Integrating deep learning and geometry-based microstructural analysis of bamboo technical fibres for bio-composite engineering [* Cross-Reference *]

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Garcia-Palacio, Lina; Cardona, Carlos I.; Alzate, Jesús Alejandro; Dieden, Reiner; Delfrari, Doriane; Cardona-Morales, Oscar; Bravo-Ortiz, Mario; Fuentes, C. A., 2026, "Integrating deep learning and geometry-based microstructural analysis of bamboo technical fibres for bio-composite engineering [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.8UR2BW, LIST Open Repository, V1
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Description
Hierarchical microstructural analysis of plant extracted technical fibres, such as bamboo, is critical for advancing sustainable composite development in what concerns to geometric modelling, properties prediction, and manufacturing optimization. Precise segmentation of anatomical phases such as elementary fibres, middle lamella, and lumen is essential for quantifying morphological gradients and structural heterogeneity. These aspects directly influence interfacial stress transfer mechanisms and the mechanical performance in natural fibre composites. Yet conventional threshold-based methods struggle with the overlapping boundaries, anisotropic growth patterns, and high variability inherent to natural fibres. This study systematically evaluates five deep learning segmentation models: RootPainter, U-Net, SwinUNETR, SegFormer, and the Segment Anything Model (SAM) using high-resolution optical micrographs of bamboo fibre cross-sections. RootPainter achieved the highest performance (Dice: 0.9096; IoU: 0.8341), producing spatially coherent segmentation masks that preserved fine structural details, followed by U-Net (Dice: 0.8707; IoU: 0.7758). The best performing model was subsequently applied to automated morphometric analysis, enabling anatomical classification, structural area quantification, and interactive visualization through a web-based interface. Critically, this approach enables quantification of the middle lamella sub-micron inter cellular bonding layer which is unresolvable by conventional µCT imaging. Middle lamella area fractions alongside elementary fibre wall and lumen areas provide essential input parameters for micromechanical stress transfer and stiffness prediction models. The framework reveals systematic morphological gradients across fibre cross sections, reflecting the functionally graded microstructure optimized for bending loading. By minimizing dependence on manual thresholding and improving reproducibility, this framework advances precise morphological characterization, supports accurate micromechanical modelling through phase-resolved geometric data. (2026-10-01)
***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1016/j.compositesa.2026.109949 for the original and latest version of the dataset and data downloads*** (2026-06-15)
Subject Physics
Keyword BambooFibre, CompositeMaterials, Crosssection, DeepLearning, microstructure
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