Hostname: page-component-7479d7b7d-c9gpj Total loading time: 0 Render date: 2024-07-13T13:20:42.347Z Has data issue: false hasContentIssue false
  • English
  • Français

LIMITATIONS OF DESIGN SPACE-BASED INDICATORS FOR EARLY ROBUSTNESS ASSESSMENT

Published online by Cambridge University Press:  27 July 2021

Herle Bagh Juul-Nyholm ,
Nökkvi S. Sigurdarson ,
Martin Ebro  and
Tobias Eifler
Herle Bagh Juul-Nyholm*
Affiliation:
Technical University of Denmark;
Nökkvi S. Sigurdarson
Affiliation:
Technical University of Denmark; Novo Nordisk A/S
Martin Ebro
Affiliation:
Novo Nordisk A/S
Tobias Eifler
Affiliation:
Technical University of Denmark;
*
Juul-Nyholm, Herle Bagh, Danmarks Tekniske Universitet / Technical University of Denmark Denmark, hbaju@mek.dtu.dk

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.

This paper seeks to address the gap between qualitative Robust Design principles and parameter optimization. The former often fails to consider the challenging amount of details in embodiment and configuration design, while the latter is the widely accepted main thrust in traditional Robust Design. The gap is addressed by exploring the value of five quantitative robustness indicators for Design Space Exploration based on variables, objectives and constraints: The set level indicators, Design Space Size and Pareto Set Dispersion, and the point level indicators, Neighbourhood Performance, Failure Rate and Distance to Failure. As a background for the discussion of the limitations of these indicators an industrial case is presented. The case is an incremental encoder and includes two configurations for comparison, five objectives, eight variables, and a range of constraints. The design spaces are sampled and they show conflicting objectives, dispersed spaces and variables dependencies. Based on this it is suggested that set level indicators are more suitable than point level indicators of early robustness evaluation, but the available indicators are limited in their considerations of design space discontinuity and conflicts.

Keywords

Robust designComputational design methodsEvaluation, Design Space ExplorationConfiguration Design
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1977 - 1986
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), 2021. Published by Cambridge University Press

References

Barrico, C. & Antunes, C. H. (2006) Robustness Analysis in Multi-Objective Optimization Using a Degree of Robustness Concept. 2006 IEEE Congress on Evolutionary Computation, 1887–1892.10.1109/CEC.2006.1688537CrossRefGoogle Scholar
Beyer, H. G. & Sendhoff, B. (2007). Robust optimization – a comprehensive survey. Computer methods in applied mechanics and engineering, 196(33-34), 3190–3218.10.1016/j.cma.2007.03.003CrossRefGoogle Scholar
Blanding, D. L. (1999). Exact constraint: machine design using kinematic principles. American Society of Mechanical Engineers.10.1115/1.800857CrossRefGoogle Scholar
Deb, K. &Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary computation, 14(4), 463–494.10.1162/evco.2006.14.4.463CrossRefGoogle ScholarPubMed
Frank, C. P., Marlier, R. A., Pinon-Fischer, O. J., & Mavris, D. N. (2018). Evolutionary multi-objective multi-architecture design space exploration methodology. Optimization and Engineering, 19(2), 359–381.10.1007/s11081-018-9373-xCrossRefGoogle Scholar
Göhler, S. M. & Howard, T. J. (2015). The contradiction index (ci): a new metric combining system complexity and robustness for early design stages. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 57175, p. V007T06A023). American Society of Mechanical Engineers.Google Scholar
Göhler, S. M., Eifler, T., & Howard, T. J. (2016) Robustness metrics: Consolidating the multiple approaches to quantify robustness, Journal of Mechanical Design, 138(11).Google Scholar
Göhler, S. M., Frey, D. D., & Howard, T. J. (2016). A model-based approach to associate complexity and robustness in engineering systems. Research in Engineering Design, 28(2), 223–234.10.1007/s00163-016-0236-1CrossRefGoogle Scholar
Harbrecht, H., Tröndle, D., & Zimmermann, M. (2019). A sampling-based optimization algorithm for solution spaces with pair-wise-coupled design variables. Structural and Multidisciplinary Optimization, 60(2), 501–512.10.1007/s00158-019-02221-xCrossRefGoogle Scholar
Hasenkamp, T., Arvidsson, M., & Gremyr, I. (2009) A review of practices for robust design methodology, Journal of Engineering Design, 20(6), 645–657.10.1080/09544820802275557CrossRefGoogle Scholar
Jugulum, R. & Frey, D.D. (2007) Toward a taxonomy of concept designs for improved robustness, Journal of Engineering Design, 18(2), 193–256.10.1080/09544820600731496CrossRefGoogle Scholar
Riquelme, N., von Lücken, C., & Baran, B. (2015). Performance metrics in multi-objective optimization. In 2015 Latin American Computing Conference (CLEI) (pp. 1–11). IEEE.Google Scholar
Rötzer, S., Thoma, D., & Zimmermann, M. (2020). Cost Optimization of Product Families Using Solution Spaces. In Proceedings of the Design Society: DESIGN Conference (Vol. 1, pp. 1087–1094). Cambridge University Press.Google Scholar
Sigurdarson, N., Eifler, T., & Ebro, M. (2019). Functional Trade-offs in the Mechanical Design of Integrated Products - Impact on Robustness and Optimisability. Proceedings of the 20th International Conference on Engineering Design.10.1017/dsi.2019.356CrossRefGoogle Scholar
Suh, N.P. (2001) Axiomatic Design: Advances and Applications. United States, OUP.Google Scholar
Taguchi, G., Chowdhury, S. & Wu, Y. (2005) Taguchi's Quality Engineering Handbook, Wiley & Sons.Google Scholar
Yannou, B., Troussier, N., Chateauneuf, A., Boudaoud, N. & Scaravetti, D. (2007) Design exploration, robust design and reliable design: Three successive and complementary approaches. International Conference on Engineering Design, ICED'07.Google Scholar