1. MSE 490 Research Final Report
Research on β phase fraction in HPDC Magnesium Alloys
Manual point counting and Algorithm counting Method
Faculty Research Advisor: Professor John Allison
Research Supervisor: Tracy Berman
Student: Zhenjie Yao
ycollin@umich.edu

2. Introduction
Magnesium alloys have many attractive properties, for example, its high strength to weight ratio makes
them a very common material for automobile, aerospace and electronic industries. High pressure die
casting (HPDC) method is used to manufacture over 90% of the commercial magnesium products. The
cooling rate of HPDC or super vacuum die casting (SVDC) is very high, ranged from 10 to 1000o
C/s.
Under such condition, which is far from equilibrium condition, the solidification kinetics, phase
transformation and the redistribution of alloying elements are very different from the equilibrium
thermodynamics prediction. Although the magnesium alloys are widely manufactured and used, there is
no systematic, quantitative information on the eutectic phase formation or microsegregation in HPDC
components, as shown in Figure 1.
Problem Statement
Existing models such as the Scheil model or rapid solidification (no time for segregation and solutes
remain in solid solution) are often used to calculate the β phase fraction. However, when calculating the β
phase fraction in the HPDC magnesium alloys, the results are over-predicted. β phase volume fraction
decreases as the cooling rate increases. The cooling rate of the HDPC is between rapid solidification and
Scheil (local equilibrium) solidification, as a result, both models are not accurate enough to quantitatively
describe the β phase. Figure 2 also shows HPDC solidification model compared with other two models.
Scheil model gives theoretical values of concentration of Al in the α phase. Rapid solidification has the
most Al trapped in the α-Mg; HPDC has some of the Al in the β phase, and Scheil predicts even more in
the beta phase (and therefore less in the α phase). The total amount of Al in all conditions is the same.
As a result, a new model that can predict the β phase in the HPDC alloys needs to be developed. In order
to do so, a systematic study, including the manual point counting method and MATLAB phase separation
algorithm will be used to quantitatively understand the β phase fraction and its distribution.
Figure 1. Microstructure of different phases in HPDC alloy Figure 2. HPDC solidification model compare with rapid
solidification model and Scheil model

3. Project Description
The goal of this project is to quantize the volume of β phase in the grain boundary and in the eutectic
region as a function of alloy, plate thickness (2.5 or 5.0 mm), and location (edge or center). From the
initial results, it is known that microsegregation that occurs during the rapid solidification is different
between the edge of the plate and the center. This project will first study the AM series alloys (AM60 and
AM70), learning how to prepare and polish sample surface, observe the microstructure with SEM, and
quantize the β phase using manual point counting in ImageJ. In addition, a routine will be built in Matlab
to separate out the different constituents. This routine is an image processing algorithm to quantitatively
characterize the size and spatial arrangement of microconstituents. The accuracy of the routine will be
determined by comparing the MATLAB results to those obtained using the ASTM standard for volume
fraction (point count method).
Manual Point Counting study
In this project, ImageJ was used to conduct the manual point counting od SEM image. The following
routine is a standard procedure for the manual counting:
Manual Point counting procedure
ImageJ:
1. Analysis--Set scale
pixel: 2048
known distance:200 (viewfield)
2. crop: get rid of the scale bar
3. Plugins--Analyze--Grid
100 point Cross(400/um^2)
Figure 3 is a schematic representation how to do the manual point counting in the ImageJ.
Figure 3. Key steps when set up manual point counting in ImageJ

4. Figure 4. AM70 5.0mm microstructure (with 16 indents)
(a). BSE – not etched (b). SE – etched (c). BSE – etched
Figure 5. AM70 5.0mm microstructure at the same indent position with different SEM mode and sample preparation. (a). BSE –
not etched; (b). SE – etched; (c). BSE – etched.
Another factor should be taken in to consideration is that different SEM mode and preparation method
could have a large influence on the manual counting. For example, an experiment was conducted in the
following way as shown in the Figure 4. 16 points were indented on the surface of AM70 5.0mm sample.
Then different preparation methods were applied to the alloy surface, and different SEM mode was used
to observe the microstructure of the same position. Figure 5 shows the microstructure at the same indent
position. Notice that even these SEM figures were taken in the same position, the contract between
different phases are not the same due to the fact that these sample surface were treated under different
condition (etched or un-etched); and then the sample were observed under the different SEM mode (SE or
BSE). The difference in methods of observing microstructure will results in the different SEM image, and
eventually could affect the value of manual point counting.

5. Figure 6. Difference in solute rich-α and β phase between different observation method. (red line represents solute rich-α phase
and blue line represents β phase)
To determine which method is the most suitable one for manual point counting, the result values were
compared between different methods. The criteria are to minimize the variance and determine which one
is clear to separate the β phase by eyes. From the Figure 6, the variance between different methods is
relatively small for β phase compared with solute rich-α phase. However, these variances were resulted
from the different contrast affect; for BSE-not etched sample, solute rich-α phase are likely over-counted
due to the low contrast and the β phase can not be seen clearly.
In this project and the following MATLAB phase separation algorithm, SE-etched method was chosen for
its bright contrast in the SEM image. The β phase are clearly shown in the image, which can minimize the
error in the value.
Phase Separation Algorithm
The routine created in the MATLAB is a quantitative characterization algorithm used to help
systematically understand the processing-property-microstructure correlation of HPDC magnesium
alloys. The routine includes several important parameters: montage creation and separation of different
phases. The accuracy and reproducibility of the routine will be compared with the manual point counting
values and ASTM standard.
-­‐‑10
0
10
20
30
Difference between BSE-Etched and Unetched
Difference=Unected - Etched
-­‐‑15
-­‐‑10
-­‐‑5
0
5
10
15
20
Difference between Etched-BSE and SE
Difference=BSE - SE

6. Figure 7. Flow chart of the phase separation algorithm
Figure 8. β phase and Bright edges in the SEM image
The flow chart of the code structure is shown in the Figure 7. In the separation process, one key step is
worth noting, as highlighted in the Figure 8. The bright edges around pores have similar brightness as to
the β phase, as a result, when running the phase separation algorithm, these edges will be count as β
phase. In order to eliminate the edge effect, a montage method was added before calculating the actual β
phase.
The montage method will select these bright edge out of the whole area, after that, the pure β phase can
be obtain by subtracting these bright edges. A schematic representation displays how this method works,
as shown in the Figure 9.
Import image
Crops off scale
bar
Set the intensity
level for each
unit
Binarize the
image
Separate out the
pores
Subtract the
pores
Select the beta
phase particles
Calculate the
beta phase
volume fraction

7.
(a) Binarization of the original image (b) dilation of the image
(c). Subtracting the bright edges from the binary image
Figure 9. Image processing in the Phase separation algorithm. (a) Binarization of the original image; (b) dilation of the image;
(c). Subtracting the bright edges from the binary image
Results
The MATLAB phase separation algorithm was applied to calculating the β phase of AM60 and AM70
alloys and compare the results with another algorithm written by Tracy Berman. The algorithm was used
to calculate the β phase results of AM70 with indents sample to test its accuracy compared with manual
point counting.
Table 1 summarizes the average value of β phase at 3 different locations. a) Edge: 0 to 50 µm from the
surface of the casting; b) Near Edge: 200 to 400 µm from the surface of the casting (centered at 300); c)
Center: 1/2 thickness of casting (1250 or 2500 µm) +/- 100 µm. Figure 10 and 11 graphically represent
the difference between two different phase separation algorithms for AM series alloy, and AM70 indents,
respectively.

8. Table 1 Summary of the average value of β phase at 3 different position on the surface of the alloys: edge; near edge; center.
Tracy’s
Algorithm
Algorithm
Ave
Std
Ave
Std
Difference
AM70_5p0_edge
3
0.6
3.3
1
-­‐0.3
AM70_5p0_near_edge
5
0.5
5.1
0.7
-­‐0.1
AM70_5p0_center
4.2
0.6
4.1
0.5
0.1
AM70_2p5_edge
7.5
0.9
6.5
1.1
1
AM70_2p5_near_edge
9.8
1.3
8.6
1.4
1.2
AM70_2p5_center
6.6
0.5
5.4
0.4
1.2
AM60_5p0_edge
1.5
0.6
1.4
0.6
0.1
AM60_5p0_near_edge
2.1
0.4
2
0.4
0.1
AM60_5p0_center
1.8
0.2
1.7
0.2
0.1
AM60_2p5_edge
2.2
0.3
2.1
0.4
0.1
AM60_2p5_near_edge
5.7
0.3
5.1
0.3
0.6
AM60_2p5_center
3.6
0.3
3.3
0.3
0.3
Figure 10. Plot of β phase distribution for different AM series alloys

9. Figure 11. Plot of β phase distribution for AM70 indents
Figure 10 graphically shows the data presented in the Table 1. From the results, some conclusions can be
made about the phase separation algorithm. The values return from the algorithm are similar to Tracy’s
algorithm results, even when there is some offset, the offset in different location are consistent, indicating
that the algorithm is reproducible. The offset was because the different threshold value chosen in the
algorithm. In Tracy’s algorithm, an average grey value was calculated and use this value as a standard to
binarize the image. In my algorithm, MATLAB can identify the threshold value automatically, and use
this default value to binarize the image. However, when comparing with the manual counting result of
AM70 indents, the difference varies along the location. The reason behind this is that, when doing the
AM60 and 70 series alloy manual counting, at least 10 images were taken at the same location, and the
results were averaged before comparing, which minimize the variance in the results; there is another
possibility that different people will have different manual point counting results, so the accuracy of this
algorithm is promising, but still need to test more image and compare with manual counting results from
different. After adequate data was collected, then we can draw our conclusion.
There is still some improvement can be made in this algorithm. For example, when there are some large
pores existing in the microstructure or the fraction of the pores is very high, the algorithm will return a
wrong value. This results from the fact that when doing the montage process, the area selected was too
large and the MATLAB focus on the wrong main area. When binarizing the image, most area will be
considered bright areas and return as “1” in the image matrix, and the outcome will be shown as white
occupying most surface area. There are two solutions might be applicable. First is that when doing the
SEM observation, those image with large pores should be avoided and areas with a clearer microstructure
should be selected. Second solution is used when we need to study the phase distribution near the pores.
The selection process should be added in the algorithm that when there are large pores existing in the
image, the average value of the image matrix is much lower than the normal one. When this situation
happens, the algorithm should select out this image and increase it contrast after the montage process.
This improvement will help solve the problem when meet the images with large pores in the
microstructure.
Figure 12. Image after montage process and binarization when there are large pores in the microstructure

10. Conclusion
In this this project, in order to eventually create the phase separation algorithm, several necessary
experimental steps were included. Experimental techniques included sample preparation (mounting,
grinding, and polishing), some experience with the SEM and the manual point counting using ImageJ. For
the manual counting, after comparing different result between different mode and sample preparation, SE-
etched was selected for use in both manual counting and phase separation algorithm. The benefit is that it
can clearly indicate whether we have a consistent result between manual counting and the algorithm. For
the phase separation algorithm, it separates the β phase successfully, and reduces the pore edge effect.
This algorithm could be improved by solving the problem for images with large fraction of pores and by
testing the reproducibility and accuracy with more experimental data.
References
1. ASTM. Standard Test Method for Determining Volume Fraction by Systematic Manual Point Count. 2014
2. Prakash, Regener. Quantitative characterization of Mg17Al12 phase and grain size in HPDC AZ91
magnesium alloy. 2008
3. John E. Allison. Phase transformation kinetics and alloy microsegregation in high pressure die cast
magnesium alloys. 2014