Summary of Accounting For Plasticity: An Extension Of Inelastic Constitutive Artificial Neural Networks, by Birte Boes et al.
Accounting for plasticity: An extension of inelastic Constitutive Artificial Neural Networks
by Birte Boes, Jaan-Willem Simon, Hagen Holthusen
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers introduce a new type of artificial neural network called Constitutive Artificial Neural Networks (CANNs), which have been shown to be effective in predicting elastic and inelastic material behavior. The focus of the study is on extending and applying CANNs to capture plasticity, including predicting elastic and plastic Helmholtz free energies, an inelastic flow rule, and a yield condition that defines the onset of plasticity. The researchers use four feed-forward networks combined with a recurrent neural network and train the model using the second Piola-Kirchhoff stress measure. The resulting formulation captures both associative and non-associative plasticity, as well as kinematic hardening effects. This allows for a wider range of applications to different materials. The paper demonstrates the capabilities of the framework by training on artificially generated data and comparing results to experimental data from X10CrMoVNb9-1 steel. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how artificial neural networks, specifically Constitutive Artificial Neural Networks (CANNs), can be used to understand plasticity in materials. The researchers extended CANNs to capture plasticity, including predicting how materials will behave under different loads and conditions. They tested their model using computer-generated data and found it matched real-world data from a type of steel. This could help us better understand and predict the behavior of many different materials. |
Keywords
» Artificial intelligence » Neural network
