A review of image processing and quantification analysis for solid oxide fuel cell
作者机构：Faculty of Electrical Engineering&TechnologyUniversiti Malaysia PerlisPauh Putra CampusPauh02600PerlisMalaysia Faculty of Mechanical Engineering&TechnologyUniversiti Malaysia PerlisPauh Putra CampusPauh02600PerlisMalaysia Faculty of Information Science&TechnologyUniversiti Kebangsaan MalaysiaBangi43600SelangorMalaysia
出 版 物：《Energy and AI》 (能源与人工智能（英文）)
年 卷 期：2024年第16卷第2期
页 面：464-482页
核心收录：
主 题：Digital image processing Deep learning Triple phase boundary Solid oxide fuel cell Microstructure
摘 要：The purpose of this study is to investigate the approaches applied to analyze solid oxide fuel cell (SOFC) microstructural properties. Both manual and automated image processing approaches applied on SOFC microstructural images which are obtained from several types of tomography such as dual-beam focused ion beam with scanning electron microscopy (FIB-SEM), Electron Backscatter Diffraction (EBSD) and others are discussed. In fact, to achieve a realistic and accurate SOFC microstructural properties, such as average diameter, volume fraction, triple phase boundary (TPB), area interface density and tortuosity factor, the approaches of image processing and quantification are crucial for a reliable image generation for quantification purposes. The microstructural properties are optimized to improve SOFC electrode performance. Therefore, the image processing and quantification approaches are outlined and reviewed. Despite the automated image processing and quantification algorithms significantly outperform manual image processing and quantification approaches in terms of computing speed when evaluating and measuring microstructural properties, the efficiency and productivity are still extremely taken into concern. As a result, image processing and quantification approaches are concluded and presented respectively in this paper.
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