AI-assisted study of auxetic structures
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articlePeer-reviewed
publishedVersion
Author
Grednev, Sergej
Steude, Henrik S.
Bronder, Stefan
Niggemann, Oliver
Jung, Anne
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Creative Commons Attribution 4.0 International Licensehttp://creativecommons.org/licenses/by/4.0/
openAccess
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In this study, the viability of using machine learning models to predict stress-strain curves of auxetic structures based on geometry-describing parameters is explored. Given the computational cost and time associated with generating these curves through numerical simulations, a machine learning-based approach promises a more efficient alternative. A range of machine learning models, including Artificial Neural Networks, k-Nearest Neighbors Regression, Support Vector Regression, and XGBoost, is implemented and compared regarding the aptitude to predict stress-strain curves under quasi-static compressive loading. Training data is generated using validated finite element simulations. The performance of these models is rigorously tested on data not seen during training. The Feed-Forward Artificial Neural Network emerged as the most proficient model, achieving a Mean Absolute Percentage Error of 0.367 ± 0.230.
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