Hwang, Heesu Ahn, Junsung Lee, Hyunbae Oh, Jiwon Kim, Jaehwan Ahn, Jae-Pyeong Kim, Hong-Kyu Lee, Jong-Ho Yoon, Young Hwang, Jin-Ha 2024-01-19T15:32:15Z 2024-01-19T15:32:15Z 2021-09-02 2021-02 1044-5803 https://pubs.kist.re.kr/handle/201004/117473 Quantitative microstructural interpretations were carried out without human involvement through an integrated combination of deep learning and focused ion beam-scanning electron microscopy (FIB-SEM) analytics on Ni/Y2O3-stabilized ZrO2 (Ni/YSZ) cermets. The Ni/YSZ/pore composites were analyzed for the automated extraction of microstructural parameters to prevent the subjective analysis problems and unavoidable artifacts frequently encountered in lengthy image processing tasks and eliminate biased evaluations. Considering the high volume of image data and future expectations for electron microscopy usage, FIB-SEM was efficiently combined with semantic segmentation. Traditional image processing analysis tools are combined with phase separation predictions by semantic segmentation algorithms, leading to a quantitative evaluation of microstructural parameters. The combined strategy enables one to significantly enhance poor image quality originating from artifacts in electron microscopy, including charging effects, curtain effects, out-of-focus problems, and unclear phase boundaries encountered in searching for high-efficiency solid oxide fuel cells (SOFCs). English ELSEVIER SCIENCE INC Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells Article 10.1016/j.matchar.2021.110906 1 MATERIALS CHARACTERIZATION, v.172 MATERIALS CHARACTERIZATION 172 scie scopus 000620431000005 2-s2.0-85099663630 Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science, Characterization & Testing Materials Science Metallurgy & Metallurgical Engineering Article Deep learning Microstructure features SOFC anode composites Stereology