Phase transformation and constitutive models of a hot compressed TC18 titanium alloy in the α+β regime

Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composition limits their application. In this paper, a novel artificial neural network (ANN) model was built to determine the steel flow stress with high accuracy in the wide range of the concentration of the elements in high-alloyed, corrosion-resistant steels. The additional compression tests for stainless Cr12Ni3Cu steel were carried out at the strain rates of 0.1–10 s−1 and the temperatures of 900–1200 °C using thermomechanical simulator Gleeble 3800. The ANN-based model showed high accuracy for both training (the error was 6.6%) and approvement (11.5%) datasets. The values of the effective activation energy for experimental (410 ± 16 kJ/mol) and predicted peak stress values (380 ± 29 kJ/mol) are in good agreement. The implementation of the constructed ANN-based model showed a significant influence of the Cr12Ni3Cu chemical composition variation within the grade on the flow stress at a steady state of the hot deformation.

https://www.mdpi.com/2075-4701/12/3/447/pdf?version=1646484276

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

The dynamic recrystallization (DRX) behavior of 5CrNiMoV steel was investigated through hot compression at temperatures of 830–1230°C and strain rates of 0.001–10 s−1. From the experimental results, most true stress-strain curves showed the typical nature of DRX that a single peak was reached at low strains followed by a decrease of stress and a steady state finally at relatively high strains. The constitutive behavior of 5CrNiMoV steel was analyzed to deduce the operative deformation mechanisms, and the correlation between flow stress, temperature, and strain rate was expressed as a sine hyperbolic type constitutive equation. Based on the study of characteristic stresses and strains on the true stress-strain curves, a DRX kinetics model was constructed to characterize the influence of true strain, temperature, and strain rate on DRX evolution, which revealed that higher temperatures and lower strain rates had a favorable influence on improving the DRX volume fraction at the same true strain. Microstructure observations indicated that DRX was the main mechanism and austenite grains could be greatly refined by reducing the temperature of hot deformation or increasing the strain rate when complete recrystallization occurred. Furthermore, a DRX grain size model of 5CrNiMoV was obtained to predict the average DRX grain size during hot forming.

/pdf/evolution-of-dynamic-recrystallization-in-5crnimov-steel-4gtgzmbx6l.pdf

Tian Rui Li

Papers

Dynamic Recrystallization and Flow Behavior in Low Carbon Nb–Ti Microalloyed Steel

Modeling of Precipitation Behavior of Complex Precipitates in Ferrite and Validation with Experiment