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  <doc>
    <id>7344</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>83</pageFirst>
    <pageLast>91</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">A Cyber-Physical Toolbox for Teaching Digital Construction – Technical Configuration, Learning Tactics and Hands-On Testing and Evaluation in Dedicated Courses</title>
    <abstract language="eng">The construction industry faces substantial economic, environmental, and social challenges. Simultaneously, the industry experiences one of the lowest degrees of digitalization. In this context, cyber-physical education is essential to empower learners with skills to utilize digital and physical solutions more effectively. Therefore, a modular as well as construction specific learning setting was designed. The setting facilitates a range of topics, such as production planning considering human and robot capabilities, (semi-) automated construction machines, robotics, sensing &amp; actuation and more. The digital part of the cyber-physical toolbox consists of various methods and tools to learn programming languages, frameworks, offline simulators, post-processors and more. This step is essential to enable an end-to-end data-/workflow from design (e.g., BIM-based planning methodologies) to (machine-based) production. A focus is given to human-machine/-robot collaboration and appropriate simulation tools (e.g., emaWD) enabling the incorporation of human factors. The physical setting is highly modular, and each device and its periphery can be customized to a broad variety of learning scenarios and levels. This paper describes the details of the learning setting, extracts learning strategies, and analyzes selected learning scenarios and their initial testing in various learning formats.</abstract>
    <parentTitle language="eng">Learning Factories of the Future, Proceedings of the 14th Conference on Learning Factories 2024, Volume 2</parentTitle>
    <identifier type="isbn">9783031653995</identifier>
    <identifier type="issn">2367-3370</identifier>
    <identifier type="doi">10.1007/978-3-031-65400-8_10</identifier>
    <enrichment key="OtherSeries">Lecture Notes in Networks and Systems ; 1060</enrichment>
    <enrichment key="ConferenceStatement">14th International Conference on Learning Factories (CLF 2024), April 17–19, 2024, Twente, the Netherlands</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Anne-Sophie Saffert</author>
    <author>Marc Schmailzl</author>
    <author>Michael Spitzhirn</author>
    <author>Thomas Linner</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>learning setting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production planning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>construction industry</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>human-robot collaboration</value>
    </subject>
    <collection role="institutes" number="FakBau">Fakultät Bauingenieurwesen</collection>
    <collection role="othforschungsschwerpunkt" number="16313">Gebäude und Infrastruktur</collection>
  </doc>
</export-example>
