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

نویسندگان

چکیده

Abstract The purpose of this work is the development a trained artificial neural network for surrogate modeling mechanical response elasto-viscoplastic grain microstructures. To end, U-Net-based convolutional (CNN) using results von Mises stress field from numerical solution initial-boundary-value problems (IBVPs) equilibrium in such microstructures subject to quasi-static uniaxial extension. resulting CNN (tCNN) accurately reproduces about 500 times faster than solutions corresponding IBVP based on spectral methods. Application tCNN test cases microstructure morphologies and boundary conditions not contained training dataset also investigated discussed.

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ژورنال

عنوان ژورنال: npj computational materials

سال: 2023

ISSN: ['2057-3960']

DOI: https://doi.org/10.1038/s41524-023-00991-z