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  4. 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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2021
Journal Article
Titel

Predictive analytics in quality assurance for assembly processes: Lessons learned from a case study at an industry 4.0 demonstration cell

Abstract
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 papers 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.
Author(s)
Burggräf, Peter
Chair of International Production Engineering and Management (IPEM), Universität Siegen, Germany
Wagner, Johannes
Chair of International Production Engineering and Management (IPEM), Universität Siegen, Germany
Heinbach, Benjamin
Chair of International Production Engineering and Management (IPEM), Universität Siegen, Germany
Steinberg, Fabian
Chair of International Production Engineering and Management (IPEM), Universität Siegen, Germany
Pérez M., Alejandro R.
Chair of International Production Engineering and Management (IPEM), Universität Siegen, Germany
Schmallenbach, Lennart
Chair of International Production Engineering and Management (IPEM), Universität Siegen,Germany
Garcke, Jochen
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Steffes-lai, Daniela
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Wolter, Moritz
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Zeitschrift
Procedia CIRP
Konferenz
Conference on Manufacturing Systems (CMS) 2021
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DOI
10.1016/j.procir.2021.11.108
Externer Link
Externer Link
Language
English
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Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
Tags
  • machine-learning

  • predictive quality

  • production

  • quality assurance

  • classification

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