| id |
ecaade2024_166 |
| authors |
Kapon, Gal; Blonder, Arielle; Austern, Guy |
| year |
2024 |
| title |
A Machine Learning Approach to The Inverse Problem of Self-Morphing Composites |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.293
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 293–302 |
| summary |
Composite materials are valued in architecture for their remarkable strength-to-weight ratio and ability to shape intricate structures. However, conventional methods relying on single-use molds raise environmental concerns. Recent advancements in moldless fabrication, particularly self-morphing techniques, leverage geometric frustration—internal stresses generated by material architecture. Uniaxial shrinkage in composites, traditionally seen as distortions, can be harnessed to create a self-shaping mechanism, enabling the achievement of complex geometries by varying fiber orientations. This paper addresses the inverse problem of self-morphing composites, aiming at the generation of production plans from desired designs for morphing. We propose leveraging machine learning, notably Convolutional Neural Networks (CNNs), to predict fiber layouts using 2D data matrices. The paper outlines the use of simulations to construct a dataset for training CNN models to predict the fiber layouts required to achieve design geometry. The contribution of this work is to advance digital design and simulation methods and tools towards the implementation of self-morphing matter in architectural fabrication. |
| keywords |
Self-morphing, geometric frustration, moldless fabrication, digital fabrication, inverse design, machine learning, CNN, composite materials |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,485,497 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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