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EUCLIDIAN DISTANCE TRANSFORMATION, MAIN AXIS ROTATION AND NOISY DILITATION SUPPORTED CROSS-SECTION CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS

Published online by Cambridge University Press:  27 July 2021

Martin Denk ,
Klemens Rother ,
Tobias Höfer ,
Jan Mehlstäubl  and
Kristin Paetzold
Martin Denk*
Affiliation:
Bundeswehr University Munich;
Klemens Rother
Affiliation:
Munich University of Applied Sciences, Institute for Material and Building Research;
Tobias Höfer
Affiliation:
Munich University of Applied Sciences, Competence Center Image Processing (CCBV)
Jan Mehlstäubl
Affiliation:
Bundeswehr University Munich;
Kristin Paetzold
Affiliation:
Bundeswehr University Munich;
*
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 be supplied as finite element meshes, resulting from topology optimization or from laser scanning. Especially from topology optimization, frame-like polygon meshes with slender parts are typical and often have to be converted into a CAD (Computer-Aided Design) format, e.g., for further geometrical detailing or performing additional shape optimization. Especially for such frame-like geometries, CAD designs are constructed as beams with cross-sections and beam-lines, whereby the cross-section is extruded along the beam-lines or beam skeleton. One major task in the recognition of beams is the classification of the cross-section type such as I, U, or T, which is addressed in this article. Therefore, a dataset consisting of different cross-sections represented as binary images is created. Noisy dilatation, the distance transformation, and main axis rotation are applied to these images to increase the robustness and reduce the necessary amount of samples. The resulting images are applied to a convolutional neuronal network.

Keywords

Artificial intelligenceMachine learningComputer Aided Design (CAD)Geometric Deep LearningCross-Sections
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1401 - 1410
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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