摘要
建立了以压电钻削测力仪作为测量元件的微孔钻削力在线监测系统,构造了用于对微孔钻削力进行实时数据处理的4层模糊神经网络。网络经训练后用于实时获取隐含微细钻头磨损状态信息的钻削力值,对微孔钻削过程进行在线监测实验,结果表明,适当选择监测阈值,可以有效避免微细钻头的折断。
An on-line micro hole drilling monitoring system with a piezoelectric element to measure the drilling thrust was built. A four-layor fuzzy neural network（FNN） was established for processing the real-time thrust data of the micro hole drilling. After drilling, the FNN was used to acquise the real- time thrust values which implicate the wear state information of the micro drill. The on-line tests to monitor the nicro hole drilling processes were performed with the established FNN, and the results showed that the drill break may be avoided if the monitoring threshold was selected properly.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2007年第6期1336-1340,共5页
Journal of Jilin University:Engineering and Technology Edition
基金
吉林省自然科学基金资助项目(20010574)
关键词
机械制造自动化
微孔钻削
模糊神经网络
实时监测
压电元件
mechanical manufacture and automation
micro hole drilling
fuzzy neural network
real-time monitoring
piezoelectric element
