Summary of A Machine Learning-based Viscoelastic-viscoplastic Model For Epoxy Nanocomposites with Moisture Content, by Betim Bahtiri et al.
A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content
by Betim Bahtiri, Behrouz Arash, Sven Scheffler, Maximilian Jux, Raimund Rolfes
First submitted to arxiv on: 14 May 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a deep learning-based constitutive model to investigate the cyclic behavior of nanoparticle/epoxy nanocomposites under various conditions. A long short-term memory network is trained using a combined framework, which enables accurate capture of rate-dependent stress-strain relationships and consistent tangent moduli. The model is implemented into finite element analysis, allowing for simulations that study the effect of load rate and moisture content on force-displacement response. Numerical results demonstrate improved computational efficiency compared to traditional models, with good agreement between simulation outputs and experimental data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to understand how tiny particles mix with a special kind of glue. The scientists use special computer programs called deep learning networks to simulate how the mixture behaves under different conditions. They test their model by comparing it to real-world experiments, and the results show that their method is faster and more accurate than previous approaches. |
Keywords
* Artificial intelligence * Deep learning
