<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>5538</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>215</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data-driven models for structure-property prediction in additively manufactured steels</title>
    <abstract language="eng">Data-driven models are developed to predict the mechanical properties of polycrystalline materials. The case study is the prediction of the yield strength of a 3D-printed 316L steel from morphological and crystallographic features. Three different artificial intelligence models including feed-forward (FNN), convolution (CNN), and graph (GNN) neural networks are employed to train the data-driven models and are compared in terms of performance and computational requirements. The dataset required for training is generated by performing crystal plasticity finite element simulations. The FNN model has the smallest input size and takes in some statistical parameters describing the material microstructure, but its accuracy is relatively low. The CNN approach inputs voxel-based realizations of the microstructure and is able to give accurate estimations; however, its training process is time-consuming and computationally expensive. In the GNN approach, the polycrystalline material is represented by a graph whose nodes and lines represent the grains and adjacency between grains. It is observed that GNN yields a better performance compared to the other two approaches and has the capability of handling complex tasks.</abstract>
    <parentTitle language="eng">Computational Materials Science</parentTitle>
    <identifier type="doi">10.1016/j.commatsci.2022.111782</identifier>
    <enrichment key="opus.import.date">2022-10-29T17:49:03+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Ebrahim Sadeghpour</author>
    <author>Aida Nonn</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Data-driven model</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Polycrystalline materials</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Structure-property relationships</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="institutes" number="TCNeustadt">Technologie-Campus Neustadt an der Donau</collection>
    <collection role="persons" number="nonncmm">Nonn, Aida (Prof. Dr.) - Computational Mechanics and Materials Lab</collection>
    <collection role="persons" number="tcnehno">Technologie-Campus Neustadt a. d. Donau (Prof. Ehrlich+ Prof. Nonn)</collection>
    <collection role="othforschungsschwerpunkt" number="16316">Produktion und Systeme</collection>
    <collection role="institutes" number="">Computational Mechanics and Materials Lab (CMM)</collection>
  </doc>
  <doc>
    <id>2108</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>125</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Computational analysis of the effects of geometric irregularities on the interaction of an additively manufactured  316L stainless steel stent and a coronary artery</title>
    <abstract language="eng">Customized additively manufactured (laser powder bed fused (L-PBF)) stents could improve the treatment of complex lesions by enhancing stent-artery conformity. However, geometric irregularities inherent for L-PBF stents are expected to influence not only their mechanical behavior but also their interaction with the artery. In this study, the influence of geometrical irregularities on stent-artery interaction is evaluated within a numerical framework. Thus, computed arterial stresses induced by a reconstructed L-PBF stent model are compared to those induced by the intended stent model (also representing a stent geometry obtained from conventional manufacturing processes) and a modified CAD stent model that accounts for the increased strut thickness inherent for L-PBF stents. It was found that, similar to conventionally manufactured stents, arterial stresses are initially related to the basic stent design/topology, with the highest stresses occurring at the indentations of the stent struts. Compared to the stent CAD model, the L-PBF stent induces distinctly higher and more maximum volume stresses within the plaque and the arterial wall. In return, the modified CAD model overestimates the arterial stresses induced by the L-PBF stent due to its homogeneously increased strut thickness and thus its homogeneously increased geometric stiffness compared with the L-PBF stent. Therefore, the L-PBF-induced geometric irregularities must be explicitly considered when evaluating the L-PBF stent-induced stresses because the intended stent CAD model underestimates the arterial stresses, whereas the modified CAD model overestimates them. The arterial stresses induced by the L-PBF stent were still within the range of values reported for conventional stents in literature, suggesting that the use of L-PBF stents is conceivable in principle. However, because geometric irregularities, such as protruding features from the stent surface, could potentially damage the artery or lead to premature stent failure, further improvement of L-PBF stents is essential.</abstract>
    <parentTitle language="eng">Journal of the Mechanical Behavior of Biomedical Materials</parentTitle>
    <identifier type="doi">10.1016/j.jmbbm.2021.104878</identifier>
    <identifier type="issn">1751-6161</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Lisa Wiesent</author>
    <author>Ashley Spear</author>
    <author>Aida Nonn</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser powder bed fusion (L-PBF)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cardiovascular stents</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Finite element analysis (FEA)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Geometrical imperfections</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Stent-artery interaction</value>
    </subject>
    <collection role="ddc" number="610">Medizin und Gesundheit</collection>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="institutes" number="TCNeustadt">Technologie-Campus Neustadt an der Donau</collection>
    <collection role="persons" number="nonncmm">Nonn, Aida (Prof. Dr.) - Computational Mechanics and Materials Lab</collection>
    <collection role="persons" number="tcnehno">Technologie-Campus Neustadt a. d. Donau (Prof. Ehrlich+ Prof. Nonn)</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Computational Mechanics and Materials Lab (CMM)</collection>
  </doc>
</export-example>
