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<p>Electrospun fiber mats, as a class of high-performance nonwoven materials, are widely applied in textiles, filtration, medical, and other fields. However, the precise three-dimensional characterization of their microstructure and quantification of volume fraction face challenges such as low resolution, poor computational efficiency, and reliance on expensive experimental imaging. This study aims to develop a computer modeling method independent of experiments, achieving high-precision reconstruction and performance prediction of fiber mats. Methodologically, by simulating the electrospinning deposition process, a parameterized deposition model is constructed, and a solvent-orientation coupled dynamic contact model is proposed, which integrates solvent residual concentration with von Mises orientation distribution and quantifies fiber cross-penetration behavior through adhesion offset equations. The main work includes developing an efficient voxelization algorithm that analyzes fiber-voxel interactions via multi-level detection (center point-corner point-ray penetration) and tolerance compensation mechanisms, enabling rapid calculation of volume fraction. Experimental results demonstrate that the error rate of this method is below 2%, and it remains robust in high fiber density scenarios. This model not only provides a high-precision tool for studying the relationship between microstructure and performance of electrospun materials but can also be extended to multi-process parameter optimization and multi-scale performance prediction, thereby promoting the intelligent design and application of nonwoven materials.</p>

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3D characterization of electrospun fiber felts based on a voxelized-dynamic contact coupled model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Yexin Liu,
  • Gaoming Jiang,
  • Bingxian Li,
  • Haisang Liu,
  • Hui Xu,
  • Shukai Tang

摘要

Electrospun fiber mats, as a class of high-performance nonwoven materials, are widely applied in textiles, filtration, medical, and other fields. However, the precise three-dimensional characterization of their microstructure and quantification of volume fraction face challenges such as low resolution, poor computational efficiency, and reliance on expensive experimental imaging. This study aims to develop a computer modeling method independent of experiments, achieving high-precision reconstruction and performance prediction of fiber mats. Methodologically, by simulating the electrospinning deposition process, a parameterized deposition model is constructed, and a solvent-orientation coupled dynamic contact model is proposed, which integrates solvent residual concentration with von Mises orientation distribution and quantifies fiber cross-penetration behavior through adhesion offset equations. The main work includes developing an efficient voxelization algorithm that analyzes fiber-voxel interactions via multi-level detection (center point-corner point-ray penetration) and tolerance compensation mechanisms, enabling rapid calculation of volume fraction. Experimental results demonstrate that the error rate of this method is below 2%, and it remains robust in high fiber density scenarios. This model not only provides a high-precision tool for studying the relationship between microstructure and performance of electrospun materials but can also be extended to multi-process parameter optimization and multi-scale performance prediction, thereby promoting the intelligent design and application of nonwoven materials.