A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning

Abstract

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 weatherresponsive 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.

Cardiac Biometrics and Perceived Workload Regression Analysis Using Random Forest Regressor in Cognitive Manufacturing Tasks

“…Regression models have developed rapidly over the years and implemented in various industries. These models have been widely used in fields like architecture [21] , supply chain [14] and manufacturing [10] . Results from these studies show high accuracy in their performances according to their respective functions.…”

Analysis of The Corrosion Inhibition Efficacy of Camellia Sinensis Extract on Aluminium 6061 Alloys using Artificial Neural Network (ANN)

“…This paper is to study the optimum concentration of camellia sinensis extract as corrosion inhibitor in a 1M HCl environment, on Aluminium 6061, by utilizing supervised machine learning model. Machine learning models have been used widely in various applications such as predicting human fatigue [20] and perceived workload [21], architectural material selections [22] and also in predicting corrosion and analysing inhibitor effectivity on metals [23], [24]. In corrosion studies, the artificial neural networks (ANN), was proven effective to analyze corrosion inhibition of 304SS in sulfuric acid solution using timoho leaf extract [23], which is considerable to be used in this study.…”