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Fiber-reinforced ceramic-matrix composites are advanced materials resistantto high temperatures, with application to aerospace engineering. Their analysisdepends on the detection of embedded fibers, with semi-supervised techniquesusually employed to separate fibers within the fiber beds. Here we present anopen computational pipeline to detect fibers in ex-situ X-ray computedtomography fiber beds. To separate the fibers in these samples, we tested fourdifferent architectures of fully convolutional neural networks. When comparingour neural network approach to a semi-supervised one, we obtained Dice andMatthews coefficients greater than $92.28 \pm 9.65\%$, reaching up to $98.42\pm 0.03 \%$, showing that the network results are close to thehuman-supervised ones in these fiber beds, in some cases separating fibers thathuman-curated algorithms could not find. The software we generated in thisproject is open source, released under a permissive license, and can be freelyadapted and re-used in other domains. All data and instructions on how todownload and use it are also available.

A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks