Metadata only
Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
In this study, a neural network model is developed to describe the large deformation response of a multi-phase material, i.e., a two-dimensional perforated plate. Using the finite element, virtual experiments are performed to generate stress–strain data for monotonic biaxial loading paths. Subsequently, a combination of fully connected and recurrent neural network models are trained and validated using the results from the virtual experiments. The predictions of a network show a remarkable good agreement with all the experimental data. The suggested neural network-based constitutive model does provide a robust solution to the problem at hand, providing a fully anisotropic, three-dimensional material model capable of covering all physical material properties. The suggested procedure promises to be generally applicable to any material class and can be paired with any numerical method. Show more
Publication status
publishedExternal links
Book title
Forming the FutureJournal / series
The Minerals, Metals & Materials SeriesPages / Article No.
Publisher
SpringerEvent
Subject
Artificial intelligence; Fully connected neural network; Recurrent neural network; Plasticity; Multi-phase materialOrganisational unit
09473 - Mohr, Dirk / Mohr, Dirk
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ETH Bibliography
yes
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