Optimization of thermomechanical processes in Cu-Cr-Zr lead frame alloy using neural networks and genetic algorithms

The thermomechanical treatment process is effective in enhancing the properties of the lead frame copper alloy. In this study, an optimal pattern of the thermomechanical processes for Cu-Cr-Zr was investegated using an intelligent control technique consisting of neural networks and genetic algorithms. The input parameters of the artificial neural network (ANN) are the reduction ratio of cold rolling, aging temperature and aging time. The outputs of the ANN model are the two most important properties of hardness and conductivity. Based on the successfully trained ANN model, genetic algorithms (GA) are used to optimize the input parameters of the model and select perfect combinations of thermomechanical processing parameters and properties. The good generalization performance and optimized results of the integrated model are achieved. Copyright by Science in China Press 2005.