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.
应用推荐