Microstructure models are used to investigate bulk properties of a material
given images of it’s microstructure. Through their use the effect of microstructural
features can be investigated independently. Processes can then
be optimised to give the desired selection of microstructural features.
Currently automatic methods of segmenting SEM images either miss
cracks leading to large overestimates of properties such as thermal conductivity
or use unjustifiable methods to select a threshold point which class
cracks as porosity leading to over estimates of porosity.
In this work a novel automatic image segmentation method is presented
which produces maps for each phase in the microstructure and an additional
phase of cracks. The selection of threshold points is based on the assumption
that the brightness values for each phase should be normally distributed. Additional
image processing is used to ensure results remain physically relevant.
The image segmentation method has been compared to other available
methods and shown to be as or more repeatable with changes of brightness
and contrast of the input image than relevant alternatives. The resulting
modelling route is able to predict density and specific heat to within experimental
error, while the expected under predictions for thermal conductivity
are observed
