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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
*
lukas.krischer@tum.de

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.

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