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OPTIMIZING REQUIREMENTS FOR MAXIMUM DESIGN FREEDOM CONSIDERING PHYSICAL FEASIBILITY

Published online by Cambridge University Press:  19 June 2023

Eduardo Rodrigues Della Noce  and
Markus Zimmermann
Eduardo Rodrigues Della Noce*
Affiliation:
Technical University of Munich (TUM)
Markus Zimmermann
Affiliation:
Technical University of Munich (TUM)
*
Rodrigues Della Noce, Eduardo Technical University of Munich (TUM), Germany, [email protected]

Abstract

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ABSTRACT

Solution spaces are sets of designs that meet all quantitative requirements of a given design problem, aiding requirement management. In previous works, ways of calculating subsets of the complete solution space as hyper-boxes, corresponding to a collection of permissible intervals for design variables, were developed. These intervals can be used to formulate independent component requirements with built-in tolerance. However, these works did not take physical feasibility into account, which has two disadvantages: first, solution spaces may be useless, when the included designs cannot be realized. Second, bad designs that are not physically feasible unnecessarily restrict the design space that can be used for requirement formulation.

In this paper, we present the new concept of a requirement space that is defined as the largest set of designs that (1) allows for decomposition (e.g., into intervals when it is box-shaped), (2) maximizes the useful design space (good and physically feasible), and (3) excludes the non-acceptable design space (bad and physically feasible). A small example from robot design illustrates that requirement spaces can be significantly larger than solution spaces and thus improve requirement decomposition.

Keywords

RequirementsComplexityConcurrent Engineering (CE)Solution SpacesDecomposition
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2865 - 2874
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
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Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Al-Ashaab, A., Howell, S., Usowicz, K., Anta, P. and Gorka, A. (2009), “Set-based concurrent engineering model for automotive electronic/software systems development”, Competitive Design - Proceedings of the 19th CIRP Design Conference.Google Scholar
Avriel, M., Wilde, D.J. and Rijckaert, M.J. (1973), Optimization and design; International Summer School on the Impact of Optimization Theory on Technological Design, Prentice-Hall Englewood Cliffs, N.J.Google Scholar
Beyer, H.G. and Sendhoff, B. (2007), “Robust optimization - a comprehensive survey”, Computer Methods in Applied Mechanics and Engineering, Vol. 196 No. 33, pp. 3190–3218, http://doi.org/10.1016/jxma.2007.03.003.CrossRefGoogle Scholar
Boyd, S., Boyd, S., Vandenberghe, L. and Press, C.U. (2004), Convex Optimization, No. Teil 1 in Berichte uber verteilte messysteme, Cambridge University Press.Google Scholar
Daub, M., Duddeck, F. and Zimmermann, M. (2020), “Optimizing component solution spaces for systems design”, Structural and Multidisciplinary Optimization, Vol. 61, http://doi.org/10.1007/s00158-019-02456-8.Google Scholar
Dullen, S., Verma, D., Blackburn, M. and Whitcomb, C. (2021), “Survey on set-based design (sbd) quantitative methods”, Systems Engineering, Vol. 24 No. 5, pp. 269–292, http://doi.org/10.1002/sys.21580.CrossRefGoogle Scholar
Eichstetter, M., Muller, S. and Zimmermann, M. (2015), “Product family design with solution spaces”, Journal of Mechanical Design, Vol. 137 No. 12, p. 121401, http://doi.org/10.1115/L4031637.CrossRefGoogle Scholar
Erschen, S., Duddeck, F., Gerdts, M. and Zimmermann, M. (2018), “On the optimal decomposition of high- dimensional solution spaces of complex systems”, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Vol. 4 No. 2, pp. -, http://doi.org/10.111571.4037485. Solution space method, design optimization.Google Scholar
Fender, J. (2013), Solution spaces for vehicle crash design, Ph.D. thesis, Technische Universitat Munchen, Konstanz. PhD thesis.Google Scholar
Graff, L., Harbrecht, H. and Zimmermann, M. (2016), “On the computation of solution spaces in high dimen¬sions”, Structural and Multidisciplinary Optimization, Vol. 54 No. 4, pp. 811–829, http://doi.org/10.1007/s00158-016-1454-x.CrossRefGoogle Scholar
Gunawan, S. and Papalambros, P.Y. (2006), “A Bayesian Approach to Reliability-Based Optimization With Incomplete Information”, Journal of Mechanical Design, Vol. 128 No. 4, pp. 909–918, http://doi.org/10.1115/1.2204969.CrossRefGoogle Scholar
Harbrecht, H., Trondle, D. and Zimmermann, M. (2019), “A sampling-based optimization algorithm for solution spaces with pair-wise-coupled design variables”, Structural and Multidisciplinary Optimization, Vol. 60 No. 2, pp. 501–512, http://doi.org/10.1007/s00158-019-02221-x.CrossRefGoogle Scholar
Harbrecht, H., Trondle, D. and Zimmermann, M. (2021), “Approximating solution spaces as a product of polygons”, Structural and Multidisciplinary Optimization, http://doi.org/10.1007/s00158-021-02979-z.CrossRefGoogle Scholar
Jiao, J., Simpson, T. and Siddique, Z. (2007), “Product family design and platform-based product development”, Journal of Intelligent Manufacturing, Vol. 18, pp. 1–3, http://doi.org/10.1007/s10845-007-0001-4.CrossRefGoogle Scholar
Krischer, L. and Zimmermann, M. (2021), “Decomposition and optimization of linear structures using meta models”, Structural and Multidisciplinary Optimization, Vol. 64, http://doi.org/10.1007/s00158-021-02993-1.CrossRefGoogle Scholar
Le Maitre, O.P. and Knio, O.M. (2010), Spectral Methods for Uncertainty Quantification, Scientific Computation, http://doi.org/10.1007/978-90-481-3520-2.CrossRefGoogle Scholar
Lehar, M. and Zimmermann, M. (2012), “An inexpensive estimate of failure probability for high-dimensional systems with uncertainty”, Structural Safety, Vol. 36-37, pp. 32–38, http://doi.org/10.1016/j.strusafe.2011.10.001.CrossRefGoogle Scholar
Mourelatos, Z.P. and Liang, J. (2005), “A Methodology for Trading-Off Performance and Robustness Under Uncertainty”, Journal of Mechanical Design, Vol. 128 No. 4, pp. 856–863, http://doi.org/10.1115/1.2202883.CrossRefGoogle Scholar
Padilla-Garcia, E.A., Rodriguez-Angeles, A., ReseNdiz, J.R. and Cruz-Villar, C.A. (2018), “Concurrent optimization for selection and control of ac servomotors on the powertrain of industrial robots”, IEEE Access, Vol. 6, pp. 27923–27938, http://doi.org/10.1109/ACCESS.2018.2840537.CrossRefGoogle Scholar
Parkinson, A., Sorensen, C. and Pourhassan, N. (1993), “A General Approach for Robust Optimal Design”, Journal of Mechanical Design, Vol. 115 No. 1, pp. 74–80, http://doi.org/10.1115/L2919328.CrossRefGoogle Scholar
Ponn, J. and Lindemann, U. (2011), Konzeptentwicklung und Gestaltung technischer Produkte: Systematisch von Anforderungen zu Konzepten und Gestaltlosungen, VDI-Buch, Springer Berlin Heidelberg.Google Scholar
Rotzer, S., Berger, V. and Zimmermann, M. (2022), “Cost optimization of product families using solution spaces: Application to early-stage electric vehicle design”, in: P. of the Design Society (Editor), Design 2022, Cambridge University Press (CUP).Google Scholar
Rotzer, S., Thoma, D. and Zimmermann, M. (2020), “Cost optimization of product families using solution spaces”, in: Proceedings of the Design Society: DESIGN Conference, Cambridge University Press (CUP), pp. 1087–1094, http://doi.org/10.1017/dsd.2020.178.Google Scholar
Shallcross, N., Parnell, G.S., Pohl, E. and Specking, E. (2020), “Set-based design: The state-of-practice and research opportunities”, Systems Engineering, Vol. 23 No. 5, pp. 557–578, http://doi.org/10.1002/sys.21549.CrossRefGoogle Scholar
Sobek, D., Ward, A. and Liker, J. (1999), “Toyota's principles of set-based concurrent engineering”, Sloan Management Review, Vol. 40.Google Scholar
Stumpf, J., Condor Lopez, J.G., Naumann, T. and Zimmermann, M. (2022), “Systems design using solution- compensation spaces with built-in tolerance applied to powertrain integration”, in: Design 2022, Zagreb, Croatia, http://doi.org/10.1017/pds.2022.202.Google Scholar
Suh, N.P. (2005), “Complexity in engineering”, CIRP Annals, Vol. 54 No. 2, pp. 46–63, http://doi.org/10.1016/S0007-8506(07)60019-5.CrossRefGoogle Scholar
Vogt, M., Duddeck, F., Wahle, M. and Zimmermann, M. (2018), “Optimizing tolerance to uncertainty in systems design with early-and late-decision variables”, IMA Journal of Management Mathematics, Vol. 30, http://doi.org/10.1093/imaman/dpy003.Google Scholar
Zhao, Y.G. and Ono, T. (1999), “A general procedure for first/second-order reliability method (form/sorm)”, Structural Safety, Vol. 21 No. 2, pp. 95–112, http://doi.org/10.1016/S0167-4730(99)00008-9.CrossRefGoogle Scholar
Zimmermann, M. and von Hoessle, J.E. (2013), “Computing solution spaces for robust design”, International Journal for Numerical Methods in Engineering, Vol. 94 No. 3, pp. 290–307, http://doi.org/10.1002/nme.4450.CrossRefGoogle Scholar
Zimmermann, M., Konigs, S., Niemeyer, C., Fender, J., Zeherbauer, C., Vitale, R. and Wahle, M. (2017), “On the design of large systems subject to uncertainty”, Journal of Engineering Design, Vol. 28 No. 4, pp. 233–254, http://doi.org/10.1080/09544828.2017.1303664.CrossRefGoogle Scholar