Modeling of Fiber Structures
GeoApp: Generate Nonwoven Statistical Digital Twin
The GeoApp Generate Nonwoven Statistical Digital Twin was originally designed to create digital twins of nonwoven structures. Today, however, it is much more than that: it is equally suitable for modeling a wide range of fiber-based materials, including fiber-reinforced composites, fiber-based gas diffusion layers in fuel cells, and various types of filter media.
Before starting digital twin generation, the original sample is first prepared in GeoDict. This process includes importing and segmenting the target structure using ImportGeo-Vol, followed by analysis with FiberFind-AI or, alternatively, directly beginning the analysis of a pre-segmented sample.
Although creating structures with curved fibers is a challenging task, GeoApp makes it straightforward by automatically optimizing the digital twin to match with the stochastic geometric properties of the original material.
The process begins by selecting the target results file IdentifyFibers.gdr (by FiberFind-AI) and setting up the optimization parameters. GeoApp maintains fixed values for key characteristics, such as fiber diameter, length, orientation, and fiber volume fraction, while dynamically adjusting fiber curvature and curliness. This ensures that the final digital twin accurately replicates the unique features of the original sample.
GeoDict Publications
[1] Grießer A., Westerteiger R., Glatt E., Hagen H., Wiegmann A., Identification and analysis of fibers in ultra-large micro-CT scans of nonwoven textiles using deep learning, The Journal of The Textile Institute, 114(11), 1647–1657, 2023. https://www.tandfonline.com/doi/full/10.1080/00405000.2022.2145429
[2] Grießer A., Westerteiger R., Glatt E., Hagen H., Wiegmann A., Deep learning based segmentation of binder and fibers in gas diffusion layers, Next Materials, Volume 6, January 2025, 100411. https://www.sciencedirect.com/science/article/pii/S2949822824003083
Optimization of through-thickness density distribution
For fiber structures with increased complexity, additional digital twin optimization can be performed based on through-thickness density distribution. To achieve this, use an extra results file, Thickness Estimation.gdr, from the MatDict module. This file provides 1D statistical data on the density distribution of the original structure, which can guide further refinement of the digital twin.
Following modules are often used in combination with the "Generate Nonwoven Statistical Digital Twin" GeoApp:
Import & Image Processing | ImportGeo-Vol | |
Image Analysis | FiberFind-AI | MatDict |
Material Modeling | FiberGeo | |
Simulation & Prediction | ||
Interfaces |