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RECONSTRUCTION OF TOPOLOGY OPTIMIZED GEOMETRY WITH CASTING CONSTRAINTS IN A FEATURE-BASED APPROACH

Published online by Cambridge University Press:  19 June 2023

Johannes Mayer ,
Martin Denk  and
Sandro Wartzack
Johannes Mayer*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Engineering Design
Martin Denk
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Engineering Design
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Engineering Design
*
Mayer, Johannes, Friedrich-Alexander-Universität Erlangen-Nürnberg, Engineering Design, Germany, mayer@mfk.fau.de

Abstract

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Topology Optimization (TO) is an established method for the development of high strength and lightweight structural components. However, its results have to be geometrically revised to obtain a computational model that meets product development requirements. Design has to be in accordance with manufacturing constraints. Geometry reconstruction therefore still is a typically manual and tedious task, but increasingly supported by computational and automated approaches. In this paper, the inclusion of a casting constraint in an automated Medial Axis based reconstruction method is presented. Since the Medial Axis provides cross-section values by the computation of maximally inscribed spheres, this information is used for geometry reconstruction and even further for the purposeful adaption of the cross-section to match Heuver's circle method. Thereby, the directed solidification of molten material is considered. With a predefined feeder position, the demonstrator of a suspension control arm is used to show the application of the method. Resulting CAD-models are also structurally evaluated for their stiffness characteristics.

Keywords

Topology OptimizationMedial Axis TransformComputational design methodsComputer Aided Design (CAD)Lightweight design
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3015 - 3024
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), 2023. Published by Cambridge University Press

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