Papers
A Data-Driven Workflow For Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations With Machine Vision And Machine Learning
Zuardin Akbar Institute for Computational Design and Construction (ICD), University of Stuttgart
Dylan Wood Institute for Computational Design and Construction (ICD), University of Stuttgart
Laura Kiesewetter Institute for Computational Design and Construction (ICD), University of Stuttgart
Achim Menges Institute for Computational Design and Construction (ICD), University of Stuttgart
Thomas Wortmann Institute for Computational Design and Construction (ICD), University of Stuttgart
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 (R²>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