Rapidly predicting the effect of tool geometry on the wrinkling of biaxial NCFs during composites manufacturing using a deep learning surrogate model
(2023)
Journal Article
Viisainen, J., Yu, F., Codolini, A., Chen, S., Harper, L., & Sutcliffe, M. (2023). Rapidly predicting the effect of tool geometry on the wrinkling of biaxial NCFs during composites manufacturing using a deep learning surrogate model. Composites Part B: Engineering, 253, Article 110536. https://doi.org/10.1016/j.compositesb.2023.110536
A deep learning surrogate model is developed to rapidly predict the wrinkling patterns of a biaxial non-crimp fabric (NCF) layup for any given tool geometry during forming. The underlying dataset of finite element simulations is used to investigate t... Read More about Rapidly predicting the effect of tool geometry on the wrinkling of biaxial NCFs during composites manufacturing using a deep learning surrogate model.