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Active-Learning Combined with Topology Optimization for Top-Down Design of Multi-Component Systems

Published online by Cambridge University Press:  26 May 2022

L. Krischer ,
A. Vazhapilli Sureshbabu  and
M. Zimmermann
L. Krischer*
Affiliation:
Technical University of Munich, Germany
A. Vazhapilli Sureshbabu
Affiliation:
Technical University of Munich, Germany
M. Zimmermann
Affiliation:
Technical University of Munich, Germany
*
[email protected]

Abstract

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In top-down design, optimal component requirements are difficult to derive, as the feasible components that satisfy these requirements are yet to be designed and hence unknown. Meta models that provide feasibility and mass estimates for component performance are used for optimal requirement decomposition in an existing approach. This paper (1) extends its applicability adapting it to varying design domains, and (2) increases its efficiency by active-learning. Applying it to the design of a robot arm produces a result that is 1% heavier than the reference obtained by monolithic optimization.

Keywords

topological optimisationartificial intelligence (AI)data-driven designsystems engineering (SE)
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1629 - 1638
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), 2022.

References

Albers, A. and Ottnad, J. (2008), “System based topology optimization as development tools for lightweight components in humanoid robots”, in Humanoids 2008: 2008 8th IEEE-RAS International Conference on Humanoid Robots; Daejeon, South Korea, 1 - 3 December 2008, 12/1/2008 - 12/3/2008, Daejeon, IEEE, Piscataway, NJ, pp. 674–680Google Scholar
Beernaert, T.F. and Etman, L.F.P. (2019), “Multi-level Decomposed Systems Design: Converting a Requirement Specification into an Optimization Problem”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 3691–3700. 10.1017/dsi.2019.376Google Scholar
Bendsøe, M.P. and Sigmund, O. (2004), Topology Optimization: Theory, Methods, and Applications, Second Edition, Corrected Printing, Springer, Berlin, Heidelberg. 10.1007/978-3-662-05086-6CrossRefGoogle Scholar
Cortes, Corinna and Vapnik, Vladimir (1995), “Support-vector networks”, Machine Learning, Vol. 20 No. 3, pp. 273–297. 10.1007/BF00994018Google Scholar
Eberhart, R. and Kennedy, J. (1995), “A new optimizer using particle swarm theory”, in Proceedings of the Sixth International Symposium on Micro Machine and Human Science: Nagoya Municipal Industrial Research Institute, October 4 - 6, 1995, 4-6 Oct. 1995, Nagoya, Japan, IEEE Service Center, Piscataway, NJ, pp. 39–43Google Scholar
Eckert, C. and Clarkson, J. (2005), “The reality of design”, in Clarkson, J. and Eckert, C. (Eds.), Design process improvement: A review of current practice, SpringerLink Bücher, Springer London, London, pp. 1–29. 10.1007/978-1-84628-061-0_1Google Scholar
Ertekin, S., Huang, J., Bottou, L. and Giles, L. (2007), “Learning on the border”, in Silva, M.J., Falcão, A.O., Laender, A.A.F., Baeza-Yates, R., McGuinness, D.L., Olstad, B. and Olsen, Ø.H. (Eds.), Proceedings of the 2007 ACM Conference on Information and Knowledge Management, 11/6/2007 - 11/10/2007, Lisbon, Portugal, ACM, New York, NY, p. 127Google Scholar
Forsberg, K. and Mooz, H. (1991), “The Relationship of System Engineering to the Project Cycle”, INCOSE International Symposium, Vol. 1 No. 1, pp. 57–65. 10.1002/j.2334-5837.1991.tb01484.xGoogle Scholar
Guyan, R.J. (1965), “Reduction of stiffness and mass matrices”, AIAA Journal, Vol. 3 No. 2, p. 380. 10.2514/3.2874Google Scholar
Kim, B.J., Yun, D.K., Lee, S.H. and Jang, G.-W. (2016), “Topology optimization of industrial robots for system-level stiffness maximization by using part-level metamodels”, Structural and Multidisciplinary Optimization, Vol. 54 No. 4, pp. 1061–1071. 10.1007/s00158-016-1446-xGoogle Scholar
Kremer, J., Steenstrup Pedersen, K. and Igel, C. (2014), “Active learning with support vector machines”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 4 No. 4, pp. 313–326. 10.1002/widm.1132Google Scholar
Krischer, L., Sureshbabu, A.V. and Zimmermann, M. (2020), “Modular Topology Optimization of a Humanoid Arm”, 2020 3rd International Conference on Control and Robots (ICCR). 10.1109/ICCR51572.2020.9344316Google Scholar
Krischer, L. and Zimmermann, M. (2021), “Decomposition and optimization of linear structures using meta models”, Structural and Multidisciplinary Optimization. 10.1007/s00158-021-02993-1Google Scholar
Liu, G.-R. and Quek, S.S. (2013), The finite element method: A practical course, Second, Butterworth-Heinemann, Oxford, UKGoogle Scholar
Martins, J.R.R.A. and Lambe, A.B. (2013), “Multidisciplinary Design Optimization. A Survey of Architectures”, AIAA Journal, Vol. 51 No. 9, pp. 2049–2075. 10.2514/1.J051895Google Scholar
Ramu, M., Prabhu Raja, V. and Thyla, P.R. (2013), “Establishment of structural similitude for elastic models and validation of scaling laws”, KSCE Journal of Civil Engineering, Vol. 17 No. 1, pp. 139–144. 10.1007/s12205-013-1216-xCrossRefGoogle Scholar
Svanberg, K. (1987), “The method of moving asymptotes—a new method for structural optimization”, International Journal for Numerical Methods in Engineering, Vol. 24 No. 2, pp. 359–373. 10.1002/nme.1620240207Google Scholar
Wang, X., Zhang, D., Zhao, C., Zhang, P., Zhang, Y. and Cai, Y. (2019), “Optimal design of lightweight serial robots by integrating topology optimization and parametric system optimization”, Mechanism and Machine Theory, Vol. 132, pp. 48–65. 10.1016/j.mechmachtheory.2018.10.015Google 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. 10.1002/nme.4450CrossRefGoogle Scholar
Zimmermann, M., Königs, 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. 10.1080/09544828.2017.1303664Google Scholar