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SUBDIVISION SURFACE MID-SURFACE RECONSTRUCTION OF TOPOLOGY OPTIMIZATION RESULTS AND THIN-WALLED SHAPES USING SURFACE SKELETONS

Published online by Cambridge University Press:  27 July 2021

Martin Denk ,
Klemens Rother  and
Kristin Paetzold
Martin Denk*
Affiliation:
Bundeswehr University Munich, Institute for Technical Product Development
Klemens Rother
Affiliation:
Munich University of Applied Sciences, Institute for Material and Building Research
Kristin Paetzold
Affiliation:
Bundeswehr University Munich, Institute for Technical Product Development
*
Denk, Martin, Bundeswehr University Munich, Insitute for Technical Product Development, Germany, martin.denk@unibw.de

Abstract

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Polygon meshes and particularly triangulated meshes can be used to describe the shape of different types of geometry such as bicycles, bridges, or runways. In engineering, such polygon meshes can occur as finite element meshes, resulting from topology optimization or laser scanning. This article presents an automated parameterization of polygon meshes into a parametric representation using subdivision surfaces, especially in topology optimization. Therefore, we perform surface skeletonization on a volumetric grid supported by the Euclidian distance transformation and topology preserving and shape-preserving criterion. Based on that surface skeleton, an automated conversation into a Subdivision Surface Control grid is established. The final mid-surface-like parametrization is quite flexible and can be changed by variating the control gird or the local thickness.

Keywords

Computational design methodsComputer Aided Design (CAD)Lightweight designSkeletonisationTopology Optimization

Information

Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2771 - 2780
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), 2021. Published by Cambridge University Press

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