Effects of aging parameters on hardness and electrical conductivity of Cu-Cr-Sn-Zn alloy by artificial neural network

Abstract

In order to predict and control the properties of Cu-Cr-Sn-Zn alloy, a model of aging processes via an artificial neural network (ANN) method to map the non-linear relationship between parameters of aging process and the hardness and electrical conductivity properties of the Cu-Cr-Sn-Zn alloy was set up. The results show that the ANN model is a very useful and accurate tool for the property analysis and prediction of aging Cu-Cr-Sn-Zn alloy. Aged at 470–510 °C for 4-1 h, the optimal combinations of hardness 110–117 (HV) and electrical conductivity 40.6–37.7 S/m are available respectively.

Keywords

Cu-Cr-Sn-Zn alloy / aging parameter / hardness / electrical conductivity / artificial neural network