Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Feldmann, Sebastian</dc:creator>
  <dc:creator>Schmiedt, Michael</dc:creator>
  <dc:creator>Schlosser, Julian</dc:creator>
  <dc:creator>Rimkus, Wolfgang</dc:creator>
  <dc:creator>Stempfle, Tobias</dc:creator>
  <dc:date>2022-01-31</dc:date>
  <dc:description>In industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project SimKI, conventional metal forming plants were digitalized under use of perception-based sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects prior to their occurrence. The SimKI-method additionally combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by a) generic datasets of a finite-element simulation, b) real component images of a 3D imaging device, and c) a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm in order to identify the position and deviation from the agreed quality. The high degree of innovation is based on obtaining real-time component quality information under use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2022.16</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:74-x1</dc:identifier>
  <dc:identifier>mcid:2022.16</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1202</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Artificial Intelligence</dc:subject>
  <dc:subject>Gocator</dc:subject>
  <dc:subject>Defect Detection</dc:subject>
  <dc:title>Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>