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

作     者：SU Juanhua1,2, LIU Ping2, DONG Qiming2 & LI Hejun1 1. College of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China 2. College of Materials Science and Engineering, Henan University of Science and Technology, Luoyang 471003, China 

作者机构：College of Materials Science and Engineering Northwestern Polytechnical University Xi’an China College of Materials Science and Engineering Henan University of Science and Technology Luoyang China 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2005年第48卷第5期

页      面：510-520页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the State“863 Plan"(Grant No.2002AA331112) the Doctorate Foundation of Northwestern Polytechnical University(Grant No.CX200409) the Science Research Foundation of Henan University of Science and Technology(Grant No.2004zy039) 

主　　题：Cu-Cr-Zr alloy, thermomechanical processing optimization, artificial neural network, genetic algorithms. 

摘      要：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.