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).

通过对Ni / Y ₂ O ₃稳定的ZrO ₂进行深度学习和聚焦离子束扫描电子显微镜（FIB-SEM）分析的集成，无需人工干预即可进行定量的微观解释。（Ni / YSZ）金属陶瓷。对Ni / YSZ /孔复合材料进行了分析，以自动提取微观结构参数，以防止主观分析问题和冗长的图像处理任务中经常遇到的不可避免的伪影，并消除了有偏见的评估。考虑到大量的图像数据以及对电子显微镜使用的未来期望，FIB-SEM有效地与语义分割相结合。传统的图像处理分析工具通过语义分割算法与相分离预测相结合，从而对微观结构参数进行定量评估。结合使用这种策略，可以显着提高源自电子显微镜伪像的不良图像质量，包括电荷效应，幕效应，散焦问题，