Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells

Title
Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells
Authors
이종호안재평김홍규안준성황희수이현배오지원김재환윤영황진하
Keywords
Microstructure; anode; Stereology; Deep learning; SOFC
Issue Date
2021-02
Publisher
Materials characterization
Citation
VOL 172, 110906
Abstract
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).
URI
http://pubs.kist.re.kr/handle/201004/72770
ISSN
1044-5803
Appears in Collections:
KIST Publication > Article
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