<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns="http://datacite.org/schema/kernel-4" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.18453/rosdok_id00004605</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Westphal, Erik</creatorName>
      <givenName>Erik</givenName>
      <familyName>Westphal</familyName>
      <nameIdentifier nameIdentifierScheme="GND" schemeURI="http://d-nb.info/gnd/">http://d-nb.info/gnd/1333065299</nameIdentifier>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org/">https://orcid.org/0000-0002-4514-9445</nameIdentifier>
    </creator>
  </creators>
  <titles>
    <title>Digital quality assurance system for additive manufacturing based on machine learning methods and blockchain technology</title>
  </titles>
  <publisher>Universität Rostock</publisher>
  <publicationYear>2023</publicationYear>
  <resourceType resourceTypeGeneral="Text" />
  <subjects>
    <subject xml:lang="en" schemeURI="http://dewey.info/" subjectScheme="dewey">600 Technology (Applied sciences)</subject>
    <subject xml:lang="en" schemeURI="http://dewey.info/" subjectScheme="dewey">620 Engineering &amp; allied operations</subject>
    <subject xml:lang="en" schemeURI="http://dewey.info/" subjectScheme="dewey">670 Manufacturing</subject>
  </subjects>
  <dates>
    <date dateType="Created">2023</date>
  </dates>
  <language>en</language>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="PURL">https://purl.uni-rostock.de/rosdok/id00004605</alternateIdentifier>
    <alternateIdentifier alternateIdentifierType="URN">urn:nbn:de:gbv:28-rosdok_id00004605-6</alternateIdentifier>
  </alternateIdentifiers>
  <descriptions>
    <description descriptionType="Abstract">This thesis examines technological innovations in the context of process digitalisation and develops a digital quality assurance system for additive manufacturing based on specific artificial intelligence (AI) methods and blockchain technology to enable more reproducible and predictable AM processes and parts. To this end, this cumulative work considers the implementation of special AI algorithms, a digitalised quality management system based on blockchain and the combination of both into a real-time digital quality assurance system for AM as three main areas of investigation.</description>
  </descriptions>
</resource>
