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

Abstract

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 CeO2 and 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.