Autor bzw. Ersteller  Blarr, Juliane 

Schlagwörter  Fiber reinforced polymer, Fiber orientation tensor, Machine Learning, Artificial neural network, Computer tomography, Spatial interpolation, Linear Algebra, Scale bridging, Scarce data, Quaternions, Deep Learning 
Klassifizierungen 

Art der Forschungsdaten  Dataset 
Erstellungsjahr  2022 
Herausgeber  KITBibliothek 
Jahr der Veröffentlichung  2022 
DOI  10.5445/IR/1000153725 
Abstract 
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 (LFTD) 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 preprocessing 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 .datfiles 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/27462diffusiontensorfielddtivisualization.

Lizenz  CC BYNCSA 4.0 
Liesmich 
In the folder "code" there are three Python scripts. The "component_averaging_method.py" and the "decomposition_method.py" work the same: The script needs an input .txtfile with coordinates and the corresponding fiber orientation tensors (the example used in the publication is given (file "Input_file_FOT.txt")). After running the code you are asked in the console for the name of the output file and for lower and upper x and y limit, which are 1 and 13, respectively, in the given case. The scripts then calculate the fiber orientation tensors at all missing positions with the respective method, which are then written into a MATLAB file (which is named the way you input in the console). This MATLAB file is structured in a way that the fiber orientation tensors can be plotted directly with the tensor glyph visualization function of Barmpoutis ("plotDTI") given in the abstract. The Jupyter Notebook "ANN_method.ipynb" works a bit differently as it is an artificial neural network. There, .csvfiles are needed as input data. The components of the tensors are given to the network in separate files and the coordinates of the positions in another separate .csvfile. This is all documented in the paper as well. The output again is a .csvfile that has to be transferred into MATLAB if users want to use the same visualization function. The folder "scans_and_FOT" includes all nine scans and respective fiber orientation tensors used for the publication. The scans are given as .mhd and .rawfiles, the orientation tensors are given in the .datfiles. To generate the fiber orientation tensors from the images, the code by Pinter et al., which is given in the abstract, was used. This C++ code writes out a vector valued image with the orientations per voxel. From this, again with another MATLAB file, which composes the orientation tensor from the vectorvalued image, these .dat files can be generated. As this is not the main focus of the publication, and the functionality of the python scripts can be verified with the given orientation tensors, this MATLAB script is not part of this dataset. Please consider the paper or contact the author Juliane Blarr for further questions.

Zugriffszähler  869 

Downloadzähler  14 
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Name  Dateigröße  Hochgeladen  Prüfsumme (MD5) 

ANN_method.ipynb  69.48 MiB  21.12.22 14:24:07  09da8486fde083a2b859851d900a98b1 
Input_file_FOT.txt  1.07 KiB  21.12.22 14:24:06  e5e7b336c5f7f0e56d8049098409d23d 
LL.mhd  166 Byte  21.12.22 14:24:04  91137b6b8730e16685376458e81d9333 
LL.raw  1.16 GiB  21.12.22 14:23:23  55f5c761a22a9e94e1cd69ad7f44d298 
LL_OT.dat  472 Byte  21.12.22 14:23:23  5fc3ce92a64db17d5ab80298eb0e5299 
LM.mhd  164 Byte  21.12.22 14:23:21  974fc03a7fef3cd2ca5810809dc496c2 
LM.raw  251.91 MiB  21.12.22 14:23:11  9d1326639021d9410a5775ac29fa400e 
LM_OT.dat  526 Byte  21.12.22 14:23:09  80b02a217a11193c3719a56fc8adb72f 
LR.mhd  166 Byte  21.12.22 14:23:09  6ea912f17bcbaa26c2174085429b7fa9 
LR.raw  2.11 GiB  21.12.22 14:21:29  71df55f8e8bb3735a291a86b9806ce5d 