Hiroyuki Kumehara

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The fast monitoring of tool wears by using various Cutting signals and the prediction models developed rapidly in recent years. Comparatively, various wear forecast models based on artificial neural networks (ANN) perform much better in accuracy and speediness than the conventional prediction models. Combining the prominent dynamic properties of back(More)
In this paper, combination of Wavelet Packet Decomposition n (WPD) and Neural Networks (NN) was used to identification the experimental cutting torque data of drilling operations previously. It consists of three steps: firstly, decomposition cutting torque from the original signals by WPD; secondly, extracting wavelet coefficients of different wear states(More)
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