FiberFind Module

The FiberFind module

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The FiberFind module is the next step towards precise object recognition in micro-CT images, combining the power of fast parameter prediction with the unsurpassed precision and speed of neural networks for accurate fiber and binder detection. FiberFind embodies the work in GeoDict that aims to understand 3D scans of fibrous materials, like nonwovens and fibrous composites.

3D models are obtained after importing and segmenting computer tomography or FIB/SEM scans of the material, and three distinct approaches are followed in FiberFind:

  • Identification of individual fibers by classical image processing methods
  • Analysis of statistical properties of fibers: Diameter distribution, orientation distribution, curvature distribution
  • FiberFind-AI and BinderFind-AI: Identification of individual fibers and binder by Artificial Intelligence (AI) approaches

By classical image processing methods, pores are separated from solids. The solids consist of individual fibers and (possibly) some binder which usually have the same gray values in the images, so that gray value-based identification is not possible. FiberFind makes possible to differentiate binder from fibers (BinderFind-AI) and, then, to identify and separate the fibers (FiberFind-AI) automatically through trained Neural Networks.

The results of FiberFind separation and identification can be used as input to reproduce isotropic and anisotropic fibrous structures using the FiberGeo module. By being used together, FiberFind and FiberGeo are intended to close the digital fibrous material design loop and to help in reducing the number of laboratory experiments needed.

Subsequently, the statistical parameters of the modelled structure may be easily varied to investigate the effect of material structure on the performance of materials by using one of the property prediction modules (-Dict) in GeoDict. For example, the fiber orientation can be computed for each material voxel, producing an orientation field. This makes possible to simulate materials with transverse isotropic properties in studies with the ElastoDict (mechanics) and ConductoDict (thermal conductivity) modules.

FiberFind is particularly well suited for the analysis in fibrous structures made of long non-hollow fibers with circular cross-section.

Tutorials showing the possibilities of FiberFind for two application cases are available:

  • Analysis of different carbon paper gas diffusion layers used for a proton-exchange membrane fuel cell. Download
  • (Not ready!) Identification of fibers of a glass-fiber reinforced polymer, together with the identification of pores with PoroDict. Download
The tutorials require a password to unzip. Click and request it by email

FiberFind features

  • The fiber diameter estimation computes the average fiber diameter, as well as its standard deviation which can be sufficient for unimodal distributions. More detailed results are provided in the form of a diameter histogram plotting the fiber diameter vs. the volume fraction of fibers of that diameter. Fiber diameter distributions (discrete or continuous) can then be entered in the FiberGeo module to reproduce structure models with matching distributions.

  • The orientation distribution computes an orientation tensor characterizing the orientation of fibers. This analysis can be performed globally, over the whole sample, or for individual sub-regions in the model. The latter functionality can be used e.g. to analyze each layer of a layered material separately or to study heterogeneity in fiber orientation across the sample volume. As explained before for fiber diameter, the orientation tensor can be entered directly into the FiberGeo module to reproduce structures with those orientation distributions. Orientations can also be computed per voxel and stored as a 3D orientation field. This field can be loaded into the ElastoDict or ConductoDict module to consider transverse isotropic material behavior (different material constants along vs. across the fiber).

  • The curvature estimation produces a histogram of fiber curvatures by extracting individual fibers of the micro-CT image.

  • BinderFind-AI and FiberFind-AI are based on neural networks trained to differentiate between fibers and binder and to identify separated fibers. The unique structure generation capabilities of GeoDict (FiberGeo module) provide the ground truth data to train the neural networks.

Examples of FiberFind applications

  • Modelling: Together with the FiberGeo module, in material modelling, to create structure models matching a physical sample.
  • Analysis of binder content: Separate binder from fibers in 3D scans and study the effect of varying binder content on the performance of the nonwoven.
  • Material optimization: Reduce experimental costs by studying the predicted effects of a change of material properties before manufacturing material prototypes.
  • Material quality control: For the study of heterogeneities and deviations in the diameter, orientation, and curvature of fibers.

Additional modules needed?

  • The GeoDict Base package is needed for basic functionality.
  • Some installation steps are necessary to set up FiberFind-AI and BinderFind-AI for GPUs and CPUs.
  • The ImportGeo-VOL module is needed to import and segment micro-CT images and create the structure models for analysis.
  • The FiberGeo module can be used to model structures that (statistically) match the analyzed micro-CT images.
  • FiberFind might be needed by other modules, such as ElastoDict and ConductoDict which can use the fiber orientation field computed by FiberFind to enable simulation of transverse isotropic materials.