Details
| Original language | English |
|---|---|
| Article number | 116293 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 415 |
| Early online date | 7 Aug 2023 |
| Publication status | Published - 1 Oct 2023 |
Abstract
Keywords
- Deep-Learning, Finite element, Nanocomposite, Recurrent neural network, Viscoelasticity-viscoplasticity
ASJC Scopus subject areas
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
- Physics and Astronomy(all)
- General Physics and Astronomy
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Computational Mechanics
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In: Computer Methods in Applied Mechanics and Engineering, Vol. 415, 116293, 01.10.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A machine learning-based viscoelastic–viscoplastic model for epoxy nanocomposites with moisture content
AU - Bahtiri, Betim
AU - Arash, Behrouz
AU - Scheffler, Sven Sigo
AU - Jux, Maximilian
AU - Rolfes, Raimund
N1 - This work originates from the following research project: “Challenges of industrial application of nanomodified and hybrid material systems in lightweight rotor blade construction” (“HANNAH - Herausforderungen der industriellen Anwendung von nanomodifiziertenund hybriden Werkstoffsystemen im Rotorblattleichtbau”), funded by the Federal Ministry for Economic Affairs and Energy, Germany. The authors wish to express their gratitude for the financial support.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress–strain relationship and consistent tangent moduli. In addition, the DL-based constitutive model is implemented into finite element analysis. Finite element simulations are performed to study the effect of load rate and moisture content on the force–displacement response of nanoparticle/epoxy samples. Numerical examples show that the computational efficiency of the DL model depends on the loading condition and is significantly higher than the conventional constitutive model. Furthermore, comparing numerical results and experimental data demonstrates good agreement with different nanoparticle and moisture contents.
AB - In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress–strain relationship and consistent tangent moduli. In addition, the DL-based constitutive model is implemented into finite element analysis. Finite element simulations are performed to study the effect of load rate and moisture content on the force–displacement response of nanoparticle/epoxy samples. Numerical examples show that the computational efficiency of the DL model depends on the loading condition and is significantly higher than the conventional constitutive model. Furthermore, comparing numerical results and experimental data demonstrates good agreement with different nanoparticle and moisture contents.
KW - Deep-Learning
KW - Finite element
KW - Nanocomposite
KW - Recurrent neural network
KW - Viscoelasticity-viscoplasticity
UR - http://www.scopus.com/inward/record.url?scp=85169621887&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2305.08102
DO - 10.48550/arXiv.2305.08102
M3 - Article
VL - 415
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
M1 - 116293
ER -