摘要
提出了一种基于集成神经网络识别铣刀磨损量的监测方法.利用小波包变换将切削力和振动信号分解为不同频带的时间序列,从每个信号中选择与刀具磨损状态最相关的3组频段的均方根作为监测特征;通过信号的组合和不同子网络输出决策间的融合,集成神经网络输出刀具磨损的识别结果.试验和仿真分析表明,此方法能够满足刀具磨损量实时监测的要求.
A tool wear condition monitoring approach based on integrated neural networks was proposed to recognize and predict tool wear conditions in milling operations. In this approach, vibration and cutting force signals are decomposed into time sequences in different frequency bands by wavelet packet transform, and the root mean square values of each signal in three frequency bands, extracted from decomposed signals, with a close relation to wear conditions are selected as monitoring features. The final recognition results of tool wear are given by the integrated neural networks through the combination of signals and the decision fusion of different subnets. Experiments and simulations show that the proposed approach can meet the requirements of on-line monitoring of tool wear conditions.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2005年第5期641-644,653,共5页
Journal of Southwest Jiaotong University
关键词
刀具磨损监测
多传感器
小波包
集成神经网络
tool wear monitoring
multi-sensor
wavelet packet
integrated neural network
作者简介
高宏力（1971-），男，博士研究生，研究方向为智能制造技术、智能化状态监测与故障诊断技术．
