Model of Predicting Polymer Melt Temperature Field by BP Neural Networks

摘要：

　　Temperature of the polymer melt is one of the most important parameters for the polymer continuous extrusion molding process.There are many factors influence the distribution of the melt temperature,these factors have the coupling and nonlinear relationship which is difficult to measure accurately by the traditional measuring method.In this study,a BP neural networks-based model approach is presented in which the effects of the die wall temperature and screw speed and the wall temperature of the transition section and the measurement section in the continuous extrusion molding are investigated.Comparison of the BP neural networks model predictions with the experimental data yields very good agreement and demonstrates that the BP neural networks model can predict the polymer melt temperature field with a high degree of precision (the mean square error within 0.03).

关键词： BP Neural Networks Polymer melt Temperature field Die wall temperature Screw speed Transition section

作者: Meng Shan Li Bing Xiang Liu Yan Wu

作者单位: School of Information Engineering, JingDeZhen Ceramic Institute, JiangXi, China

会议类型: 国际会议

会议名称: the 2012 International Conference on Materials Science and Nanotechnology (2012年材料科学与纳米技术国际会议(ICMSN 2012))

会议地点: 广州

会议语种:英文

页码: 425-428

在线出版日期: 2012-11-16（万方平台首次上网日期，不代表论文的发表时间）