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The hot deformation behavior of the 40MnBH alloy is investigated by isothermal compression tests at the temperature of 900-1200 °C with the strain rate of 0.05, 0.5, and 5s−1, and the degree of compression ranging from 15 to 60%. The effects of deformation parameters are studied through the true stress-strain curve under different deformation conditions. The processing maps are constructed based on the dynamic material model. Microstructures of 40MnBH steel at different deformation conditions are observed. The results show that the stress of the alloy has a dependence on strain rate and deformation temperature. A constitutive model is built, which described the flow stress behavior of the alloy. The processing maps reveal that with the increase in the degree of compression, the instability regions decrease. The results of processing maps have been verified through the microstructures of the 40MnBH steel. [ABSTRACT FROM AUTHOR]

Abstract: The hot flow behaviors of 20CrMnTiH steel are studied using isothermal compression tests corresponding to the temperature range from 973 to 1123K and the strain rates range from 0.01 to 10s−1. The flow behaviors of 20CrMnTiH steel are represented, which are based on the analysis of true stress–true strain curves. The results show that there exist typical dynamic recovery (DRV) and dynamic recrystallization (DRX) during hot deformation. The effect of hot deformation on microstructure is investigated. There exists a dual phase feature within the deformation temperature. A modified Zerilli–Armstrong (ZA) model, based upon the original ZA model and Johnson–Cook model, is proposed to describe the flow stress under different deformation conditions. Compared with experimental data, the modified ZA model has a good accuracy in describing the constitutive relation of the material. [Copyright &y& Elsevier]

Abstract: Arrhenius-type constitutive equation considering strain compensation and BP neural network model are used to analyze the hot deformation behavior of XC45 steel. The experimental data are acquired from hot compression tests in Gleeble-1500 thermal–mechanical simulator in the range of temperature from 1053K to 1253K and strain rates from 0.01s−1 to 10s−1. The predictabilities of Arrhenius-type constitutive model and back propagation (BP) neural network model can be evaluated based on the correlation coefficient (R), root mean square error (RMSE) and average absolute relative error (AARE), and their predictabilities can be compared through several verification methods. It can be found that the BP neural network model is better in the predictable ability than the Arrhenius-type constitutive model. [Copyright &y& Elsevier]