Establishing process-structure linkages using Generative Adversarial Networks
Abstract
The microstructure of material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Thus, establishing a Process-Structure-Property relationship is a crucial task in material design and is of interest in many engineering applications. We develop a GAN (Generative Adversarial Network) to synthesize microstructures based on given processing conditions. This approach is devoid of feature engineering, needs little domain awareness, and can be applied to a wide variety of material systems. Results show that our GAN model can produce high-fidelity multi-phase microstructures which have a good correlation with the given processing conditions.
- Publication:
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arXiv e-prints
- Pub Date:
- July 2021
- DOI:
- 10.48550/arXiv.2107.09402
- arXiv:
- arXiv:2107.09402
- Bibcode:
- 2021arXiv210709402S
- Keywords:
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- Condensed Matter - Materials Science;
- Computer Science - Machine Learning
- E-Print:
- 16 pages, 9 figures