摘要
为了提高机床加工过程中刀具磨损的监测能力,选择主轴电流和进给电流为主要信息,基于小波分解及软测量模型进行电流信号的多特征提取,从加工进给和主轴驱动两方面反映刀具磨破损信息;在此基础上,基于Parzen视窗法进行多特征信息的数据融合,构建智能报警模型,并依据拉依达法则确定报警边界,从而实现刀具状态的智能报警。将该技术应用到机床的加工中,实验证明可以实时地监测刀具运行状态并进行磨破损报警。
In order to improve the ability of tool wear monitoring in the machining process,the spindle current and feed current are selected as the main information.The multi-feature extraction of current signal is based on the wavelet decomposition and soft-sensing model.The information of tool wear and break can be reflected from the processing of feed and spindle driving.On the basis of above all,the data fusion of the multi-feature information and the construction of intelligent alarm model can be implemented based on Parzen windows.The alarm boundary is determined based on PaTa criterion,so the intelligent alarm of tool condition can be achieved.When applying this technology to tool machining,it is shown from the experiment results that this technology can monitor tool condition in real-time and alarm in time.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2013年第3期377-381,522,共5页
Journal of Vibration,Measurement & Diagnosis
基金
云南省省校科技合作专项项目(2010AD011)
国家重大科技专项项目(2010ZX04014-015)
国家自然科学基金资助项目(51075323)
关键词
刀具磨损
智能监测
电机定子电流特征分析
软测量模型
tool wear,intelligent monitoring,motor stator current signature analysis,soft-sensing model
