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Physics-Informed Data-Driven Surrogate Modeling for Full-Field 3D Microstructure and Micromechanical Field Evolution of Polycrystalline Materials

  • Informatics-Enabled Design of Structural Materials
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Abstract

We have developed a machine learning-based crystal plasticity surrogate model (CP-SM) that can directly learn highly nonlinear material behavior during plastic deformation. CP-SM provides fast inference of spatially resolved three-dimensional (3D) microstructure and micromechanical fields and their evolution during plastic deformation, predicting the 22-dimensional material characteristics including a four-dimensional (4D) quaternion-based representation of crystal orientation, six-dimensional (6D) elastic and plastic strain tensors, and 6D stress at each location in the 3D structure. The predictions from CP-SM are orders of magnitude faster than and show good agreement with the deformation fields predicted by performing direct numerical simulations using spectral solvers. The fidelity of the CP-SM is further tested by assessing how well physics-based constraints are satisfied by the predicted 3D fields. We demonstrate our results on numerical simulations of uniaxially loaded copper.

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Acknowledgements

This work was supported by the U.S. Department of Energy through Los Alamos National Laboratory. Los Alamos National Laboratory (LANL) is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the US Department of Energy (Contract No. 89233218CNA000001).

Funding

The work was funded by LANL’s LDRD-ECR Project No. 20190571ECR.

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Authors and Affiliations

  1. Los Alamos National Laboratory, Materials Science and Technology Division, Los Alamos, NM, 87545, USA

    Reeju Pokharel & Anup Pandey

  2. Accelerator Operations and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

    Alexander Scheinker

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  1. Reeju Pokharel
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  2. Anup Pandey
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  3. Alexander Scheinker
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Correspondence to Reeju Pokharel.

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Pokharel, R., Pandey, A. & Scheinker, A. Physics-Informed Data-Driven Surrogate Modeling for Full-Field 3D Microstructure and Micromechanical Field Evolution of Polycrystalline Materials. JOM 73, 3371–3382 (2021). https://doi.org/10.1007/s11837-021-04889-3

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