Automated quality assurance as an intelligent cloud service using machine learning

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

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)185-191
Number of pages7
JournalProcedia CIRP
Volume86
DOIs
StatePublished - 2020
Event7th CIRP Global Web Conference, CIRPe 2019 - Berlin, Germany
Duration: 16 Oct 201919 Oct 2019

Keywords

  • Machine learning
  • Methodology
  • Quality assurance

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