An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders
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This paper describes a machine learning approach to determine the abrasive belt wear of wide belt sanders used in industrial processes based on acoustic data, regardless of the sanding process-related parameters, Feed speed, Grit Size, and Type of material. Our approach utilizes Decision Tree, Random Forest, k-nearest Neighbors, and Neural network Classifiers to detect the belt wear from Spectrograms, Mel Spectrograms, MFCC, IMFCC, and LFCC, yielding an accuracy of up to 86.1 achieved with different Decision Tree Classifiers specialized in different sanding parameter configurations. The classifiers could also determine with an accuracy of 97 accuracy of 98.4 Size. We can show that low-dimensional mappings of high-dimensional features can be used to visualize belt wear and sanding parameters meaningfully.
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