基于小波模糊神经网络刀具监控系统研究

STUDY ON TOOL MONITORING SYSTEM USING WAVELET FUZZY NEURAL NETWORK

针对切削过程中振动信号和ＡＥ信号的特点，利用小波分析技术提取信号深层特征，建立了新型的基于模糊推理的神经网络模型，该模型能融合振动和ＡＥ信号的特征和描述信号特征与刀具状态的非线性关系，以此识别刀具状态。试验表明小波模糊神经网络对提高在线刀具监控系统的可靠性极为有效。

The features of vibration signal and AE signal in cutting proceeding is analyzed, the signal intrinsic features are extracted using wavelet analysis, the signals are fused and the nonlinear relationship between signal feature and tool condition is described in order to identify tool condition using new wavelet fuzzy neural network. The experimental results show it is very effective to improve tool monitoring system reliability.