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。
Tool condition monitoring is the key technique in automatic and unmanned machining process.
刀具状态监测是实现自动化加工和无人化加工的关键技术。
After a brief introduction the importance of tool condition monitoring, the paper derived the relationship between the spindle current and tool wear theoretically.
在简单介绍监测刀具状态的重要性的基础上，文章从理论上推导了主轴电流与刀具磨损量之间的关系式。
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 proposes a composite neural model for tool condition monitoring. It is composed with several neural networks and the number of neural networks is equal to the number of tool faults.
提出了一种用于刀具状态监测的复合神经网络模型，模型由多个神经网络组成，神经网络的数目等于要监测的刀具故障数目。
Blind sources separation is a special and dominant tool for analyzing and processing signals blindly, which is promising in condition monitoring and fault diagnosis of machinery.
盲源分离是一个很有优势的盲信号分析与处理工具，在机械状态监测与故障诊断领域有较好的应用前景。
Blind sources separation is a special tool for analyzing and processing signals blindly, which is promising in condition monitoring and fault diagnosis of machinery.
盲源分离是一个很独特的盲信号分析与处理工具，在机械设备状态监测与故障诊断领域有较好的应用前景。
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。
应用推荐