DCM microstructures
DCM technology has been implemented as DCM software which takes X-ray CT images as input and produces compositional microstructures and some material properties as output.
The challenge
Advanced X-ray CT imaging facilities, such as synchrotrons and lab CT machines, are becoming common for high resolution 3D imaging. Up-to-date, analysis of such images is generally threshold segmentation, which reduces the high resolution 16 bit images into 1-2 bit low resolution images with significant loss of information. DCM addresses such challenges by integration of complete image information with statistical physics and underlying materials science knowledge to produce a more accurate and complete representation of the material microstructures. This enables accurate and quantitative predictive modelling of materials with desirable properties to reduce materials development time and cost. It also helps in characterization and modelling of naturally occurring materials with applications in unconventional resources exploration and development.
Our response
Quantitative knowledge of material microstructures is important in various science and engineering applications including advanced materials development and utilization, energy resources exploration and development, bio and medical sciences. Considerable experimental, theoretical and numerical efforts have been devoted to 3D microscopic characterization for material samples. X-ray CT has been widely used as a sample non-destructive method for microstructures characterization. However, X-ray CT is not always adequate to discriminate material compositions, such as where different material phases exhibit similar X-ray absorptions.
A level of success has been achieved using data-constrained modeling (DCM) with multi-energy (spectrum) X-ray CT in characterization of microscopic compositional distributions. In DCM, it assumes that the total volume of a voxel is the sum of sub-volumes of its constituent materials. It also assumes that, when an X-ray beam propagates through a voxel, the total attenuation is the sum of attenuations by its constituent compositions. The latter assumption is valid where the voxel size is small. That is, the X-ray attenuation of a voxel is small which is generally true with X-ray CT. The assumptions are formulated as a linear optimization problem with non-negativity constraints, using multi-energy X-ray CT data as constraints. The linear optimization problem can be solved efficiently using standard routines such as Simplex method. Such DCM approach has been used in compositional microstructure characterization for a range of materials including mineral phases distribution in hydrocarbon reservoir rocks, corrosion inhibitor distribution in paint primers, and gold particles distribution in nano-functional materials.