| 1 | Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis显示文摘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. | Jaimyun Jung Juwon Na Hyung Keun Park Jeong Min Park Gyuwon Kim Seungchul Lee Hyoung Seop Kim | 2021 | npj Computational Materials2021，，1： | 0 |
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