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Multi-view FMEA Re-validation: Efficient Risk and Engineering Knowledge Integration in Agile Production Systems Engineering

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Model-Driven Engineering and Software Development (MODELSWARD 2021, MODELSWARD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1708))

Abstract

In agile Production Systems Engineering (PSE), multi-disciplinary teams work concurrently on various PSE artifacts in an iterative process that can be supported by common concept and Product-Process-Resource (PPR) modeling. However, keeping track of the interactions and effects of changes across engineering disciplines and their implications for risk assessment is exceedingly difficult in such settings. To tackle this challenge and systematically co-evolve Failure Mode and Effects Analysis (FMEA) and PPR models during PSE, it is necessary to propagate and validate changes across engineering artifacts. To this end, we design and evaluate a FMEA-linked-to-PPR assets (FMEA+PPR) meta model to represent relationships between FMEA elements and PSE assets and trace their change states and dependencies in the design and validation lifecycle. Furthermore, we design and evaluate the FMEA+PPR method to efficiently re-validate FMEA models upon changes in multi-view PSE models. We evaluate the model and method in a feasibility study on the quality of a joining process automated by a robot cell in automotive PSE. The study results indicate that the FMEA+PPR method is feasible and addresses requirements for FMEA re-validation better than alternative traditional approaches. Thereby, the FMEA+PPR approach facilitates a paradigm shift from traditional, isolated PSE and FMEA activities towards an integrated agile PSE method.

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Notes

  1. 1.

    APIS: http://www.apis-iq.com/.

  2. 2.

    Neo4J: https://neo4j.com/.

  3. 3.

    SemanticWeb: www.w3.org/standards/semanticweb/.

  4. 4.

    FMEA-PAN.NEO4J: https://github.com/tuw-qse/fmea-revalidation-resources.

  5. 5.

    Cypher: www.opencypher.org/.

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Acknowledgement

The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development is gratefully acknowledged. This work has been partially supported and funded by the Austrian Research Promotion Agency (FFG) via “Austrian Competence Center for Digital Production” (CDP) under contract nr. 881843. This work has also received funding from the Teaming.AI project, which is part of the European Union’s Horizon 2020 research and innovation program under grant agreement No 957402.

Author information

Authors and Affiliations

  1. CDL for Security and Quality Improvement in the Production System Lifecycle, TU Wien, Vienna, Austria

    Felix Rinker, Kristof Meixner & Dietmar Winkler

  2. Institute of Information Systems Engineering, Technische Universität Wien, Vienna, Austria

    Felix Rinker, Kristof Meixner, Laura Waltersdorfer, Dietmar Winkler & Stefan Biffl

  3. Center for Digital Production, Vienna, Austria

    Sebastian Kropatschek, Thorsten Steuer & Stefan Biffl

  4. Institute of Data, Process, and Knowledge Engineering, WU Wien, Vienna, Austria

    Elmar Kiesling

  5. Neuman Aluminium, CAG Holding GmbH, Lilienfeld, Austria

    Patrik Sommer

  6. IAF at FMB, Otto-v.Gueriche University, Magdeburg, Germany

    Arndt Lüder

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  1. Felix Rinker
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Correspondence to Felix Rinker .

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Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Luís Ferreira Pires

  2. ESEO, ERIS, Angers, France

    Slimane Hammoudi

  3. Model Driven Solutions, Herndon, VA, USA

    Edwin Seidewitz

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Rinker, F. et al. (2023). Multi-view FMEA Re-validation: Efficient Risk and Engineering Knowledge Integration in Agile Production Systems Engineering. In: Pires, L.F., Hammoudi, S., Seidewitz, E. (eds) Model-Driven Engineering and Software Development. MODELSWARD MODELSWARD 2021 2022. Communications in Computer and Information Science, vol 1708. Springer, Cham. https://doi.org/10.1007/978-3-031-38821-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-38821-7_4

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