2020 AFS Proceedings of the 124th Metalcasting Congress Paper 2020-085 (6 pages)

Effect of Defect Size on Tensile Elongation in Aluminum

Anthony J. Koprowski General Motors Powertrain, Saginaw, Michigan

Dr. Qigui Wang, Dr. Peggy Jones, Dr. Thomas Gustafson General Motors, Pontiac, Michigan

Copyright 2020 American Society

ABSTRACT shuts, etc. These may be small in comparison to the entire but when looked at in the cross section of a test This paper will study the effect of defect size (area) on % bar, they become a premature failing point. Elongation of tensile bars for various alloys, heat treatments, bar types, and bar sizes. Although this Many studies have been done to understand the effect of particular study will not take into account the effect of the tensile behavior of aluminum alloys on entrapped gas type of defects, i.e. gas, shrink, intermetallics, etc. nor porosity, shrink porosity, oxide films, and . It has will it take into account β€œas cast” skin on die cast tensile been shown that tensile properties of a test bar have little bars, irregular shapes of flat tensile bars cut from parts, it or no correlation to the bulk (larger area) material quality will look at the effect that total defect volume versus the but are consistent with the area fraction of the defects in fracture surface area has on the % elongation of the test fracture surface of the sample(1) which tends to be bar. It will show that there is good correlation to total localized. To state this another way, the sizes and shape of defect size and the reduction of % elongation on the the defects as well as the type of tensile sample will also samples. determine the degree of effect that the defects have on the Keywords: Elongation, defect, tensile, ECD, failure, bar. The smaller the cross section the smaller the defect volume fraction needed to affect the mechanical properties(2). Samples with large and/or abnormally high numbers of intermetallics can also affect the behavior of the tensile bars. It has also been shown that the solidification cooling INTRODUCTION rate, correlated to Secondary Dendritic Arm Spacing (SDAS), can affect the mechanical properties(3). Ceschini Tensile properties are known to provide a wealth of et al. showed that the change in cooling rate from 45 oC/s information about the products and materials being made. to 20 oC/s for the same alloy can produce an 80% change The tensile property data can show whether the proper in elongation. This is the change in SDAS of 50 Vs 10 material and manufacturing process, especially heat m. It also affects the UTS by as much as 25% and Ys as treatment, is used. It further acts as a valuable quality much as 13%. Finally, the heat treatment(3) can play a control tool that shows trends and can point to possible significantπœ‡πœ‡ role in the outcome of tensile properties. problems before they occur.

In addition, sample geometry can significantly affect the The tensile testing process is based on Hook’s Law and tensile properties for the same bulk material. Anilchandra designed using the stress strain behavior of the material, et al. (6) evaluated the tensile properties of aluminum see appendix β€œA” for a full explanation. The important foundry alloys through reference castings and found that aspects of the testing process are that it gives the just the shape of the bar can cause as much as a 50% manufacturer a snapshot of the material conditions of a reduction in elongation and a reduction of ultimate tensile product from the perspective of the ultimate tensile strength as much as 11%. The yield strength does not strength, the yield strength, modulus of elasticity, and seem to be affected. In their study, the surface finish of elongation. These can be compared to the print or product samples was, Table, 1removed. The crosshead speed used specification(s) and can be used to accept or reject a was 2 mm/min and the strain was measured using a 25- batch, heat, load, etc. Following the trends of the mm extensometer. Experimental data were collected and properties can reveal shifts in the process. The material processed to provide yield stress (YS or 0.2% proof then can be investigated and adjusted before a critical stress), ultimate tensile strength (UTS), and elongation to level is reached. In some instances, it is a regulatory issue. fracture (%EL). At least 10 tensile tests were conducted The data helps confirm that critical components will not for each condition. The specimens were maintained at fail under normal conditions. One problem is that test bars room temperature for five months before testing. Table 1 from castings are susceptible to fail prematurely because summarizes the tensile strength of the alloys tested.” of natural defects in the material, voids, oxides, cold

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DEFECT ANALYSIS PROCESS:

Aluminum tensile bars normally fail by microvoid coalescence (MVC). This proceeds in 3 phases of growth during testing. The first is nucleation. This is the damage to the microstructure during the beginning of plastic deformation or stretching. It can be responsible for the onset of plastic instability in the sample. The second is growth of cylindrical and spherical voids in the material by plastic flow or linking of voids as it is plastically deformed. This can be exaggerated by pre-existing voids or other defects. This is the point where visible necking can be seen in a tensile specimen. The final phase is void coalescence where the voids link to such an extent as to produce the failure of the test bar.

Because of heat treatment and alloy differences, the tensile samples have been assembled into 4 groups of Fig. 2 Original magnification 40X SEM image This is parts: the components made from A356-T6 Aluminum; the same tensile bar area as seen by SEM in Fig. 1. the components made from A356-T6 Aluminum with a This gives a much clearer view of the defect 0.5% Copper addition; the components made from boundaries and defines the perimeter of the defect. AlSi8MnMg+Sr; and the components made from The SEM confirms this is shrink porosity. 319T7+Sr Aluminum. It should be noted that the types of defects, i.e. shrink porosity, oxides, gas porosity, Further, the accuracy of measuring the defect area is laminations, cold shuts, etc., were not separated out while higher using the SEM versus the stereoscope (Figs. 3 and taking the data. 4). In this study, a stereoscope at 20 to 40X was used to locate and map the defects on the fracture surfaces. Prior The defects on the fracture surfaces were examined with to examination, the samples were then cleaned using the SEM. The use of the SEM gives a better, clear vision Acetone and an ultrasonic cleaner for approximately 20 of the fracture surface over a stereoscope or depth of field minutes. After cleaning, the samples were rinsed in microscope (Figs. 1 and 2). alcohol and blown dry with compressed clean air. The fracture surfaces were examined using the SEM/EDAX equipment at SMCO (Hitachi S-4000/EDAX Octane Plus).

Fig. 1. Original magnification 30X stereoscope fracture surface of a tensile bar showing a large defect. The defect appears to be an area of dendritic shrink porosity. Fig. 3 Original magnification 30X stereoscope. This is the same area as in Fig. 1 after it was measured using Clemex software. Note the edge is not well defined; getting exact measurements is difficult because of the depth of field. Compare this to Fig. 4, which is the measured picture of the SEM image.

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Fig. 4 Original magnification 40X SEM image shown in Fig. 2 after measuring the defect with Clemex software. Note that the depth of field is clearer, and the ability to capture the full area of defect easier. When compared to Fig. 3, the defect area, perimeter and ECD are larger than with a stereoscope image. Fig. 5 Original magnification 40X SEM image The SEM allows for a composite of the fracture surface to be made and this allows defects to be easily seen and A composite of the tensile fracture surface was made at mapped. Defects can be measured at higher 40X, Fig. 5. This allowed for defects to be examined. As magnifications if needed. part of the examination of the defects, EDAX data was taken on the largest defects to determine if they were related to intermetallics, oxides, etc. The defects were digitally photographed as TIF files along with the calibrated marker that EDAX places on the image. The digital pictures were then exported to Adobe Photoshop CC 2017. This program allows one to calibrate the drawing function to the micron marker and set an accurate scale to allow for measurements. The lines, squares, and circles are now calibrated. These geometric figures are automatically measured and can be recorded. The β€œLasso Tool” function of Photoshop allows for irregular areas to be measured and recorded. The data can be put on the photographs, see Fig. 6. This allows the area and perimeter of the defects to be measured accurately. The field of view was also measured to allow for a volume fraction to be calculated for the defect.

Fig. 6 Original magnification 100X SEM image. One section of a flat bar from AlSi8MnMg+Sr showing multiple defects.

The total defect area for this portion of the bar is 60.952 Β΅m2. The ECD value for this portion of the bar is 179 Β΅m. When the entire test bar was measured, it had a defect area of 632,604 Β΅m2 and an ECD for the sample of 962 Β΅m. The % Elongation for this sample is 5.29% and the print requires 6% minimum. This falls in line with the paper’s predicted requirements to pass % Elongation of defects with areas less than 350,000 Β΅m2 and an ECD of less than 740 Β΅m.

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The data was placed into an Excel spread sheet to allow (Fig. 9). Volume fraction was determine as shown in for graphic interpretation of the data. The data was sorted Equation 2. using the % elongation from low to high. The chart function in Excel was used to produce the graphs used in ( ) = this report. (Figure 7). ( ) Eqn. 2 𝑽𝑽𝑽𝑽𝑻𝑻𝑽𝑽𝑽𝑽𝑽𝑽 𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭𝑭 𝑽𝑽𝑽𝑽 𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨⁄𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨 𝒐𝒐𝒐𝒐 𝒕𝒕𝒕𝒕𝒕𝒕 𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻 𝑩𝑩𝑩𝑩𝑩𝑩 𝑿𝑿 𝟏𝟏𝟏𝟏𝟏𝟏

Fig. 7. Data from our flat test bar, die cast, from Fig. 9. Data from our flat test bar, die cast, from AlSi8MnMg+Sr showing that when the total area of the AlSi8MnMg+Sr showing that when the Volume total reaches 350,000 Β΅m2 the bars will not pass the Fraction of the total defect size reaches 1.7% the bars 6% elongation requirement of the print. will not pass the 6% Elongation requirement of the print. A second way of displaying the data is to show % Elongation vs ECD (Fig. 8). ECD is defined as the RESULTS AND DISCUSSION β€œEquivalent Circle Diameter.” Once the defect area is Based on the data taken from our tensile test bars, there is measured and the data collected, the defect area can be a reasonable correlation between defect size and the % calculated to ECD using Equation 1: elongation achieved by the test samples. The correlation becomes more accurate when the defect area is converted to (ECD). The critical defect size and elongation = Eqn. 1 correlation is dependent upon the tensile bar size, shape, alloy used, and heat treatment. The curve fitting of the % 𝑬𝑬𝑬𝑬𝑬𝑬 οΏ½πŸ’πŸ’ 𝑿𝑿 𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨 ⁄𝝅𝝅 Elongation and ECD value of the defect size provides usable value for an assignable cause as to why the test bars do not meet the print requirements for % Elongation, but may meet the requirements of Ultimate Tensile Strength, Yield Strength, Modules of Elasticity, and Reduction of Area. This was originally demonstrated by Lee, et al. (5).

The two charts (Fig. 10) show a change in UTS and YS of about 10% whereas the % elongation changes by about 75% for volumetric porosity. The change is more striking for Fractographic Porosity where the UTS and YS changes 26% and where the % elongation changes by 90%. It should be noted that the Volumetric Porosity (%) is performed on a cross section whereas the Fractographic Porosity (%) is performed on the actual fracture surface, Fig. 8. Data from our flat test bar, die cast, from similar to the evaluation in this paper. AlSi8MnMg+Sr showing that when the ECD of the total defect size reaches 740 Β΅m the bars will not pass the 6% Elongation requirement of the print.

The third method of examining the data was to use Volume Fraction, i.e. % Elongation Vs Volume Fraction

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Fig. 12. β€œThere is a good correlation between defect size and fatigue life. The larger the defect size, the shorter the fatigue life. To meet 100% plus fatigue life@77Mpa, the maximum allowable defect size (ECD – equivalent circle diameter) is about 250Β΅m.” (7,8)

CONCLUSIONS 1. The data shows that there is a reasonable correlation Fig. 10. Tensile properties of A356 alloy on the between the total defect size (area) and % Elongation microporosity variation; the volumetric porosity (a) that can be used as a guide for reasons that tensile and fractographic porosity (b).(5) bars do not meet print requirements. This will allow For fatigue bars, Jones and Wang(7,8) have shown that for the decision to accept or reject the product to be the defect size is critical size in order to reach required based on more than just the tensile bar elongation fatigue life. For 6 mm fatigue samples this value is numbers. approximately 250 Β΅m. Once this size is exceeded, the 2. The components made from A356-T6 Aluminum and defect will cause the bars to fail at less than 100% Stress AlSi8MnMg+Sr Aluminum samples have enough Time Percent (STP) regardless of casting processes and data to be considered statistically acceptable. chemistry. The data further indicates that the bar may not 3. The data for The components made from Aluminum represent the bulk properties of the castings being tested A356-T6 with a 0.5% copper addition and 319 T7+Sr (Figs. 11 and 12). Aluminum samples needs to have more data points to ensure that the values are accurate but should be used as a guideline until a statistically significant number of data points are obtained. 140 y = 327.63x-0.2827 R2 = 0.85 Solid Triangles=Gravity LF 4. Based on this data, the following table was created to 120 Open Triangles=LP LF Open Circle=Handtmann Gr LF indicate critical flaw size (area, ECD, or Volume A356+Sr+Ti, Pressure 100 Circle=LP PS Fraction) for the different products tested. Square = 5. These critical sizes only affect the tensile bar data 80 Open Diamond=Squeeze and do not necessarily reflect a critical problem with 60 the casting; therefore, each case must be examined to 40

10e7 HCF (MPa) Strength ensure that there is not a significant deviation in the Away from 20 normal process when tensile failures are found.

0 0 100 200 300 400 500 600 700 800 Critical Flaw Size Product Type Maximum Pore Size (microns, metallographic measurement) Alloy AL 356 AlSi7Mg(Fe) AL 365A AL 319 Print % Elongation 1.8 6.0 1.5 1.0 Fig. 11. Fatigue strength primarily controlled by ECD (Β΅m) 650 740 1,550 1,000 maximum pore size(7,8) Area (Β΅m2) 700,100 350,000 1,950,000 800,000 Volume Fraction (%) 2.5 1.7 6.8 1.25 SDAS Average 28 16 38 21 Mean % Porosity 0.18 0.22 0.13 0.11 Number of Samples 41 91 20 31 REFERENCES 1. CΓ‘ceres, C. H., and Selling, B. I., β€œCasting Defects and the Tensile Properties of an AlSiMg

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Alloy,” Materials Science and Engineering: A, vol. 220, no. 1-2, pp. 109–116 (1996) doi:10.1016/s0921-5093(96)10433-0 2. Teng, X., Mae, H., Bai, Y., and Wierzbicki, T., β€œPore size and fracture ductility of aluminum low pressure die casting,” Engineering Fracture Mechanics, 76(8), pp. 983-996 (2009). doi:10.1016/j.engfracmech.2009.01.001 3. L Ceschini, L., Boromei, I., Morri, A., Seifeddine, S., and Svensson, I. L., β€œMicrostructure, tensile and fatigue properties of the Al–10%Si–2%Cu alloy with different Fe and Mn content cast under controlled conditions,” Journal of Materials Processing Technology, 209 (15–16), 5669–5679 (2009) doi:10.1016/j.jmatprotec.2009.05.030 4. Callister, W. D., and Rethwisch, D. G., β€œMaterials science and engineering: An introduction,” Wiley, Hoboken, NJ (2014). 5. Lee, C. D., β€œEffects of microporosity on tensile properties of A356 aluminum alloy,” Materials Science and Engineering: A, 464(1–2), 249–254 (2007) doi:10.1016/j.msea.2007.01.130 6. Anilchandra, A., Arnberg, L., Bonollo, F., Fiorese, E., and Timelli, G., β€œEvaluating the Tensile Properties of Aluminum Foundry Alloys through Reference Castingsβ€”A Review,” Materials, 10(9), 1011 (2017). doi:10.3390/ma10091011 7. Jones, P., and Wang, Q., β€œWhy are our Fatigue Data highly variable? What should we do about it?” GM Presentation (March 5, 2013). 8. Wang, Q., β€œV8 Fatigue Failures (Metallurgical Analysis),” GM Presentation (August 20, 2015).

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