Simulation of aging process of lead frame copper alloy by an artificial neural network

The aging hardening process makes it possible to get higher hardness and electrical conductivity of lead frame copper alloy. The process has only been studied empirically by trial-and-error method so far. The use of a supervised artificial neural network (ANN) was proposed to model the non-linear relationship between parameters of aging process with respect to hardness and conductivity properties of Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of aging process was established via sufficient data mining by the network. The results show that the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Zr alloy.