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Author: Yeh-Liang Hsu, Chia-Chieh Yu (2005-10-18); recommended: Yeh-Liang Hsu (2007-08-02)
Note: This paper is published in Proceedings of the I MECH E Part B Journal of Engineering Manufacture, Vol. 220, No. 2, February, 2006, p. 203-211.

Computer Simulation of Casting Process of Aluminum Wheels – A Case Study

Abstract

Aluminum disc wheels intended for normal use in passenger cars are commonly produced by gravity casting. If the cooling process and the initial temperature of the mold are not well controlled, shrinkage cavity will occur after solidification, causing leakage in the disc wheel.

In this research, a casting simulation software is used to simulate the casting process of aluminum wheels. The casting simulation is done iteratively till the mold temperature converges to a stable temperature. A “shrinkage index” (SI) is defined to provide a quantified index of casting quality of aluminum wheels, based on the phenomenon of liquid entrapped at the joints of rim and spokes of the wheel where shrinkage cavity usually happens. This shrinkage index shows good correlation with the aluminum wheel leakage test results. This paper also discusses the influence of cooling process parameters on SI, including initial mold temperature, and geometry of the wheel, which verifies engineers’ empirical data. This iterative simulation process and SI can be used to predict the casting quality of aluminum wheels and to find the optimal parameters of the casting process.

Keywords: aluminum disc wheels, casting, shrinkage cavity, liquid entrapped.

Introduction

Aluminum disc wheels intended for normal use on passenger cars are commonly produced by gravity casting. Figure 1 shows the four casting molds, namely top mold, side mold, bottom mold, and support mold, for an aluminum disc wheel. The cooling conditions are applied to the molds. If the cooling process and the initial temperatures of molds are not well controlled, shrinkage cavity can occur after solidification, causing leakage in the disc wheel. Several practical strategies are often employed to prevent such. They include drilling air vessels to increase the rate of heat transferred from the joints of rim and spokes of the wheel, and spraying vapor on the bottom mold to increase cooling rate. In aluminum wheel manufacturing, these strategies are applied currently on a “trial-and-error” basis, and depend heavily on the experience of engineers.

Figure 1. CAD models of casting molds for an aluminum disc wheel

To improve the quality of foundry products has long been a research issue in manufacturing industry. Numerical models are developed to predict the mechanical characteristics, shrinkages, and porosities. The casting process and the effective parameters are carefully studied to address the improvement schemes.

Tiwari [1] used neural networks to build an intelligent shrinkage minimization module, which learns the real behavior of the solidification process so that it can perform the task of casting design feature modification in real time and intensify the process of directional solidification. Seetharamu et al. [2] used the finite element method to simulate the heat transfer process accompanying the solidification process. The results of residual stresses, shrinkage and thermal stresses were compared with available experimental data. Bounds et al. [3] modeled the formation of macro defects, macro porosity, mis-runs, and pipe shrinkage, explicitly as a function of the interaction among free-surface fluid flow, heat transfer, and solidification in arbitrarily complex three-dimensional geometries. Midea et al. [4] illustrated four examples when casting process modeling is combined with other computer modeling to optimize cast component manufacturability. Shenefelt et al. [5] used “criteria functions (CF’s)” based on thermal environment to provide a means for estimating shrinkage porosity within a casting.

In particular, the use of mold filling and solidification commercial simulation software to investigate the filling patterns, velocities, and temperature distributions, has become increasingly popular. Spittle et al. [6] used MAVIS, a heat transfer/solidification simulation package, to predict the temperature distributions in a permanent mold. Drezet et al. [7] analyzed a nominal ingot using the finite element software ABAQUS to compare two different casting speeds and free mold designs and obtained more uniform thickness. Kreziak et al. [8] utilized SIMULOR, a filling and solidification simulation software, to simulate a quarter of an automobile wheel cast. The results were validated by experimental data that temperatures were measured from different positions. In the meantime, they used a simple sample to study the effects of cycle time, preheat temperature and die coatings.

This paper presents a case study of using computer simulation for casting process of aluminum wheels of a local manufacturer in order to establish a process to find out the optimal cooling conditions to avoid shrinkage cavity. The “liquid entrap” phenomenon at the joints of rim and spokes of the wheel during the casting process causes shrinkage cavity of the final aluminum wheel. In this research, a “shrinkage index” (SI) is defined to describe the amount of entrapment of liquid. It provides a quantified index of casting quality of aluminum wheels. The casting simulation is done iteratively till the mold temperature converges to a stable temperature.

This paper starts by describing the simulation model, the simulation process, and defining SI. Next, correlation of the shrinkage index with the aluminum wheel leakage test results of the local manufacturer is investigated using the iterative simulation.  Then the paper discusses the influences of cooling process parameters on SI, including initial mold temperature, and geometry of the wheel.

Simulation of Casting Process of Aluminum Wheels Using ProCAST

Many major foundries use commercial software such as ProCAST and MagmaSOFT, to simulate filling and solidification of castings. In this paper, ProCAST is used to simulate the casting process of aluminum wheels. ProCAST uses the finite element method and can be employed to analyze a wide variety of fully coupled thermal, fluid, stress, and microstructure prediction problems in casting process [9].

Figure 2 shows the finite element model of an 15” aluminum wheel and its molds. The interfaces of each part are coincident in the model. Four-noded tetrahedral elements are used. The material used is AlSi7Mg (ASTM A356, JIS AC4C) casting aluminum alloy, which can be found in ProCAST’s material database. The heat capacity of the alloy is 963 J/(Kg K), and the latent heat is J/Kg. Table 1 lists other thermal characteristics of the alloy, which are functions of temperature.

Table 1. The material properties of the alloy

Fraction Solid

Density

Thermal conductivity

Temperature (C)

%

Temperature (C)

Kg/m3

Temperature (K)

W/(mK)

557.98

100

25.00

2702.00

406.67

146.50

558.00

92.10

656.00

2540.00

420.16

153.26

562.00

90.16

664.00

2380.00

432.26

154.94

564.00

88.20

700.00

2369.00

477.02

166.76

566.00

82.81

 

589.52

167.43

568.00

71.54

666.13

166.08

570.00

53.90

 

570.50

45.10

574.00

44.10

578.00

41.65

582.00

38.70

586.00

34.40

590.00

28.42

594.00

22.54

598.00

15.20

602.00

7.06

605.00

0.00

Figure 2. The finite element model of an aluminum wheel and its molds

The boundary conditions of the simulation are based on the wheel manufacturer’s real parameters. In casting, one or more of the following remedies are often used to prevent unfilled cavity: preheating the die, insulating some/all of the cavity surface with die coating, and increasing the filling velocity. In our simulation, the temperature of the melted alloy is 720 oC. The cavity volume of the model is 7.474e-3 m3, and the fill duration is 16 seconds. The filled velocity is 0.673 m/sec through the sectional area of 0.693 m3. The die is preheated to 360 oC, while the ambient temperature is 30 oC. For a coated die with a metal-mold heat transfer coefficient of 300 W/m2C, the whole cavity can be filled in the simulation.

Figure 2 also shows the locations of air cooling (by blowing cold air to the side mold) and water cooling (by spraying water to the bottom mold). In the simulation, cooling parameters are also based on the wheel manufacturer’s real parameters. The heat transfer coefficients of air cooling is 700 W/m2C and its affected area is 4,800 mm2. The heat transfer coefficients of water cooling is 2,200 W/m2C and its affected area is 88,822 mm2. The filling of melted metal completes after 16th seconds. Air cooling starts at 66th second and continues till the end of casting (240 seconds). Water cooling starts at 126th second and lasts for 40 seconds. Finally, the casting wheel is picked out of the cavity at the end of casting. The mold is cooled down for 30 seconds, and the next casting cycle starts.

Figure 3 shows the solidification process of a typical aluminum wheel (referred to as “Type A” in this paper) from ProCAST. After about 150 seconds into the casting process, the liquid starts to be entrapped at the intersection between the spokes and rim. At the positions indicated by the circles, the surrounding regions become solidified. Both the central riser and the rim riser cannot provide liquid metal. The position of entrapped liquid is coincident with a volume where the aluminum wheel actually fails, as shown in Figure 4.

Figure 3. The solidification process of a typical aluminum wheel from ProCAST

Figure 4. The position where the aluminum wheel actually fails

The simulation shown in Figure 3 assumes constant initial mold temperature everywhere. However, this is not true in reality. Figure 5 shows the temperature distribution of the casting process of an aluminum wheel simulated by ProCAST. The temperature scale range is 250 oC – 466 oC. During the casting process, the temperature distribution changes after filling, air cooling, water cooling, and casting out of cavity. In a continuous casting process, this final temperature distribution of mold becomes the initial temperature distribution of mold for the next casting.

Figure 5. Temperature distributions of mold during the casting process

As mentioned earlier, Spittle et al. [6] used MAVIS to predict the temperature distributions in a permanent mold. The mold for the production of AlSi7Mg alloy castings had been used to assess the influence of mold design modifications and water cooling on the steady-state temperature distribution in the mold and the freezing characteristics of the casting. Results matched very well between experiment and simulation, where a steady state was assumed to be achieved in any batch run without water cooling after 20 castings.

In our research, we also try to evaluate the cooling parameters only after a steady-state mold temperature is reached. ProCAST simulation is performed continuously for 10 cycles. In the first simulation, the mold temperature is assumed to be constant everywhere at 360 oC. The final temperature distribution of mold of the simulation is then used as the initial temperature distribution of mold in the next simulation. Figure 6 shows the results of 10 simulations. Figure 6 shows the maximum temperature (D), the minimum temperature (), and the mean temperature (o) of the casting mold in each simulation. The temperature distribution of the casting mold reaches a steady state after 10 simulations – the relative changes in maximum temperature, minimum temperature, and mean temperature are all less than 1%. Therefore, in this research the casting process simulation results are observed after 10 cyclic simulations.

MATLAB Handle Graphics

Figure 6. The maximum, minimum, and mean temperature of the casting mold in each simulation

Definition of a Shrinkage Index

Figure 7 shows the solidification of the vertical section of wheel Type A. The solidification scale of Figure 7 is from 0.3 to 0.9 (0 for pure liquid and 1 for solid). Critical fraction solid, the point at which the alloy is solid enough that liquid feed metal can no longer flow, is assumed to be 0.7. As shown in the left of Figure 7, the solidification scale reaches 0.7 in the rim of the wheel indicated by the dashed circle after about 131 second. The riser on the top of the rim (rim riser) cannot provide melted alloy to the joint of rim and spokes. As the cooling process continues, the solidification scale reaches 0.7 in the spoke of the wheel after about 173 seconds. This portion is thicker and is located further from the risers, so longer solidification time is required. Now the central riser cannot provide melted alloy to the joint of rim and spokes. Therefore, liquid entrapment occurs at the joint of rim and spokes.

Figure 7. Liquid entrapment at the joint of rim and spokes

Kreziak et al. [8] showed that there is no risk of shrinkage under where the solidification presents an orientated temperature gradient from the top to the running system, and the critical solid fraction isochronal chart does not show liquid entrapped areas. When a part of the casting is locally being solidified without feeding from the system, this is a high-risk area.

Therefore, the volume of the liquid-entrapped portion in the wheel can be used to indicate the level of shrinkage in the wheel. We employed a “shrinkage index (SI)” in order to define quantitatively the level of shrinkage from the simulation results by ProCast. Figure 8 shows the portion of the wheel where shrinkage cavity usually happens. ProCAST can output the solid fraction at each node at a certain instant. Since it is difficult to output the volume of a portion of the casting in ProCAST, and the sizes of the finite elements are almost equal, we used as our shrinkage index SI the number of nodes with solid fractions less than 0.7 at the instant when both risers become invalid as our shrinkage index SI.

Figure 8. The portion of the wheel where shrinkage cavity usually happens

As discussed in the previous section, in this research, we simulated the wheel casting process continuously for 10 cycles in order to reach a steady-state mold temperature. Figure 9 shows the SI of these 10 cycles for wheel Type A. SI is high for the first several simulations, but soon converges to about 20 at the 8th to 10th simulation. This also shows that a reasonable simulation result can be obtained after a steady-state mold temperature is reached.

MATLAB Handle Graphics

Figure 9. SI with 10 cyclic simulations

In aluminum wheel manufacturing, every casting wheel must pass a “leakage test” to guarantee that the air will not leak through shrinkage cavities. The “leakage ratio” is the ratio of the number of wheels with leakage and the total number of wheels tested. Table 2 shows the test data for three types of wheels (Types A, B and C) from the local aluminum wheel manufacturer and their corresponding SI from our simulation. Types C-1, C-2, and C-3 are almost identical wheels with slightly different geometries, which will be discussed in later sections. The leakage ratios of Type C-1 and C-2 were very high and were not approved for mass production. Therefore, there were only 20 test samples of Types C-1 and C-2 available for testing. Types A, B, and C-3 are mass-produced wheels.

Table 2. Comparison of leakage test data and SI

Type

A

B

C-3

C-2

C-1

Total number of wheels tested

917

1135

1139

20

20

Number of wheels with leakage

86

203

316

8

12

Leakage ratio %

9.4

17.9

27.7

40

60

SI

20

73

111

122

132

Figure 10 shows the relation between leakage ratio and SI. When SI is high, the leakage ratio will be high. If we accumulate more data, SI can be a good index for predicting the leakage ratio in the leakage test.

MATLAB Handle Graphics

Figure 10. SI vs. leakage ratio

Effect of Cooling Parameters

Using the cyclic simulation process and the shrinkage index described in the previous sections, this section discusses the influences of the timing of air cooling and water cooling on SI.

Table 3 shows the SI and the time when liquid is entrapped with variation in starting time of air cooling from 16 second to 146 second. The time when liquid is entrapped is not influenced by air cooling because air cooling is applied on the side mold. Only the rim riser is affected. The central riser is not affected. As expected, SI increases if air cooling is applied late and thus solidification at the joint of rim and spokes is slower. However, if air cooling is applied earlier than at 66th second (the starting time of the current casting process), no decrease in SI is observed. If air cooling is applied at 16th second (right after filling is completed), SI increases slightly because the rim riser cools down too fast and soon becomes inactive.

Table 3. Effect of starting time of air cooling

Starting time

16-240 sec.

26-240 sec.

66-240 sec.

106-240 sec.

146-240 sec.

SI

21

20

20

26

35

Liquid entrapped

163 sec

164 sec

164 sec

164 sec

164 sec

Table 4 shows the SI and the time when liquid is entrapped with variation in duration of water cooling from 20 second to 70 second (water cooling still starts at 66th second). Water cooling is applied to the bottom and has a significant effect on the central riser. Liquid entrapment occurred earlier when the duration of water cooling is long, and SI increases. Water cooling for less than 40 seconds (the water cooling time of the current casting process) will not further decrease SI. However, when water cooling is less than 20 seconds, the casting will not solidify at the end of the casting (240 seconds) because there is not enough cooling.

Table 4. Effect of duration of water cooling

Duration

20 sec.

30 sec.

40 sec.

50 sec.

60 sec.

70 sec.

SI

X

20

20

26

37

45

Liquid entrapped

X

169 sec

168 sec

160 sec

153 sec

151 sec

The initial mold temperature is also considered in the casting process. Table 5 shows the SI and the time when liquid is entrapped with variation in the initial temperature of the mold in the range of 300 oC – 450 oC. When the initial temperature of the mold is high, the solidification is slow and SI is high, although the time when liquid is entrapped is long. Decreasing the initial mold temperature to less than 360 oC (the mold temperature of the current casting process) will not further reduce SI because liquid is entrapped early. From the analysis of results given in Tables 3, 4 and 5, it follows that the timing for air cooling and water cooling, as well as the initial mold temperature in the current casting process of the local manufacturer, obtained from the engineers’ empirical adjustment, seems already to be close to the optimal.

Table 5. Effect of initial mold temperature

Initial mold temperature

300 oC

330 oC

360 oC

390 oC

420 oC

450 oC

SI

20

21

20

30

39

41

Liquid entrapped

155 sec

159 sec

168 sec

168 sec

172 sec

176 sec

Effect of Wheel Geometry

Figure 11 shows the different geometries of wheel Type A and Type C. Wheel Type A has 5 spokes and the cross-sectional area of a spoke is 1060.7mm2. Wheel Type C has 10 spokes and the cross-sectional area of a spoke is 405.3mm2. Table 6 shows the current cooling parameters for wheel Type C. Comparing with wheel Type A, the filling of wheel Type C takes longer because the cross-sectional area of the spoke is smaller. Air cooling is applied right after filling is completed and the duration of water cooling is extended.

Figure 11. The geometry and cross section of wheel Type A and Type C

Table 6. Cooling conditions of simulation

 

Casting parameters

Alloy filling time

24 sec.

Air cooling time

24-210 sec.

Water cooling time

74-134 sec.

Initial mold temp.

360

Wheel solidification time

210 sec.

For comparison purpose, both wheel Type A and wheel Type C were simulated using the cooling conditions given in Table 6. As shown in Table 7, the SI for wheel Type A is much better than that of wheel Type C, which indicates that the geometry of the wheel makes a great difference in the casting process. For wheel Type A, the rim riser becomes inactive much earlier than the central riser. For wheel Type C, both risers become inactive at about the same time because the cross sectional areas of the spokes are small.

Table 7. Simulation results

 

Type A

Type C

Rim riser inactive

124sec

116sec

Central riser inactive

161sec

116sec

SI

31

132

Besides adjusting cooling parameters, engineers also often modify the geometry of casting cavity to improve the quality of the casting. For example, in wheel Type C, engineers decided to thicken the portion of the rim cavity (Figure 12), so that melted alloy would not solidify too fast in this portion and the rim riser can provide enough melted alloy into the joint of the rim and spokes. Table 8 shows the SI and the time when liquid is entrapped with variation in thickness of the rim of wheel Type C. When the thickness of the rim increases, liquid is entrapped late, and SI decreases. Type C-3 has the best SI; however, it is also the heaviest among the three. As shown in Table 2, the leakage ratio in the leakage test of Type C-1 is 60%, and that of Type C-2 is 40%, while the leakage ratio of Type C-3 drops to 27.7%.

Figure 12. Thickened portion of the rim cavity

Table 8. Different thickening of rim

Wheel type

C-1

C-2

C-3

Thickening of rim

0

+0.75mm

+1.5mm

SI

132

122

111

Liquid entrapped

116 sec.

127 sec.

130 sec.

Conclusion

Numerical simulation is a powerful tool for many industrial applications. While efforts are made to produce accurate simulation results, it is often difficult to accurately model the boundary and loading conditions in many real industrial applications, such as the casting of aluminum wheels. In many applications, it is also difficult to accurately validate the simulation results with physical measurement data. However, numerical simulation still provides the correct “trend”, if not 100% numerically accurate, of the performance and quality of the final product. On the other hand, to implement numerical simulation as part of the everyday manufacturing process, a standardized simulation process needs to be established, and simple indices, which correctly describe the trend of the performance and quality of the final product, should be obtained from the simulation results.

In this paper, casting simulation software ProCAST is used to simulate the casting process of aluminum wheels of a local manufacturer. A cyclic simulation process is established to properly simulate the temperature distribution of the mold in real casting process. The shrinkage index SI is defined to describe quantitatively the level of casting shrinkage from casting simulation. Matching with the leakage test results of the 5 different wheels, SI shows good correlation with the aluminum wheel leakage test results. The effects of cooling parameters and geometry of the mold cavity on SI are also discussed. Engineers’ empirical data concerning modification of cooling parameters can be verified.

If enough leakage test data for a given aluminum wheel manufacturer is accumulated, the relation between SI and leakage ratio of final casting wheels can be established. Leakage ratio of a new wheel can be predicted using SI.

For future research, we are also investigating optimization of casting parameters to obtain the best casting quality. The objective can be to minimize SI (therefore the leakage ration of final casting wheels). Casting simulation described in this paper can be used as the function generator of SI.

References

1.      Tiwari, M.K., Roy, D., 2002. “Minimization of internal shrinkage in castings using synthesis of neural networks,” International Journal of Smart Engineering System Design, v4, n3, p205-214.

2.      Seetharamu, K.N., Paragasam, R., Quadir, G.A., Zainal, Z.A., Prasad, B.S., Sundararajan, T., 2001. “Finite element modeling of solidification phenomena,” Sadhana-Academy Proceedings in Engineering Sciences, v26, n1-2, p103-120.

3.      Bounds, S., Moran, G., Pericleous, K., Cross, M., Croft, T.N., 2000. “Computational model for defect prediction in shape castings based on the interaction of free surface flow, heat transfer, and solidification phenomena,” Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science, v31, n3, p515-527.

4.      Midea, A., Nariman, R., Yancey, B., Faivre, T., 2000. “Using Computer Modeling to Optimize Casting Processes” Modern Casting, v90, n5, 4pp.

5.      Shenefelt, Jeffrey R., Luck, Rogelio, Berry, John T., Taylor, Robert P., 1999.  “Solidification modeling and porosity control in aluminum alloy castings,” Manufacturing Science and Engineering, Nov14-Nov19, Nashville, TN, USA, p507-511.

6.      Spittle, J.A., Brown, S.G..R., Wishart, H., 1999. “Experimental and computational evaluation of the influence of permanent mould design on solidification of A17SiMg casting,” Light Metals: Proceedings of Sessions, TMS Annual Meeting, Proceedings of the 1999 128th TMS annual Meeting ‘Light Metals 1999’, Feb 28-Mar 4, San Diego, CA, USA, p951-958.

7.      Drezet, J.-M, Rappaz, M., Krahenbuhl, Y., 1996. “Modelling of thermomechanical effects during direct chill casting of AA1201 aluminum alloy,” Materials Science Forum, v217-222, pt1.

8.      Kreziak, G., Rigaut, C., Santarini, M., 1993. “Low pressure permanent mould process simulation of a thin wall aluminum casting,” Materials Science & Engineering A: Structural Materials: Properties, Microstructure and Processing, v A173, n1-2.10.

9.      ProCAST, “User’s manual and technical reference,” Based on ProCAST version 3.1.0, C1-C57.