The second part of this work follows on the work carried out in\ud Part 1 where the investigations were made between the grinding phenomena:\ud cutting, ploughing and rubbing. The demarcation between each of the\ud phenomenon was identified from Acoustic Emission (AE) signals being\ud converted to the frequency-time domains using Short-Time Fourier Transforms\ud (STFTs). Other digital signal processing techniques were used and discussed;\ud however, the more update and successful tests only required STFTs. This part\ud of the paper looks at the classification using both Neural Networks (NNs) and\ud fuzzy-c clustering/Genetic Algorithm (GA) techniques. After the cutting,\ud ploughing and rubbing gave a high confidence in terms of classification\ud accuracy, 1 μm and 0.1 mm grinding test data were applied to the classifiers.\ud Interesting output results sufficed from both classifiers signifying a distinction\ud that there is more cutting utilisation than both ploughing and rubbing as the\ud interaction between grit and workpiece become more in contact with one\ud another (measured depth of cut increases)
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.