Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis

The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all re...

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Bibliographic Details
Published in:npj computational materials 2021-06, Vol.7 (1), p.1-11, Article 96
Main Authors: Jung, Jaimyun, Na, Juwon, Park, Hyung Keun, Park, Jeong Min, Kim, Gyuwon, Lee, Seungchul, Kim, Hyoung Seop
Format: Article
Language:English
Subjects:
639/301/1023/303
639/301/930/2735
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Deep learning
Finite element method
Grain boundaries
Heat treating
Image resolution
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Mechanical analysis
Mechanical properties
Microstructure
Stress concentration
Stress distribution
Theoretical
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Summary:The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the microstructures. The resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical modeling. Here, a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution (LR) microstructure data for microstructure characterization and finite element (FE) mechanical analysis. Microstructure characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution (HR) data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution, which can be only partially considered or entirely disregarded in LR data-based analysis.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-021-00568-8