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。
Tool condition monitoring is the key technique in automatic and unmanned machining process.
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。
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.
本文通过集成多个传感器的信息，建立了一个基于神经网络的刀具状态监测的智能系统。
This paper establishes an intelligent system based on Unsupervised neural network for monitoring the state of cutting tool by integrating information from a variety of sensors.
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。
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.
声发射是一种新的切削刀具状态的监测方法。
Acoustic emission (AE) is a new method for cutting tool condition monitoring.
在对刀具切削状态进行监测的众多方法中，声发射方法是一种最有前途的方法。
Among the various way to monitor the condition of cutting tool, the way through Acoustic Emission (AE) is a most promising method.
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。
This paper researches on the tool wear condition monitoring by cutting sound signal and workpiece surface texture based on analysis of the relative situation.
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。
After a brief introduction the importance of tool condition monitoring, the paper derived the relationship between the spindle current and tool wear theoretically.
应用声发射传感器对微铣削过程中的刀具状态进行监测。
Acoustic emission (AE) sensors are used to monitor tool conditions in micro-milling operations.
应用声发射传感器对微铣削过程中的刀具状态进行监测。
Acoustic emission (AE) sensors are used to monitor tool conditions in micro-milling operations.
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