{"id":2406,"date":"2021-07-13T17:47:06","date_gmt":"2021-07-13T15:47:06","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=2406"},"modified":"2022-07-08T12:19:06","modified_gmt":"2022-07-08T10:19:06","slug":"assembly-battery-production","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/assembly-battery-production\/","title":{"rendered":"2021 Assembly & Battery Production"},"content":{"rendered":"\n
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To access the paper presentation page (paper info, video viewing, Q&A posting), please click on the paper title.<\/p>\n\n\n\n

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  1. Knowledge-based Implementation of Deep Reinforcement Learning Agents in Assembly<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Marcus Roehler<\/span>, Johannes Schilp (Germany)<\/em><\/em><\/li>
  2. Innovative technologies for virtual assembly validation in multi-user scenarios<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Maik Franz Tiemann<\/span>, Simon Kind, Rainer Stark (Germany)<\/em><\/li>
  3. Towards an intelligent disruption management system based on the maximal network plan – Development of a prioritization algorithm for disruptions in production processes<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Jan Wagner<\/span>, Gesa Wimberg, Roland Larek, Sven Oppermann, Heiko Grendel (Germany)<\/em><\/li>
  4. Unsupervised domain adaptive object detection for assembly quality inspection<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Xiaomeng Zhu<\/span>,  Atsuto Maki, Lars Hanson (Sweden)<\/em><\/li>
  5. PREVIS – A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Patrick Ruediger<\/span>, Felix Claus, Viktor Leonhardt, Hans Hagen, Jan C. Aurich, Christoph Garth (Germany)<\/em><\/li>
  6. Information Model for Hybrid Prototyping in Design Reviews of Assembly Stations<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Iris Graessler, Stefan Hillebrand<\/span>, Daniel Roesmann, Jens Pottebaum (Germany)<\/em><\/li>
  7. Towards accurate failure prediction in manual assembly by establishing a System Dynamics model<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Robin Guenther<\/span>, Junjie Liang, Robin Exner, Robert H. Schmitt (Germany)<\/em><\/li>
  8. Extensible Worker Assistance (EWA): Presenting a Comprehensive Framework for Context-Aware Assistance in Manual Assembly<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Eva Binder, Michael Romer, Patrick Engesser<\/span>, Jannes Lehwald (Germany)<\/em><\/li>
  9. Production planning and process optimization of a cell finishing process in battery cell manufacturing<\/a><\/strong>
    Johannes Wanner<\/span>, Max Weeber, Kai Peter Birke, Alexander Sauer (Germany)<\/em><\/li>
  10. Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteries<\/a><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>
    Benjamin Maschler<\/span>, Sophia Tatiyosyan, Michael Weyrich (Germany)<\/em><\/li>
  11. Multi-Body Simulation of a Novel Electrode Stacking Process for Lithium-Ion Battery Production<\/a><\/strong><\/strong>
    Christina von Boeselager<\/span>, Merlin Oliver Kapelar, Klaus Droeder (Germany)<\/em><\/li>
  12. Potential of a machine learning based cross-process control in lithium-ion battery production<\/a><\/strong>
    Julia Meiners<\/span>, Arian Froehlich, Klaus Droeder (Germany)<\/em><\/li><\/ol>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    To access the paper presentation page (paper info, video viewing, Q&A posting), please click on the paper title. Knowledge-based Implementation of Deep Reinforcement Learning Agents in AssemblyMarcus Roehler, Johannes Schilp (Germany) Innovative technologies for virtual assembly validation in multi-user scenariosMaik Franz Tiemann, Simon Kind, Rainer Stark (Germany) Towards an intelligent…<\/p>\n

    Continue reading<\/span><\/i><\/a> <\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-2406","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2406","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=2406"}],"version-history":[{"count":7,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2406\/revisions"}],"predecessor-version":[{"id":3542,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2406\/revisions\/3542"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=2406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}