In the process of end milling , chatter is considered as one of the most important causes of instability in the machining processes .It would lead to very poor surface finish , low material removal rate , severe tool wear , and noisy workplace .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 occures .Therefore, chatter detection system should find chatter chracteristics in the early state .In the chatter-emerging processes , the amplitude of vibration signal increases in time domain , and the energy spectrum density of vibration signal transfers from high frequency to low frequency in frequency domain .A method to catch the chatter features was proposed in the transi-tion state by considering the two vibration signal characteristics .The energy ratio and the singular spectrum entropy ratio of vibration signal in the chatter-emerging frequency band were extracted as two chatter features .An artificial neural network ( ANN) model was de-veloped to identify the cutting of chatter characertistics .The chatter detection system , consisting of the feature extraction and identifica-tion, can accurately distinguish the stable , transition and chatter states in end milling processes .%在立铣加工过程中，颤振是加工过程失稳的一个最重要的原因。颤振将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。在颤振发生过程中，振动信号具有在时域中不断增大，在频域中能量频移的特性。考虑这两个振动信号特征，提出了一种颤振特征提取方法。提取颤振发生频带中振动信号的能量比和奇异谱熵系数作为两个颤振特征，并通过人工神经网络模型实现切削颤振的识别。文中提出的颤振监测系统包括特征提取和分类，能够精确辨识立铣加工中的稳定、过渡和颤振状态。
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