titanium alloy in a wide temperature range of 1073-1323 K and strain rate range of 0.01-10 s(-1) with a reduction of 60% were conducted on a Gleeble-1500 thermo-mechanical simulator. The flow stress shows a complex non-linear intrinsic relationship with strain, strain rate and temperature, meanwhile the strain-softening behavior articulates dynamic recrystallization mechanism in alpha phase, dynamic recovery mechanism in beta phase and their comprehensive function during phase transformation (alpha + beta). Based on the experimental data, an artificial neural network (ANN) was trained with standard back-propagation learning algorithm to generalize the complex deformation behavior characteristics. In the present ANN model, strain and temperature were taken as inputs, and flow stress as output. A comparative study has been made on ANN model and improved Arrhenius-type constitutive model, and their predictability has been evaluated in terms of correlation coefficient (R) and average absolute relative error (ARRE). During alpha, alpha + beta and beta phase regime, R-value and ARRE-value for the improved Arrhenius-type model are 0.9824% and 6.02%, 0.9644% and 21.02%, and 0.9627% and 12.38%, respectively, while the R-value and ARRE-value for the ANN model are 0.9992% and 0.91%, 0.9996% and 1.47%, and 0.9975% and 2.17%, respectively. The predicted strain-stress curves outside of experimental conditions articulate the similar intrinsic relationships with experimental strain-stress curves. The results show that the feed-forward back-propagation ANN model can accurately tracks the experimental data in a wide temperature range and strain rate range associated with interconnecting metallurgical phenomena, and in further it has a good capacity to model complex hot deformation behavior of titanium alloy outside of experimental conditions. (c) 2013 Elsevier Ltd. All rights reserved.