Physics-Informed Data-Driven Surrogate Modeling for Full-Field 3D Microstructure and Micromechanical Field Evolution of Polycrystalline Materials
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...
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| Veröffentlicht in: | JOM (1989) 2021-11, Vol.73 (11), p.3371-3382 |
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| container_title | JOM (1989) |
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| creator | Pokharel, Reeju Pandey, Anup Scheinker, Alexander |
| description | 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. |
| doi_str_mv | 10.1007/s11837-021-04889-3 |
| format | Article |
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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. 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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. 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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.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11837-021-04889-3</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-0563-6142</orcidid></addata></record> |
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| subjects | Chemistry/Food Science Crystal structure Datasets Deformation Direct numerical simulation Earth Sciences Engineering Environment Evolution Experiments Fourier transforms Informatics-Enabled Design of Structural Materials Inverse problems Machine learning Mathematical functions Mathematical models Microstructure Neural networks Partial differential equations Physics Plastic deformation Quaternions Simulation Strain Tensors |
| title | Physics-Informed Data-Driven Surrogate Modeling for Full-Field 3D Microstructure and Micromechanical Field Evolution of Polycrystalline Materials |
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