Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T09:27:27.540Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of the methodical framework for the management of uncertainty in the context of the integration of sensory functions

Published online by Cambridge University Press:  16 May 2024

Peter Welzbacher ,
Sawa Vinzenz Witt ,
Yanik Koch  and
Eckhard Kirchner
Peter Welzbacher*
Affiliation:
Technical University of Darmstadt, Germany
Sawa Vinzenz Witt
Affiliation:
Technical University of Darmstadt, Germany
Yanik Koch
Affiliation:
Technical University of Darmstadt, Germany
Eckhard Kirchner
Affiliation:
Technical University of Darmstadt, Germany
*
peter.welzbacher@tu-darmstadt.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

As digitalization progresses, the development and integration of sensory functions in technical systems become increasingly important. Managing uncertainty, especially in the early phase of this process, is crucial to ensure the reliability of the data provided. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was presented in prior works. In this contribution, the effectivity of the framework is evaluated by applying it to a sensory function for rotational speed and offset measurement of a disk pack coupling using sensor integrating bolts.

Keywords

robust designuncertaintymethodical designsensory functionevaluation
Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 835 - 844
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Breimann, R., Fett, M., Küchenhof, J., Gomberg, I. and Kirchner, E., et al. (2023), “A method for optimizing product architectures for the management of disturbance factors”, Procedia CIRP, Vol. 119, pp. 1041–1046. 10.1016/j.procir.2023.02.179.Google Scholar
De Weck, O., Eckert, C.M. and Clarkson, P.J. (2007), “A Classification of Uncertainty for Early Product and System Design”, Proceedings of ICED 2007, Vol. 42, pp. 1–12.Google Scholar
Grebici, K., Goh, Y. and McMahon, C. (2008), “Uncertainty and risk reduction in engineering embodiment processes”, Proceedings of DESIGN 2008, Vol. 48, pp. 143–156.Google Scholar
Hausmann, M., Koch, Y. and Kirchner, E. (2021), “Managing the uncertainty in data-acquisition by in situ measurements: a review and evaluation of sensing machine element-approaches in the context of digital twins”, International Journal of Product Lifecycle Management, Vol. 13 No. 1, pp. 48–65. 10.1504/IJPLM.2021.115700.CrossRefGoogle Scholar
International Organization for Standardization (2009), Risk management: vocabulary (ISO Guide 73), November 2009, Beuth, Berlin.Google Scholar
Kirchner, E., Wallmersperger, T., Gwosch, T., Menning, J.D.M. and Peters, J., et al. (2024), “A Review on Sensor-integrating Machine Elements”, Advanced Sensor Research. 10.1002/adsr.202300113.Google Scholar
Kraus, B., Matzke, S., Welzbacher, P. and Kirchner, E. (2022), “Utilizing a graph data structure to model physical effects and dependencies between different physical variables for the systematic identification of sensory effects in design elements”, Proceedings of DFX 2022, Vol. 33, pp. 1–10. 10.35199/dfx2022.09.Google Scholar
Kraus, B., Welzbacher, P., Schwind, J., Hahn, T. and Kirchner, E. (2023), “Improvement, digitalization and validation of a development method for enabling the utilization of sensory functions in design elements”, Procedia CIRP, Vol. 119, pp. 272–277. 10.1016/j.procir.2023.02.135.Google Scholar
Kreye, M.E., Goh, Y. and Newnes, L.B. (2011), “Manifestation of uncertainty. A classification”, Proceedings of ICED 2011, 68-6, pp. 96–107.Google Scholar
Malmiry, R.B., Pailhès, J., Qureshi, A.J., Antoine, J.-F. and Dantan, J.-Y. (2016), “Management of product design complexity due to epistemic uncertainty via energy flow modelling based on CPM”, CIRP Annals, Vol. 65 No. 1, pp. 169–172. 10.1016/j.cirp.2016.04.048.CrossRefGoogle Scholar
Mathias, J., Kloberdanz, H., Engelhardt, R. and Birkhofer, H. (2010), “Strategies and principles to design robust products”, Proceedings of DESIGN 2010, Vol. 60, pp. 341–350.Google Scholar
Matt, D.T. and Rauch, E. (2020), “SME 4.0: The role of small- and medium-sized enterprises in the digital transformation”, in Zsifkovits, H., Modrák, V. and Matt, D.T. (Eds.), Industry 4.0 for SMEs, Springer; OAPEN Foundation, pp. 3–36. 10.1007/978-3-030-25425-4_1.CrossRefGoogle Scholar
McManus, H. and Hastings, D. (2005), “A Framework for Understanding Uncertainty and its Mitigation and Exploitation in Complex Systems”, INCOSE International Symposium, Vol. 15 No. 1, pp. 484–503. 10.1002/j.2334-5837.2005.tb00685.x.CrossRefGoogle Scholar
Westerhausen, Meyer zu, Schneider, S., and Lachmayer, J., R. (2023), “Reliability analysis for sensor networks and their data acquisition: a systematic literature review”, Proceedings of ICED 2023, Vol. 3, pp. 3065–3074. 10.1017/pds.2023.307.Google Scholar
Vorwerk-Handing, G., Gwosch, T., Schork, S., Kirchner, E. and Matthiesen, S. (2020a), “Classification and examples of next generation machine elements”, Forschung im Ingenieurwesen, Vol. 84 No. 1, pp. 21–32. 10.1007/s10010-019-00382-1.CrossRefGoogle Scholar
Vorwerk-Handing, G., Welzbacher, P. and Kirchner, E. (2020b), “Consideration of uncertainty within the conceptual integration of measurement functions into existing systems”, Procedia Manufacturing, Vol. 52, pp. 301–306. 10.1016/j.promfg.2020.11.050.CrossRefGoogle Scholar
Vorwerk-Handing, G., Welzbacher, P. and Kirchner, E. (2023), “A multipole-based effect catalog system for the systematic identification of potential measurands”, Design Science, Vol. 9. 10.1017/dsj.2023.30.Google Scholar
Walker, W.E., Harremoës, P., Rotmans, J., van der Sluijs, J.P. and van Asselt, M.B.A., et al. (2003), “Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support”, Integrated Assessment, Vol. 4 No. 1, pp. 5–17. 10.1076/iaij.4.1.5.16466.CrossRefGoogle Scholar
Welzbacher, P., Geipl, A., Kraus, B., Puchtler, S. and Kirchner, E. (2023), “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”, Proceedings of ICED 2023, Vol. 3, pp. 141–150. 10.1017/pds.2023.15.Google Scholar
Welzbacher, P., Puchtler, S., Geipl, A. and Kirchner, E. (2022), “Uncertainty Analysis of a Calculation Model for Electric Bearing Impedance”, Proceedings of DESIGN 2022, Vol. 2, pp. 653–662. 10.1017/pds.2022.67.Google Scholar
Welzbacher, P., Vorwerk-Handing, G. and Kirchner, E. (2021), “A control list for the systematic identification of disturbance factors”, Proceedings of ICED 2021, Vol. 1, pp. 51–60. 10.1017/pds.2021.6.Google Scholar