共 52 条
Refined nonlinear micromechanical models using artificial neural networks for multiscale analysis of laminated composites subject to low-velocity impact
被引:8
作者:
Hochster, Hadas
[1
]
Bernikov, Yevheniia
[1
]
Meshi, Ido
[1
]
Lin, Shiyao
[2
]
Ranatunga, Vipul
[3
]
Shemesh, Noam N. Y.
[4
]
Haj-Ali, Rami
[1
]
机构:
[1] Tel Aviv Univ, Sch Mech Engn, Tel Aviv, Israel
[2] Univ Michigan, Ann Arbor, MI 48109 USA
[3] AF Res Lab, Wright Patterson AFB, OH 45433 USA
[4] IAF Aeronaut Engn Branch, Tel Aviv, Israel
基金:
以色列科学基金会;
关键词:
Micromechanics;
PHFGMC;
Composite;
Artificial Neural Network;
Low -Velocity Impact;
FIDELITY-GENERALIZED-METHOD;
CONSTITUTIVE MODELS;
DAMAGE;
FORMULATION;
PREDICTION;
BEHAVIOR;
TESTS;
CELLS;
D O I:
10.1016/j.ijsolstr.2023.112123
中图分类号:
O3 [力学];
学科分类号:
08 ;
0801 ;
摘要:
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 several composite materials. The computational effort required for studying the nonlinear and damage 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 analysis of composite structures. This is due to the thousands or more RUC models required at the integration points within a multiscale finite-element (FE) model of laminated structures. To that end, we propose a new artificial neural network (ANN) based micromechanical modeling framework, termed ANN-PHFGMC, for exploring the nonlinear behavior of fiberreinforced 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 simulated training data is founded on the PHFGMC-RUC results based on a hexagonal RUC. 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 5% error in the verified predictions. The ANN-PHFGMC can be used as a stand-alone or embedded as a surrogate proxy model within a multiscale analysis of composite structures. Next, the ANNPHFGMC model is integrated within a commercial explicit FE code for low-velocity impact (LVI) analysis of laminated composite plates. Multiscale LVI analyses are performed for two composite plates with different layups. Further, results are compared to experimental data to demonstrate the new model's ability to integrate refined nonlinear micromechanical models within a multiscale analysis.
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页数:12
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