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.

Hot deformation is one of the main technological stages of products made from metallic materials. It is strictly required to decrease the costs of developing optimized technologies at this stage without a significant decrease in the products’ quality. The present investigation offers an algorithm to unite three different models to predict the hot deformation behavior, fracture, and microstructure evolution. The hot compression and tension tests of the AISI 316Ti steel were conducted using the thermomechanical simulator Gleeble 3800 for the models’ construction. The strain-compensated constitutive model and the Johnson–Mehl–Avrami–Kolmogorov (JMAK)-type model of the grain structure evolution show a satisfactory accuracy of 4.38% and 6.9%, respectively. The critical values of the modified Rice and Tracy fracture criteria were determined using the experimental values of the relative cross-section reduction and finite element calculation of the stress triaxiality. The developed models were approved for the stainless AISI 316Ti steel by the hot torsion with tension test.

The development of new lightweight materials is required for the automotive industry to reduce the impact of carbon dioxide emissions on the environment. The lightweight, high-manganese steels are the prospective alloys for this purpose. Hot deformation is one of the stages of the production of steel. Hot deformation behavior is mainly determined by chemical composition and thermomechanical parameters. In the paper, an artificial neural network (ANN) model with high accuracy was constructed to describe the high Mn steel deformation behavior in dependence on the concentration of the alloying elements (C, Mn, Si, and Al), the deformation temperature, the strain rate, and the strain. The approval compression tests of the Fe–28Mn–8Al–1C were made at temperatures of 900–1150 °C and strain rates of 0.1–10 s−1 with an application of the Gleeble 3800 thermomechanical simulator. The ANN-based model showed high accuracy, and the low average relative error of calculation for both training (5.4%) and verification (7.5%) datasets supports the high accuracy of the built model. The hot deformation effective activation energy values for predicted (401 ± 5 kJ/mol) and experimental data (385 ± 22 kJ/mol) are in satisfactory accordance, which allows applying the model for the hot deformation analysis of the high-Mn steels with different concentrations of the main alloying elements.

The deformation behavior of lightweight Fe-35Mn-10Al-1C steel with an elevated concentration of Mn was investigated. Hot compression tests at temperatures of 950–1150 °C and strain rates of 0.1–10 s−1 were carried out using the thermomechanical simulator, Gleeble 3800. Strain compensated constitutive model of hot deformation behavior with high accuracy (error was 4.6%) has shown significant increases in the effective activation energy (410–460 kJ/mol) in comparison with low Mn steels. The significant influence of the strain rate and temperature on the grain size was shown. The grain size decreases from the initial value of 42 ± 6 μm to the value of 3.5 ± 0.7 μm after the deformation at 1050 °C and 10 s−1. The model of the microstructure evolution of the investigated steel was constructed. The average error of the constructed model was 8.5%. The high accuracy of the constructed models allows for their application for the optimization of the hot deformation technologies using finite element simulation.