Chen, Xun and Griffin, James Marcus (2007) Grinding acoustic emission classification in terms of mechanical behaviours. Key Engineering Materials, 329. pp. 15-20. ISSN 1662-9795Metadata only available from this repository.
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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