Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells
- Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells
- 이종호; 안재평; 김홍규; 안준성; 황희수; 이현배; 오지원; 김재환; 윤영; 황진하
- Microstructure; anode; Stereology; Deep learning; SOFC
- Issue Date
- Materials characterization
- VOL 172, 110906
- 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).
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