Tessellation-based stochastic modelling of 3D coating structures imaged with FIB-SEM tomography
Journal article, 2021

To facilitate printing, coatings are typically applied to paperboard used for packaging to provide a good surface for application. To optimise the performance of the coating, it is important to understand the relationship between the microstructure of the material and its mass transport properties. In this work, three samples of paperboard coating are imaged using combined focused ion beam and scanning electron microscope (FIB-SEM) tomography data appropriately segmented to characterise the internal microstructure. These images are used to inform a parametric, tessellation-based stochastic three-dimensional model intended to mimic the irregular geometry of the particles that can be seen in the coating. Parameters for the model are estimated from the FIB-SEM image data, and we demonstrate good agreement between the real and virtual structures both in terms of geometrical measures and mass transport properties. The development of this model facilitates exploration of the relationship between the structure and its properties.

Gaussian random field

Permeability

Laguerre tessellation

Paperboard coatings

FIB-SEM tomography

Stochastic modelling

Author

Philip Townsend

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

RISE Research Institutes of Sweden

Torben Nilsson Pingel

Chalmers, Physics, Nano and Biophysics

Niklas Lorén

RISE Research Institutes of Sweden

Chalmers, Physics, Nano and Biophysics

Tobias Gebäck

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Eva Olsson

Chalmers, Physics, Nano and Biophysics

Aila Särkkä

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Magnus Röding

RISE Research Institutes of Sweden

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Computational Materials Science

0927-0256 (ISSN)

Vol. 197 110611

Subject Categories

Manufacturing, Surface and Joining Technology

Other Materials Engineering

Medical Image Processing

DOI

10.1016/j.commatsci.2021.110611

More information

Latest update

6/24/2021