npj Computational Materials (Mar 2023)

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

  • Mohammad S. Khorrami,
  • Jaber R. Mianroodi,
  • Nima H. Siboni,
  • Pawan Goyal,
  • Bob Svendsen,
  • Peter Benner,
  • Dierk Raabe

DOI
https://doi.org/10.1038/s41524-023-00991-z
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 10

Abstract

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