基于神经网络技术的注塑成型注射压力和熔体温度预测  

Prediction of Injection Pressure and Melt Temperature of Injection Molding by Radial Basis Function Network

作　　者：姜勇道[1] 周华民[2] 傅建[1] 李德群[2] 

机构地区：[1]四川工业学院材料科学与工程系,四川成都610039 [2]华中科技大学模具技术国家重点实验室,湖北武汉430074

出　　处：《塑料工业》2003年第6期27-29,共3页China Plastics Industry

基　　金：华中科技大学模具技术国家重点实验室开放课题 (0 2 -0 1);中国博士后科学基金(2 0 0 2 0 3 12 5 2资助

摘　　要：建立了基于径向基函数网络的注塑成型注射压力和熔体温度的预测模型 ,与BP神经网络模型和CAE结果进行了对比。结果表明 ,径向基函数网络在精度。A radial basis function network model on injection pressure and melt temperature of injection molding is established in this paper The prediction model based on radial basis function network is trained through injection molding CAE data and verified by additional data successfully Another network model based on back propagation network is also trained for comparison The results show that for the problem studied in this paper, the radial basis function network is much better than back propagation network in accuracy and speed of training

关 键 词：神经网络技术 注塑成型 注射压力 熔体温度 预测模型 

分 类 号：TQ320.662[化学工程—合成树脂塑料工业]