An Artificial Neural Network (ANN) Model for Predicting Instability Regimes in Copper–Aluminum Alloys

Abstract

Materials workability is one of the important aspects for any process design to achieve quality products. Identifying optimum process parameters like temperature, strain rate, and strain are normally done by trial and error. In recent years, processing maps are used in choosing these parameters for hot working of materials. Identification of these parameters requires certain high-level expertise as well as detailed microstructural evidences. In this study, using the available copper–aluminum alloy data, an Artificial Neural Network (ANN) model has been developed to classify the hot-working process parameters, like temperature, strain rate, flow stress for instability regime, directly from the corrected flow stress data without applying the Dynamic Materials Model (DMM). This model uses four compositions of Cu–Al system, ranging from 0.5% to 6% Aluminum. Details about the ANN architecture, and the training and testing of these models are explained. The results obtained using the ANN model are compared and validated with those obtained from the processing maps using DMM. It is further shown that even with smaller data set the development of an ANN model is possible as long as the data has some pattern in it.