Thickness and surface estimation of thin porous media based on 3D image data

Abstract

Measuring the thickness of thin porous materials provides valuable insights into their structure, properties, and performance, including key properties such as porosity and permeability, and is highly beneficial for a range of industrial applications, particularly for ensuring effective quality control processes. A novel approach for estimating the thickness of porous media and their surfaces is proposed based on voxel sets of 3D images, such as 3D scans and segmented scan data. Initially, the solid volume fraction (SVF) is computed for each voxel layer perpendicular to the through direction. Then, fitting functions consisting of piecewise linear segments are chosen to ensure an accurate representation of the layer data. Each function is associated with various thickness regions of the medium, including the medium itself and its surface. An optimization problem is then solved to find the best-fitting function based on the squared area between the SVF and the fitting function. The thickness of the medium and its surfaces is determined based on the identified optimal fit. This robust, reliable, and fast approach aims to provide not only a non-intrusive method for thickness estimation of porous media represented by voxel sets but also a precise alternative to existing methodologies.