The GeoDict-AI module covers the functionalities in GeoDict that aim to reconstruct 3D models obtained from segmented computer tomography or FIB/SEM images of different multi-component materials, e.g. nonwovens and electrodes.

The GeoDict-AI module offers the possibility of using Artificial Intelligence (AI) approaches to separate the different materials during image segmentation.

Often, different materials, as for example binder and fibers, have the same gray values in the images. Therefore, an automatic separation based on the gray value is not possible. However, it is possible to separate the different materials based on their shape in the image. A neural network can be trained to identify binder in a grain structure, to differentiate two fiber types with different shapes or even to identify the individual fibers.

GeoDict-AI is the starting point to analyze physical properties on the segmented 3D-scans considering the different materials. GeoDict-AI can be used for nearly every structure that can be generated with the GeoDict structure generator modules, e.g. grain structures with binder in GrainGeo or fiber structures in FiberGeo.

Start with inspecting 3D samples of the structures to analyze all available statistical information to find the right parameter ranges for the training structures. Then, create a Generation Script (.py) using the GeoDict structure generator modules. Select the right parameter ranges to match the statistical properties for the considered materials that should be distinguished, e.g. the object diameters, the object orientations, and the solid volume percentages.

Set the Design of Experiments defining the number of training structures and the varying parameter ranges according to inspected 3D samples.

Create the actual Training Data and Testing Data with the defined parameter ranges based on the design of experiments.

Use the training data to Train a Neural Network that can identify the target material or the individual fibers in every segmented 3D-scan containing materials matching the statistical properties caught in the training data.

Validate the Performance of the trained neural network by applying it to all generated test structures.

Finally, use Apply Neural Network to apply the trained network to segmented 3D-scans and identify the desired target material.

Find detailed theory about the underlying model and all parameters in the GeoDict-AI User Guide.