摘要
采集车刀切削过程中的振动信号,对其进行时域与频谱分析,找出影响振动信号的主要因素。采用信号的模式滤波法将车刀振动信号分解为一系列子波信号,研究不同工况下车刀子波参数分布的差异性,结合数字化音频技术实现了子波的分类、分离与识别处理,并最终将这些子波在β2-α特征平面内划分为17个不同的区域,获得了每个特征区域子波参数分布与车刀工况之间的联系。在此基础上开展了不同工况特征区域车刀振动子波参数变化规律的研究。研究表明:同类及不同类子波参数之间存在着很强的关联性;通过引入同伴、竞争等关系,获得了这些参数彼此间的消长变化规律,以及这些变化与车刀磨损工况之间的联系,从而为定量化识别车刀工况创造了条件。
The main affecting factors of cutting vibration signals for machining were found by spectrum and time-frequency analysis to samples from the cutting process. The signals were decomposed into a series of signal operation units （SOU） by pattern filtering calculation （PFC）. The differences among the SOU parameters were investigated in different cutting wear condition by pattern filtering method （PFM）. All SOU signals were separated, classified and identified by the use of digital audio technology. The characteristic plane of/i2-a parameters for SOU was divided into 17 regions. The relationship between tool wear and SOU parameters＇ variation for each region was discovered. It is shown that strong correlations for SOU parameters are existed in the same and different kind of signal operation units. The relationship between SOU and cutting condition as well as the link of different SOU parameters are established by introducing relationship of associates and competition. Thereby a good foundation is laid for quantitative identification of tool wear condition.
出处
《机床与液压》
北大核心
2012年第1期65-71,102,共8页
Machine Tool & Hydraulics
关键词
切削
振动
信号处理
模式滤波法
车刀工况识别
Cutting
Vibration
Signal processing
Pattern filtering method
Cutting tool wear condition identification
