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。
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