Guoliang Ji

TL;DR: In this article, the experimental stress-strain data from isothermal hot compression tests on a Gleeble-3800 thermo-mechanical simulator was employed to develop the Arrhenius-type constitutive model and artificial neural network (ANN) model, and their predictability for high-temperature deformation behavior of Aermet100 steel was further evaluated.

TL;DR: In this article, 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.

TL;DR: In this paper, an artificial neural network (ANN) model is developed to predict the hot deformation behavior of the ultra-high strength steel of Aermet100, where the inputs of the neural network are strain, strain rate and temperature, whereas flow stress is the output.

TL;DR: In this article, the suitability levels of these two models were evaluated by comparing both the correlation coefficient R and the average absolute relative error (AARE), and the number of material constants involved in the two models was also compared.

TL;DR: In this article, a 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.