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。
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。
This paper researches on the tool wear condition monitoring by cutting sound signal and workpiece surface texture based on analysis of the relative situation.
本论文在分析现状的基础上，从切削声信号和工件表面纹理这两个方面对刀具磨损状态监测技术进行了研究。
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