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�������պ����ѧѧ�� 2011, Vol. 37 Issue (1) :106-109    DOI:
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State recognition of tool wear based on wavelet neural network
Nie Peng, Chen Xin, Xu Tao, Sun Baolin*
Mechanical and Electrical Engineering Institute, Shenyang Aerospace University, Shenyang 110136, China

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ժҪ ��Ժ�������ļӹ��ص�,�ɼ������ڲ�ͬĥ��״̬�µ�������(AE,Acoustic Emission)�ź�,��AE�źŽ���ʱƵ������С���任,���ÿ��ٸ���Ҷ�任(FFT, Fast Fourier Transform)�Լ�db8С��5��ֽ�,��ȡAE�źŷ�ֵ�ľ�������������Ƶ�ε�������Ϊ��������,�������������й�һ���������Ϊ����������С�����������ѵ��.С�����������ò��������㷨,��Ȩֵ����ֵ�������м��붯����.���Խ������:AE�źŶԵ���ĥ�����е�Ƶ�ʷ�Χ��10~150�CkHz,����ʵ�������������������С��0.03,�÷����ܹ��Ե��߲�ͬĥ��״̬������ȷ��ʶ��.
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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 10-150�CkHz. 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.
Keywords�� aviation machining   tool wear   wavelet neural network   condition identification     
Received 2009-11-12;
Fund:

�������˲�����ר�����������Ŀ(07SYRC04); ����ʡ�������ص�ʵ������Ŀ(LS2010117)

About author: �� ��(1972-),��,����ʡ��������,������,niehit@163.com.
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����, ����, ����, �ﱦ��.����С��������ĺ��յ���ĥ��״̬ʶ��[J]  �������պ����ѧѧ��, 2011,V37(1): 106-109
Nie Peng, Chen Xin, Xu Tao, Sun Baolin.State recognition of tool wear based on wavelet neural network[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2011,V37(1): 106-109
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