id |
caadria2022_277 |
authors |
Akbar, Zuardin, Wood, Dylan, Kiesewetter, Laura, Menges, Achim and Wortmann, Thomas |
year |
2022 |
title |
A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning |
doi |
https://doi.org/10.52842/conf.caadria.2022.1.393
|
source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 393-402 |
summary |
This paper develops a workflow to train machine learning (ML) models with a small dataset from physical samples to predict the curvatures of self-shaping wood bilayers based on local variations in the grain. In contrast to state-of-the-art predictive models, specifically 1.) a 2D Timoshenko model and 2.) a 3D numerical model with a rheological model, our method accounts for natural and unavoidable material variations. In this paper, we only focus on local grain variations as the main driver for curvatures in small-scale material samples. We extracted a feature matrix from grain images of active and passive layers as a Grey Level Co-Occurrence Matrix and used it as the input for our ML models. We also analysed the impact of grain variations on the feature matrix. We trained and tested several tree-based regression models with different features. The models achieved very accurate predictions for curvatures in each sample (R;0.9) and extend the range of parameters that is incalculable by a Timoshenko model. This research contributes to the material-efficient design of weather-responsive shape-changing wood structures by further leveraging the use of natural material features and explainable data-driven modelling and extends the topic in ML for material behaviour-driven design among the CAADRIA community. |
keywords |
data-driven model, machine learning, material programming, smart material, timber structure, SDG 12 |
series |
CAADRIA |
email |
zuardin.akbar@icd.uni-stuttgart.de |
full text |
file.pdf (814,223 bytes) |
references |
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last changed |
2022/07/22 07:34 |
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