A follow-up on the methodical framework for the identification, analysis and consideration of uncertainty in the context of the integration of sensory functions by means of sensing machine elements

DS 122: Proceedings of the Design Society: 24th International Conference on Engineering Design (ICED23)

Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad├Ęge Troussier
Author: Welzbacher, Peter; Geipl, Anja; Kraus, Benjamin; Puchtler, Steffen; Kirchner, Eckhard
Series: ICED
Institution: Institute for Product Development and Machine Elements (pmd), Technical University of Darmstadt
Section: Design Methods
Page(s): 0141-0150
DOI number: https://doi.org/10.1017/pds.2023.15
ISSN: 2732-527X


When integrating sensing machine elements for in-situ measurements in technical systems, special attention must be paid to uncertainty to ensure the reliability of the provided information. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was already developed in prior research, which still offers room for improvement regarding the included methods and tools. Therefore, in this contribution, the initially proposed methods and tools are adapted and extended to enhance their efficiency and applicability and to reduce their error proneness in order to increase the acceptance of the framework in practice. First, the identification of uncertainty is improved by means of an extended effect graph for an automated identification of disturbance factor induced data and model uncertainty. Second, the significance of the subsequent evaluation of uncertainty is enhanced by replacing the initially proposed local sensitivity analysis with a global sensitivity analysis. Finally, a flowchart is proposed that supports the identification of applicable and promising strategies for the development of measures to consider critical disturbance factor induced uncertainty.

Keywords: Uncertainty, Robust design, Design methods, sensory function, effect graph

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