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PERSPECTIVES ON ROBUST DESIGN – AN OVERVIEW OF CHALLENGES AND RESEARCH AREAS ACROSS INDUSTRY FIELDS

Published online by Cambridge University Press:  19 June 2023

Tobias Eifler ,
Felician Campean ,
Stephan Husung  and
Benjamin Schleich
Tobias Eifler*
Affiliation:
Technical University of Denmark;
Felician Campean
Affiliation:
University of Bradford;
Stephan Husung
Affiliation:
Technische Universität Ilmenau;
Benjamin Schleich
Affiliation:
Technische Universität Darmstadt
*
Eifler, Tobias, Technical University of Denmark, Denmark, [email protected]

Abstract

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Robust Design offers a coherent and widely appreciated approach for the parametric exploration of the design space by means of simulation or experimentation, which is well-established in the quality-by design domain. From the perspective of design research, however, this only addresses a relatively narrow part of the design process and is not fully integrated with other design decisions such as concept exploration, the suitable configuration of system elements, or the design of interfaces. Particularly in light of the growing importance of developing technologically advanced and “smart” systems, it seems that a new methodical perspective on Robust Design is needed. Against this background, this paper consolidates knowledge and insights from different research fields and industry sectors. On this basis, new angles to the discussion on product robustness in different domains are explored in order to suggests directions for action and new research areas, both with respect to a methodical RD approach as well as the question of systematic research procedures.

Keywords

Decision makingDesign engineeringRobust design
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2885 - 2894
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), 2023. Published by Cambridge University Press

References

Bagchi, S. et al. (2020), “Vision Paper: Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures”, IEEE Open Journal of the Computer Society, 1, https://doi.org/10.1109/OJCS.2020.3006807.CrossRefGoogle Scholar
Box, G. (1988), “Signal-to-noise ratios, performance criteria, and transformations”, Technometrics, 30(1),pp.1–40.CrossRefGoogle Scholar
Brix, T. and Husung, S. (2022), “Research and Teaching on Robust Design in early Design Phases”, RD Research Seminar, https://doi.org/10.17632/n9pjyhxkht.1.CrossRefGoogle Scholar
Breyfogle, F.W. (2003), Implementing Six Sigma, Second Edition: Smarter Solutions Using Statistical Methods, Productivity Press, ISBN 9780471265726.Google Scholar
Campean, F., Uddin, A., Bridges, J., Fannon, S. and Yildirim and, U. (2022), “Evaluation of the Impact of Collaborative Research on Robust Design Methodologies: A Large Scale Empirical Case Study with an Automotive OEM”, Design Conference 2022. https://doi.org/10.1017/pds.2022.1.CrossRefGoogle Scholar
Clausing, D. (1994), Total Quality Management - A Step by Step Guide to World Class Concurrent Engineering, ASME Press. https://doi.org/10.1115/1.800695.Google Scholar
Clausing, D. and Frey, D.D. (2005), “Improving System Reliability by Failure-Mode Avoidance Including Four Concept Design Strategies”, Systems Engineering, 8:3:245–262, https://doi.org/10.1002/sys.20034.CrossRefGoogle Scholar
Davis, T.P. (2004), “Measuring Robustness as a parameter in a Transfer Function”, SAE 2004 World Congress & Exhibition, https://doi.org/10.4271/2004-01-1130.CrossRefGoogle Scholar
Eifler, T. and Schleich, B. (2021), “A Robust Design Research Landscape - Review on the Importance of Design Research for Achieving Product Robustness”, ICED conference, Gothenburg, Cambridge University Press, pp. 211–220, https://doi.org/10.1017/pds.2021.22.CrossRefGoogle Scholar
Forslund, K., Karlsson, M. and Söderberg, R. (2013), “Impacts of geometrical manufacturing quality on the visual product experience”, International Journal of Design, 7(1), 69–84.Google Scholar
Götz, S., Schleich, B. and Wartzack, S. (2020), “Integration of robust and tolerance design in early stages of the product development process”, Research in Engineering Design, 31, pp. 157–173. https://dx.doi.org/10.1007/s00163-019-00328-2.CrossRefGoogle Scholar
Harfensteller, F., Henning, S., Zentner, L. and Husung, S. (2022): “Modelling of corner-filleted flexure hinges under various loads”, Mechanism and Machine Theory, 175.CrossRefGoogle Scholar
Henshall, E. and Campean, F. (2010), “Design Verification as a Key Deliverable of Failure Mode Avoidance”, SAE Int. J. Mater. Manuf. 3(1), https://doi.org/10.4271/2010-01-0708.CrossRefGoogle Scholar
Grove, DM. Davis, TP (1992), Engineering, quality and experimental design, Longman. ISBN 978–0582066878.Google Scholar
Haley, B., Dong, A. and Tumer, I. (2015), “Measuring functional robustness with network topological robustness metrics”, 20th International Conference on Engineering Design (ICED 15).Google Scholar
Hansen, F. (1970), Adjustment of Precision Mechanisms. Lliffe Books, London.Google Scholar
Höhne, G, Brix, T. and Sperlich, H. (2005), “Learning from design failures - the method of weak point analysis and virtual deviations”. 15th International Conference on Engineering Design (ICED 2005).Google Scholar
Hu, F., Lu, Y., Vasilakos, A.V., Hao, Q., Ma, R., Patil, Y., Zhang, T., Lu, J, Li, X., Xiong, N.N. (2016), “Robust Cyber–Physical Systems: Concept, models, and implementation”. Future Generation Computer Systems.CrossRefGoogle Scholar
Ihueze, C., Okpala, C.C., Okafor, C. E. and Ogbobe, P. (2017), “Robust Design and Optimization of Production Wastes: An Application for Industries”, World Academy of Science, Engineering and Technology, 76 2013, Available at SSRN: https://ssrn.com/abstract=2902171.Google Scholar
Juul-Nyholm, H. B., Eifler, T. and Ebro, M. (2020), “Robust Design for IoT - on the relevance of mechanical design for robust sensor integration in connected devices”. DESIGN Conference, Croatia. https://doi.org/10.1017/dsd.2020.326.CrossRefGoogle Scholar
Juul-Nyholm, H. B., Ebro, M. and Eifler, T. (2021), “Barriers for Industrial Sensor Integration Design - An Exploratory Interview Study”, ASME Journal of Mechanical Design, 143(7), https://doi.org/10.1115/1.4050078.CrossRefGoogle Scholar
Lee, W.C., Balu, S., Cobden, D., Joshi, A.V., and Pashos, C.L. (2006), “Medication adherence and the associated health-economic impact among patients with type 2 diabetes mellitus converting to insulin pen therapy”, Clinical Therapeutics, 28(10), https://doi.org/10.1016/j.clinthera.2006.10.004.CrossRefGoogle ScholarPubMed
Lowe, D. (2019), “The Latest on Drug Failure and Approval Rates”, Science and Translational Medicine, URL: https://blogs.sciencemag.org/pipeline/archives/2019/05/09/the-latest-on-drug-failure-and-approval-rates.Google Scholar
Manske, E., Theska, R. and Fröhlich, T. (2021), “Foreword to the Special Issue on Tip- and Laser-Based 3D Nanofabrication in Extended Macroscopic Working Areas”. Nanomanuf Metrol, 4, 131. https://doi.org/10.1007/s41871-021-00113-7.CrossRefGoogle Scholar
McDermott, T.A. (2019), “A Rigorous System Engineering Process for Resilient Cyber-Physical Systems Design”. International Symposium on Systems Engineering (ISSE), Edinburgh, UK, 2019, pp. 1–8. https://doi.org/10.1109/ISSE46696.2019.8984569.CrossRefGoogle Scholar
Nair, VN., Taam, W., and Ye, KQ. (2002), “Analysis of functional responses from robust design studies”, Journal of Quality Technology, 34, pp. 355–370.CrossRefGoogle Scholar
Rungger, M. and Tabuada, P. (2016), “A Notion of Robustness for Cyber-Physical Systems”, IEEE Trans Automatic Control, 61(8), https://doi.org/10.1109/TAC.2015.2492438.CrossRefGoogle Scholar
Schienbein, R., Fern, F. and Theska, R. (2021), “Fundamental Investigations in the Design of Five-Axis Nanopositioning Machines for Measurement and Fabrication Purposes”. Nanomanuf Metrol, 4, pp. 156–164. https://doi.org/10.1007/s41871-021-00102-w.CrossRefGoogle Scholar
Schleinkofer, U., Dazer, M., Lucan, K., Mannuß, O., Bertsche, B. and Bauernhansl, T. (2019) “Framework for Robust Design and Reliability Methods to Develop Frugal Manufacturing Systems”, Procedia CIRP, 81:518–523, https://doi.org/10.1016/j.procir.2019.03.148.CrossRefGoogle Scholar
Shafique, M et al. (2020), “Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead”. IEEE Design and Test, 37(2), pp. 30–57, https://doi.org/10.1109/MDAT.2020.2971217.CrossRefGoogle Scholar
Shahrokni, A. and Feldt, R., (2013), “A systematic review of software robustness”. Information and Software Technology, 55(1), https://doi.org/10.1016/j.infsof.2012.06.002.CrossRefGoogle Scholar
Taguchi, G. (1986), Introduction to quality engineering – designing quality into products and processes, Asian Productivity Association. ISBN: 978–9283310846.Google Scholar
Saxena, A., Davis, T. and Jones, J.A. (2015), “A failure mode avoidance approach to reliability”. Annual Reliability and Maintainability Symposium (RAMS). https://doi.org/10.1109/RAMS.2015.7105062.CrossRefGoogle Scholar
Spruegel, T.C., Walter, M. and Wartzack, S. (2014), “Robust Tolerance Design of systems with varying ambient temperature influence due to worldwide manufacturing and operation”, Design Conference, pp. 1189–1198.Google Scholar
Welzbacher, P., Puchtler, S., Geipl, A., Kirchner, E. (2022). Uncertainty Analysis of a Calculation Model for Electric Bearing Impedance”. In Design Conference 2022. https://doi.org/10.1017/pds.2022.67.CrossRefGoogle Scholar