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Abstract: Hot compression tests of T24 ferritic steel were carried out using Gleeble-3500 thermo mechanical simulator in the temperature range of 1323–1473K with the strain rate of 0.01–10s−1 and the height reduction of 60%. The flow behavior of T24 ferritic steel was characterized based on analysis of the true stress–strain curves. Constitutive equations incorporating the effects of temperature, strain rate and strain have been developed to model the hot deformation behavior of T24 ferritic steel. Material constants α, n, ln A and activation energy Q in the constitutive equations were calculated as a function of strain. The flow stress values of T24 ferritic steel predicted by the proposed constitutive equations show a good agreement with experimental results, which indicated that the developed constitutive equations could give an accurate and precise prediction for the flow stress of T24 ferritic steel. [Copyright &y& Elsevier]

Abstract: The true stress–strain data from isothermal hot compression tests on Gleeble-3500 thermo mechanical simulator, in a wide range of temperatures (1323–1473K) and strain rates (0.01–10s−1), were employed to establish the constitutive equations based on modified Zerilli–Armstrong and strain-compensated Arrhenius-type models respectively, and develop the artificial neural network model to predict the high-temperature flow stress of T24 steel. Furthermore, a comparative study has been made on the capability of the three models to represent the elevated temperature flow behavior of this steel. Suitability of the three models were evaluated by comparing the accuracy of prediction of deformation behavior, correlation coefficient and average absolute relative error of prediction, the number of material constants, and the time needed to evaluate these constants. The results showed that the predicted values by the modified Zerilli–Armstrong model could agree well with the experimental values except under the strain rate of 0.01s−1. The predicted flow stress of the other two models shows good agreement with the experimental data. However, the artificial neural network model could track the deformation behavior more accurately throughout the entire temperature and strain rate range though it is strongly dependent on extensive high quality data and characteristic variables and offers no physical insight. [Copyright &y& Elsevier]