Euclidian distance transformation, main axis rotation and noisy dilitation supported cross-section classification with convolutional neural networks
Denk M, Rother K, Höfer T, Mehlstäubl J, Paetzold K (2021)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2021
Publisher: Cambridge University Press
Book Volume: 1
Pages Range: 1401-1410
Conference Proceedings Title: Proceedings of the Design Society
Event location: Gothenburg, SWE
DOI: 10.1017/pds.2021.140
Open Access Link: https://doi.org/10.1017/pds.2021.140
Abstract
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.
Authors with CRIS profile
Involved external institutions
Universität der Bundeswehr München
Germany (DE)
Hochschule für angewandte Wissenschaften München
Germany (DE)
Germany (DE)
Hochschule für angewandte Wissenschaften München
Germany (DE)
How to cite
APA:
Denk, M., Rother, K., Höfer, T., Mehlstäubl, J., & Paetzold, K. (2021). Euclidian distance transformation, main axis rotation and noisy dilitation supported cross-section classification with convolutional neural networks. In Proceedings of the Design Society (pp. 1401-1410). Gothenburg, SWE, SE: Cambridge University Press.
MLA:
Denk, Martin, et al. "Euclidian distance transformation, main axis rotation and noisy dilitation supported cross-section classification with convolutional neural networks." Proceedings of the 23rd International Conference on Engineering Design, ICED 2021, Gothenburg, SWE Cambridge University Press, 2021. 1401-1410.
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