3D µCT images of specimens of carbon fiber reinforced polyamide 6 plaque, fiber orientation tensor data of these images, and three Python code files for two different algebraic and one machine learning based tensor interpolation algorithms

This dataset includes 3D µCT images of nine different specimen of 10 mm \times 10 mm of a carbon fiber reinforced polyamide 6 plaque produced in the long fiber reinforced thermoplastic direct (LFT-D) process. The position of the specimen in the plaque can be learned from the referenced publication (Blarr et al., Implementation and comparison of algebraic and machine learning based tensor interpolation methods applied to fiber orientation tensor fields obtained from CT images, Computational Materials Science, 2022). After small pre-processing steps, the fiber orientation tensor of each of the image stacks is determined with the help of the structure tensor based implementation of Pinter et al. The code can be found here: https://sourceforge.net/p/composight/code/HEAD/tree/trunk/SiOTo/StructureTensorOrientation/FibreOrientation/StructureTensorOrientationFilter.cxx#l186. Hence, nine .dat-files containing the fiber orientation tensor of second order are also included in this dataset. Most importantly, this dataset contains three different Python codes. The author implemented a different interpolation method in each of those codes; two algebraic and one machine learning based one. The component averaging method is the simplest; the decomposition method is mathematically more difficult. It works with the decomposition of the tensor into shape and orientation and subsequent separate invariant and quaternion weighting, before reassembling the then interpolated tensor. The deep learning based method is the only Jupyter notebook in this dataset, where an ANN is implemented for the same interpolation task. Please consider the reference paper mentioned before for details. For the visualization of the tensor glyphs, a Matlab function by Barmpoutis is used, which can be found here: https://de.mathworks.com/matlabcentral/fileexchange/27462-diffusion-tensor-field-dti-visualization.