<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>35050</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>110</pageFirst>
    <pageLast>119</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>41</volume>
    <type>conferenceobject_ref</type>
    <publisherName>Materials Research Forum LLC</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improving the structural integrity of challenging to manufacture LPBF components with toolpath correction</title>
    <abstract language="eng">This work deals with the influence of optimised exposure strategies on the distortion and microstructure of components susceptible to overheating and warpage. Therefore, different distortion-prone specimen geometries of 316L were fabricated with the standard parameters, as well as with exposure strategies optimised by machine learning, which were generated using the&#13;
AMAIZE software package. The manufactured samples were analysed with regard to distortion. The results of the distortion analysis were then linked with the results of the digital tomography from AMAIZE. Furthermore, components were manufactured that tend to overheat due to their geometry and orientation on the substrate plate. The influence of overheating during the LPBF process on the microstructure and porosity was investigated along the build-up direction by means&#13;
of an EBSD analysis and a porosity analysis. With the presented approach for optimising the&#13;
exposure strategy with AMAIZE, it could be shown that a successful production of distortion-&#13;
prone components with a porosity of less than 1 % is possible in the first trial.</abstract>
    <parentTitle language="eng">Materials Research Proceedings</parentTitle>
    <identifier type="issn">2474-395X</identifier>
    <identifier type="url">https://d21zja6o12zyp0.cloudfront.net/9781644903131.pdf</identifier>
    <identifier type="doi">10.21741/9781644903131-12</identifier>
    <enrichment key="Fprofil">5 Sonstige / Other</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Felix</firstName>
      <lastName>Jensch</lastName>
    </author>
    <submitter>
      <firstName>Felix</firstName>
      <lastName>Jensch</lastName>
    </submitter>
    <author>
      <firstName>Katharina</firstName>
      <lastName>Eissing</lastName>
    </author>
    <author>
      <firstName>Williams</firstName>
      <lastName>Richard</lastName>
    </author>
    <author>
      <firstName>Marcus</firstName>
      <lastName>Trautmann</lastName>
    </author>
    <author>
      <firstName>Yitong</firstName>
      <lastName>Yang</lastName>
    </author>
    <author>
      <firstName>Sergej</firstName>
      <lastName>Dubinin</lastName>
    </author>
    <author>
      <firstName>Sebastian</firstName>
      <lastName>Härtel</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LPBF-Process, Machine Learning, Microstructural Investigation</value>
    </subject>
    <collection role="institutes" number="3405">FG Hybride Fertigung</collection>
  </doc>
</export-example>
