Prediction of properties in thermomechanically treated Cu-Cr-Zr alloy by an artificial neural network

A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results show that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.