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Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

Journal Key Engineering Materials (Volume 329)
Volume Advances in Abrasive Technology IX
Edited by Dongming Guo, Tsunemoto Kuriyagawa, Jun Wang and Jun’ichi Tamaki
Pages 15-20
DOI 10.4028/www.scientific.net/KEM.329.15
Citation Xun Chen et al., 2007, Key Engineering Materials, 329, 15
Online since January, 2007
Authors Xun Chen, James Griffin
Keywords Acoustic Emission (AE), Grinding, Neural Network (NN), Short Time Fourier Transfer, Wavelet Transfer
Abstract

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

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