Modeling of the hot deformation behavior of a high phosphorus steel using artificial neural networks

Abstract

The hot deformation behavior of high phosphorus steels were investigated through thermo-mechanical simulations for temperatures ranging from 750 °C to 1050 °C and with strain rates of 0.001 s⁻¹, 0.01 s⁻¹, 0.1 s⁻¹, 0.5 s⁻¹, 1.0 s⁻¹ and 10 s⁻¹. Using a combination of temperature, strain and strain rate as input parameters and the obtained experimental stress as a target, a multi-layer artificial neural network (ANN) model based on a feed-forward back-propagation algorithm with ten neurons is trained, to predict the values of flow stress for a given processing condition. A comparative study of predicted stress using ANN and experimental stress shows the reliability of the predictions. A processing map for true strain of 0.7 was plotted with the help of the predicted values of flow stress, and the optimum processing conditions were investigated, at low temperatures and moderate to high strain rates, as well as at moderate to high temperatures and low to moderate strain rates.