Artificial intelligence approach to the internal variable-based rheological model for steels

The paper is a continuation of the authors´ earlier work dealing with application of artificial neural networks to the prediction of yield stress in hot forming of metals. At present, the task of the network is to predict a time-derivative of the dislocation density during hot deformation. The inputs are the state of the material defined by the current dislocation density and by the time-integral of strain, the current strain rate and temperature. The flow stress curve is determined from the dislocation density vs. strain function, which is calculated using a finite difference technique in which the time-derivative of the dislocation density is supplied by the artificial neural network. Examples of calculations are presented for the axi-symmetrical compression of low carbon steel