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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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posted on 09.04.2021, 18:42 by Peter Burggraef, Johannes WagnerJohannes Wagner, Benjamin HeinbachBenjamin Heinbach, Fabian SteinbergFabian Steinberg, Alejandro PerezAlejandro Perez, Lennart Schmallenbach, Jochen Garcke, Daniela Steffes-lai, Moritz Wolter
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.

Funding

This work was funded by the European Regional Development Fund (ERDF) within the project ”ManuBrain”.

History

Email Address of Submitting Author

fabian.steinberg@uni-siegen.de

ORCID of Submitting Author

0000-0002-9842-5343

Submitting Author's Institution

University of Siegen

Submitting Author's Country

Germany