Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours |
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| Journal | Key Engineering Materials (Volume 329) |
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| 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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