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

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 1000oC/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  

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

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.

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.

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Difference between BSE-Etched and UnetchedDifference=Unected - Etched

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Difference between Etched-BSE and SEDifference=BSE - SE

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

unitBinarize the

image

Separate out the pores

Subtract the pores

Select the beta phase particles

Calculate the beta phase

volume fraction

 

(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.

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

 

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

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