Adaptive Clustering SOFC Image Segmentation Based on Particle Swarm Optimization

Microstructural parameters are important for analyzing the chemistry and performance of solid oxide fuel cells (SOFCs). Aiming at the YSZ / Ni anode optical microscopy (OM) image of SOFC, in this paper, particle swarm intelligent optimization algorithm is used to improve the fuzzy C-means clustering algorithm for image segmentation. Particle swarm optimization is used to adaptively search the initial clustering center, helping to avoid local optimization and preserve more image detail. The experimental results show that the proposed method can improve the segmentation accuracy of images. At the same time, it can accurately segment the SOFC three-phase and provide effective image segmentation results for the microstructure parameters.