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    <publishedYear>2022</publishedYear>
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    <completedDate>2023-01-28</completedDate>
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    <title language="eng">Paper Tissue Softness Rating by Acoustic Emission Analysis</title>
    <abstract language="eng">Softness is one of the essential properties of hygiene tissue products. Reliably measuring it is of utmost importance to ensure the balance between customer expectations and cost-effective tissue production. This study presents a method for assessing softness by analyzing acoustic emissions produced while tearing a tissue specimen. The aim was to train neural network models using the corrected results of human panel tests as the ground truth labels and to predict the tissue softness in two- and three-class recognition tasks. We also investigate the possibility of predicting some production parameters related to the softness property. The results proved that tissue softness and production parameters could be reliably estimated only by the tearing noise.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="issn">2076-3417</identifier>
    <identifier type="doi">10.3390/app13031670</identifier>
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    <enrichment key="Artikelnummer">1670</enrichment>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
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    <author>
      <firstName>Frank</firstName>
      <lastName>Schubert</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>acoustic emission</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>tissue softness analysis</value>
    </subject>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
  </doc>
  <doc>
    <id>36208</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
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    <language>deu</language>
    <pageFirst>249</pageFirst>
    <pageLast>254</pageLast>
    <pageNumber>6</pageNumber>
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    <issue>4-5</issue>
    <volume>68</volume>
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    <publisherName>Springer Science and Business Media LLC</publisherName>
    <publisherPlace>Berlin ; Heidelberg</publisherPlace>
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    <completedDate>2025-06-12</completedDate>
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    <title language="deu">Materialdiagnose und integrale Prüfverfahren für keramische Bauteile</title>
    <abstract language="deu">Hochleistungskeramiken findet man heute häufig als kritische Komponente in neuentwickelten Systemen für Zukunftsanwendungen. Die Zuverlässigkeit des gesamten Systems basiert hierbei oft auf der kritischen keramischen Komponente. Für diese oft neuentwickelten keramischen Materialien werden neue Methoden für die Prozesssteuerung, Materialdiagnostik und Strukturüberwachung benötigt. In diesem Artikel werden drei für die Keramikcharakterisierung am Fraunhofer-Institut für Keramische Technologien und Systeme IKTS weiter entwickelte Technologien und Verfahren beschrieben und deren Einsatz anhand von Beispielen illustriert. Dazu werden die Laser-Speckle-Photometrie, die optische Kohärenztomographie und die Klanganalyse in Kombination mit einer entsprechenden akustischen Mustererkennung als leistungsfähige Verfahren für die Materialdiagnostik im Bereich der keramischen Materialien vorgestellt.</abstract>
    <parentTitle language="deu">Keramische Zeitschrift</parentTitle>
    <additionalTitle language="eng">Materials diagnostics and integrated testing technology for ceramic parts</additionalTitle>
    <identifier type="doi">10.1007/BF03400267</identifier>
    <identifier type="issn">0023-0561</identifier>
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    <author>
      <firstName>Joerg</firstName>
      <lastName>Opitz</lastName>
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    <submitter>
      <firstName>Stefanie</firstName>
      <lastName>Jannasch</lastName>
    </submitter>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wunderlich</lastName>
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    <author>
      <firstName>B.</firstName>
      <lastName>Bendjus</lastName>
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      <lastName>Cikalova</lastName>
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      <firstName>C.</firstName>
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      <firstName>A.</firstName>
      <lastName>Lehmann</lastName>
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      <firstName>M.</firstName>
      <lastName>Barth</lastName>
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      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
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      <firstName>B.</firstName>
      <lastName>Köhler</lastName>
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      <firstName>K.</firstName>
      <lastName>Tschöke</lastName>
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      <firstName>T.</firstName>
      <lastName>Windisch</lastName>
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      <firstName>Constanze</firstName>
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      <firstName>T.</firstName>
      <lastName>Moritz</lastName>
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    <author>
      <firstName>U.</firstName>
      <lastName>Scheithauer</lastName>
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    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
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  <doc>
    <id>36204</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
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    <language>eng</language>
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    <pageLast>21</pageLast>
    <pageNumber>21</pageNumber>
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    <publisherName>MDPI AG</publisherName>
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    <completedDate>2025-06-12</completedDate>
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    <title language="eng">Inline monitoring of battery electrode lamination processes based on acoustic measurements</title>
    <abstract language="eng">Due to the energy transition and the growth of electromobility, the demand for lithium-ion batteries has increased in recent years. Great demands are being placed on the quality of battery cells and their electrochemical properties. Therefore, the understanding of interactions between products and processes and the implementation of quality management measures are essential factors that requires inline capable process monitoring. In battery cell lamination processes, a typical problem source of quality issues can be seen in missing or misaligned components (anodes, cathodes and separators). An automatic detection of missing or misaligned components, however, has not been established thus far. In this study, acoustic measurements to detect components in battery cell lamination were applied. Although the use of acoustic measurement methods for process monitoring has already proven its usefulness in various fields of application, it has not yet been applied to battery cell production. While laminating battery electrodes and separators, acoustic emissions were recorded. Signal analysis and machine learning techniques were used to acoustically distinguish the individual components that have been processed. This way, the detection of components with a balanced accuracy of up to 83% was possible, proving the feasibility of the concept as an inline capable monitoring system.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="doi">10.3390/batteries7010019</identifier>
    <identifier type="issn">2313-0105</identifier>
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Great demands are being placed on the quality of battery cells and their electrochemical properties. Therefore, the understanding of interactions between products and processes and the implementation of quality management measures are essential factors that requires inline capable process monitoring. In battery cell lamination processes, a typical problem source of quality issues can be seen in missing or misaligned components (anodes, cathodes and separators). An automatic detection of missing or misaligned components, however, has not been established thus far. In this study, acoustic measurements to detect components in battery cell lamination were applied. Although the use of acoustic measurement methods for process monitoring has already proven its usefulness in various fields of application, it has not yet been applied to battery cell production. While laminating battery electrodes and separators, acoustic emissions were recorded. 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Power Sources"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"A5163","DOI":"10.1149\/2.0251903jes","article-title":"X-ray Based Visualization of the Electrolyte Filling Process of Lithium Ion Batteries","volume":"166","author":"Schilling","year":"2018","journal-title":"J. Electrochem. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Frankenberger, M., Trunk, M., Seidlmayer, S., Dinter, A., Dittloff, J., Werner, L., Gernh\u00e4user, R., Revay, Z., M\u00e4rkisch, B., and Gilles, R. (2020). SEI Growth Impacts of Lamination, Formation and Cycling in Lithium Ion Batteries. 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