computer simulation of casting process of aluminium wheels

Computer simulation of casting processof aluminium wheels – a case studyYeh-Liang Hsu* and Chia-Chieh Yu

Department of Mechanical Engineering, Yuan Ze University, Taiwan, Republic of China

The manuscript was received on 9 May 2005 and was accepted after revision for publication on 20 October 2005.

DOI: 10.1243/09544054JEM381

Abstract: Aluminium disc wheels intended for normal use in passenger cars are commonlyproduced by gravity casting. If the cooling process and the initial temperature of the mouldare not well controlled, shrinkage cavity will occur after solidification, causing leakage inthe disc wheel. In this research, a casting simulation software is used to simulate thecasting process of aluminium wheels. The casting simulation is done iteratively until themould temperature converges to a stable temperature. A ‘shrinkage index’ (SI) is defined toprovide a quantified index of casting quality of aluminium wheels, based on thephenomenon of liquid entrapped at the joints of rim and spokes of the wheel whereshrinkage cavity usually happens. This shrinkage index shows good correlation with thealuminium wheel leakage test results. This paper also discusses the influence of coolingprocess parameters on SI, including initial mould temperature, and geometry of the wheel,which verifies engineers’ empirical data. This iterative simulation process and SI can be usedto predict the casting quality of aluminium wheels and to find the optimal parameters of thecasting process.

Keywords: aluminium disc wheels, casting, shrinkage cavity, liquid entrapped

1 INTRODUCTION

Aluminium disc wheels intended for normal use onpassenger cars are commonly produced by gravitycasting. Figure 1 shows the four casting moulds –top mould, side mould, bottom mould, and supportmould – for an aluminium disc wheel. The coolingconditions are applied to the moulds. If the coolingprocess and the initial temperatures of moulds arenot well controlled, shrinkage cavity can occur aftersolidification, causing leakage in the disc wheel.Several practical strategies are often employed toprevent this. These include drilling air vessels toincrease the rate of heat transferred from the jointsof rim and spokes of the wheel, and spraying vapouron the bottom mould to increase the cooling rate.In aluminium wheel manufacturing, these strategiesare applied currently on a ‘trial-and-error’ basis, anddepend heavily on the experience of engineers.

To improve the quality of foundry productshas long been a research issue in manufacturingindustry. Numerical models are developed to predictthe mechanical characteristics, shrinkages, andporosities. The casting process and the effectiveparameters are carefully studied to address theimprovement schemes.

Tiwari and Roy [1] used neural networks to buildan intelligent shrinkage minimization module,which learns the real behaviour of the solidificationprocess so that it can perform the task of castingdesign feature modification in real time andintensify the process of directional solidification.Seetharamu et al. [2] used the finite element methodto simulate the heat transfer process accompanyingthe solidification process. The results of residualstresses, shrinkage, and thermal stresses were com-pared with available experimental data. Boundset al. [3] modelled the formation of macro defects,macro porosity, mis-runs, and pipe shrinkage, expli-citly as a function of the interaction among free-surface fluid flow, heat transfer, and solidificationin arbitrarily complex three-dimensional geo-metries. Midea et al. [4] illustrated four examples

*Corresponding author: Department of Mechanical Engineer-

ing, Yuan Ze University, 135 Yuan Tung Road, Chungli,

Taiwan, Republic of China. email: [email protected]

JEM381 � IMechE 2006 Proc. IMechE Vol. 220 Part B: J. Engineering Manufacture

CASE STUDY 203

in which casting process modelling is combined withother computer modelling to optimize cast com-ponent manufacturability. Shenefelt et al. [5] used‘criteria functions (CFs)’ based on thermal environ-ment to provide a means for estimating shrinkageporosity within a casting.

In particular, the use of mould filling and solid-ification commercial simulation software to investi-gate the filling patterns, velocities, and temperaturedistributions, has become increasingly popular.Spittle et al. [6] used MAVIS, a heat transfer/solidification simulation package, to predict thetemperature distributions in a permanent mould.Drezet et al. [7] analysed a nominal ingot usingthe finite element software ABAQUS to comparetwo different casting speeds and free mould designsand obtained more uniform thickness. Kreziak et al.[8] utilized SIMULOR, a filling and solidificationsimulation software, to simulate a quarter of anautomobile wheel cast. The results were validated

by experimental data that temperatures were mea-sured from different positions. In the meantime,they used a simple sample to study the effects ofcycle time, preheat temperature, and die coatings.

The current paper presents a case study of usingcomputer simulation for the casting process of alu-minium wheels of a local manufacturer in order toestablish a process to find out the optimal coolingconditions to avoid shrinkage cavity. The ‘liquidentrap’ phenomenon at the joints of rim and spokesof the wheel during the casting process causesshrinkage cavity of the final aluminium wheel. Inthis research, a ‘shrinkage index’ (SI) is defined todescribe the amount of entrapment of liquid. Itprovides a quantified index of casting quality ofaluminium wheels. The casting simulation is doneiteratively until the mould temperature convergesto a stable temperature.

This paper starts by describing the simulationmodel, the simulation process, and defining SI.Correlation of the SI with the aluminium wheelleakage test results of the local manufacturer isthen investigated using the iterative simulation.The influences of cooling process parameters on SIare then discussed, including initial mould tempera-ture and geometry of the wheel.

2 SIMULATION OF CASTING PROCESS OFALUMINIUM WHEELS USING ProCAST

Many major foundries use commercial softwaresuch as ProCAST and MagmaSOFT, to simulatefilling and solidification of castings. In this paper,ProCAST is used to simulate the casting process ofaluminium wheels. ProCAST uses the finite elementmethod and can be employed to analyse a widevariety of fully coupled thermal, fluid, stress, andmicrostructure prediction problems in the castingprocess [9].

Figure 2 shows the finite element model of a15 in aluminium wheel and its moulds. The inter-faces of each part are coincident in the model.

Top mould

Side mould

Button mould Support mold

Fig. 1 CAD models of casting moulds for an aluminiumdisc wheel

Air cooling Water cooling

Fig. 2 The finite element model of an aluminium wheel and its moulds

204 Yeh-Liang Hsu and Chia-Chieh Yu

Proc. IMechE Vol. 220 Part B: J. Engineering Manufacture JEM381 � IMechE 2006

Four-noded tetrahedral elements are used. Thematerial used is AlSi7Mg (ASTM A356, JIS AC4C)casting aluminium alloy, which can be found inProCAST’s material database. The heat capacity ofthe alloy is 963 J/(kg K), and the latent heat is3.98 · 105 J/kg. Table 1 lists other thermalcharacteristics of the alloy, which are functions oftemperature.

The boundary conditions of the simulation arebased on the wheel manufacturer’s real parameters.In casting, one or more of the following remediesare often used to prevent unfilled cavity: preheatingthe die, insulating some/all of the cavity surfacewith die coating, and increasing the filling velocity.In the current simulation, the temperature of themelted alloy is 720 �C. The cavity volume of themodel is 7.474e�3 m3, and the fill duration is 16 s.The filled velocity is 0.673 m/s through the sectionalarea of 0.693 m3. The die is preheated to 360 �C,while the ambient temperature is 30 �C. For a coateddie with a metal–mould heat transfer coefficient of300 W/m2 C, the whole cavity can be filled in thesimulation.

Figure 2 also shows the locations of air cooling(by blowing cold air to the side mould) and watercooling (by spraying water to the bottom mould).In the simulation, cooling parameters are alsobased on the wheel manufacturer’s real parameters.The heat transfer coefficient of air cooling is700 W/m2 C and its affected area is 4800 mm2. Theheat transfer coefficient of water cooling is2200 W/m2 C and its affected area is 88 822 mm2.The filling of melted metal completes after 16 s.Air cooling starts at 66 s and continues until theend of casting (240 s). Water cooling starts at 126 sand lasts for 40 s. Finally, the casting wheel ispicked out of the cavity at the end of casting. The

mould is cooled down for 30 s, and the next castingcycle starts.

Figure 3 shows the solidification process of atypical aluminum wheel (referred to as ‘Type A’ inthis paper) from ProCAST. After approximately150 s into the casting process, the liquid starts tobe entrapped at the intersection between the spokesand rim. At the positions indicated by the circles,the surrounding regions become solidified. Boththe central riser and the rim riser cannot provideliquid metal. The position of entrapped liquid iscoincident with a volume where the aluminiumwheel actually fails, as shown in Fig. 4.

The simulation shown in Fig. 3 assumes constantinitial mould temperature everywhere. However,this is not true in reality. Figure 5 shows thetemperature distribution of the casting processof an aluminium wheel simulated by ProCAST.The temperature scale range is 250–466 �C. Duringthe casting process, the temperature distributionchanges after filling, air cooling, water cooling, andcasting out of cavity. In a continuous casting pro-cess, this final temperature distribution of themould becomes the initial temperature distributionof the mould for the next casting.

As mentioned earlier, Spittle et al. [6] usedMAVIS to predict the temperature distributionsin a permanent mould. The mould for the produc-tion of AlSi7Mg alloy castings had been usedto assess the influence of mould design modifi-cations and water cooling on the steady statetemperature distribution in the mould and thefreezing characteristics of the casting. Resultsmatched very well between experiment and simula-tion, where a steady state was assumed to beachieved in any batch run without water coolingafter 20 castings.

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 2702.00 406.67 146.50558.00 92.10 656.00 2540.00 420.16 153.26562.00 90.16 664.00 2380.00 432.26 154.94564.00 88.20 700.00 2369.00 477.02 166.76566.00 82.81 589.52 167.43568.00 71.54 666.13 166.08570.00 53.90570.50 45.10574.00 44.10578.00 41.65582.00 38.70586.00 34.40590.00 28.42594.00 22.54598.00 15.20602.00 7.06605.00 0.00

Computer simulation of casting process of aluminium wheels 205


In the current research, an attempt is made toevaluate the cooling parameters only after a steadystate mould temperature is reached. ProCAST simu-lation is performed continuously for ten cycles. Inthe first simulation, the mould temperature is assu-med to be constant everywhere at 360 �C. The finaltemperature distribution of mould of the simulationis then used as the initial temperature distribution ofmould in the next simulation. Figure 6 shows theresults of ten simulations and shows the maximumtemperature (D), the minimum temperature (r),

131 s 152 s 173 s

Fig. 3 The solidification process of a typical aluminium wheel from ProCAST

Fig. 4 The position where the aluminium wheel actuallyfails

Fig. 5 Temperature distributions of mould during thecasting process



and the mean temperature (o) of the casting mouldin each simulation. The temperature distributionof the casting mould reaches a steady state afterten simulations – the relative changes in maximumtemperature, minimum temperature, and meantemperature are all less than one per cent. Therefore,in this research the casting process simulationresults are observed after ten cyclic simulations.

3 DEFINITION OF A SHRINKAGE INDEX

Figure 7 shows the solidification of the verticalsection of wheel Type A. The solidification scale of

Fig. 7 is from 0.3 to 0.9 (0 for pure liquid and 1 forsolid). Critical fraction solid, the point at which thealloy is solid enough that liquid feed metal can nolonger flow, is assumed to be 0.7. As shown in theleft of Fig. 7, the solidification scale reaches 0.7 inthe rim of the wheel indicated by the dashed circleafter approximately 131 s. The riser on the top ofthe rim (rim riser) cannot provide melted alloy tothe joint of rim and spokes. As the cooling processcontinues, the solidification scale reaches 0.7 in thespoke of the wheel after about 173 s. This portionis thicker and is located further from the risers,so longer solidification time is required. Now thecentral riser cannot provide melted alloy to the jointof rim and spokes. Therefore, liquid entrapmentoccurs at the joint of rim and spokes.

Kreziak et al. [8] showed that there is no risk ofshrinkage under where the solidification presentsan orientated temperature gradient from the top tothe running system, and the critical solid fractionisochronal chart does not show liquid entrappedareas. When a part of the casting is locally beingsolidified without feeding from the system, this is ahigh-risk area.

Therefore, the volume of the liquid-entrappedportion in the wheel can be used to indicate the levelof shrinkage in the wheel. An SI is employed in orderto define quantitatively the level of shrinkage fromthe simulation results by ProCast. Figure 8 showsthe portion of the wheel where shrinkage cavityusually happens. ProCAST can output the solidfraction at each node at a certain instant. It is diffi-cult to output the volume of a portion of thecasting in ProCAST, and the sizes of the finiteelements are almost equal. Therefore, the number

Fig. 6 The maximum, minimum, and mean temperature of the casting mould in each simulation

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



of nodes with solid fractions less than 0.7 at theinstant when both risers become invalid areemployed as the SI.

As discussed in the previous section, in thisresearch, the wheel casting process was simulatedcontinuously for ten cycles in order to reach a steadystate mould temperature. Figure 9 shows the SI ofthese ten cycles for wheel Type A. SI is high forthe first several simulations, but soon converges toapproximately 20 at the eighth to tenth simulation.This also shows that a reasonable simulationresult can be obtained after a steady state mouldtemperature is reached.

In aluminium wheel manufacturing, every castingwheel must pass a ‘leakage test’ to guarantee thatthe air will not leak through shrinkage cavities. The‘leakage ratio’ is the ratio of the number of wheelswith 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 aluminium wheelmanufacturer and their corresponding SI from our

simulation. Types C-1, C-2, and C-3 are almostidentical wheels with slightly different geometries,which will be discussed in later sections. The leak-age ratios of Type C-1 and C-2 were very high andwere not approved for mass production. Therefore,there were only 20 test samples of Types C-1 andC-2 available for testing. Types A, B, and C-3 aremass-produced wheels.

Figure 10 shows the relation between leakageratio and SI. When SI is high, the leakage ratio willbe high. If more data are accumulated, SI can be agood index for predicting the leakage ratio in theleakage test.

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

Fig. 9 SI with ten cyclic simulations

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 20Number of wheels with leakage 86 203 316 8 12Leakage ratio % 9.4 17.9 27.7 40 60SI 20 73 111 122 132



4 EFFECT OF COOLING PARAMETERS

Using the cyclic simulation process and the SIdescribed in the previous sections, this section dis-cusses the influences of the timing of air coolingand water cooling on SI. Table 3 shows the SI andthe time when liquid is entrapped with variation instarting time of air cooling from 16 s to 146 s. Thetime when liquid is entrapped is not influenced byair cooling because air cooling is applied on theside mould. Only the rim riser is affected. The centralriser is not affected. As expected, SI increases if aircooling is applied late and thus solidification at thejoint of rim and spokes is slower. However, if aircooling is applied earlier than at 66 s (the startingtime of the current casting process), no decrease inSI is observed. If air cooling is applied at 16 s (imme-diately after filling is completed), SI increases slightlybecause the rim riser cools down too fast and soonbecomes inactive.

Table 4 shows the SI and the time when liquidis entrapped with variation in duration of watercooling from 20 to 70 s (water cooling still starts at66 s). Water cooling is applied to the bottomand has a significant effect on the central riser.Liquid entrapment occurred earlier when the dura-tion of water cooling is long, and SI increases. Watercooling for less than 40 s (the water cooling time ofthe current casting process) will not further decreaseSI. However, when water cooling is less than 20 s,the casting will not solidify at the end of the casting(240 s) because there is not enough cooling.

The initial mould temperature is also consideredin the casting process. Table 5 shows the SI and thetime when liquid is entrapped with variation in theinitial temperature of the mould in the range of300 to 450 �C. When the initial temperature of themould is high, the solidification is slow and SI ishigh, although the time when liquid is entrappedis long. Decreasing the initial mould temperatureto less than 360 �C (the mould temperature of thecurrent casting process) will not further reduce SIbecause liquid is entrapped early. From the analysisof results given in Tables 3 to 5, it follows that thetiming for air cooling and water cooling, as well asthe initial mould temperature in the current casting

Fig. 10 SI versus leakage ratio

Table 3 Effect of starting time of air cooling

Starting time 16–240 s 26–240 s 66–240 s 106–240 s 146–240 sSI 21 20 20 26 35Liquid

entrapped163 s 164 s 164 s 164 s 164 s

Table 4 Effect of duration of water cooling

Duration 20 s 30 s 40 s 50 s 60 s 70 sSI X 20 20 26 37 45Liquid entrapped X 169 s 168 s 160 s 153 s 151 s

Table 5 Effect of initial mould temperature

Initial mouldtemperature

300 �C 330 �C 360 �C 390 �C 420 �C 450 �C

SI 20 21 20 30 39 41Liquid

entrapped155 s 159 s 168 s 168 s 172 s 176 s



process of the local manufacturer, obtained from theengineers’ empirical adjustment, seems already tobe close to the optimum.

5 EFFECT OF WHEEL GEOMETRY

Figure 11 shows the different geometries of wheelType A and Type C. Wheel Type A has fivespokes and the cross-sectional area of a spoke is1060.7 mm2. Wheel Type C has ten spokes and thecross-sectional area of a spoke is 405.3 mm2. Table6 shows the current cooling parameters for wheelType C. Comparing with wheel Type A, the fillingof wheel Type C takes longer because the cross-sectional area of the spoke is smaller. Air coolingis applied right after filling is completed and theduration of water cooling is extended.

For comparison purposes, both wheel Type A andwheel Type C were simulated using the cooling con-ditions given in Table 6. As shown in Table 7, the SIfor wheel Type A is much better than that of wheel

Type C, which indicates that the geometry of thewheel makes a great difference in the castingprocess. For wheel Type A, the rim riser becomesinactive much earlier than the central riser. Forwheel Type C, both risers become inactive at aboutthe same time because the cross-sectional areas ofthe spokes are small.

In addition to adjusting cooling parameters,engineers also often modify the geometry of thecasting cavity to improve the quality of the casting.For example, in wheel Type C, engineers decidedto thicken the portion of the rim cavity (Fig. 12), sothat a melted alloy would not solidify too fast inthis portion and the rim riser can provide enoughmelted alloy into the joint of the rim and spokes.Table 8 shows the SI and the time when liquid isentrapped with variation in thickness of the rimof wheel Type C. When the thickness of the rimincreases, liquid is entrapped late, and SI decreases.Type C-3 has the best SI; however, it is also theheaviest among the three. As shown in Table 2, theleakage ratio in the leakage test of Type C-1 is60 per cent, and that of Type C-2 is 40 per cent, whilethe leakage ratio of Type C-3 drops to 27.7 per cent.

6 CONCLUSION

Numerical simulation is a powerful tool for manyindustrial applications. While efforts are made toproduce accurate simulation results, it is oftendifficult to model accurately the boundary and load-ing conditions in many real industrial applications,

Fig. 11 The geometry and cross-section of wheel Type Aand Type C

Table 6 Cooling conditions of simulation

Casting parameters

Alloy filling time 24 sAir cooling time 24–210 sWater cooling time 74–134 sInitial mould temp. 360 �CWheel solidification time 210 s

Table 7 Simulation results

Type A Type C

Rim riser inactive 124 s 116 sCentral riser inactive 161 s 116 sSI 31 132

Thickening

Fig. 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 þ0.75 mm þ1.5 mmSI 132 122 111Liquid entrapped 116 s 127 s 130 s



such as the casting of aluminium wheels. In manyapplications, it is also difficult to validate accuratelythe simulation results with physical measurementdata. However, numerical simulation still providesthe correct ‘trend’, if not 100 per cent numericallyaccurate, of the performance and quality of the finalproduct. On the other hand, to implement numericalsimulation as part of the everyday manufacturingprocess, a standardized simulation process needs tobe established, and simple indices, which correctlydescribe the trend of the performance and qualityof the final product, should be obtained from thesimulation results.

In this paper, casting simulation software Pro-CAST is used to simulate the casting process ofaluminium wheels of a local manufacturer. A cyclicsimulation process is established to simulateproperly the temperature distribution of the mouldin real casting process. SI is defined to describequantitatively the level of casting shrinkage fromcasting simulation. Matching with the leakage testresults of the five different wheels, SI shows goodcorrelation with the aluminium wheel leakage testresults. The effects of cooling parameters and geo-metry of the mould cavity on SI are also discussed.Engineers’ empirical data concerning modificationof cooling parameters can be verified. If enoughleakage test data for a given aluminium wheelmanufacturer are accumulated, the relation betweenSI and leakage ratio of final casting wheels can beestablished. Leakage ratio of a new wheel can bepredicted using SI.

For future research, the current authors are alsoinvestigating optimization of casting parametersto obtain the best casting quality. The objective canbe to minimize SI (therefore the leakage ratio of finalcasting wheels). Casting simulation described in thepresent paper can be used as the functiongenerator of SI.

REFERENCES

1 Tiwari, M. K. and Roy, D. Minimization of internalshrinkage in castings using synthesis of neuralnetworks. Int. J. Smart Engng System Des. 2002, 4(3),205–214.

2 Seetharamu, K. N., Paragasam, R., Quadir, G. A.,Zainal, Z. A., Prasad, B. S., and Sundararajan, T. Finiteelement modeling of solidification phenomena. Sadhana-Acad. Proc. Engng Sci., 2001, 26, (1–2), 103–120.

3 Bounds, S., Moran, G., Pericleous, K., Cross, M.,and Croft, T. N. Computational model for defect pre-diction in shape castings based on the interactionof free surface flow, heat transfer, and solidificationphenomena. Metall. Mater. Trans. B: Process Metall.Mater. Processing Sci., 31, (3), 515–527.

4 Midea, A., Nariman, R., Yancey, B., and Faivre, T.Using computer modeling to optimize casting pro-cesses. Modern Casting, 2000, 90 (5), 4.

5 Shenefelt, J. R., Luck, R., Berry, J. T., and Taylor, R. P.Solidification modeling and porosity control inaluminum alloy castings. Manufacturing scienceand engineering, 14–19 November 1999, Nashville,Tennessee, USA, pp. 507–511.

6 Spittle, J. A., Brown, S. G. R., and Wishart, H.Experimental and computational evaluation of theinfluence of permanent mould design on solidifica-tion of A17SiMg casting. Light Metals: Proceedings ofSessions, TMS Annual Meeting. Proceedings of the1999 128th TMS Annual Meeting ‘Light Metals 1999’,28 February–4 March 1999, San Diego, California,USA, pp. 951–958.

7 Drezet, J.-M, Rappaz, M., and Krahenbuhl, Y.Modelling of thermomechanical effects during directchill casting of AA1201 aluminum alloy. Mater. Sci.Forum, 1996, 217, Part I, 305–310 .

8 Kreziak, G., Rigaut, C., and Santarini, M. Lowpressure permanent mould process simulation of athin wall aluminum casting. Mater. Sci. Engng A: Struct.Mater.: Properties, Microstruct. and Processing, 1993,A173 (1–2), 10.

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



computer simulation of casting process of aluminium wheels

Documents