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March 30, 2026Materials & Design1 citationsOpen Access

Efficient method for numerically generating High-Density wood fiber network microstructure models

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BCBin ChenEOErfan OliaeiLBLars A. Berglund

Key Points

  • The research focuses on developing a method to create realistic 3D microstructure models of high-density wood fiber networks.
  • Proposes a geometrical method for generating 3D HD-WFN models with low porosity.
  • Utilizes a displacement field to compress an initially sparse structure.
  • Allows shaping of fibers and manipulation of structural parameters such as orientation and material thickness.
  • Simulates optical scattering using ray tracing methods.
  • Achieves fiber network models with porosity as low as 21.5% without overlapping fibers.
  • Generates HD-WFN models optimized for various applications like composite materials.
  • Provides an open-source code for generating extensive datasets for further modeling.

Abstract

• A method is proposed to generate 3D high-density wood fiber network (WFN) models. • The numerically generated WFN microstructures can reach a porosity as low as 21.5%. • It is a simple and efficient geometrical method with open-source code. • Fiber segmentation, local fiber orientation, WFNs with molded shapes can be obtained. • The optical scattering of WFN composites is simulated using the generated 3D models. Realistic 3D microstructure models of wood fiber networks (WFNs, e.g., paper, molded fibers, hot-pressed fibers, etc.) are of interest for numerical modeling of mechanical, optical, and other physical properties. One challenge is to numerically describe 3D high-density WFN (HD-WFN) models with complex fiber shapes without fiber overlapping. An efficient method is proposed to generate 3D HD-WFN microstructures with porosities as low as 21.5% while without fiber overlapping. The HD-WFN microstructures are obtained by compressing an initially sparse structure using a geometrically designed 3D displacement field. The sparse structure can be optimized to obtain a very low-porosity HD-WFN by reducing the local fiber clustering. The method can generate HD-WFNs with designated shapes (e.g., molded shapes) and varied structural parameters, including fiber width, orientation, location, and material thickness. Each interfiber bond and local material axis in the HD-WFN models can be determined for numerical simulation of properties. The optical scattering of transparent HD-WFN models (polymer matrix composites, transparent paper) is numerically studied using ray tracing methods. The open-source code is available on GitHub, and it can be used to obtain a large dataset for deep learning modeling.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69ca12d4883daed6ee0950dfhttps://doi.org/10.1016/j.matdes.2026.115937
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