Hot Deformation Characterization of Pure Aluminum Using artificial neural network (ANN) and Processing Map Considering Initial Grain Size

Abstract

In this investigation, processing maps and artificial neural network (ANN) models were carried out to describe and predict the flow behavior of pure aluminum at various initial grain sizes in the hot working conditions. The elevated temperature flow behavior of AA1070 aluminum was done through isothermal hot compressive tests in a large range of initial grain size (IGS) (50–450 µm), strain rate (0.005–0.5 s−1) and temperature (623–773 K). Consequences showed that the flow stress can be remarkably influenced by the initial grain size at high temperatures. Based on the results, the ANN model trained with a feed-forward back-propagation learning algorithm which was prepared to describe the flow behavior of pure aluminum at the elevated temperatures. In which the initial grain size, strain, temperature and strain rate were taken as input data and true stress was used as target data. The results showed that the developed ANN model was a powerful method to predict the complex non-linear of the hot flow behavior of pure aluminum. The processing map was plotted and analyzed via the dynamic material model as “stable” and “unstable” regions were determined by observing the microstructure evolution. Based on this, The optimum ranges for temperature and strain rate were 623–773 K and 0.05 s−1 respectively, for fine-grained microstructure (lower than 50 µm) and were 650–720 K and 0.005–0.5 s−1 respectively, for coarse-grained microstructures (over than 50 µm).