Journal article Embargoed Access

Melt pool segmentation for additive manufacturing: A generative adversarial network approach

Weibo Liu; Zidong Wang; Lulu Tian; Stanislao Lauria; Xiaohui Liu


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  <identifier identifierType="URL">https://zenodo.org/record/6226688</identifier>
  <creators>
    <creator>
      <creatorName>Weibo Liu</creatorName>
      <affiliation>Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom</affiliation>
    </creator>
    <creator>
      <creatorName>Zidong Wang</creatorName>
      <affiliation>Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom</affiliation>
    </creator>
    <creator>
      <creatorName>Lulu Tian</creatorName>
      <affiliation>School of Automation Engineering, University of Electronic Science and Technology of China, Sichuan 611731, China</affiliation>
    </creator>
    <creator>
      <creatorName>Stanislao Lauria</creatorName>
      <affiliation>Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom</affiliation>
    </creator>
    <creator>
      <creatorName>Xiaohui Liu</creatorName>
      <affiliation>Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Melt pool segmentation for additive manufacturing: A generative adversarial network approach</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Additive manufacturing</subject>
    <subject>Generative adversarial network</subject>
    <subject>Defect detection</subject>
    <subject>Image processing</subject>
    <subject>Image segmentation</subject>
    <subject>Thermal image</subject>
  </subjects>
  <dates>
    <date dateType="Available">2023-06-05</date>
    <date dateType="Accepted">2021-05-05</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6226688</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.compeleceng.2021.107183</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/integradde</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/embargoedAccess">Embargoed Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Additive manufacturing (AM) is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. To ensure the stability of the AM process, it is of critical importance to obtain high-quality thermal images by using image processing techniques. In this paper, a novel image processing method is put forward with aim to improve the contrast ratio of the thermal images for image segmentation.&lt;br&gt;
To be specific, an image-enhancement generative adversarial network (IEGAN) is developed, where a new objective function is designed for the training process. To verify the superiority and feasibility of the proposed IEGAN, the thermal images captured from an AM process are utilized for image segmentation. Experiment results demonstrate that the developed IEGAN outperforms the original GAN in improving the contrast ratio of the thermal images.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/820776/">820776</awardNumber>
      <awardTitle>Intelligent data-driven pipeline for the manufacturing of certified metal parts through Direct Energy Deposition processes</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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