State recognition of tool wear based on wavelet neural network
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摘要: 针对航空零件的加工特点,采集刀具在不同磨损状态下的声发射(AE,Acoustic Emission)信号,对AE信号进行时频分析和小波变换,运用快速傅里叶变换(FFT, Fast Fourier Transform)以及db8小波5层分解,提取AE信号幅值的均方根和主能量频段的能量作为特征向量,对特征向量进行归一化处理后作为输入向量对小波神经网络进行训练.小波神经网络运用参数调整算法,在权值和阈值的修正中加入动量项.测试结果表明:AE信号对刀具磨损敏感的频率范围在10~150kHz,网络实际输出与期望结果的误差小于0.03,该方法能够对刀具不同磨损状态进行正确的识别.Abstract: In connection with the processing characteristics of aviation parts, acoustic emission(AE) signals of tool which in different wear state were acquired. Time-frequency analysis and wavelet transform were utilized on the AE signals. Fast fourier transform and db8 wavelet decomposition were used to extract the amplitude root-mean-square value and the main band energy value which were considered as eigenvectors of AE signals. Then the eigenvectors were normalized and taken as input vector for the training of wavelet neural network. Parameter adjustment algorithm was applied to add momentum term to weights and threshold for amendment in the wavelet neural network. The results indicate that the frequency range of tool AE signals which sensitive to different wear states is between 10-150kHz. The error between the trained wavelet neural network actual output and expect output is less than 0.03. Different tool wear states can be recognized correctly and effectively by this method.
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Key words:
- aviation machining /
- tool wear /
- wavelet neural network /
- condition identification
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