Integrated application of semantic segmentation-assisted deep learning to quantitative multi-phased microstructural analysis in composite materials: Case study of cathode composite materials of solid oxide fuel cells
Integrated application of semantic segmentation-assisted deep learning to quantitative multi-phased microstructural analysis in composite materials: Case study of cathode composite materials of solid oxide fuel cells
复制标题
DOI：
10.1016/j.jpowsour.2020.228458
复制
发表时间：
2020-09
影响因子：
9.2
通讯作者：
Heesu Hwang;S. Choi;Jiwon Oh;Seung-Muk Bae;Jonghyeok Lee;Jae-Pyeong Ahn;Jeong-O Lee;Ki-Seok An;Young Yoon;Jina Hwang
中科院分区：
文献类型：
--
作者：
Heesu Hwang;S. Choi;Jiwon Oh;Seung-Muk Bae;Jonghyeok Lee;Jae-Pyeong Ahn;Jeong-O Lee;Ki-Seok An;Young Yoon;Jina Hwang
自动语义分割应用于固体氧化物燃料电池（SOFC）三相复合阴极材料（即GDC/LSC/Pore）微观结构特征的量化，其中GDC代表Gd2O3掺杂的CeO2，LSC代表La0.6Sr0.4CoO3-δ。我们的目标是消除人类专家的繁琐参与和相关错误。大量图像信息集是使用自动采集系统生成的，该系统涉及聚焦离子束扫描电子显微镜，通过所谓的切片视图程序。通过将语义分割与图像处理辅助立体摄影工具相结合，可以在没有任何人为参与的情况下自动客观地定量提取以下详细的微观结构特征：基于二维图像的尺寸分布、表面（或等效地，体积）分数、两相边界的长度和三相边界的密度。提取的二维信息与三维重建分析相结合。考虑到能源导向设备中高性能电极结构的有效分析和设计，讨论了语义分割在 SOFC 中的含义。
Automated semantic segmentation is applied to the quantification of microstructural features in three-phase composite cathode materials of solid oxide fuel cells (SOFCs), i.e., GDC/LSC/Pore where GDC stands for Gd2O3-doped CeO2and LSC for La0.6Sr0.4CoO3-δ. Our aim is to eliminate the tedious involvement of human experts and the associated errors. The high volume of image information sets is generated using automatic acquisition systems involving focused-ion beam scanning electron microscopy through a so-called slice-view procedure. Through the integration of semantic segmentation with image processing-assisted stereography tools, the following detailed microstructural features are quantitatively extracted automatically and objectively without any human involvement: size distribution, surface (or equivalently, volume) fraction, lengths of two-phase boundaries, and density of triple-phase boundaries based on two-dimensional images. The extracted two-dimensional information is connected with three-dimensional reconstruction analysis. The implications of semantic segmentation in SOFCs are discussed considering efficient analysis and design of high-performance electrode structures in energy-oriented devices.