Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T17:30:57.111Z Has data issue: false hasContentIssue false

ON THE TREATMENT OF REQUIREMENTS IN DFAM: THREE INDUSTRIAL USE CASES

Published online by Cambridge University Press:  19 June 2023

Felix Endress*
Affiliation:
Laboratory for Product Development and Lightweight Design, TUM School of Engineering and Design, Technical University of Munich, Germany
Jasper Rieser
Affiliation:
Laboratory for Product Development and Lightweight Design, TUM School of Engineering and Design, Technical University of Munich, Germany
Markus Zimmermann
Affiliation:
Laboratory for Product Development and Lightweight Design, TUM School of Engineering and Design, Technical University of Munich, Germany
*
Endress, Felix, Technical University of Munich, TUM School of Engineering and Design, Germany, felix.endress@tum.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.

Optimization-driven design offers advantages over traditional experience-based mechanical design. As an example, topology optimization can be a powerful tool to generate body shapes for Additive Manufacturing (AM). This is helpful, when (1) load paths are non-intuitive due to complex design domains or boundary conditions, or (2) the design process is to be automated to minimize effort associated with experience-based design. However, practically relevant boundary conditions are often difficult to put into a formal mathematical language to, for example, either feed it into a topology optimization algorithm, or provide precise quantitative criteria for CAE-supported manual design. This paper presents a survey of three industry use cases and identifies three types of requirements: the first can be directly cast into parts of an optimization problem statement (∼ 40%), the second is considered indirectly by adapting the optimization problem without explicit reference to the requirement (∼ 20%), and the third is only assessed after the design is finalized (∼ 40%). For categories 2 and 3 we propose directions of improvement to support formulating complex design tasks as unambiguous design problems.

Type
Article
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

Bendsøe, M.P. and Sigmund, O. (2004), Topology Optimization: Theory, Methods, and Applications, Springer International Publishing, Cham.CrossRefGoogle Scholar
Bruggi, M. and Duysinx, P. (2012), “Topology optimization for minimum weight with compliance and stress constraints”, Structural and Multidisciplinary Optimization, Vol. 46 No. 3, pp. 369384, http://doi.org/10.1007/s00158-012-0759-7.CrossRefGoogle Scholar
Da Silva, G.A., Aage, N., Beck, A.T. and Sigmund, O. (2021), “Local versus global stress constraint strategies in topology optimization: A comparative study”, International Journal for Numerical Methods in Engineering, Vol. 122 No. 21, pp. 60036036, http://doi.org/10.1002/nme.6781.CrossRefGoogle Scholar
Glinz, M. (2007), “On non-functional requirements”, in: Requirements Engineering Conference, 2007. RE '07. 15th IEEE International, IEEE / Institute of Electrical and Electronics Engineers Incorporated, pp. 2126, http://doi.org/10.1109/RE.2007.45.Google Scholar
Hirshorn, S. R. (2016), NASA systems engineering handbook: NASA SP-2016-6105 Rev2, National Aeronautics and Space Administration, Washington, D.C.Google Scholar
Holmberg, E., Torstenfelt, B. and Klarbring, A. (2014), “Fatigue constrained topology optimization”, Structural and Multidisciplinary Optimization, Vol. 50 No. 2, pp. 207219, http://doi.org/10.1007/s00158-014-1054-6.CrossRefGoogle Scholar
Klahn, C., Meboldt, M., Fontana, F., Leutenecker-Twelsiek, B. and Jansen, J. (2018), Entwicklung und Konstruktion fur die Additive Fertigung: Grundlagen und Methoden fur den Einsatz in industriellen Endkundenprodukten, Vogel Business Media, Wuerzburg.Google Scholar
Koelsch, G. (2016), Requirements Writing for System Engineering, Apress, Berkeley, CA.CrossRefGoogle Scholar
Lachmayer, R., Rettschlag, K. and Kaierle, S. (2021), Konstruktion fur die Additive Fertigung 2020, Springer, Berlin.CrossRefGoogle Scholar
Larsson, J., Wennhage, P. and Goransson, P. (2022), “Mass minimization with conflicting dynamic constraints by topology optimization using sequential integer programming”, Finite Elements in Analysis and Design, Vol. 200, p. 103683, http://doi.org/10.1016/jj.finel.2021.103683.CrossRefGoogle Scholar
Ma, Z.D., Cheng, H.C. and Kikuchi, N. (1994), “Structural design for obtaining desired eigenfrequencies by using the topology and shape optimization method”, Computing Systems in Engineering, Vol. 5 No. 1, pp. 7789, http://doi.org/10.1016/0956-0521(94)90039-6.CrossRefGoogle Scholar
Pahl, G., Beitz, W., Feldhusen, J. and Grote, K.H. (2007), Engineering design: A systematic approach, Springer, London.CrossRefGoogle Scholar
Pedersen, N.L. (2000), “Maximization of eigenvalues using topology optimization”, Structural and Multidisciplinary Optimization, Vol. 20 No. 1, pp. 211, http://doi.org/10.1007/s001580050130.CrossRefGoogle Scholar
Rodriguez, T., Montemurro, M., Le Texier, P. and Pailhes, J. (2020), “Structural displacement requirement in a topology optimization algorithm based on isogeometric entities”, Journal of Optimization Theory and Applications, Vol. 184 No. 1, pp. 250276, http://doi.org/10.1007/s10957-019-01622-8.CrossRefGoogle Scholar
Sigmund, O. (2022), “On benchmarking and good scientific practise in topology optimization”, Structural and Multidisciplinary Optimization, Vol. 65 No. 11, http://doi.org/10.1007/s00158-022-03427-2.CrossRefGoogle Scholar
Tsai, T.D. and Cheng, C.C. (2013), “Structural design for desired eigenfrequencies and mode shapes using topology optimization”, Structural and Multidisciplinary Optimization, Vol. 47 No. 5, pp. 673686, http://doi.org/10.1007/s00158-012-0840-2.CrossRefGoogle Scholar
Tyflopoulos, E. and Steinert, M. (2019), “Messing with boundaries - quantifying the potential loss by pre-set parameters in topology optimization”, Procedia CIRP, Vol. 84, pp. 979985, http://doi.org/10.1016/j.procir.2019.04.307.CrossRefGoogle Scholar
van de Ven, E., Maas, R., Ayas, C., Langelaar, M. and van Keulen, F. (2021), “Overhang control in topology optimization: a comparison of continuous front propagation-based and discrete layer-by-layer overhang control”, Structural and Multidisciplinary Optimization, Vol. 64 No. 2, pp. 761778, http://doi.org/10.1007/s00158-021-02887-2.CrossRefGoogle Scholar
Yi, B., Zhou, Y., Yoon, G.H. and Saitou, K. (2019), “Topology optimization of functionally-graded lattice structures with buckling constraints”, Computer Methods in Applied Mechanics and Engineering, Vol. 354, pp. 593619, http://doi.org/10.1016/jj.cma.2019.05.055.CrossRefGoogle Scholar
Zhou, M. and Fleury, R. (2016), “Fail-safe topology optimization”, Structural and Multidisciplinary Optimization, Vol. 54 No. 5, pp. 12251243, http://doi.org/10.1007/s00158-016-1507-1.CrossRefGoogle Scholar
Zhu, J.H., Zhang, W.H. and Xia, L. (2016), “Topology optimization in aircraft and aerospace structures design”, Archives of Computational Methods in Engineering, Vol. 23 No. 4, pp. 595622, http://doi.org/10.1007/s11831-015-9151-2.CrossRefGoogle Scholar
Zimmermann, M. and de Week, O. (2020), “Formulating engineering systems requirements”, in: Maier, A., Oehmen, J. and Vermaas, P.E. (Editors), Handbook of Engineering Systems Design, Springer International Publishing, Cham, pp. 152, http://doi.org/10.1007/978-3-030-46054-9U33-1.Google Scholar