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  <doc>
    <id>1694</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1711</pageFirst>
    <pageLast>1721</pageLast>
    <pageNumber>11 Seiten</pageNumber>
    <edition/>
    <issue>3</issue>
    <volume>36</volume>
    <type>article</type>
    <publisherName>Springer US</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-03-01</completedDate>
    <publishedDate>2024-03-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Vision based process monitoring in wire arc additive manufacturing (WAAM)</title>
    <abstract language="eng">A stable welding process is crucial to obtain high quality parts in wire arc additive manufacturing. The complexity of the process makes it inherently unstable, which can cause various defects, resulting in poor geometric accuracy and material properties. This demands for in-process monitoring and control mechanisms to industrialize the technology. In this work, process monitoring algorithms based on welding camera image analysis are presented. A neural network for semantic segmentation of the welding wire is used to monitor the working distance as well as the horizontal position of the wire during welding and classic image processing techniques are applied to capture spatter formation. Using these algorithms, the process stability is evaluated in real time and the analysis results enable the direction independent closed-loop-control of the manufacturing process. This significantly improves geometric fidelity as well as mechanical properties of the fabricated part and allows the automated production of parts with complex deposition paths including weld bead crossings, curvatures and overhang structures.</abstract>
    <parentTitle language="eng">Journal of Intelligent Manufacturing (ISSN: 1572-8145)</parentTitle>
    <identifier type="issn">0956-5515</identifier>
    <identifier type="issn">1572-8145</identifier>
    <identifier type="doi">10.1007/s10845-023-02287-x</identifier>
    <identifier type="urn">urn:nbn:de:101:1-2405052141549.085870766506</identifier>
    <enrichment key="opus.import.date">2025-07-29T13:01:47+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">20978d6dd03618d6ea6717fe4f856091</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Jan Franke</author>
    <author>Florian Heinrich</author>
    <author>Raven T. Reisch</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wire arc additive manufacturing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vision based monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nozzle-to-work distance monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Contact tube wear off detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spatter detection</value>
    </subject>
    <collection role="ddc" number="604">Spezielle Themen</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="">Fakultät für Informatik und Mathematik</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="Transformationsvertrag" number="">DEAL Springer Nature</collection>
    <thesisPublisher>Universität Passau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-uni-passau/files/1694/10845_2024_Article_2287.pdf</file>
  </doc>
</export-example>
