<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2227</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>*</pageFirst>
    <pageLast>*</pageLast>
    <pageNumber/>
    <edition/>
    <issue>57/102798</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-05-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Increased relative density and characteristic melt pool Signals at the edge in PBF-LB/M</title>
    <abstract language="eng">Limited process control can cause metallurgical defect formation and inhomogeneous relative density in laser powder bed fusion manufactured parts. This study shows that process monitoring, based on optical melt-pool signal analysis is capable of tracing relative density variations: Unsupervised machine learning, applied to cluster multiple-slice monitoring data, reveals characteristic patterns in this noisy time-series signal, which can be co-registered with geometrical positions in the build part. For cylindrical 15–5 PH stainless steel specimens, manufactured under constant process parameters and post-analyzed by µ-computer tomography, correlations between such patterns and an increased local relative density at the edge have been observed. Finite element method (FEM) modeling of thermal histories at exemplary positions close to the edge suggest pre-heating effects caused by neighboring laser scan trajectories as possible reasons for the increased melt pool intensity at the edge.</abstract>
    <parentTitle language="eng">Additive Manufacturing</parentTitle>
    <identifier type="url">https://www.sciencedirect.com/science/article/pii/S2214860422001993?via%3Dihub</identifier>
    <identifier type="doi">https://doi.org/10.1016/j.addma.2022.102798</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="HAB_Review">ja</enrichment>
    <enrichment key="opus.source">publish</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>Jorrit Voigt</author>
    <author>Thomas Bock</author>
    <author>Uwe Hilpert</author>
    <author>Ralf Hellmann</author>
    <author>Michael Möckel</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Maschinelles Lernen</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schmelze</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="institutes" number="">Medizinische IT &amp; Simulation</collection>
  </doc>
</export-example>
