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

  • 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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Metadaten
Author:Maximilian Bundscherer, Thomas H. Schmitt, Sebastian P. BayerlORCiD, Thomas Auerbach, Tobias BockletORCiD
DOI:https://doi.org/10.1109/SENSORS52175.2022.9967324
ISBN:978-1-6654-8464-0
Parent Title (English):2022 IEEE Sensors
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Reviewed:Begutachtet/Reviewed
Release Date:2024/07/02
Tag:Abrasive belt wear; Acoustic sensors; Industrial process; Machine learning; Tool wear; Wide belt sanding machines
Volume:2022
Pagenumber:4
Konferenzangabe:2022 IEEE Sensors, 30 October 2022 - 02 November 2022, Dallas, TX, USA
institutes:Fakultät Informatik
Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
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