TY  - CONF
A1  - Bundscherer, Maximilian
A1  - Schmitt, Thomas H.
A1  - Bayerl, Sebastian P.
A1  - Auerbach, Thomas
A1  - Bocklet, Tobias
T1  - An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders
T2  - 2022 IEEE Sensors
N2  - 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.
KW  - Acoustic sensors
KW  - Abrasive belt wear
KW  - Tool wear
KW  - Machine learning
KW  - Industrial process
KW  - Wide belt sanding machines
Y1  - 2022
UR  - https://opus4.kobv.de/opus4-ohm/frontdoor/index/index/docId/1820
SN  - 978-1-6654-8464-0
VL  - 2022
PB  - IEEE
ER  -