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Automated quality assurance as an intelligent cloud service using machine learning

: Schreiber, M.; Klöber-Koch, J.; Bömelburg-Zacharias, J.; Braunreuther, S.; Reinhart, G.

Volltext ()

Procedia CIRP 86 (2019), S.185-191
ISSN: 2212-8271
Global Web Conference "Towards Shifted Production Value Stream Patterns through Inference of Data, Models, and Technology" (CIRPe) <7, 2019, Online>
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IGCV ()

The amount of data generated in production systems increases continuously due to the integration of cyber-physical systems and additional sensors. This data contains potentially useful knowledge that can be used to improve production processes and product quality. Machine learning algorithms offer the potential of improvements for quality assurance. However, companies often do not have the necessary know-how to extract this knowledge. This publication therefore presents a service-based system for optical quality assurance using machine learning algorithms. The intelligent cloud service is tested and validated by an industrial use case for transparent injection molded parts.