湖南大学学报（自然科学版）2011，Vol.38Issue(4)：31-35,5.
基于RBF神经网络的难加工金属材料数控加工控制方法研究
Study on Control Method of Numerical Control Machining Processes for Difficult Processed Metal Materials Based on RBF Neural Network
摘要
Abstract
To solve the problems existing in numerical control machining processes for difficult processed metal materials, such as titanium alloy, a control method of numerical control machining processes for difficult processed metal materials based on RBF neural network was developed. And combined with CNC machining equipment, the corresponding real-time monitoring system was established. Application effect shows that, through RBF neural network control of numerical control machining processes, the surface roughness and roundness errors of workpiece are much lower than those machined in conventional machining methods. The average error of surface roughness reduced more than 50 ％, while the average error of roundness reduced more than 70 ％.关键词
难加工金属材料/神经网络/数控加工/加工精度Key words
difficult processed metal materials/ neural network/ numerical control machining/ machining accuracy分类
信息技术与安全科学引用本文复制引用
曾谊晖,左青松,李翼德,黄红华,陈恒,王亚风..基于RBF神经网络的难加工金属材料数控加工控制方法研究[J].湖南大学学报（自然科学版）,2011,38(4):31-35,5.基金项目
湖南省教育厅优秀大学生创新实验项目(2010244-394) （2010244-394）
湖南省教育厅优秀青年科研项目(10B58) （10B58）
