声发射和小波包分解技术在刀具磨损状态中的应用

Acoustic emission and wavelet analysis-based estimation of tool wear

随着现代加工工业的发展,对刀具磨损的监测在保障生产安全和产品质量中发挥着越来越重要的作用。声发射技术是刀具磨损监测的一种新方法。在车削加工过程中采集声发射信号,用声发射信号对刀具磨损状态进行识别。利用小波包分解技术对信号进行分析,得到有效的特征量作为BP神经网络的输入样本,并对网络进行学习训练,完成对刀具磨损状态的有效识别。

Accompanied with the development of modem machining industry, tool wear monitoring becomes more and more important. Acoustic Emission （AE） is a useful and effective technique in tool wear monitoring. This paper uses Daubechies Wavelet to analyze AE signal and select features of the tools. The selected features are then considered as inputs to BP neural network to complete recognition of the status of the cutting tool.