A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel

For predicting high-temperature deformation behaviour in Aermet100 steel, the experimental stress-strain data from isothermal hot compression tests on a Gleeble-3800 thermo-mechanical simulator, in a wide range of temperatures (1073-1473K) and strain rates (0.01-50s^-1), were employed to develop the Arrhenius-type constitutive model and artificial neural network (ANN) model, and their predictability for high-temperature deformation behaviour of Aermet100 steel was further evaluated. The predictability of two models was quantified in terms of correlation coefficient (R) and average absolute relative error (AARE). The R and AARE for the Arrhenius-type constitutive model were found to be 0.9861 and 7.62% respectively, while the R and AARE for the feed-forward back-propagation ANN model are 0.9995 and 2.58% respectively. The breakdown of the Arrhenius-type constitutive model at the instability regimes (i.e. at 1073K and 1173K in 0.1, 1, 10 and 50s^-1, and at 1373K in 50s^-1) is possibly due to that physical mechanisms in the instability regimes, where microstructure exhibits cracking, shear bands and twin kink bands, are far different from that of the stability regimes where dynamic recovery and recrystallization occur. But the feed-forward back-propagation ANN model can accurately track the experimental data across the whole hot working domain, which indicates it has good capacity to model the complex high-temperature deformation behaviour of materials associated with various interconnecting metallurgical phenomena like work hardening, dynamic recovery, dynamic recrystallization, flow instability, etc.