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。
Based on acoustic emission, power monitoring and improved neural network technology and using cutting tool monitoring software, the normal or abnormal condition of cutting tools can be identified.
以声发射、功率监控和改进的神经网络技术，借助刀具监控软件，识别刀具切削工作的正常和失效状态。
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。
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。
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