Conference paper Open Access
Mid-Curve Completion Using Convolutional Neural Network
Boussuge, Flavien; Marc, Raphaël
Although of great interest for Finite Element (FE) analysis, the automatic generation of a lower dimensional representation of shapes is not straightforward. One-dimensional (1D) mid-curve model contains entities such as lines, circles and more generally curves representing higher dimension entities like surfaces to be located in the middle of a thin-walled model following its shape. In this paper we present a novel approach to adapt a given medial axis of a polygon in order to generate a dimensionally reduced mid-curve model suitable for FE analysis. The novelty is to use a deep learning completion network [1] which is trained to automatically modify the perturbed regions of the medial axis (end and connection regions). The network takes as input a local image containing the polygon boundary, a mask of the regions to complete and the surrounding valid mid-curves. It returns a predicted mid-curve image which is inserted back into the global mid-curve model.
| Name | Size | |
|---|---|---|
|
03-Boussuge.pdf
md5:e2e0d1c5312cb1a8f270c0495e38bf39 |
4.0 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 80 | 80 |
| Downloads | 39 | 39 |
| Data volume | 154.1 MB | 154.1 MB |
| Unique views | 77 | 77 |
| Unique downloads | 38 | 38 |
- Publication date:
- October 9, 2021
- DOI:
-
- Keyword(s):
- dimensional reduction mid-curve generation machine learning convolutional neural network
- Meeting:
- 29th International Meshing Roundtable (IMR), Virtual Conference, June 21-25, 2021
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International