Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21
Online ISSN : 2424-3086
ISSN-L : 2424-3086
セッションID: D002
会議情報
D002 Drill Wear Prediction with Features Extracted From the Static & Dynamic Components of Forces by Wavelet Packet Transform Using Back Propagation Neural Network
Jie XUKeiji YAMADAKatsuhiko SEKIYARyutaro TANAKAYasuo YAMANE
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会議録・要旨集 フリー

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抄録
This paper defines static and dynamic component parameters based on the method that converts thrust and torque detected during drilling process into equivalent thrust force and principal force. The features of the parameters are extracted by wavelet packet transform (WPT) and then they are transferred to a back propagation neural network (BPNN) as inputs to predict the drill wear. Experiments with different drilling conditions and workpiece materials were conducted to prove the reliability of this method.
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© 2013 一般社団法人 日本機械学会
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