TY - GEN
T1 - Integrated Proxy Micromechanical Models in Multiscale Analysis Using Deep Learning for Laminated Composites Subject to Low-Velocity Impact
AU - Hochster, Hadas
AU - Lin, Shiyao
AU - Ranatunga, Vipul
AU - Shemesh, Noam N.Y.
AU - Haj-Ali, Rami
N1 - Publisher Copyright:
© 2023 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Multiscale nonlinear micromechanical approaches can analyze laminated composite structures. In contrast, current classical macromechanical modeling approaches depict composite materials as anisotropic homogenized media. This research proposes alternative refined micromechanics that can generate the local mechanical behavior of fiber and matrix constituents and accurately depict the microstructure. The parametric high-fidelity generalized method of cells (PHFGMC) is an advanced micromechanical method that can be used for the nonlinear and failure analysis of different composite material systems. The computational effort required for generating the nonlinear multiaxial behavior is relatively small, depending on the size of the discretized repeating unit cell (RUC). However, it is computationally challenging, if not impossible, to integrate refined nonlinear micromechanical models within a multiscale finite element (FE) analysis of composite structures. To that end, we propose a new artificial neural network (ANN) based micromechanical modeling framework, termed ANN-PHFGMC, for depicting the nonlinear behavior of fiber-reinforced polymeric (FRP) materials. Pre-simulated mechanical stress-strain responses and behaviors are determined using the PHFGMC to generate a multiaxial training database for the ANN micromodel. The PHFGMC effective stress-strain responses for different applied multiaxial strain paths are divided into two sets of data; one for the training and the other for verifying the trained ANN-PHFGMC model. The resulting trained ANN-PHFGMC is accurate, with less than a 5% error in the verified predictions. Next, the ANN-PHFGMC model can be integrated within a commercial explicit FE code for multiscale low-velocity impact (LVI) analysis of laminated composite plates.
AB - Multiscale nonlinear micromechanical approaches can analyze laminated composite structures. In contrast, current classical macromechanical modeling approaches depict composite materials as anisotropic homogenized media. This research proposes alternative refined micromechanics that can generate the local mechanical behavior of fiber and matrix constituents and accurately depict the microstructure. The parametric high-fidelity generalized method of cells (PHFGMC) is an advanced micromechanical method that can be used for the nonlinear and failure analysis of different composite material systems. The computational effort required for generating the nonlinear multiaxial behavior is relatively small, depending on the size of the discretized repeating unit cell (RUC). However, it is computationally challenging, if not impossible, to integrate refined nonlinear micromechanical models within a multiscale finite element (FE) analysis of composite structures. To that end, we propose a new artificial neural network (ANN) based micromechanical modeling framework, termed ANN-PHFGMC, for depicting the nonlinear behavior of fiber-reinforced polymeric (FRP) materials. Pre-simulated mechanical stress-strain responses and behaviors are determined using the PHFGMC to generate a multiaxial training database for the ANN micromodel. The PHFGMC effective stress-strain responses for different applied multiaxial strain paths are divided into two sets of data; one for the training and the other for verifying the trained ANN-PHFGMC model. The resulting trained ANN-PHFGMC is accurate, with less than a 5% error in the verified predictions. Next, the ANN-PHFGMC model can be integrated within a commercial explicit FE code for multiscale low-velocity impact (LVI) analysis of laminated composite plates.
UR - http://www.scopus.com/inward/record.url?scp=85178629826&partnerID=8YFLogxK
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AN - SCOPUS:85178629826
T3 - Proceedings of the American Society for Composites - 38th Technical Conference, ASC 2023
SP - 312
EP - 321
BT - Proceedings of the American Society for Composites - 38th Technical Conference, ASC 2023
A2 - Maiaru, Marianna
A2 - Odegard, Gregory
A2 - Bednarcyk, Brett
A2 - Pineda, Evan
PB - DEStech Publications
T2 - 38th Technical Conference of the American Society for Composites, ASC 2023
Y2 - 18 September 2023 through 20 September 2023
ER -