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arXiv · 2210.13273

An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders

Maximilian Bundscherer·Thomas H. Schmitt·Sebastian Bayerl·Thomas Auerbach·Tobias Bocklet
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Abstract

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% on five levels of belt wear. A 96% accuracy could be achieved with different Decision Tree Classifiers specialized in different sanding parameter configurations. The classifiers could also determine with an accuracy of 97% if the machine is currently sanding or is idle and with an accuracy of 98.4% and 98.8% detect the sanding parameters Feed speed and Grit 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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Maximilian Bundscherer, Thomas H. Schmitt, Sebastian Bayerl, Thomas Auerbach, Tobias Bocklet. 2022-10-24. An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders. https://doi.org/10.1109/sensors52175.2022.9967324

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