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    <title language="eng">An Acoustical Machine Learning Approach to Determine Abrasive Belt Wear of Wide Belt Sanders</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">2022 IEEE Sensors</parentTitle>
    <identifier type="isbn">978-1-6654-8464-0</identifier>
    <identifier type="doi">10.1109/SENSORS52175.2022.9967324</identifier>
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    <author>Maximilian Bundscherer</author>
    <author>Thomas H. Schmitt</author>
    <author>Sebastian P. Bayerl</author>
    <author>Thomas Auerbach</author>
    <author>Tobias Bocklet</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Acoustic sensors</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Abrasive belt wear</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tool wear</value>
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      <language>eng</language>
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      <value>Machine learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Industrial process</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wide belt sanding machines</value>
    </subject>
    <collection role="institutes" number="">Fakultät Informatik</collection>
    <collection role="Forschungsschwerpunkt" number="5">Digitalisierung &amp; Künstliche Intelligenz</collection>
    <collection role="institutes" number="">Zentrum für Künstliche Intelligenz (KIZ)</collection>
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