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Caglar, Baris (author), Broggi, G.C. (author), Ali, Muhammad A. (author), Orgéas, Laurent (author), Michaud, Véronique (author)
Knowledge of permeability of fibrous microstructures is crucial for predicting the mold fill times and resin flow path in composite manufacturing. Herein we report a method to rapidly predict the permeability of 3D fibrous microstructures. Our method relies on predicting the permeability of 2D cross-sections via deep neural networks and...
conference paper 2022
document
Caglar, Baris (author), Broggi, G.C. (author), Ali, Muhammad A. (author), Orgéas, Laurent (author), Michaud, Véronique (author)
Permeability of fibrous microstructures is a key material property for predicting the mold fill times and resin flow path during composite manufacturing. In this work, we report an efficient approach to predict the permeability of 3D microstructures from deep learning based permeability predictions of 2D cross-sections combined via a circuit...
journal article 2022