立铣加工过程中的颤振会严重影响工件表面质量和材料去除率，加剧刀具磨损和恶化工作环境。虽然大部分颤振监测系统可以监测到颤振发生，但颤振发生时已经对工件和刀具产生了严重的损伤，因此，需要提前监测到颤振特征。由于加工过程的非线性导致振动信号频率成分复杂，单一的时频分析方法难于得到可靠的颤振特征。通过小波包分解确定颤振发生频段并重构该频段信号，通过颤振发生频段的倒频谱辨识稳定、过渡和颤振状态。研究结果表明，该方法可以有效识别立铣加工过程的稳定、过渡和颤振状态。%Chatter in the process of end milling would lead to poor surface finish , low material removal rate , severe tool wearand noisy workplace , and etc .Chatter vibration can be detected by most chatter detection system , however , it already has serous effects on the surface quality of workpieces as well as the cutting tools when it occurring .Therefore, chatter detection system must find chatter characteristicsin the early state .The nonlinear machining process would cause complex frequency componentsin the vibration signal , so it was difficult to obtain reliable chatter feature using a single time-frequency analysis method .Wavelet packet decomposition was em-ployed to define the chatter emerging frequency range , and it was reconstructed .The cepstrum of chatter emerging frequency range wa-susedto identify the stable , transition and chatter states .The study results show that the method can accurately distinguish the stable , transition and chatter states in end milling processes .
展开▼
