PREDICTION OF MECHANICAL BEHAVIOR OF WOVEN COMPOSITE VIA DEEP NEURAL NETWORK
- Authors
- Kim, Dug-Joong; Baek, Jeong-Hyeon; Kim, Gyu-Won; Kim, Hak Sung
- Issue Date
- Jun-2022
- Publisher
- Composite Construction Laboratory (CCLab), Ecole Polytechnique Federale de Lausanne (EPFL)
- Keywords
- Carbon fiber-reinforced plastics (CFRP); Deep-learning; Deepneural- network (DNN); Finite-element-method (FEM)
- Citation
- ECCM 2022 - Proceedings of the 20th European Conference on Composite Materials: Composites Meet Sustainability, v.4, pp.862 - 867
- Indexed
- SCOPUS
- Journal Title
- ECCM 2022 - Proceedings of the 20th European Conference on Composite Materials: Composites Meet Sustainability
- Volume
- 4
- Start Page
- 862
- End Page
- 867
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/184842
- Abstract
- The mechanical behavior of CFRP was trained by deep-neural-network (DNN). For an accurate analysis of composite properties, micromechanics of failure based multi-scale simulation method was introduced for progressive damage analysis of composite materials. The meso-scale and micro-scale representative volume was used for multi-scale simulation, and stress transfer between meso-micro scale model, was performed by applying stress amplification factor (SAF). With the developed simulation method, stress-strain curves of CFRP were derived depending on constituent properties and yarn structures. The databases of mechanical behavior were trained by deep-neural-network, which use stress-strain curves as training output, and mechanical, geometrical properties as training input, respectively. As a result, mechanical behavior of CFRP could be predicted by the developed method in a very fast time with high accuracy.
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