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Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell
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  • Peter Burggraef ,
  • Johannes Wagner ,
  • Benjamin Heinbach ,
  • Fabian Steinberg ,
  • Alejandro Perez ,
  • Lennart Schmallenbach ,
  • Jochen Garcke ,
  • Daniela Steffes-lai ,
  • Moritz Wolter
Peter Burggraef
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Johannes Wagner
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Benjamin Heinbach
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Fabian Steinberg
University of Siegen, University of Siegen, University of Siegen

Corresponding Author:[email protected]

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Alejandro Perez
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Lennart Schmallenbach
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Jochen Garcke
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Daniela Steffes-lai
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Moritz Wolter
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Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.
2021Published in Procedia CIRP volume 104 on pages 641-646. 10.1016/j.procir.2021.11.108