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 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.

Automated summary

Digital microstructures are crucial in modern materials research, but their resolution-sensitive features may be overlooked due to inadequate resolution, limiting the accuracy of microstructure characterization and material analysis. Super-resolution imaging based on deep learning can enhance the quality of low-resolution microstructure data, allowing for more accurate microstructure characterization and finite element mechanical analysis.