Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform
Abstract Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-53865-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850179759085977600 |
|---|---|
| author | Brayan Murgas Joshua Stickel Luke Brewer Somnath Ghosh |
| author_facet | Brayan Murgas Joshua Stickel Luke Brewer Somnath Ghosh |
| author_sort | Brayan Murgas |
| collection | DOAJ |
| description | Abstract Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained prior particles and ultra-fine grains (UFG) and additively manufactured (AM) Ti64 alloys with alpha laths in beta substrates. The paper introduces an approach strategically integrating a Generative Adversarial Network (GAN) for multi-modal microstructures with a synthetic microstructure builder DREAM.3D for packing grains conforming to statistics in electron backscatter diffraction (EBSD) maps for generating SEVMs of CSF and AM alloy microstructures. A robust multiscale model is subsequently developed for self-consistent coupling of crystal plasticity finite element model (CPFEM) for coarse-grained crystals with an upscaled constitutive model for UFGs. Sub-volume elements are simulated for efficient computations and their responses are averaged for overall stress-strain response. The methods developed are important for image-based micromechanical modeling that is necessary for microstructure-property relations. |
| format | Article |
| id | doaj-art-fb9d90fd91f64acc97d42183dadd8bfc |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-fb9d90fd91f64acc97d42183dadd8bfc2025-08-20T02:18:25ZengNature PortfolioNature Communications2041-17232024-11-0115111610.1038/s41467-024-53865-3Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platformBrayan Murgas0Joshua Stickel1Luke Brewer2Somnath Ghosh3Department of Civil & Systems Engineering, Johns Hopkins UniversityDepartment of Civil & Systems Engineering, Johns Hopkins UniversityDepartment of Materials Science & Engineering, University of AlabamaDepartment of Civil & Systems Engineering, Johns Hopkins UniversityAbstract Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained prior particles and ultra-fine grains (UFG) and additively manufactured (AM) Ti64 alloys with alpha laths in beta substrates. The paper introduces an approach strategically integrating a Generative Adversarial Network (GAN) for multi-modal microstructures with a synthetic microstructure builder DREAM.3D for packing grains conforming to statistics in electron backscatter diffraction (EBSD) maps for generating SEVMs of CSF and AM alloy microstructures. A robust multiscale model is subsequently developed for self-consistent coupling of crystal plasticity finite element model (CPFEM) for coarse-grained crystals with an upscaled constitutive model for UFGs. Sub-volume elements are simulated for efficient computations and their responses are averaged for overall stress-strain response. The methods developed are important for image-based micromechanical modeling that is necessary for microstructure-property relations.https://doi.org/10.1038/s41467-024-53865-3 |
| spellingShingle | Brayan Murgas Joshua Stickel Luke Brewer Somnath Ghosh Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform Nature Communications |
| title | Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform |
| title_full | Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform |
| title_fullStr | Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform |
| title_full_unstemmed | Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform |
| title_short | Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform |
| title_sort | modeling complex polycrystalline alloys using a generative adversarial network enabled computational platform |
| url | https://doi.org/10.1038/s41467-024-53865-3 |
| work_keys_str_mv | AT brayanmurgas modelingcomplexpolycrystallinealloysusingagenerativeadversarialnetworkenabledcomputationalplatform AT joshuastickel modelingcomplexpolycrystallinealloysusingagenerativeadversarialnetworkenabledcomputationalplatform AT lukebrewer modelingcomplexpolycrystallinealloysusingagenerativeadversarialnetworkenabledcomputationalplatform AT somnathghosh modelingcomplexpolycrystallinealloysusingagenerativeadversarialnetworkenabledcomputationalplatform |