Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Materials Characterization Laboratory
Lab In-charge: Dr. S.K. Vajpai
List of Experiments
Experiment#1: Digital Image Analysis using Image-J.
Experiment#2: Determining the crystal structure, lattice parameter, and Identification of
Phases of crystalline materials through X-Ray Diffraction Method and diffraction data analysis.
Experment#3: Determination of Mean Grain Size of Single Phase Bulk Crystalline Materials by
Line-Intercept Technique using (i) Manual Method, and (ii) Digital Image-analysis method.
Experiment#4: Determination of Mean Grain Size and Grain Size Distribution of Bulk
Polycrystalline Materials by Individual Grain Size Measurement Technique using Digital
Image-analysis method.
Experiment#5: Determination of volume fraction of constituent phases in multi-phase Bulk
Crystalline Materials using (i) Point Count Method, and (ii) Digital Image-analysis method.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Experiment#1: Learning Digital Image Analysis using Image-J.
Background: ImageJ is a Java-based image processing program developed at the National
Institutes of Health and the Laboratory for Optical and Computational Instrumentation (LOCI,
University of Wisconsin). ImageJ was designed with an open architecture that provides
extensibility via Java plugins and recordable macros. Custom acquisition, analysis and
processing plugins can be developed using ImageJ's built-in editor and a Java compiler. Userwritten plugins make it possible to solve many image processing and analysis problems, from
three-dimensional live-cell imaging to radiological image processing, multiple imaging system
data comparisons to automated hematology systems. ImageJ's plugin architecture and builtin development environment has made it a popular platform for teaching image processing.
ImageJ can display, edit, analyze, process, save, and print 8-bit color and grayscale, 16-bit
integer, and 32-bit floating point images. It can read many image file formats, including TIFF,
PNG, GIF, JPEG, BMP, DICOM, and FITS, as well as raw formats. ImageJ supports image stacks,
a series of images that share a single window, and it is multithreaded, so time-consuming
operations can be performed in parallel on multi-CPU hardware. ImageJ can calculate area
and pixel value statistics of user-defined selections and intensity-thresholded objects. It can
measure distances and angles. It can create density histograms and line profile plots. It
supports standard image processing functions such as logical and arithmetical operations
between images, contrast manipulation, convolution, Fourier analysis, sharpening,
smoothing, edge detection, and median filtering. It does geometric transformations such as
scaling, rotation, and flips. The program supports any number of images simultaneously,
limited only by available memory.
ImageJ and its Java source code are freely available and in the public domain. No license is
required.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Objectives: Learning basic functions of Image-J Image Analysis Software used for analyzing
microstructures.
Procedure:
1. ImageJ and its Java source code are freely available and in the public domain. No license is
required.
2. Download using following URL and Install in your computer: https://imagej.nih.gov ›
download
3. Use the provided user manual and related video to learn following:
i. To open and load image file and editing options such as area selection, copying a portion
from an image, generating duplicate image, etc.
ii. Type of required image file for image processing.
iii. To set scale of the digital image.
iv. To set brightness and contrast to enhance image quality.
v. To adjust Threshold and color threshold in an image.
vi. To learn using line tool, selection tool, angle tool, text tool, measure and record the
results.
vii. To process image and applying smoothing and sharpening.
viii. To measure and analyze particles, and generate its summary record.
ix. To set measurements, calibrate, set scale, and generate histogram.
x. Calculating area fraction and point fraction.
Report: Report the following:
Report the procedure of using these tools in pointwise manner.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Experiment#2:
Objective: Determining the crystal structure, lattice parameter, and Identification of Phases
of crystalline materials through X-Ray Diffraction Method and diffraction data analysis.
Background: We need to know about crystal structures because structure, to a large extent,
determines properties. X-ray diffraction (XRD) is one of a number of experimental tools that
are used to identify the structures of crystalline solids.
The XRD patterns, the product of an XRD experiment, are somewhat like fingerprints in that
they are unique to the material that is being examined. The information in an XRD pattern is
a direct result of two things:
(1) The size and shape of the unit cells determine the relative positions of the diffraction
peaks;
(2) Atomic positions within the unit cell determine the relative intensities of the diffraction
peaks (remember the structure factor?).
Taking these things into account, we can calculate the size and shape of a unit cell from the
positions of the XRD peaks and we can determine the positions of the atoms in the unit cell
from the intensities of the diffraction peaks.
Full identification of crystal structures is a multi-step process that consists of:
(1) Calculation of the size and shape of the unit cell from the XRD peak positions;
(2) Computation of the number of atoms/unit cell from the size and shape of the cell, chemical
composition, and measured density;
(3) Determination of atom positions from the relative intensities of the XRD peaks
We will only concern ourselves with step (1), calculation of the size and shape of the unit cell
from XRD peak positions.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Procedure for Indexing XRD Patterns
Interplanar spacings in cubic crystals can be written in terms of lattice parameters using the
plane spacing equation:
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Report:
You will be provided the XRD patterns of different materials, preferably cubic. Index the XRD
patterns, find out the correct crystal structure, and lattice parameter. Report your results with
proper calculations as demonstrated above.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Experment#3:
Objectives: Determination of Mean Grain Size of Single Phase Bulk Crystalline Materials by
Line-Intercept Technique using (i) Manual Method, and (ii) Digital Image-analysis method.
Background: The relationship between microstructure and property (also known as structureproperty correlation) of materials is well established. One such relationship for single-phase
Polycrystalline materials is the well-known Hall-Petch equation: s=so+Kd-1/2; where s is the
Flow stress, d is the grain size, so and K are constants. The microstructural observations are
Generally made on 2-dimensional sections (or plane of polish) which cut through the 3dimensional structure of materials. Many methods have been developed to obtain
parameters of the 3-dimensioanl structure from measurements made on the plane of polish.
These methods constitute what is known as the field of stereology.
Estimation of grain size of polycrystalline materials is probably one of the most important
parameters because of its strong influence on properties. However, the problem of
determining the size of 3-dimensional grains constitutes one of the hard-problems in
stereology. Therefore, one generally determines a single value and uses it to represent an
average planar grain size. Several different measurements can be used to represent this
average value: average diameter, average area, number of grain per unit area, average
intercept length, number of grain per unit volume, average diameter based on average grain
volume.
The mean intercept length is probably the most accepted method for the estimation of grain
size. In the intercept length method, the polycrystalline microstructure is generally
superimposed by a grid of parallel lines (see figure) and the number of intercepts
(intersections with grain boundaries) per unit length (PL) is obtained. The mean intercept
1
length (𝑙 ̅ ) is given by: 𝑙 ̅ = 𝑃
𝐿
The intercept method involves an actual count of the number of grains intercepted by a test
line or the number of grain boundary intersections with a test line, per unit length of test line,
used to calculate the mean lineal intercept length, 𝑙 .̅ 𝑙 ̅ is used to determine the ASTM grain
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
size number, G. The precision of the method is a function of the number of intercepts or
intersections counted. A precision of better than ±.25 grain size units can be attained with a
reasonable amount of effort. Results are free of bias; repeatability and reproducibility are less
than ±.5 grain size units. Because an accurate count can be made without need of marking
off intercepts or intersections, the intercept method is faster than the other methods for the
same level of precision. The intercept procedure is particularly useful for structures consisting
of elongated grains. No attempt should be made to estimate the average grain size of heavily
cold-worked material. Partially recrystallized wrought alloys and lightly to moderately coldworked material may be considered as consisting of non-equiaxed grains, if a grain size
measurement is necessary.
It is important, in using these test methods, to recognize that the estimation of average grain
size is not a precise measurement. A metal structure is an aggregate of three-dimensional
crystals of varying sizes and shapes. Even if all these crystals were identical in size and shape,
the grain cross sections, produced by a random plane (surface of observation) through such
a structure, would have a distribution of areas varying from a maximum value to zero,
depending upon where the plane cuts each individual crystal. Clearly, no two fields of
observation can be exactly the same. In general, if the grain structure is equiaxed, any
specimen orientation is acceptable. However, the presence of an equiaxed grain structure in
a wrought specimen can only be determined by examination of a plane of polish parallel to
the deformation axis. If the grain structure on a longitudinally oriented specimen is equiaxed,
then grain size measurements on this plane, or any other, will be equivalent within the
statistical precision of the test method. If the grain structure is not equiaxed, but elongated,
then grain size measurements on specimens with different orientations will vary. In this case,
the grain size should be evaluated on at least two of the three principle planes, transverse,
longitudinal, and planar (or radial and transverse for round bar) and averaged to obtain the
mean grain size.
Good judgment on the part of the observer is necessary to select the magnification to be
used, the proper size of area (number of grains), and the number and location in the specimen
of representative sections and fields for estimating the characteristic or average grain size. It
is not sufficient to visually select what appear to be areas of average grain size. Grain size
estimations shall be made on three or more representative areas of each specimen section.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Linear Intercept Procedure:
1. Estimate the average grain size by counting (on the ground-glass screen, on a
photomicrograph of a representative field of the specimen, or on the specimen itself) the
number of grains intercepted by one or more straight lines sufficiently long to yield at least
50 intercepts. It is desirable to select a combination of test line length and magnification such
that a single field will yield the required number of intercepts. The precision of grain size
estimates by the intercept method is a function of the number of grain interceptions counted.
Because the ends of straight test lines will usually lie inside grains, precision will be reduced
if the average count per test line is low. If possible, use either a longer test line or a lower
magnification.
2. Make counts first on three to five blindly selected and widely separated fields to obtain a
reasonable average for the specimen. If the apparent precision of this average is not
adequate, make counts on sufficient additional fields to obtain the precision required for the
specimen average.
3. An intercept is a segment of test line overlaying one grain. An intersection is a point where
a test line is cut by a grain boundary. Either may be counted, with identical results in a single
phase material. When counting intercepts, segments at the end of a test line which penetrate
into a grain are scored as half intercepts. When counting intersections, the end points of a
test line are not intersections and are not counted except when the end appears to exactly
touch a grain boundary, when 1⁄2 intersection should be scored. A tangential intersection
with a grain boundary should be scored as one intersection. An intersection apparently
coinciding with the junction of three grains should be scored as 1-1⁄2 .With irregular grain
shapes, the test line may generate two intersections with different parts of the same grain,
together with a third intersection with the intruding grain. The two additional intersections
are to be counted.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Procedure using Printed Photomicrographs:
1. Draw random test lines on the micrographs and count number of intercepts.
2. Calculate number of intercept per unit length of test line, i.e. P L.
1
2. Using the formula calculate the average grain size, i.e. 𝑙 ̅ = 𝑃 .
𝐿
Procedure using Digital Image Analysis System:
1. Open the provided image/micrograph in the ImageJ software.
2. Set-scale using the length scale provided on the micrograph.
3. Draw test lines of desired lengths.
4. Follow rest of the procedure as described for the case of printed micrographs.
Report:
1. Objectives.
2. Type of Microstructure Analyzed
3. Procedure of measurement
4. Measurements: No. of Test lines, length of test line, number of intercepts on each test lines.
5. Calculation of average grain size.
6. Comparison of results for both the methods.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Experiment#4:
Objectives: Determination of Mean Grain Size and Grain Size Distribution of Bulk Crystalline
Materials by Individual Grain Size Measurement Technique using Digital Image-analysis
method.
Background: The relationship between microstructure and property (also known as structureproperty correlation) of materials is well established. One such relationship for single-phase
Polycrystalline materials is the well-known Hall-Petch equation: s=so+Kd-1/2; where s is the
Flow stress, d is the grain size, so and K are constants. The microstructural observations are
Generally made on 2-dimensional sections (or plane of polish) which cut through the 3dimensional structure of materials. Many methods have been developed to obtain
parameters of the 3-dimensioanl structure from measurements made on the plane of polish.
These methods constitute what is known as the field of stereology.
Estimation of grain size of polycrystalline materials is probably one of the most important
parameters because of its strong influence on properties. However, the problem of
determining the size of 3-dimensional grains constitutes one of the hard-problems in
stereology. Therefore, one generally determines a single value and uses it to represent an
average planar grain size. Several different measurements can be used to represent this
average value: average diameter, average area, number of grain per unit area, average
intercept length, number of grain per unit volume, average diameter based on average grain
volume.
There are certain conditions, such as multiphase materials, the highly anisotropic
microstructures, or the large variation in grain size, where general line-intercept method does
not provide the real representation of the microstructure. In such cases, grain size of the
individual grains can be measured digitally and the data can be presented in the relevant
form. For example, along with the average grain size, the grain size distribution and the
average aspect ratio of the grains can be estimated for single-phase as well as all the individual
phases present in a multiphase materials.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Procedure:
1. Open the micrograph in ImageJ and adjust Brightness and contrast in such a way that grain
boundaries and phase boundaries appear with clarity.
2. Set-scale for measuring distance in either mm or m.
3. Select line tool and start measuring the diameter of individual grains.
4. For each grain, make two measurements in perpendicular directions.
5. If the microstructure consists of elongated grains, the two measurements should be carried
out on smallest width and largest width of the grains.
6. The data should be recorded for each grain and the direction, say X-direction and Ydirection.
7. Make such measurements on atleast 100 grains. Take an average of the diameter from
these measurements for each phase.
Report:
1. Objectives
2. Details of microstructure and Procedure as carried out by you.
3. Data recorded and calculation of average grain size and standard deviation.
4. Plot grain size distribution from these measurements.
5. Report average aspect ratio of grains.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Experiment#5:
Objectives: Determination of volume fraction of constituent phases in multi-phase Bulk
Crystalline Materials using (i) Point Count Method, and (ii) Digital Image-analysis method.
Background: The relationship between microstructure and property (also known as structureproperty correlation) of materials is well established. One such relationship for single-phase
Polycrystalline materials is the well-known Hall-Petch equation: s=so+Kd-1/2; where s is the
Flow stress, d is the grain size, so and K are constants. The microstructural observations are
generally made on 2-dimensional sections (or plane of polish) which cut through the 3dimensional structure of materials. Many methods have been developed to obtain
parameters of the 3-dimensioanl structure from measurements made on the plane of polish.
Many properties of bulk materials, especially multiphase materials, depends on the size,
shape, spatial distribution, and volume fraction of various constituent phases present in the
materials. In particular, volume fraction of the constituent phases is an important key
parameters. A simple illustration of the estimation of volume fraction of a particular phase in
a multi-phase alloy is illustrated in the figure. A grid of points is superimposed on the
microstructural image. The ratio, PP, of the number of points (n) falling in the phase of interest
(the black graphite nodules in figure) to the total number of grid points (N) is obtained. This
ratio is directly the statistical estimate of the volume fraction of the phase, VV:
VV = PP = n/N.
The above method is simply termed as the Point Count Technique.
Similarly, in the technique of Areal Analysis, the volume fraction is equated to the estimate of
the area fraction (AA) of the phase on the 2-dimensional section. The method of area fraction
can be easily applied using digital image analysis, and the corresponding volume fraction can
be estimated from area fraction measurements.
Department of Metallurgical and Materials Engineering
National Institute of Technology, Jamshedpur
Figure 1:Illustration of the Point Count Technique for the estimation of volume fraction of graphite
Procedure:
1. For manual systematic point count method:
i. Take a suitable size transparent grid. Place this grid randomly on the different areas of the
microstructure.
ii. Count the number of points falling on the desired phase. Repeat the procedure for several
micrographs taken from different areas of the specimen.
iii. Calculate the fraction of these points with respect to total number of grid points. The
resulting point fraction would give an estimate of volume fraction of the selected phase.
2. Using digital Image Analysis:
i. Open the image in ImageJ, convert it to 8 bit image, and set-scale.
ii. Adjust the brightness and contrast, and proceed for thresholding to generate binary image.
iii. Adjust the level of thresholding to as to cover the desired phase appropriately.
iv. Apply appropriate function, i.e. calculate particles, to estimate area fraction and sizes of
particles.
v. Repeat it for several micrographs taken from different areas of the specimen.
Report:
1. Objectives
2. Description of Sample/Microstructure and Procedure followed
4. Results, i.e. observations, using both the methods.
5. Volume fraction and size distribution of second phase.