Toward Automated Topology Optimization: Identification of Non-Design Features of CAD Models Using Graph Neural Networks
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
This paper presents an automated identification of non-design features of CAD models for topology optimization using learning-based segmentation. The CAD files are taken from a large database of industry-relevant components. Based on the geometry and topology of the components, a graph structure is created and processed by a deep neural network. The results show good match with real cases and can be continuously improved by training with additional data.
| Original language | English |
|---|---|
| Title of host publication | Industrializing Additive Manufacturing : Proceedings of AMPA2023 |
| Editors | Christoph Klahn, Mirko Meboldt, Julian Ferchow |
| Number of pages | 13 |
| Place of Publication | Cham |
| Publisher | Springer International Publishing |
| Publication date | 2024 |
| Pages | 267-279 |
| ISBN (print) | 978-3-031-42982-8 |
| ISBN (electronic) | 978-3-031-42983-5 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |
| Event | 3th International Conference on Additive Manufacturing in Products and Applications - AMPA 2023 - ETH Zürich, Zürich, Switzerland Duration: 12.09.2023 → 14.09.2023 Conference number: 3 https://ampa.ethz.ch/ |
Bibliographical note
Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Engineering - Design Automation, Topology Optimization, Graph Neural Networks