Paper ID #8867

Defect Simulation of AL319 in Lost Foam Casting – an REU UndergraduateResearch Experience

Dr. Ahmed H. Elsawy, Tennessee Technological University

Dr. Ahmed ElSawy joined Tennessee Technological University (TTU) as a Professor and Chairperson,Department of Manufacturing and Industrial Technology in July 1, 1999. He holds B.Sc., M.Sc. andPh.D. degrees in Mechanical Engineering with emphasis on Materials processing and Manufacturingengineering. Prior joining TTU Dr. ElSawy held several industrial and academic positions in the USAand abroad. Dr. ElSawy teaching and research interests are in the areas of material processing, metallurgyand manufacturing systems. Dr. ElSawy received ˜ $2M of state, federal, and industrial grants in supportof his laboratory development and research activities. He advised several masters and doctoral studentswho are holding academic and industrial positions in the USA, Germany and Taiwan. Dr. ElSawy hasnumerous publications in national and international conferences and refereed journals.

Prof. Mohamed Abdelrahman, Texas A&M University-KingsvilleDr. Sally J. Pardue, Tennessee Technological University

Sally Pardue, Ph.D. is an Associate Professor of Mechanical Engineering at Tennessee Tech University,and Director of the Oakley Center for Excellence in the Teaching of Science, Technology, Engineering,and Mathematics.

c©American Society for Engineering Education, 2014

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Defect Simulation of Aluminum Silicon 319 Alloy in Lost Foam Casting - a Summer

Research Experience for Undergraduates

Lauren J. Addie*, Ahmed H. ElSawy**, Sally Pardue***, and Mohamed Abdelrahman****

* Former Undergraduate Student of Manufacturing and Engineering technology,

College of Engineering, Tennessee Technological University

** Professor & Chair, Department of Manufacturing & Engineering Technology,

College of Engineering, Tennessee Technological University

*** Professor of Mechanical Engineering and Director of STEM Center, Tennessee

Technological University

**** Professor, Department of Electrical Engineering & Computer Science & AVP for

Research & Graduate Studies, Texas A&M University-Kingsville.

ABSTRACT The primary field of a multi-year Research Experience for Undergraduates (REU) project at

Tennessee Technological University (TTU) was industrial application of sensing, modeling and

control. The NSF funded research focus was in the metal casting industry, a multibillion dollar

industry that has been struggling as a result of foreign competition and lack of research

innovation. The industrial partners were General Motors, Foseco Morval, Inc, and Metal

Casting Technology, with government agency partnership at the Oak Ridge National Laboratory

(ORNL). The research process and results presented are an example of the undergraduate

research outcomes that were achieved with the “Student as Principal Investigator (SPI)”

mentoring model that the REU project employed over three years with more than 30 students.

This exemplar project examines the simulation of the solidification of Al319 in the Lost Foam

casting process and inspection of defects after rapid cooling techniques. SOLIDCast simulation

was used to generate images of the ideal metal fill, cooling data, and solidification images. The

simulation outcome was used to predict possible locations of defects. An experimental sand and

coating were used during production. It was found that the combination of styromol coating

in the experimental mullite sand produced the fastest cooling rate, and the combination of the

experimental coat in the control mullite sand produced the cast with the least number of internal

defects. The undergraduate student participated in this research experience received credits toward

her senior project capstone culminating experience in engineering technology. Moreover, the

student demonstrated her compliance with Criteria 3-Student Outcomes: a, b, c, d, f, and g.

Currently she is employed by GM Smyrna plant as Production Supervisor & Group Leader.

REU BACKGROUND

The REU project “Industrial Application of Sensing, Modeling, and Control” sought to enhance

the image of the metal casting industry as a source of high technology research opportunities via

multiple engineering and technology disciplines2. The project was based on a set of

multidisciplinary student-generated research questions pursued by teams of undergraduate

students and mentors from electrical, mechanical, and chemical engineering and industrial

technology. Each summer of the 3-year project, five students were recruited from TTU and 5

students were recruited through a nationw ide search with particular focus on minority institutes

and principally undergraduate institutions. These 10 students participated in a rigorous 9-week

summer schedule with some students conducting ongoing work in the following academic year.

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A key feature of the TTU REU project design was the “Student as the Principal Investigator

(SPI)” model1. The SPI model was developed by the PI and Co-PI as a result of their shared

commitment for developing students’ research capability through immediate engagement of

the student in their own learning, a holistic strategy inspired by “Design for Significant

Learning Experiences5”. The SPI instructional process began with an intensive One Week

orientation to the overall research program, and targeted training in research methods and project

management. Active learning techniques7 were used to introduce the multidisciplinary research

areas of casting and to generate group discussion of the primary steps to conducting engineering

research. The REU participants each formulated their own research question, developed

hypotheses, and planned the required steps to obtain the data needed answer their question. The

students took a public path of question development through the use of hand-written posters

in the meeting space. Peers, as well as the technical mentors, reviewed the posters throughout

the first week and made suggestions via post-it notes to refine the students’ research question and

their research plans. By the end of the first week, the students had become the PI of their own

research program. The TTU faculty mentors, graduate students, R&D engineers, and industry

mentors then became facilitators to the student PIs. Rigorous weekly reporting requirements

kept the projects on task and helped the mentors know what coaching might be required to help

the students make changes in their research approach. The REU participants’ outcomes at the

end of the nine weeks were publically showcased with a written report, a verbal presentation, and

a poster session.

The comprehensive approach of the SPI model ensured students were not only exposed to more

traditional aspects of “how-to-conduct specific research skills” with limited autonomy11, but also

how to independently design their own research program and be solely responsible for the

outcomes – start to finish. The body of this paper presents a student exemplar of the

undergraduate research achieved at TTU using the SPI model. The student- generated research

question was “How do production options such as coatings, sand, and vibration affect the nature

and quantity of defects in lost foam cast aluminum alloy parts?”

INTRODUCTION

In the casting industry, there is little and inconsistent data about the conditions that cause casting

defects. Aluminum alloys are seeing increased use due to their castability, high strength to

weight ratio, and corrosion resistance. Al 319 is a good choice for lost foam casting because of its

resistance to hot tearing, pressure tightness, and good fluidity8. Al 319 casts are prone to

defects such as gas porosity, shrinkage, oxide inclusions, and shrinkage porosity9. Other defects

prevalent in castings are folds, blisters, internal porosity, fatigue cracks, shrinkage, blowouts,

and voids10. It is thought that shorter solidification times will improve the quality of the Al-

alloy casts8.

In one experiment, Tschopp10 tested aluminum alloys 206, 319, 356, and 380. He found that small

blisters were found in the 206, and 319 alloys, while few or no folds were found in the 206 and

380 alloys. In another experiment, he found that the higher the velocity of the molten metal, the

more the number of defects observed increased10.

Coatings in the lost foam casting process are instrumental in the solidification of the cast. The

coating determines the shakeout time and the rate of metal heat loss14. Shakeout time is critical

to the manufacturability of a cast and, while heat loss is essential to improving the quality of the

cast. Boron nitride, BN, is a dry-film lubricant that is used as a releasing agent, a corrosion

inhabitant in high-temperature processes.

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Table 1 - Properties of water based Boron Nitride, BN

Molecular

Weight

Density

(g/cm)

Dielectric

Strength

(volts/mil)

Dielectric

Constant

Coefficient

of Friction

Thermal

Expansion

Specific

Heat at

298K

Thermal

Conductivity at

293K

24.83

2.27

800-1000

4

0.2-0.7

25-1000

0.117

cal/g-K

0.08

cal/(cm-sec-K)

In comparison to most ceramic materials, boron nitride has a high thermal conductivity. This

makes boron nitride an ideal additive to increase the thermal conductivity of the Styromol

coating. Boron nitride is completely inorganic and inert. It is non-reactive and non-wetted with

most molten metals. The water-based boron nitride coating is used for a variety of uses and was

used in this experiment to ensure thorough and complete mixing.

EXPERIMENTAL PROGRAM

SOLIDCast A high fusion foam pattern was provided. Dimensions were taken and the pattern was drawn in

ProEngineer Wildfire 2.0. After the reproduction was complete, the file was saved with an .stl

extension. The .stl file was imported to the SOLIDCast Program. SOLIDCast is a solidification

modeling software. Solidification simulation is the process of numerically modeling what

happens when metal is poured into a mold and the metal cools and solidifies. It is important to

model the shape of the casting, gating and risers for the program to properly function. Materials

and conditions are input and the simulation is run. Information such as fill time, cast weight,

sand weight, solidification curves and shrinkage porosity are outputs of the SOLIDCast program.

In addition to these outputs, a colored image is provided to show the temperature gradient of the

cast. The parameters were added and the simulation was run. The output data stated that the ideal

cast weight was 14.68 pounds and the ideal sand weight was 74.49 pounds. The mold fill time

was set for fifteen minutes. The program output the solidification curve. Figure 1 is the graphical

display of Al319. The white line is a curve of shrinkage as the metal cools. The blue line is the

curve of solidification as the metal cools.

Fig. 1 - SOLIDCast solidification Al319 solidification curve

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The pour simulation was run and images were captured as the cast began cooling in the sand.

The simulation ran for nine minutes. The metal was poured at 1130ºF and data ended at 740ºF.

The sample reached 100 percent solidification at 742ºF. Data was collected over a time of fifteen

minutes with a maximum temperature of 1278ºF and a final temperature of 728ºF.

The images in Figure 2 were taken during the pour simulation. Figure 2(a) was taken 3.5 minutes

after the simulation began and it shows molten metal entering the mold. Figure 2(b) was taken

5.9 minutes after the simulation began, showing the metal completely filling the mold as the

temperature begins to decrease. Figure 2(c) was taken 7.8 minutes after the simulation was

started. It shows the beginning of solidification. Figure 2(d) was taken 8.2 minutes after the

simulation began and it shows the continued solidification. Figure 2(e) was captured 8.9 minutes

after the simulation began, showing the final images of solidification.

SOLIDCast provided colored images of the temperature and temperature gradient as the pour

was completed. Figure 3(a) shows images of the temperature. This image shows that the cast

cools first at the gating system. Figure 3(b) is the temperature gradient. This allows predictions

as to where the hot spots are located. Where there is a hot spot, there is concern that a defect will

occur. As the cast cools the areas around the cylinders are the last to loose heat. This confirms

the fact that the main area of concern for defects is near the cylinders.

(a) (b)

(c) (d)

Page 24.355.5

(e)

Fig. 2 - SolidCast solidification simulation images

(a) (b)

Fig. 3 - SOLIDCast (a) Temperature and (b) Temperature Gradient

SAND TESTING Three mullite sands were available for testing. The sands were labeled according to their color

properties, red, brown, and green. Placing a soldering iron tip with a thermocouple attached in a

mold and covering the tip with sand tested the heat flow of each. The LabView data acquisition

program was turned on to gather data and the heat flow is given by subtracting the minimum

temperature from the maximum temperature. The heat flow was tested three times for each sand

type. The sand with the best heat flow rate for this experiment was the red sand. Figure 4 shows

the heat flow of the red sand is similar to that of the green sand.

COATING SELECTION

Styromol coating was used as a base for the coating selection. Water-based boron nitride was added

in 0, 25, 50, 75, and 100 percent concentrations in a 800mL mixture. The boron nitride and styromol

were thoroughly mixed. A series of tests were run on each coating to gather information to ensure

the best combination of the coatings was used in the experiment. Mesh were weighed and coated to

gather information on coating mass and permeability. Permeability was tested, with the Digital

Absolute Perimeter, by forcing air through the coated mesh. Viscosity was tested, with the

Brookfield DV-E Viscometer, by submerging the spindle in a flask containing 600mL of coating.

Page 24.355.6

Fig. 4 - Rate of heat flow for experimental sand

The heat flow of each sample was tested by placing the coating on a soldering iron tip with a

thermocouple attached. The LabView data acquisition program gathered data and the heat flow is

given by subtracting the minimum temperature from the maximum temperature. The heat flow

was tested three times for each coating. The average heat loss of the coatings of pure styromol,

25 percent, 50 percent, 75 percent, and pure boron nitride were 153.8, 158.6, 160.8, 164.2,

and169.0, respectively. With the information from the permeability, viscosity, and thermal

conductivity readings the 600mL styromol, 200mL boron nitride, 100mL water coating was

chosen as the coating to be used in the experiment.

Table 2 - Coating Tests

Styromol/BN/Water 800/0/0 600/200/100 400/400/100 200/600/0 0/800/0

Mesh Weight 1.53 1.58 1.55 1.59 1.56

Coat and Mesh Weight 3.23 2.84 2.71 2.51 2.34

Weight of Coat 1.7 1.26 1.16 0.92 0.78

Permeability

Run 1 17.2 15.2 13.7 8.6 2.7

Run 2 17.2 15.2 13.8 8.7 2.7

Average 17.2 15.2 13.75 8.65 2.7

Viscosity

Run 1 136.4 ERROR 383.4 ERROR 328.1

Run 2 123.3 ERROR 384.8 ERROR 330

Average 129.85 ERROR 384.1 ERROR 329.05

2.96

1.70 1.63 1.68

AIR

GR

EEN

SA

ND

BR

OW

N S

AN

D

RED

SA

ND

Ch

ange

in t

em

pe

ratu

re o

ver

tim

e (

F/se

c)

Heat Flow of sands

Heat Flow

Page 24.355.7

As seen in table 2, it was found that the experimental coating with the least mass was the pure boron

nitride sample at 0.78 grams, and the mass of the pure styromol coat was 1.7 grams. The experimental

coating with the best permeability was 25 percent concentration of boron nitride at 15.2, while the

styromol coating had a permeability of 17.2. Viscosity of each coating was also tested. The viscosity

test was run and two of the samples were too thick to gather a viscosity reading. ERROR was given

for the initial tests. 100mL of water was added to the two coatings. This did not improve the viscosity

readings, or lack thereof.

Fig. 5 - Rate of heat flow of coatings

PATTERN PREPARATION Three high fusion patterns were obtained from the foam inventory. The extraneous sections of

the patterns were removed and the pattern was cut into thirds using a band saw. The patterns

were marked prior to being cut to ensure they were exact in dimensions and cut location. Each

section of the pattern encases a cylinder. The cylinder is the main focus of the study and is the

area of concern when looking for both internal and external defects.

Fig. 6 - Images of the high fusion pattern provided for experimentation

2.353230769 2.366

2.440615385

2.474461538

2.526

2.599692308

AIR

80

0/0

60

0/2

00

40

0/4

00

20

0/6

00

0/8

00

Ch

ange

in t

em

pe

ratu

re o

ver

tim

e (

F/se

c)

Coating Heat Flow Rate

Heat Flow

Page 24.355.8

Gates were cut to six inches in length. They were applied to each of the pattern sections, in

similar locations. The gates were glued on using a wax and left to dry. While the gates were

drying, rectangles were cut into uncoated sprues. The sprues were cut to thirty inches in length

and the gating rectangles were cut three inches from the bottom of the sprue. One thermocouple

was added to each pattern in similar locations.

The patterns, attached gates, and thermocouple were labeled and dipped in the coatings. Three of

the samples were dipped in the experimental coat and the other three were dipped in the styromol.

The samples were hung to dry and checked for complete coverage. Once touch ups were

complete the patterns were left over night to dry. When the patterns were completely dried the

gates were fitted into the rectangle in the sprue and glued in place using the wax. Duct tape was

applied to the end of the sprue to keep sand from entering the mold when the molten metal was

poured, as seen in Figure 7.

Fig. 7 - Completed pattern preparation

MOLD PREPARATION AND POURING Each mold was prepared according to the variables or combination of variables that were being

tested. Chills were used in all pours.

Table 3 - Pour identification

Pour #

Coat

Sand

Viscosity of

Styromol

Duration of

Vibration

Pour 1 Styromol Mullite 120 ---

Pour 2 Boron

Nitride

Green 120 0:05:09

Pour 3 Styromol Green 120 0:05:35

Pour 4 Boron

Nitride

Mullite 120 0:04:21

Pour 5 Boron

Nitride

Mullite 136.1 ---

Pour 6 Styromol Green 136.1 ---

The proper sand and coatings were combined by placing enough sand in the bottom of the mold

to allow the sprue and pattern to stand freely. The thermocouple was stretched outside of the

Page 24.355.9

mold. The chill was placed in the center of each cylinder and the mold was carefully filled. The

molds were lightly vibrated to ensure that the sand was thoroughly packed around the foam

pattern.

Fig. 8 - Images of filling of mold cavity

The LabView data acquisition was turned on and the metal was poured. If the pour variables

called for vibration, the vibration table located in the Foundry was turned on low once the mold

was filled and continued until the solidification temperature of 960ºF was met. Once cooled, the

mold was emptied and the part was set aside to cool. This process was completed for each pour.

CAST CLEANUP AND SAMPLE GATHERING The cast cleanup required a wire brush and compressed air. The casts with the experimental coat

were slightly more difficult to clean, but produced a smoother, lubricated finish. The casts that

used vibration had external defects that were present. The vibration caused the coating to crack

and the metal took the shape of the sand grains. The vibrated casts were difficult to clean and had

many sharp edges, as the cracks took place primarily on the edges of the cast. These defects were

not removed or polished prior before the sample was taken.

Fig. 9 (L to R) Images of the control cast and the experimental cast

with vibration prior to cleanup

Samples were gathered by cutting metal from the same location from each cast. The sample was

cut to include the inner cylinder. Each sample was labeled and cut smaller to be mounted and

Page 24.355.10

examined under the microscope. The samples were mounted, polished, and etched. The etchant

used was Keller’s 3A. Keller’s 3A is composed of 190mL distilled water, 5mL HNO3, 3mL HCl,

2mL HF (ASM Handbook). The solution was thoroughly mixed. The etchant was applied to each

sample using a cotton swab for twelve seconds and dried. The sample was then examined under

the microscope.

METALLOGRAPHY

Each sample was examined under a magnification of 100x and 400x. The 100x magnification

provided images of where the defects were located. Each defect was then looked at with 200x

magnification. The center of each sample was used to compare the number of internal defects in

each sample. Pour five, which consisted of the experimental coat in the experimental sand, had

the highest number of internal defects. Pour two, which consisted of the experimental coat in the

control sand with vibration, has the fewest number of internal defects. In figure 10, the black

areas represent holes in the sample space.

Fig. 10 (L to R) Images of 100x magnification of pours five and two

The 400x magnification was used to examine the silicon structures and the smaller, less

detrimental defects. Pour one, which consisted of the experimental coat in the control sand, had

the highest number of small defects. Pour two, which consisted of the experimental coat in the

control sand with vibration, had the fewest number of small defects. Figure 11 shows the silicon

in the sample. The spherical shapes in the images are the less detrimental defects.

Fig. 11 (L to R) Images of 400x magnification of pours one and two

Page 24.355.11

CONCLUSION

Exciting research endeavors in traditional manufacturing processes provide undergraduate

students with opportunities to understand the scientific research process. The 9-weeks period for

the summer research experience may not be long enough to completely answer a comprehensive

research question. However, the student as PI model offered the student a chance to learn the

fundamentals of proposing a research question and design an experimental methodology to

address the question. Through this REU experience, the student showed that the experimental

coating provided the fewest number of internal defects, but the highest number of external

defects. The experimental sand provided the highest number of internal defects. The use of

vibration caused severe internal and external defects.

The experimental results allowed for conjecturing that: 1) The introduction of more effective

methods of cooling the cast should help reach a more readily seen conclusion; 2) The testing of

different pouring mediums should be beneficial in many different aspects; 3) Using a

standardized method for quantifying defects will lead to more statistically significant data.

ACKNOWLEDGMENTS

This work was supported by the National Science Foundation grant number EEC-0552860,

Research Experiences for Undergraduates (REU) Industrial Applications of Sensing, Modeling,

and Control. Additional thanks to Dr. Mike Baswell for his assistance in the foundry pouring

molten aluminum and to Mr. Wayne Hawkins for his assistance in preparing specimens for

metallography and analysis.

BIBLIOGRAPHY

1. Abdelrahman, M. and Pardue, S., “An REU Experience on the Industrial Applications of

Sensing, Modeling And Control,” Conference Proceedings of ASEE-SE Regional

Conference, April 2008, Memphis, TN.

2. Abdelrahman, M., Pardue, S., Baswell, M., K. Currie, K.,“Addressing the Image and

Human Resource Issues of Casting Industry through Multidisciplinary Research

Experience for Undergraduates,” AFS Transactions 2007, Vol. 115, Paper 07-051(13).

3. Alagarsamay, A., “Casting Defect Analysis Procedure and Case History,” Keith Mills

Symposium on Ductile Iron (2003).

4. Baskerud, L., Chai, G., Tamminen, J., Solidification Characteristics of Aluminum Alloy

Volume 2: Foundry Alloys, pp. 64-65, 86-87, 128-129, American Foundry

Society/Skanaluminum, Des Plaines, IL (1990).

5. Fink, L. Dee, Creating Significant Learning Experiences: An Integrated Approach to

Designing College Courses, Jossey-Bass Publishers, San Francisco, 2003.

6. Finite Solutions. Solid Cast: PC Based Casting Solidification Modeling Software, volume

6.2. Finite Solutions, Incorporated, 2003.

7. Johnson, David W., Roger T. Johnson, and Karl A. Smith, Active Learning: Cooperation

in the College Classroom, Interaction Book Company, Edina, Minnesota, 1998

8. Hess, D.R., “Comparison of Aluminum Alloys and EPS Foams for Use in the Lost Foam

Casting Process,” AFS Transactions, vol 112 (2004).

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9. Hess, D.R, Durham, B., Ramsay, C.W., Askeland, D.R., “Observations on the Effect of

Pattern and Coating Properties on Metal Flow and Deformation in Aluminum Lost Foam

Castings,” AFS Transactions, vol 110 (2002).

10. Tschopp, M.A., “Mechanisms of Formation of Pyrolysis Defects in Aluminum Lost

Foam Castings,” M.S Thesis, University of Missouri-Rolla, 1999.

11. Thurgood, Lori, Christopher Ordowich, and Prudy Brown. 2010. “Research Experiences

for Undergraduates (REU) in the Directorate for Engineering (ENG): Follow-up of FY

2006 Student Participants.” Technical Report to the National Science Foundation. SRI

International: Arlington, VA.

12. Vander Voort, George F. AMS Handbook: Metallography and Microstructures, volume 9.

ASM Intl,, 2004.

13. Wang, Q.G., Aeplian, D., Arnberg, L., Gulbrandsen-Dahl, S., Hjelen, J., “Solidification

of the Eutectic Phase in Hypoeutectic Al-Si Alloys,” AFS Transactions, vol 107 (1999).

14. Zhao, Q., Biederman, S., Flemings, M., “The Effects of Coating on the Heat Transfer in

Lost Foam Aluminum Process,” AFS Transactions, vol 114 (2006).

15. Zhao, Q., Wang, H., Biederman, S., Jason, D., Parish, J.S., “Lost Foam Casting Coating

Characterization: Heat and Mass Transfer,” AFS Transactions, vol 113 (2005).

16. http://www.finitesolutions.com/

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