| Abstract Scope |
Microstructure reconstruction problems are usually limited to the representation with finitely many numbers of phases, e.g. binary and ternary. However, images of microstructure obtained through experimental, for example, using a microscope, are often represented as an RGB or grayscale image. In this talk, a microstructure reconstruction method based on image inpainting techniques, which produces statistically equivalent microstructures at the fidelity of experiments, is presented without introducing any physics-based microstructure descriptor. The image texture is fully preserved, while the resulting microstructure images are of high quality. A significant advantage of the proposed method is to remedy the data scarcity problem in materials science, where experimental data is scare and hard to obtain. The method is demonstrated using the UltraHigh Carbon Steel micrograph DataBase (UHCSDB). Tran, A., & Tran, H. (2019). Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpainting. Acta Materialia, 178, 207-218. |