Papers by Author: James Griffin

Abstract: This paper looks at the multiple characteristics and investigations of two grinding anomalies: grinding burn and grinding chatter. A genetic programming (GP) of multiple classifications was investigated for different machining strategies and associated anomaly phenomena. Such a GP paradigm could evolve rules to provide the correlation between monitored signals and grinding phenomena. The investigation also looks at both Short-Time Fourier Transforms (STFT) and Wavelet Packet Transforms (WPT) to convert the raw acoustic emission (AE) signal into a time based frequency signal, segmented into different frequency bands. A set of encouraging results is presented.

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.