Title :
Research on the milling tool monitoring system based on wavelet neural network
Author :
Guizhong Guo ; Xinhua Mao
Author_Institution :
Xinxiang Univ., Xinxiang, China
Abstract :
Among the milling process, the signal representation tools sharply wearing in primary stage, is weaker. While the work piece accuracy have already at this time obvious change. Wavelet neural network can effectively handle various signals with different frequency, but it is possible that it can not detect the faint signal. Based on monitoring accuracy change of the workpiece, do modify the parameter of wavelet transform in time, and it can enhance the ability of monitoring faint signal, decrease missing rate and false alarm rate.
Keywords :
computerised monitoring; milling machines; neural nets; signal detection; signal representation; wavelet transforms; faint signal detection; false alarm rate; milling tool monitoring system; signal representation tools; wavelet neural network; wavelet transform; Accuracy; Biological neural networks; Force; Milling; Monitoring; Surface roughness; Wavelet transforms; Monitoring; The milling Cutter; Wavelet neural network;
Conference_Titel :
Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on
Conference_Location :
Harbin, Heilongjiang, China
Print_ISBN :
978-1-61284-087-1
DOI :
10.1109/EMEIT.2011.6023313
