结果表明，该方法实现了对刀具切削状态的特征识别。
The results obtained show that the characteristics identification of tool cutting conditions is attained by the approach.
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.
针对特征分类性能和稳定性的差异，本文采用BP神经网络方法对上述特征进行评价和选择，挑选出刀具切削状态的基础特征。
Aim at the discrepancy of classified capacity and stability of feature, this paper make use of BP neural net to remark and select above feature, pick out basic feature of cutting tool state.
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.
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.
在干切状态下，切削深度对刀具寿命影响最大。
Under dry cutting state, tool life is affected mostly by cutting depth.
本文通过实验研究了金刚石薄膜涂层刀具的破损机理及刀具表面状态、切削用量对其损坏形式的影响。
This paper studied the breakage mechanism of the diamond film-coated tools, the influence of the surface appearance, the cutting parameters on the damage forms.
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。
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.
试验表明：干切削状态下金属软化效应占主导地位，湿切削可获得更好的表面粗糙度，切削液在刀具磨损后期开始发挥作用。
The results show that the metal soften is dominant in dry turning process, a good surface can be acquired in wet turning, and the cutting fluid takes an effective effect at the end of tool wear.
采用机床主电动机功率法和声发射（AE）法来获取切削过程中发出的刀具磨损和破损信号，建立了刀具状态试验系统。
Experiments were implemented to obtain the motor power and AE signals under different cutting parameter condition(cutting velocity, feed velocity and cutting depth), tool wear and breakage conditions.
刀具的切削力和振动信号是研究刀具磨损状态的很好的手段。
The cutting force signals and vibration signals are good means to research tool wear.
通过比较实时采集的切削力与不同刀具磨损值对应的切削力大小，可确定刀具的磨损状态，并利用建立的简化模型计算刀具的精确磨损值。
The mapping relationship between machining parameters and cutting forces in different tool wear states was built up by means of B-spline neural network in this new methodology model.
通过比较实时采集的切削力与不同刀具磨损值对应的切削力大小，可确定刀具的磨损状态，并利用建立的简化模型计算刀具的精确磨损值。
The mapping relationship between machining parameters and cutting forces in different tool wear states was built up by means of B-spline neural network in this new methodology model.
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