Algorithm to determine orientation distribution function from microscopic images of fibrous networks: Validation with...

Area of Science:

• Materials Science
• Computational Modeling
• Image Analysis

Background:

• Accurate characterization of fibre orientation in random fibrous networks (RFNs) is vital for predicting material properties and performance.
• Existing 2D methods lack information on fibre orientation in the thickness direction, which is critical for dense or thick materials.
• Understanding fibre orientation impacts applications in fields like additive manufacturing and computational fluid dynamics.

Purpose of the Study:

• To introduce a fully parametric algorithm for computing the 3D fibre orientation distribution function (ODF) in RFNs.
• To evaluate the algorithm's performance on various densities of nonwoven fabrics.
• To validate the algorithm's accuracy using both experimental and virtual fibrous structures.

Main Methods:

• Generation of voxel models from experimental nonwoven webs using X-ray micro-computed tomography (µCT).
• Application of a novel parametric algorithm to compute 3D ODFs from the voxel models.
• Validation using deterministic voxelated virtual fibrous structures generated via mathematical functions.

Main Results:

• The algorithm successfully computed 3D ODFs for low-, medium-, and high-density nonwoven fabrics.
• A statistically significant increase in the fraction of fibres oriented along the thickness direction was observed with increasing fabric density.
• The algorithm demonstrated accuracy in characterizing fibre orientation across different RFN densities.

Conclusions:

• The developed algorithm provides a robust method for quantifying 3D fibre orientation in any RFN.
• Fibre density is a key factor influencing the through-thickness fibre orientation in nonwoven materials.
• This 3D orientation analysis capability enhances predictive modeling for diverse engineering applications.