Data-driven analysis of product state propagation in manufacturing systems using visual analytics and machine learning

Marc André Filz*, Sebastian Gellrich, Christoph Herrmann, Sebastian Thiede

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

The importance of quality and efficiency has increased in recent years. Moreover, the rise of computational power and the development of advanced analytics has enabled the industry to enhance the performance of manufacturing systems. Therefore, further transparency of intermediate product states is necessary to derive appropriate actions. The goal of this paper is to develop a framework to enable the data-driven analysis of product state propagation within manufacturing systems to improve the transparency of product quality related cause-effect relationships. Based on their intermediate product features, machine learning algorithms assign products to classes of similar characteristics. This approach is practically applied to a case study from the electronic production industry. By using visual analytics tools, the propagation of product states along the manufacturing process chain is exemplarily analyzed.

Original languageEnglish
Pages (from-to)449-454
Number of pages6
JournalProcedia CIRP
Volume93
DOIs
Publication statusPublished - 1 Jan 2020
Externally publishedYes
Event53rd CIRP Conference on Manufacturing Systems, CIRP CMS 2020 - Virtual Conference, Chicago, United States
Duration: 1 Jul 20203 Jul 2020
Conference number: 53

Keywords

  • Intermediate product states
  • Machine learning
  • Manufacturing systems
  • Product propagation
  • Product state classes
  • Visual analytics

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