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    <completedYear>2024</completedYear>
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    <language>eng</language>
    <pageFirst>251</pageFirst>
    <pageLast>256</pageLast>
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    <title language="eng">Machine Learning Based Early Rejection of Low Performance Cells in Li Ion Battery Production</title>
    <abstract language="eng">Lithium-ion battery cell production is conducted through a multistep production process which suffers from a notable scrap rate. Machine learning (ML) based process monitoring provides solutions to mitigate the impact of substantial scrap rates by repeated multifactorial quality predictions (virtual quality gates) along the process line. This enables an early rejection of battery cells which are unlikely to reach required specifications, avoids further waste of resources at later process steps and simplifies recycling of rejected cells. A hierarchical architecture is used to apply ML algorithms first for process-adapted feature extraction which is guided by a priori knowledge on typical production anomalies. In a second step, these features are correlated with end-of-line quality control data using explainable ML methods. The resulting predictions may lead to pass or fail of a battery cell, or -in the context of flexible production- may also trigger adjustments of later process steps to compensate for detected deficiencies. An example ML based quality control concept is illustrated for a pilot battery cell production line.</abstract>
    <parentTitle language="deu">CACML '24: Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning</parentTitle>
    <identifier type="doi">https://doi.org/10.1145/3654823.3654870</identifier>
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    <author>Xukuan Xu</author>
    <author>Michael Möckel</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="institutes" number="">Medizinische IT &amp; Simulation</collection>
    <file>https://opus4.kobv.de/opus4-h-ab/files/2500/3654823.3654870.pdf</file>
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