Constitutive Modeling of High‐Temperature Deformation Behavior of Nonoriented Electrical Steels as Compared to Machine Learning

Hot rolling is a critical thermomechanical processing step for nonoriented electrical steel (NOES) to achieve optimal mechanical and magnetic properties. Depending on the silicon and carbon contents, the electrical steel may or may not undergo austenite–ferrite phase transformation during hot rolling, which requires different process controls as the austenite and ferrite show different flow stresses at high temperatures. Herein, the high‐temperature flow behaviors of two nonoriented electrical steels with silicon contents of 1.3 and 3.2 wt% are investigated through hot compression tests. The hot deformation temperature is varied from 850 to 1050 °C, and the strain rate is differentiated from 0.01 to 1.0 s −1 . The measured stress‐strain data are fitted using various constitutive models (combined with optimization techniques), namely, Johnson–Cook, modified Johnson–Cook, Zener–Hollomon, Hensel–Spittel, modified Hensel–Spittel, and modified Zerilli–Armstrong. The results are also compared with a model based on deep neural network (DNN). It is shown that the Hensel–Spittel model results in the smallest average absolute relative error among all the constitutive models, and the DNN model can perfectly track almost all the experimental flow stresses over the entire ranges of temperature, strain rate, and strain.