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An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials

MPS-Authors
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Khorrami,  Mohammad Sarkari
Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max Planck Institute for Sustainable Materials GmbH, Max Planck Society;

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Mianroodi,  Jaber Rezaei
Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Hamidi Siboni,  Nima
Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max Planck Institute for Sustainable Materials GmbH, Max Planck Society;

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Goyal,  Pawan Kumar
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Svendsen,  Bob
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Material Mechanics, Faculty of Georesources and Materials Engineering, RWTH Aachen University, Schinkelstraße 2, D-52062 Aachen, Germany ;

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Benner,  Peter
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Raabe,  Dierk
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Sustainable Synthesis of Materials, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

Khorrami, M. S., Mianroodi, J. R., Hamidi Siboni, N., Goyal, P. K., Svendsen, B., Benner, P., et al. (2022). An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials. arXiv.


Cite as: https://hdl.handle.net/21.11116/0000-000F-AFA5-2
Abstract
The purpose of this work is the development of an artificial neural network (ANN) for surrogate modeling of the mechanical response of viscoplastic grain microstructures. To this end, a U-Net-based convolutional neural network (CNN) is trained to account for the history dependence of the material behavior. The training data take the form of numerical simulation results for the von Mises stress field under quasi-static tensile loading. The trained CNN (tCNN) can accurately reproduce both the average response as well as the local von Mises stress field. The tCNN calculates the von Mises stress field of grain microstructures not included in the training dataset about 500 times faster than its calculation based on the numerical solution with a spectral solver of the corresponding initial-boundary-value problem. The tCNN is also successfully applied to other types of microstructure morphologies (e.g., matrix-inclusion type topologies) and loading levels not contained in the training dataset.