- Conference Paper
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
Book titleForming the Future
Journal / seriesThe Minerals, Metals & Materials Series
Pages / Article No.
SubjectArtificial intelligence; Fully connected neural network; Recurrent neural network; Plasticity; Multi-phase material
Organisational unit09473 - Mohr, Dirk / Mohr, Dirk
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