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

Alexandre Fioravante de Siqueira, Daniela Mayumi Ushizima, Stéfan van der Walt

January 15, 2021

Abstract: Fiber-reinforced ceramic-matrix composites are advanced materials resistant to high temperatures, with application to aerospace engineering. Their analysis depends on the detection of embedded fibers, with semi-supervised techniques usually employed to separate fibers within the fiber beds. Here we present an open computational pipeline to detect fibers in ex-situ X-ray computed tomography fiber beds. To separate the fibers in these samples, we tested four different architectures of fully convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients greater than 92.28±9.65%, reaching up to 98.42±0.03%, showing that the network results are close to the human-supervised ones in these fiber beds, in some cases separating fibers that human-curated algorithms could not find. The software we generated in this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains. All data and instructions on how to download and use it are also available.

Featured Fellows

Alex de Siqueira

Assistant Project Scientist, Data Science Outreach Lead

Daniela Ushizima

Computational Research Division, CAMERA, LBNL
Research Affiliate

Stéfan van der Walt

Senior Research Data Scientist