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A Machine Learning-Based Approach for Quick Evaluation of Live Simulations in Embodiment Design

Published online by Cambridge University Press:  26 May 2022

C. Sauer ,
B. Gerschütz ,
J. Bernsdorf ,
B. Schleich  and
S. Wartzack
C. Sauer*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
B. Gerschütz
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
J. Bernsdorf
Affiliation:
CADFEM GmbH, Germany
B. Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
S. Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
*
[email protected]

Abstract

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Supporting product developers in early design phases with Live-Simulation can enhance the quality of early product designs. Live-Simulation can also facilitate a democratization of simulation and puts away pressure from simulation experts. In this paper, a machine learning based quick evaluation tool is proposed to support product developers in interpreting Live-Simulation results. The proposed tool enables a quick evaluation of the Live-Simulation results and enables product developers to further enhance their simulations. The tool is shown within a use case in bike rocker switch design.

Keywords

data-driven designsimulation-based designstructural analysismachine learninglive-simulation
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1757 - 1766
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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