Identification and analysis of fibers in ultra-large micro-CT scans of nonwoven textiles using deep learning
Andreas Grießer, Rolf Westerteiger, Erik Glatt, Hans Hagen, Andreas Wiegmann · Journal of the Textile Institute · 2022
Fibrous materials play a significant role in many industries, such as lightweight automotive materials, filtration, or as constituents of hygiene products. The properties of fibrous materials are governed to a large extent by their microstructure. One way to access the microstructure is to use micro-Computed Tomography (micro-CT). Completely characterizing the microstructure requires geometrically characterizing each individual fiber. To make this possible, one must identify the individual fibers. Our method achieves this by finding in segmented µCT scans the centerline of all individual fibers. It uses a convolutional neural network that was trained on automatically generated synthetic training data. From the centerlines, analytic descriptions of the individual fibers are constructed. These analytic representations allow detailed insights into the statistics of the geometric properties of the fibrous material, such as the fibers’ orientation, length, or curvature. The method is validated on artificial data sets and its usefulness demonstrated on a very large micro-CT scan of a nonwoven composed of long fibers with random curvature.