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.
| 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 |
