Huiqu Li

Bio: Huiqu Li is an academic researcher. The author has contributed to research in topics: Dynamic recrystallization & Strain rate. The author has an hindex of 4, co-authored 4 publications receiving 290 citations.

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

Abstract: 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 (1073–1473 K) and strain rates (0.01–50 s−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 1073 K and 1173 K in 0.1, 1, 10 and 50 s−1, and at 1373 K in 50 s−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.

Research on the dynamic recrystallization kinetics of Aermet100 steel

Abstract: Using the compression tests on a Gleeble-3800 thermo-mechanical simulator, the hot deformation behavior of ultrahigh strength Aermet100 steel was studied in the temperature ranges of 800–1200 °C and the strain rate ranges of 0.01–50 s−1. Dependence of the peak stress on temperature and strain rate for Aermet100 steel is described by means of the conventional hyperbolic sine equation; By regression analysis, the activation energy in the whole range of deformation temperature was determined to be Q = 489.10 kJ/mol. The dynamic recrystallization (DRX) kinetics of Aermet100 steel is established by further analysis of true stress–true strain curves. The complete DRX grain size is dependent only on Zener–Hollomon parameter (Z) and is independent of the initial grain size and accumulated strain, because Z controls the stored energy. The complete DRX grain size is a power law function of Z with an exponent of −0.24.

Development and validation of a processing map for Aermet100 steel

Abstract: Using the flow stress data obtained from the compression tests in the temperature ranges of 800–1200 °C and at strain rate ranges of 0.01–50 s −1 , the processing map of Aermet100 steel was developed based on a simple instability condition applicable to a general flow stress versus strain rate curve at any strain and temperature. Deformation mechanisms in the stable and unstable regimes were verified with the microstructure observations. The optimum hot processing windows of Aermet100 steel are at temperature ranges of 1025–1200 °C and at strain rate ranges of 0.03–15 s −1 , in which dynamic recrystallization occurs with a peak efficiency of power dissipation of 38%. The instability regimes I and II occur at low temperature ranges of 800–975 °C, and at strain rate ranges of 0.1–6 s −1 and 4.5–33 s −1 , respectively. While the instability regime III occurs at high temperature ranges of 950–1200 °C and at high strain rate ranges of 15–50 s −1 . These instability regimes, whose microstructural manifestations such as cracks, shear bands and twin kink bands are detrimental to the mechanical properties of components, need to be avoided during hot processing of Aermet100 steel.

A modified Johnson-Cook model for tensile behaviors of typical high-strength alloy steel

Abstract: The uniaxial tensile tests were conducted with the initial strain rates range of (0.0001–0.01) s−1 and the temperature range of (1123–1373) K for typical high-strength alloy steel. Based on the experimental results, the modified Johnson–Cook model, which considers the coupled effects of strain, strain rate and deformation temperature, was proposed to describe the tensile behaviors of the studied alloy steel. Results show that the stress–strain values predicted by the proposed model well agree with experimental ones, which confirmed that the modified Johnson–Cook model can give an accurate and precise estimate of the flow stress for the studied typical high-strength alloy steel.

Recrystallization of 30Cr2Ni4MoV ultra-super-critical rotor steel during hot deformation. Part I: Dynamic recrystallization

Abstract: The metadynamic recrystallization (MDRX) behavior of 30Cr2Ni4MoV ultra-super-critical (USC) rotor steel during hot deformation was investigated based on the first part of this study, in which the evolution of the dynamically recrystallized structure was studied in detail. Compression tests were performed using double hit schedules at temperatures of 970–1250 °C, strain rates of 0.001–0.1 s−1 and inter-pass time of 1–100 s. Based on the experimental results, the kinetic equations and grain size model were established. Results show that the effects of deformation parameters, including forming temperature and strain rate, on MDRX softening fractions and austenite grain size in the two-pass hot deformed 30Cr2Ni4MoV steel are significant. Results also reveal that the pre-strain (beyond the peak strain) has little influence on the MDRX behaviors in 30Cr2Ni4MoV steel. Comparisons between the experimental and the predicted results were carried out. A good agreement between the experimental and the predicted results was obtained, which verified the developed models.

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

Abstract: 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 (1073–1473 K) and strain rates (0.01–50 s−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 1073 K and 1173 K in 0.1, 1, 10 and 50 s−1, and at 1373 K in 50 s−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.