by Professor Ian Nettleship
Over the last decade the field of materials informatics has emerged in recognition
of the potential for systematic approaches to complex problem solving in materials
science and engineering1. If the necessary data
rich environments are to be accessible to the widest audience in materials science
and engineering materials informatics will require data acquisition and management
strategies similar to those previously developed in fields such as bioinformatics.While
databases exist for some basic materials information in thermodynamics and crystallography
there are no widely used collections for more complex data sets like microstructure.
Microstructure Mining, as its name suggests, is the application of data mining
concepts2, used in many branches of science and
engineering, to the analysis of materials microstructure.
At the very core of this approach is the expectation that many problems in
microstructure-property analysis are not easily addressed using experimentation
involving the characterization of only one simple microstructural lengthscale,
such as average grain size. This is especially true for materials that have
multiphase microstructures or for material properties that are dominated by
relatively rare microstructural events such the nucleation of cracks.In such
circumstances the associations between microstructural features (e.g. crystallographic
misorientation across grain boundaries) may be just as important as the average
lengthscale of the features themselves. Fortunately the continued advances in
imaging techniques, digital imaging and data storage are providing the environment
were searchable "microstructure banks" become a viable prospect,
resulting in the necessary data rich environment in which data mining techniques
could be widely applied.
To realize material informatics at the microstructure level it will be necessary
to establish standard procedures and formats for acquiring microstructure images
to be stored in databanks. The Microstructure Mining methodology is one possible
approach that can be broken down into the steps that are illustrated in figure
The steps in the Microstructure Mining process.
The steps are not meant as a strict regiment but a flexible guide to problem
solving with each step valued only by its practical utility to the problem in
question. It is therefore useful in its own right, independent of any formal
system of shared data storage. While the following discussion will be focus
on analysis of two dimensional sections there is no reason why it cannot be
applied to 3 D images. After preparation of the section the second step in Microstructure
Mining method involves a qualitative survey in which the microstructure is observed
at increasing magnification from the lowest available for the imaging technique
employed. This will result in the identification of all the important microstructural
lengthscales bearing in mind that lengthscales can be defined by the association
between microstructural features (e.g., aggregate size) as well as the feature
The decision regarding the choice of the imaging conditions, such as magnification,
for the consequent microstructure measurements is thus based on experience and
significant understanding of the microstructure variables that affect the property
in question. Once the magnification is chosen the quantitative microstructural
measurement must be defined and the sampling procedures must be decided upon,
the latter is particularly important when considering microstructural features
or associations which occur with low frequency. Standard approaches to unbiased
sampling have been developed for stereological measurements3.
Once the images are acquired and banked the image processing steps must be
assembled to prepare the images for analysis. This is best done with some familiarity
concerning the common image processing steps that are widely available in image
processing software. The specific procedures in this step depend on the nature
of the measurement. For example, simple stereological parameters can be used
to determine microstructural averages. If only a single measurement is required
for such an average it may be more appropriate to use standard manual counting
methods with a minimum of image processing. In other circumstances such as the
use of microstructural information for material processing control, more elaborate
routines will be necessary to allow information about changes in microstructure
populations to be rapidly analyzed. For example, a study, by our group, of the
relationship between the powder processing methods and the size of surface flaws
in the final ceramic required the use of a new artificial intelligence technique
termed "adaptive thresholding" to automate the processing of many
images4. This allowed the distribution of flaw sizes to be constructed and
the consequent use of extreme value analysis to predict the largest flaw size
in following image analysis and data mining steps5. Future studies will need
to combine such techniques with automated specimen preparation techniques to
fully implement rapid image acquisition methods that would allow for "machine
vision" to be extended down to the microstructural scale.
One example will be used to illustrate the design of a Microstructure Mining
parameter to solve a problem in ceramics processing. This parameter addressed
the common observation that nanocrystalline ceramic powders often lead to sintering
microstructures that consist of dense multigrain aggregates surrounded by pores.
An example of such a microstructure is shown in figure 2(a). In this circumstance,
the interpore diffusion distance will not correlate with the grains size in
the way that is assumed in the kinetic models of sintering.
(a) Microstructure of partially sintered zirconia showing dense aggregates
surrounded by pores (b) illustrates the "densification short-cuts"
in the structure compared to an example of a pore separation intercept
length that crosses a dense aggregate. The latter would overestimate
the diffusion distance in this structure.
In fact, the common power-law dependence between measured densification rate
and grain size for the zirconia ceramic shown in figure 2(a) gave an exponent
of 12, well above the expected value of 3 for volume diffusion in the combined
stage sintering model6 implemented in the master
sintering curve method7. Therefore the sintering
models will underestimate the average diffusion distance that limit the kinetics
of sintering in these aggregated microstructures. Additional consideration leads
to the conclusion that diffusion in these aggregated structures will be spatially
constrained to "diffusion short-cut" between pores on the aggregate
boundaries and will not occur across the dense aggregates illustrated in figure
Furthermore, it can also be concluded that the interpore spacing measured
using stereological techniques, which assumes random measurements, will overestimate
the true diffusion distance because many such intercept lengths will bisect
dense aggregates. Therefore measurements of the mean pore spacing will not be
appropriate for this problem. Our group used Microstructure Mining involving
a simple image processing technique, pore boundary tessellation8,
to design an effective diffusion distance parameter that would be independent
of the state of microstructural aggregation9.
The feature boundary tessellation option available on most image processing
software effectively dilates the boundary of isolated features on a two dimensional
section until the boundaries impinge. Thus boundary tessellation leads to a
segmentation of the microstructure into space filling cells, each cell containing
all the pixels in the image that are closer to the boundary of feature they
contain than the boundary of any other feature. In this case the pore boundaries
in the micrographs of the partially sintered ceramic were dilated, resulting
in cells, illustrated in figure 3, that each contain a central pore section
and the surrounding solid phase that is closest to that pore than any other
pore boundary. Therefore all the pixels on the tessellation cell boundaries
represent the limiting diffusion distances into the central pore section from
the surrounding solid.
shows the result of pore boundary tessellation for a partially sintered
ceramic. Each cell features a central pore section, colored black and
an orange cell boundary.
To bias the average diffusion distance for each cell towards the previously
illustrated "diffusion shortcuts" on the aggregate boundary, the
diffusion distances for each cell were flux weighted and then averaged over
thousands of cells taken from several randomly located images. The resulting
effective diffusion distance was then tested by using the expected power-law
dependence of densification rate on the diffusion distance. This time the exponent
was 3, agreeing with that expected for volume diffusion in the sintering models.
Therefore the effective diffusion distance correctly approximated the diffusion
length for the aggregated microstructures and can be used to replace grain size
in the sintering models. This new parameter will allow, independent of the state
of aggregation in the microstructure, predictions of sintering time to full
density based on microstructural information rather than sintering shrinkage
alone. Applications of the parameter could include the prediction of sintering
times to full density in a range of circumstances, including the sintering of
transparent nanocrystalline ceramics for optical applications where very high
densities are crucial.
- K. Rajan "Materials Informatics," Materials
Today, 8 38-45 (2005).
- I.H. Witten, E. Frank and M.A. Hall, "Data Mining:
Practical Machine Learning Tools and Techniques," 3rd Edition, published
by Elsevier Direct (2011).
- C.V. Howard and M.G. Reed, "Unbiased Stereology: Three
Dimensional Measurement in Microscopy," BIOS Scientific Publishers,
- O. Dengiz, A.E. Smith and I. Nettleship, "Two-Stage
Data Mining for Flaw Identification in Ceramic Manufacture," International
Journal of Production Research, 44 2839-2851 (2006).
- O. Dengiz, T. Chen, I. Nettleship and A.E. Smith,"The
Effect of Powder Forming Method on the Pull-Out Flaw populations Observed
on Polished Surfaces of Alumina Ceramics," Mat. Sci. & Eng. A, A427
- J.D. Hansen, R.P. Rusin, M.H. Teng and D.L. Johnson, "Combined
Stage Sintering Model," J. Am. Ceram. Soc., 75 1129-35 (1992).
- H. Sui and D. L. Johnson, "Master Sintering Curve:
A Practical Approach to Sintering," J. Am. Ceram. Soc., 79 3211-17 (1996).
- R.J. McAfee and I. Nettleship, "A Mesoscale Description
of Microstructure Evolution for the Sintering of Ceramics," Acta Mater.,
53 4305-4311 (2005).
- T. Chen, I. Nettleship, R.J. McAfee, T.R. Hinklin and K.G.
Ewsuk, "An Experimental Measurement of Effective Diffusion Distance
for the Sintering of Ceramics," J. Am. Ceram. Soc., 92 1481-1486 (2009).
Copyright AZoM.com, Professor Ian Nettleship (University of Pittsburgh)