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

TL;DR

This study presents an acoustic machine learning method to accurately assess abrasive belt wear in industrial sanders, independent of process parameters, using various classifiers and spectral features.

Contribution

It introduces a novel acoustic-based approach employing multiple classifiers to determine belt wear and sanding parameters with high accuracy, regardless of process settings.

Findings

Achieved up to 86.1% accuracy in belt wear classification.

Decision Tree classifiers reached 96% accuracy with specific configurations.

Successfully detected sanding parameters with over 97% accuracy.

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.