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Surrogate-based design optimization of the binder cover combining performance and production cost

Published online by Cambridge University Press:  16 May 2024

Pavel Eremeev ,
Hendrik Devriendt ,
Alexander De Cock  and
Frank Naets
Pavel Eremeev*
Affiliation:
KU Leuven, Belgium Flanders Make@KU Leuven, Belgium
Hendrik Devriendt
Affiliation:
KU Leuven, Belgium Flanders Make@KU Leuven, Belgium
Alexander De Cock
Affiliation:
Flanders Make, Belgium
Frank Naets
Affiliation:
KU Leuven, Belgium Flanders Make@KU Leuven, Belgium
*
pavel.eremeev@kuleuven.be

Abstract

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This study integrates surrogate models into combined design optimization of a binder cover, considering production cost and performance constraints. Results reveal that models trained on substantial datasets achieve designs close to the global optimum. Incorporating model variance into constraints prediction in surrogate-based optimization improves robustness and accuracy, especially with noisy functions. This modification enhances the likelihood of obtaining feasible designs, reducing computational demands and showcasing the potential of smaller datasets in predicting local optima.

Keywords

artificial intelligence (AI)surrogate-based design optimisationproduct design
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2029 - 2038
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), 2024.

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