Deep learning for full-feature X-ray microcomputed tomography segmentation of proton electron membrane fuel cells. (May 2022)

• Record Type: Journal Article
• Authors: Tang, Kunning; Meyer, Quentin; White, Robin; Armstrong, Ryan T.; Mostaghimi, Peyman; Da Wang, Ying; Liu, Shiyang; Zhao, Chuan; Regenauer-Lieb, Klaus; Tung, Patrick Kin Man
• Is Part Of: Computers & chemical engineering. Volume 161(2022)
• Publication Date: 2022-05
• Subjects: Proton exchange membrane fuel cell -- X-ray micro-computed tomography -- Image segmentation -- Deep learning -- Convolutional neural network -- Gas diffusion layer; PEMFC proton exchange membrane fuel cell -- MPL microporous layer -- GDL gas diffusion layer -- CNN convolutional neural network -- U-ResNet a novel convolutional neural network suited for image segmentation -- mIoU mean intersection of union; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2022.107768
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Proton exchange membrane fuel cell materials 3D structure are captured by micro-CT. A symmetrical U-ResNet convolutional neural network model is used for segmentation. CNN segmentation outperforms the manual segmentation, especially for carbon fibre and binder. CNN-based segmentation captures realistic fuel cell structure parameters. Abstract: This study demonstrates the benefit of convolutional neural networks to accurately classify the different materials of proton exchange membrane fuel cells using X-ray micro-computed tomography. Nineteen 2D orthoslices from a 3D tomography dataset were segmented with high quality and used to train a novel U-ResNet convolutional neural network (CNN) to segment the complete volume. The results were compared with a 3D manual segmentation performed under time constraints. The CNN segmented all phases with equal or greater accuracy in comparison to the manual segmentation. In particular, the CNN excelled in separating the carbon fibres and binder phase in the gas diffusion layer, which is usually completely avoided due to difficulty. Further, permeability calculations were performed on the binder void space for both segmentations, with the CNN displaying realistic results. Therefore, CNNs have been shown to be a viable and valuable method in segmenting such fuel cells with increased efficiency and accuracy.