Algorithm to determine local basis weight of random fibrous networks with X-ray microtomography and SEM images

Abstract

Analysis of the basis weight for random fibrous networks is important to understand their microstructure, properties and performance. Two-dimensional microscopical images show in-plane fibers without giving any information on their distribution in three dimensions. This research introduces a fully parametric algorithm for computing the local basis weight of random fibrous networks using three-dimensional images because out-of-plane fiber orientation is important, especially for high-density or thick networks. Voxel models of real nonwoven webs were generated by an X-ray micro-computed tomography system. The developed algorithm could accurately estimate a local basis weight value for random fibrous networks produced with various manufacturing parameters. Numerical results computed with the developed method were compared with those obtained with a physical weight measurement technique. The algorithm was tested and validated for various nonwoven fabrics with different densities. It was observed that the developed method can be used to examine and/or compare the basis weight of a wide range of random fibrous networks. In addition, it can be used to predict the basis weight for fabrics, especially in a new product development process.