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.
The unboundedness of Hausdorff operators on quasi‐Banach spaces
