Abstract
The mechanical properties of composite fiber materials are highly dependent on the orientation of the fibers. Micro-CT enables acquisition of high-resolution 3D images, where individual fibers are visible. However, manually extracting orientation information from the samples is impractical due to the size of the 3D images. In this paper, we use a Structure Tensor to extract orientation information from a large 3D image of non-crimp glass fiber fabric. We go through the process of segmenting the image and extracting the orientation distribution step-by-step using structure tensor and show the results of the analysis of the studied non-crimp fabric. The Jupyter notebooks and Python code used for the data-analysis are publicly available, detailing the process and allowing the reader to use the method on their own data. The results show that structure tensor analysis works well for determining fiber orientations, which has many useful applications.
Highlights
Fiber reinforced polymer matrix composites are used in many structural applications due to their excellent stiffness, strength, and fatigue properties and relatively low weight. These properties are very sensitive to the fiber orientations, and characterizing fiber orientations is an important part of quality control
The results shown here are available in the HF401TT-13 FoV16.5 Stitch Jupyter notebook and can be reproduced by running the code in the notebook [5]
We have described how to do this step by step and included three Jupyter notebooks and a Python module demonstrating the procedure
Summary
Fiber reinforced polymer matrix composites are used in many structural applications due to their excellent stiffness, strength, and fatigue properties and relatively low weight. To extract orientation information about the fibers, one approach is to track every fiber individually [1] This can provide detailed information about the fibers, but often requires some user interaction and can be very computationally demanding, making it less attractive for large data-sets. It is often not the location of the individual fibers but just the fiber orientation in each material point which is of relevance. An approach addressing this is the voxel-based Structure Tensor method [2, 3, 4].
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