Acoustic emission (AE) is a new method for cutting tool condition monitoring.
声发射是一种新的切削刀具状态的监测方法。
Taking off-line control test for example, the reliability of cutting tool condition identification of this monitoring system has been assessed.
并以离线控制试验为实例，考核该系统识别刀具状态的可靠性。
In order to improve cutting tool condition monitoring, a method of cutting tool fault diagnosis based on wavelet and artificial networks with relaxed structure is proposed in this paper.
为了有效的进行刀具状态监测，采用小波神经网络的松散型结合对刀具进行故障诊断。
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