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Recognition for Spindle Speeds of Drill Press Based on Sound Signals Produced from Drilling

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Abstract:

Effects of running state and spindle speeds on the sound signals produced from a drill press are investigated. And the obtained sound signals by using of a sound level meter are analyzed in both time domain and frequency domain. It is evident that there is more high frequency energy for drilling sound signals with load than without load. And spindle speeds still affect their energy distribution of drilling sound signals. Using wavelet decomposition and wavelet packet decomposition, drilling sound signals are decomposed into a number of frequency bands. And energy percentages of the divided frequency bands are extracted to be the effective characteristics to recognize spindle speeds. Meanwhile, training error of different BP networks is compared to obtain the effective network for recognition spindle speeds. By using of the obtained network structure named 16-30-5, the study rate for training samples and the recognize rate for testing samples are all above 95%.

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Advances in Machining & Manufacturing Technology IX View Preview

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Periodical:

Key Engineering Materials (Volumes 375-376)

Pages:

535-538

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.375-376.535

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Online since:

March 2008

Authors:

Xiang Feng Li, Gen Lian Yang, Dun Wen Zuo

Keywords:

Drill Press, Neural Network (NN), Sound Signal, Spindle Speed, Wavelet Decomposition, Wavelet Package Decomposition

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© 2008 Trans Tech Publications Ltd. All Rights Reserved

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