Prediction and analysis of the aging properties of rapidly solidified Cu-Cr-Sn-Zn alloy through neural network

It is known that the strength of a metal can be successfully improved by rapid solidification. The hardness of the rapidly solidified Cu-Cr-Sn-Zn alloy is much higher than that of the solution heat-treated and aged alloy. In this study, multiple-layer, feed-forward, artificial neural network (ANN) modeling has been used to study the hardness and electrical conductivity behavior of a rapidly solidified Cu-Cr-Sn-Zn alloy. The ANN model shows how the aging parameters influence the hardness and electrical conductivity of a rapidly solidified Cu-Cr-Sn-Zn alloy. The ANN modeling also provides encouraging predictions for information not included in the trained set samples, indicating that a backpropagation network is a very useful and accurate tool for property analysis and prediction.