June 15, 2014
June 15, 2014
June 18, 2014
24.355.1 - 24.355.13
Defect Simulation of AL319 in Lost Foam Casting – a REU Undergraduate Research ExperienceLauren J. Addie*, Ahmed ElSawy**, Sally Pardue***, and Mohamed Abdelrahman*****Manufacturing Engineer at GM, ** Faculty Advisor, Department of Manufacturing andEngineering Technology and ***REU Project Advisor, College of Engineering,Tennessee Technological University and ****Project Advisor, Associate VP forGraduate Studies and Research, Texas A&M Kingsville.ABSTRACTUndergraduate students who participate in hands-on research are more likely to pursuecareers in science, technology, engineering and mathematics (STEM) fields. It supportthe students learning outcomes a-k for the ETAC/ABET accreditation. Our formerstudent who went through this experience is employed currently as ProductionSupervisor/Group Leader-Power Train Assembly at GM-Springfield, TN. Thisundergraduate research experience examines the simulation of the solidification of Al319in the Lost Foam process and inspection of defects after rapid cooling techniques areapplied. SOLIDCast was used to generate images of the ideal metal fill, cooling data, andsolidification images. The outcome was used to predict possible locations of defects. Anexperimental sand and coating were used. It was found that the combination of StyromolCoating in the Experimental Mullite Sand produced the fastest cooling rate, and thecombination of the Experimental Coat in the Control Mullite Sand produced the cast withthe least number of internal defects.INTRODUCTIONIn the casting industry, there is little and inconstient data about the conditions that causecasting defects. Aluminum Alloys are seeing increased use due to their castability, highstrength to weight ratio, and corrosion resistance. Al319 is a good choice for Lost FoamCasting because of its resistance to hot tearing, pressure tightness, and good fluidity(Hess, 2004). Al319 casts are prone to defects such as gas porosity, shrinkage, oxideinclusions, and shrinkage porosity (Hess, 2002). Other defects prevalent in castings arefolds, blisters, internal porosity, fatigue cracks, shrinkage, blowouts, and voids (Tschopp,1999). It is thought that shorter solidification times will improve the quality of the Al-alloy casts (Hess, 2004).In one experiment Tschopp tested aluminum alloys. He tested 206, 319, 356, and 380. Hefound that small blisters were found in the 206, and 319 alloys, while few or no foldswere found in the 206 and 380 alloys. In another experiment, he found that the higher thevelocity of the molten metal the more the number of defects observed increased(Tschopp, 1999).Coatings in the Lost Foam Casting process are instrumental in the solidification of thecast. The coating determines the shakeout time and the rate of metal heat loss (Zhao,2006). Shakeout time is critical to the manufacturability of a cast and, heat loss isessential to improving the quality of the cast. Boron nitride, BN, is a dry-film lubricantthat is used as a releasing agent, a corrosion inhabitant in high-temperature processes. Table 1 Properties of water based Boron Nitride, BN Dielectric ThermalMolecular Density Strength Dielectric Coefficient Thermal Specific Heat Conductivity Weight (g/cm) (volts/mil) Constant of Friction Expansion at 298K at 293K 0.117 24.83 2.27 800-1000 4 0.2-0.7 25-1000 0.08 cal/g-K cal/(cm-sec-K)In comparison to most ceramic materials, boron nitride has a high thermal conductivity. This makes boronnitride an ideal additive to increase the thermal conductivity of the Styromol coating. Boron Nitride iscompletely 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 will be used in this experiment to ensurethorough and complete mixing.EXPERIMENTAL PROGRAMSOLIDCastA high fusion foam pattern was provided. Dimensions were taken and the pattern wasdrawn in ProEngineer Wildfire 2.0. After the reproduction was complete, the file wassaved with an .stl extension. The .stl file was imported to the SOLIDCast Program.SOLIDCast is a solidification modeling software. Solidification simulation is the processof simulating what happens when metal is poured into a mold and the metal cools andsolidifies, on a computer. It is important to model the shape of the casting, gating andrisers for the program to properly function. Materials and conditions are input and thesimulation is run. Information such as fill time, cast weight, sand weight, solidificationcurves and shrinkage porosity are outputs of the SOLIDCast program. In addition to theseoutputs, 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 theideal cast weight was 14.68 pounds and the ideal sand weight was 74.49 pounds. Themold 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 themetal cools. The blue line is the curve of solidification as the metal cools.The pour simulation was run and images were captured as the cast began cooling in thesand. The simulation ran for nine minutes. The metal was poured at 1130ºF and dataended at 740ºF. The sample reached 100 percent solidification at 742ºF. Data wascollected over a time of fifteen minutes with a maximum temperature of 1278ºF and afinal temperature of 728ºF.The images in figure 2 were taken during the pour simulation. Figure 2(a) was taken 3.5minutes after the simulation began. It shows molten metal entering the mold. Figure 2(b)was taken 5.9 minutes after the simulation began. It shows the metal completely fillingthe mold as the temperature begins to decrease. Figure 2(c) was taken 7.8 minutes afterthe simulation was started. It shows the beginning of solidification. Figure 2(d) was taken8.2 minutes after the simulation began. It shows the continued solidification. Figure 2(e)was captured 8.9 minutes after the simulation began. It shows the final images ofsolidification. Fig. 1 SOLIDCast solidification Al319 solidification curve(a)(b)(c)(d) (e) Fig. 2 Solidcast solidification simulation imagesSOLIDCast provided colored images of the temperature and temperature gradient as thepour was completed. Figure 3(a) shows images of the temperature. This image shows thatthe cast cools first at the gating system. Figure 3(b) is the temperature gradient. Thisallows predictions as to where the hot spots are located. Where there is a hot spot, there isconcern that a defect will occur. As the cast cools the areas around the cylinders are thelast to loose heat. This confirms the fact that the main area of concern for defects is nearthe cylinders. (a) Fig. 3 SOLIDCast (a)Temperature and (b)Temperature GradientSAND TESTINGThree mullite sands were available for testing. The sands were labeled according to theircolor properties, red, brown, and green. Placing a soldering iron tip with a thermocoupleattached in a mold and covering the tip with sand tested the heat flow of each. TheLabView data acquisition program was turned on to gather data and the heat flow isgiven by subtracting the minimum temperature from the maximum temperature. The heatflow was tested three times for each sand type. The sand with the best heat flow rate forthis experiment was the red sand. Figure 4 shows the heat flow of the red sand is similarto that of the green sand. Heat Flow of sands Change in temperature over time (F/sec) 3.50 3.00 2.50 2.00 Heat Flow 1.50 1.00 0.50 0.00 air brown green sand red sand sand Fig. 4 Rate of heat flow for experimental sandCOATING SELECTION Styromol coating was used as a base for the coating selection. Water-based boron nitridewas added in 0, 25, 50, 75, and 100 percent concentrations in a 800mL mixture. Theboron nitride and styromol were thoroughly mixed. A series of tests were run on eachcoating to gather information to ensure the best combination of the coatings was used inthe experiment. Mesh were weighed and coated to gather information on coating massand permeability. Permeability was tested, with the Digital Absolute Permmeter, byforcing air through the coated mesh. Viscosity was tested, with the Brookfield DV-EViscometer, by submerging the spindle in a flask containing 600mL of coating.As seen in table 2, it was found that the experimental coating with the least mass was thepure boron nitride sample at 0.78 grams, and the mass of the pure styromol coat was 1.7grams. The experimental coating with the best permeability was 25 percent concentrationof boron nitride at 15.2, while the styromol coating had a permeability of 17.2. Viscosityof each coating was also tested. The viscosity test was run and two of the samples weretoo thick to gather a viscosity reading. ERROR was given for the initial tests. 100mL ofwater was added to the two coatings. This did not improve the viscosity readings, or lackthereof. 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 329.05The heat flow of each sample was tested by placing the coating on a soldering iron tipwith a thermocouple attached. The LabView data acquisition program gathered data andthe heat flow is given by subtracting the minimum temperature from the maximumtemperature. The heat flow was tested three times for each coating. The average heat lossof the coatings of pure styromol, 25 percent, 50 percent, 75 percent, and pure boronnitride were 153.8, 158.6, 160.8, 164.2, and 169.0, respectively. With the informationfrom 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 theexperiment. Change in temperature over time (F/sec) Coating Heat Flow Rate 2.65 2.6 2.55 2.5 2.45 Heat Flow 2.4 2.35 2.3 2.25 2.2 600/200 400/400 200/600 800/0 0/800 Air Fig. 5 Rate of heat flow of coatingsPATTERN PREPARATIONThree high fusion patterns were obtained from the foam inventory. The extraneoussections of the patterns were removed and the pattern was cut into thirds using a bandsaw. The patterns were marked prior to being cut to ensure they were exact in dimensionsand cut location. Each section of the pattern encases a cylinder. The cylinder is the mainfocus of the study and is the area of concern when looking for both internal and externaldefects. Fig. 6 Images of the high fusion pattern provided for experimentationGates 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 gateswere drying, rectangles were cut into uncoated sprues. The sprues were cut to thirtyinches in length and the gating rectangles were cut three inches from the bottom of thesprue. 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 weredipped in the styromol. The samples were hung to dry and checked for completecoverage. Once touch ups were complete the patterns were left over night to dry. Whenthe patterns were completely dried the gates were fitted into the rectangle in the sprue andglued in place using the wax. Duct tape was applied to the end of the sprue to keep sandfrom entering the mold when the molten metal was poured, as seen in figure 7. Fig. 7 Completed pattern preparationMOLD PREPARATION AND POURINGEach mold was prepared according to the variables or combination of variables that werebeing tested. Chills were used in all pours. Table 3 Pour identification Viscosity of Duration of Coat Sand Styromol 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 ofthe mold to allow the sprue and pattern to stand freely. The thermocouple was stretchedoutside of the mold. The chill was placed in the center of each cylinder and the mold wascarefully filled. The molds were lightly vibrated to ensure that the sand was thoroughlypacked around the foam pattern. Fig. 8 Images of filling of mold cavityThe LabView data acquisition was turned on and the metal was poured. If the pourvariables called for vibration, the vibration table located in the Foundry was turned onlow once the mold was filled and continued until the solidification temperature of 960ºFwas met. Once cooled, the mold was emptied and the part was set aside to cool. Thisprocess was completed for each pour.CAST CLEANUP AND SAMPLE GATHERINGThe cast cleanup required a wire brush and compressed air. The casts with theexperimental coat were slightly more difficult to clean, but produced a smoother,lubricated finish. The casts that used vibration had external defects that were present. Thevibration caused the coating to crack and the metal took the shape of the sand grains. Thevibrated casts were difficult to clean and had many sharp edges, as the cracks took placeprimarily on the edges of the cast. These defects were not removed or polished priorbefore the sample was taken. Fig. 9 (L to R) Images of the control cast and the experimental cast with vibration prior to cleanupSamples were gathered by cutting metal from the same location from each cast. Thesample was cut to include the inner cylinder. Each sample was labeled and cut smaller tobe mounted and examined under the microscope. The samples were mounted, polished,and etched. The etchant used was Keller’s 3A. Keller’s 3A is composed of 190mLdistilled water, 5mL HNO3, 3mL HCl, 2mL HF (ASM Handbook). The solution wasthoroughly mixed. The etchant was applied to each sample using a cotton swab fortwelve seconds and dried. The sample was then examined under the microscope.METALLOGRAPHYEach sample was examined under a magnification of 100x and 400x. The 100xmagnification provided images of where the defects were located. Each defect was thenlooked at with 200x magnification. The center of each sample was used to compare thenumber of internal defects in each sample. Pour five, which consisted of the experimentalcoat 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 thefewest number of internal defects. In figure 10, the black areas represent holes in thesample space. Fig. 10 (L to R) Images of 100x magnification of pours five and twoThe 400x magnification was used to examine the silicon structures and the smaller, lessdetrimental defects. Pour one, which consisted of the experimental coat in the controlsand, had the highest number of small defects. Pour two, which consisted of theexperimental coat in the control sand with vibration, had the fewest number of smalldefects. Figure 11 shows the silicon in the sample. The spherical shapes in the images arethe less detrimental defects. Fig. 11 (L to R) Images of 400x magnification of pours one and twoCONCLUSIONThe experimental coat provided the fewest number of internal defects, but the highestnumber of external defects. The experimental sand provided the highest number ofinternal defects. The use of vibration caused severe internal and external defects.The introduction of more effective methods of cooling the cast should help reach a morereadily seen conclusion. The testing of different pouring mediums should be beneficial inmany different aspects. Using a standardized method for quantifying defects, will lead tomore statistically significant data.ACKNOWLEDGEMENTSThis work was supported by the National Science Foundation grant number EEC-0552860, Research Experiences for Undergraduates (REU) Industrial Applications ofSensing, Modeling, and Control. Sample continuation sentences: I would also like toacknowledge Mr. Mike Baswell for his assistance in pouring molten metal, Mr. MikeRenfro, R & D Engineer from TTU Manufacturing Center for his assistance with dataacquisition, and Foseco Morval for providing the patterns that were used in this work.REFERENCES Alagarsamay, A., “Casting Defect Analysis Procedure and Case History,” Keith Mills Symposium on Ductile Iron (2003). Baskerud, L., Chai, G., Tamminen, J., Solidification Characteristics of Aluminum Alloys Volume 2: Foundry Alloys, pp. 64-65, 86-87, 128-129, American Foundry Society/Skanaluminum, Des Plaines, IL (1990). Finite Solutions. Solid Cast: PC Based Casting Solidification Modeling Software, volume 6.2. Finite Solutions, Incorporated, 2003. Hess, D.R., “Comparison of Aluminum Alloys and EPS Foams for Use in the Lost Foam Casting Process,” AFS Transactions, vol 112 (2004). 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). Tschopp, M.A., “Mechanisms of Formation of Pyrolysis Defects in Aluminum Lost Foam Castings,” M.S Thesis, University of Missouri-Rolla, 1999. Vander Voort, George F. AMS Handbook: Metallography and Microstructures, volume 9. ASM Intl,, 2004. 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). Zhao, Q., Biederman, S., Flemings, M., “The Effects of Coating on the Heat Transfer in Lost Foam Aluminum Process,” AFS Transactions, vol 114 (2006). 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).
Elsawy, A. H., & Abdelrahman, M., & Pardue, S. J. (2014, June), Defect Simulation of AL319 in Lost Foam Casting – an REU Undergraduate Research Experience Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. https://peer.asee.org/20246
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