Kropatschek, S., Steuer, T., Kiesling, E., Meixner, K., Ayatollahi, I., Sommer, P., & Biffl, S. (2022). Analysis of Quality Issues in Production With Multi-view Coordination Assets. In IFAC Papers Online (pp. 2938–2943). Elsevier. http://hdl.handle.net/20.500.12708/142557
10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022
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Event date:
22-Jun-2022 - 24-Jun-2022
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Event place:
Nantes, France
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Number of Pages:
6
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Publisher:
Elsevier
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Peer reviewed:
Yes
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Keywords:
Knowledge management in production; Quality management; Monitoring of product quality and control performance; Multi-view modeling of manufacturing operations
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Abstract:
The diffusion of the Industry 4.0 paradigm has led to a proliferation of data that is generated by production assets on the shop floor. This data opens up new opportunities for the analysis of quality issues, but it also makes identifying, selecting, and correctly interpreting data all the more critical. This involves a multitude of domain experts that design, operate and maintain production equipment. Major challenges they face in this context are (i) to map and integrate their domain knowledge on potential failure modes and effects, products, processes and production assets; and (ii) to coordinate their actions to systematically investigate and address the most important issues first. To address these challenges, this paper introduces the FMEA-linked-to-PPR Asset Issue Analysis (FPI) Model, a multi-view coordination asset, to guide quality issue analyses. The model integrates cross-domain knowledge and facilitates tracking the investigation state of quality analyses in teams of domain experts. A preliminary evaluation on a real-world use case indicates the FPI model to facilitate effective cross-domain analytic processes and the efficient identification of potential causes for quality issues.
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Project title:
Verbesserung der Sicherheit von Informationsprozessen in Produktionssystemen: CDL SQI (CDG Christian Doppler Forschungsgesellschaft)
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Research Areas:
Computer Engineering and Software-Intensive Systems: 50% Information Systems Engineering: 50%