Materials Characterization application examples

MeTiS: Characterization of inclusion population

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MeTiS : software application for automatic characterization of inclusion population in the metallurgical industry

MeTiS was developed by Datamet to characterize inclusion population automatically from images from an optical microscope or a system consisting of a scanning electron microscope and EDS analyzer.

Version for electron microscopy

Integrated FEG-SEM JEOL, image analysis (based on Aphelion™) and chemical analysis (EDS PGT) to detect and analyze the inclusions in the best conditions. Metis is a complete software control of the microscope and image analysis including three separate modules:

  • Metis to measure the morphological parameters and chemical
  • meTis processing and formatting results
  • metiS for the simulation of measurement methods

Simple and user friendly interface assists MeTiS logical user sessions setting during measurement, methods of analysis and simulation, while allowing to obtain a large number of possibilities.

meTis and metiS are hardware-independent analysis (FEG-SEM and EDS). They can be used on items deported.

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Metis (measure)

A strong coupling between the FEG-SEM (JEOL JSM-6500F), the EDS analysis system and software image analysis (Aphelion) allows an optimal characterization of inclusions with diameters ranging from a few hundred nanometers to a few tens micrometers.

The main specificities of Metis are:
  • number of samples only limited by the size of the plate and the sample size
  • no limit on the number of measurement locations on each sample
  • possible measures on samples rectangular or circular
  • possibility of linking characterizations with different measurement conditions
  • no limit on the duration of measurement
  • choice of type and image acquisition conditions
  • various methods for scanning the measurement zone
  • magnification change possible during the measurement session
  • autofocus each field
  • distinction to three phases of the matrix by thresholding
  • choice of chemical elements to analyze the different phases
  • choice of analytical conditions (position of the chemical analysis, duration)
  • backup images of inclusions or fields demand
  • choice among different measurement options (image processing, filters, etc.).
  • possibility of release in semi-automatic mode (manual focus and / or manual thresholding…)
  • automatic backup results
  • work session defined by the user credentials, test, sample, etc.

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The benefits of Metis Measure are:
  • automatic characterization of inclusions having a diameter greater than 500 nm
  • characterization inclusions smaller in semi-automatic mode
  • implementation quick and easy data
  • complete parameterization methods to multiply
  • observation and measurements at different magnifications (optimization of analysis time…)
  • measurements of the parameters of chemical and morphological many inclusions (up to more than 100/hour)
  • ability to analyze multiple samples with different measurement conditions
  • optimizing the use of the microscope (24&nsbsp;H/24 operation possible)
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Examples of inclusions detected

MeTis (Processing)

Processing software, dissociated measurement software allows multiple operations data measurement files, and return the results in Microsoft Excel.

The main specificities of MeTis are:
  • possibility of different treatments on each measurement file
  • treatment of results files (merge, filtering, etc.)
  • creation of tables of results
  • creation of multiple graphs (mapping, histogram, etc.)
  • automatic recognition of inclusion population
  • processing database images inclusions
  • constitution report results (specific methods or standards)
  • functions “macros”

The benefits of MeTis Treatment are :
  • treatment independent of the measurement
  • a multiprocessor database
  • expert system to identify inclusion population
  • automatic reports with ASTM E45 (2005), DIN 50602 (1985), ISO 4967 (1998), JIS G 0555 (2003) et EN 10247 (2007)
metiS (simulation)

The simulation software provides access to statistical parameters of the various methods by measures calculated on virtual samples.

The advantages of MetiS Simulation are :
  • uncertainty calculation for given measurement method
  • comparison of different methods
  • determination of optimal measurement conditions

Download article

An article describing the application is published in the journal JEOL News in July 2006.

Version for optical microscopy

MeTiS version for optical microscopy offers the same features as the version for electron microscopy, with the exception of the following features:

  • characterization inclusions smaller than 0.5 microns
  • chemical analysis and processing of data from this analysis
  • magnification change during a session

Automatic study of Ceramics material

Development of a vertical application for the study of ceramics

ADCIS and the LERMAT, a research laboratory specialized in Material Science jointly developed automatic tools to analyze ceramics using Image Processing techniques. During the course of this 2 year project, two types of ceramics were analyzed, a ceric oxide, used for sintered analysis, and alumine-zircone, a two phase material used for grain detection and analysis.

First step

A scanning electron microscope was used to acquire black and white, very high resolution images. Some filtering operators were applied to the images to enhance their quality by removing visible noise. Segmentation of the images involved automatic thresholding techniques to detect grains and pores, and various morphological operators such as top-hat, skeleton by zone of influence, watershed with constrained markers, and other functions looking at the size distribution of particles.

The images below present an example of image processing algorithms applied to a ceric oxide sintered at a temperature of 1200 degrees during a 5 hour process.

Second step

The research work also involved an in-depth analysis of the sample to measure characteristics such as granulometry, evolution of the volumic fraction versus the temperature, kinetics laws, pore dispersion, etc. The following chart displays the evolution of the granulometry.

Third step

Finally, some probabilistic models were studied to determine a model for the micro-structure.

Fourth step

This comprehensive work was performed within the scope of a Ph.D. thesis, and was partially funded by an ITIC grant from the Normandy region in France.

All image processing and analysis functions were developed using Aphelion™ and are now available as a set of macros written in Basic Script.

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Grain boundary detection

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Grain boundary detection example

This software solves a common problem in materials science, namely the detection of grain boundaries. For example, ASTM standards refer to this detection.

The user interface appears as a combination of several windows with buttons calling some functions of image processing and mathematical morphology. The judicious combination of these features can automatically detect the grains and their boundaries in calling functions segmentation and analysis of size and shape. The user has very few parameters to be specified before the launch detection and the output image of the algorithm contains boundaries thinned and thick one pixel.

This software is absolutely expectations metallurgists who wish to automate the detection of grain material and quantify the number of phases and inclusions in steel. The software is based on a stand-alone program that takes an image in TIFF format as input and generates an image at the same output format. All measurements are exported to a file in Microsoft Excel format. The demo version is an executable Visual Basic.

The advantages of the detection software grain boundaries are:

  • Standalone software can be used from any Windows PC
  • Solves the problem non-trivial detection prior to any extent ASTM
  • No learning curve - Can be used by non expert technician in image processing

  • Automatic 99%. Very few parameters must be specified by the user
  • Fully compatible with all hardware supported by the acquisition of Aphelion™, as an optical microscope or digital microscope
  • Outputs a binary image and measures exported into a spreadsheet


The algorithm includes edge detection, two types of transformations Top Hat Form to solve the problem of non-uniform illumination, an edge detection, filtering, operations erosion and dilation to clean contours, a line algorithm Watershed and skeleton by influence zone.

Extract volume fraction of zircone grains in aluminum

Related video

Below is an application that was developed by an Aphelion™ users in the field of Material Science. The image is courtesy of ESRF Grenoble, INSA de Lyon, GEMPPM, and Ecole des Mines de Paris.

The goal of the application is to extract volume fraction of zircone grains in aluminum, to compute grain sizing and determine neighbor distribution, working on the 3D volume.

In the past, most of the 3D analysis were performed using the set of 2D sections of a 3D volume. Nowadays, thanks the processing power of computers, and the quality of sensors, it is possible to deal directly with 3D images.

Below is the description of a very innovative technique based on 3D Morphology and 3D Image Understanding to compute grain sizing and neighbor distribution, two analysis which can only be performed on the 3D data.

  • The image is acquired using a X-Ray micro-tomograph with an advanced sensor.
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    Original Image

  • The computation of the mean value of the binary image gives the zircone volumic fraction.
  • Since the contrast between the two phases is good, a simple threshold is applied to extract the zircone phase.
  • Since zircone particles are almost spherical, they all appear in the image as a stack of spheres touching each other. The use of the ClustersSplitConvex operator will help to segment all the spheres which are actually convex particles. The operator is based on the 3D implementation of the watershed, a morphological operator.
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    3D label displayed as a volume

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    3D label displayed as 3 orthogonal sections

  • To avoid biased measurements, a BorderKill operator is applied to remove all particles intersecting the edge of the image. A Miles-Lantuéjoul correction could also be applied to actually get unbiased measurements taking into account the size of the volume, and the operators involved in the process.
  • The binary image is now converted into a 3D ObjectSet based on the 26-Connectivity. Note that cubic and cubic centered face grids are supported in Aphelion 3D. All spheres are now perfectly individualized. A set of measurements based on the shape are computed, such as the sphericity, and the intercept numbers in the main directions of the grid. The size distribution of the particles is computed and displayed as an histogram, as shown in the chart below.
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    Size distribution

  • The last step of the application involves the computation of the number of neighbors for each zircone spherical grain. This number can only be accessed from the 3D data, using the notion of 3D ObjectSets. The classical method is based on the extraction of each sphere in the volume. Each sphere is dilated, and then intersected with the binary image. A geodesic reconstruction is then performed, and the number of components is computed. It has to be done for each grain, and it takes several minutes to run. We are proposing to use another technique, much faster, and based on the Aphelion ObjectSets.

With the help of ObjectSets, the computation is no longer performed on pixels, but on 3D objects. Since objects are already individualized in an ObjectSet, and available as rasters, we perform a dilation on the Objects, with the condition that grains remain individualized even if they overlap after the dilation. The final result gives the number of overlaps for each grain, and is displayed in the standard Aphelion grid as a new attribute. This computation is very quickly performed since no pixel information is required, and it really proves Aphelion has superior capability than any other software when dealing with 3D images.

The following chart gives the neighbor distribution for the Zircone image, and the grid shows the value of the neighbor attribute. As in the 2D version of Aphelion, message passing is available between the grid and the chart.

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Neighbor distribution