Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
| Autor: | Tim Hsu, Gregory A. Hackett, Hokon Kim, William K. Epting, Paul A. Salvador, Harry Abernathy, Anthony D. Rollett, Elizabeth A. Holm |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Surface (mathematics)
Materials science 0211 other engineering and technologies General Engineering Boundary (topology) 02 engineering and technology 021001 nanoscience & nanotechnology Microstructure Tortuosity Finite element method Distribution (mathematics) Volume fraction General Materials Science Solid oxide fuel cell 0210 nano-technology Biological system 021102 mining & metallurgy |
| Zdroj: | JOM. 73:90-102 |
| ISSN: | 1543-1851 1047-4838 |
| DOI: | 10.1007/s11837-020-04484-y |
| Popis: | Using a large-scale, experimentally captured 3D microstructure data set, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple-phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN-generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. The ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink