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Simulation-enabled casting product defect prediction in die casting processInternational Journal of Production Research (September 2009), 47 (18), pg. 5203-5216

M.W. Fu; M.S. Yong

CAE simulation | high pressure die casting | product quality assurance | defect prediction

In current casting industries, product development paradigm is shifting from traditional trial-and-error in the workshop toCAE-enabled simulation and ‘proof-of-concept’ by computer. The product development paradigm shift is thus fromheuristic know-how and experience to more scientific simulation, evaluation, analysis and calculation. CAE simulationplays an important role in the new product development paradigm as it models the entire casting process and reveals thedynamic behaviour of the casting system in working conditions. In addition, the product quality panorama and productdefects are explored via simulation in such a way that the root-causes of casting defects are pinpointed and the solutionsto avoid them can be determined. In this paper, the CAE technology for casting process simulation is presented. Themodelling of the casting process is first articulated and the detailed simulation issues are then described. The informationrelated to the filling and solidification process and after-ejection behaviour are revealed by CAE simulation. Through casestudies, how the CAE simulation helps identify and predict the process-related defects is illustrated and its efficiency isfinally validated.

Affiliation: 0001 a Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

0002 b Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, 638075 Singapore

Table of Content: 1. Introduction 2. Modelling of the casting process 3. Simulation of the casting process system 4. Behaviour and information revealed via CAE simulation

4.1 Filling process simulation 4.2 Solidification process simulation 4.3 Stress analysis in casting

5. Case studies 6. Conclusions

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1. Introduction

In today's casting product development, the product development paradigm is basically trial-and-error based on know-how and experience. These know-how and experience, however, are normally linked to long years of apprenticeship andskilled craftsmanship. This product development paradigm appears to be more heuristic and experience-based thandeep scientific simulation, evaluation, analysis, and calculation. It is thus time-consuming, error-prone, and needs a lot ofexperimental tryout and verification in the workshop for ‘proof-of-concept’. Currently, casting products, especially for highpressure die castings (HPDC), have been widely used in many industries due to its near-net shape or net shapecharacteristics, high productivity and complicated geometries and features. As the market demands for shorter designand manufacturing lead-times, good dimensional accuracy, overall product quality and rapid change of product designand process configuration are increasingly significant. How to meet these demands has been a bottleneck in castingproduction industries. The traditional product development paradigm is obviously handicapped in this competitivemarketplace. To address these issues, an efficient product development paradigm supported by efficient enablingtechnologies is needed. Conventionally, CAD/CAM technologies, as the efficient enabling technologies for representationof design intent and solutions and helps realisation of design physically, provide an essential part of solutions to addressthe above issues. The technologies greatly enhance design quality and shorten design and manufacturing lead-times.However, it is difficult to address some critical issues in the design of the casting process, tooling structure, productproperties configuration and finally the quality control and assurance by using CAD/CAM technologies alone.

Computer-aided engineering (CAE) simulation technology, on the other hand, fills this gap as it helps practitionersgenerate, verify, validate and optimise the design solutions before they are practically implemented and physicallyrealised. From product quality and defect prediction perspective, CAE simulation is a most technologically efficient andcost effective technology for analysis, prediction and evaluation of casting product quality and defects.

In CAE simulation, the simulation is the representation of a physical system by models that imitate the dynamic behaviourof the system in working processes and conditions. The numerical simulation employs numerical methods such as finiteelement method (FEM) or finite difference method (FDM) to quantitatively represent the working behaviour of physicalsystems. The numerical results are correspondingly related to the physical content of the physical systems to besimulated. Taking the casting process as an instance, the fluid dynamics of the metal in cavity, the thermal phenomenaand solid state transformation of the melt in filling and solidification processes need to be modelled by physical andmathematical models. The final simulation results will thus be related to the behaviour of the casting process and theproperties of the casting products. From the production process perspective, the numerical simulation results willassociate the structure, quality, property and defect issues of the products. This up-front process and casting systemsimulation is critical as 20% of design activities at the up-front design stage commits to about 80% of product cost andproduct quality issues (Fuh et al.2004 ).

Furthermore, it is reported that about 90% of product defects are related to the mistakes made in the design stage andonly 10% is due to manufacturing problems (Louvo 1997 ). In addition, it has also been calculated that the costs tochange design in the up-front design process is ten times higher in the subsequent design and manufacturing processes.From the product quality assurance and control perspective, any technologies, which predict product quality and defectsin the casting process to ensure ‘right design the first time’ and reduce trial-and-error in the workshop, will help cutproduct development cost and shorten time-to-market. CAE simulation technology is one of those technologies.

Presently, the applications of CAE simulation technology in casting product development are monolithic. They arebasically focused on casting design, process determination, flow pattern prediction, tooling design, quality control andproduct stress analysis. From the casting design perspective, CAE simulation helps casting design through fillingsimulation, solidification analysis, stress evaluation and optimisation of casting geometries and features (Sequeira etal.2001 , Sturm et al.2001 , McMillin et al.2002 ). Casting design is critical as it is the first step among the design activitiesand affects the subsequent design processes.

From the process determination point of view, simulation helps determine process routing and process parameterconfiguration (Lewis and Ravindran 2000 , Midea et al.2000 , Cleary et al.2002 , Mirbagheri et al.2002 , Barriere etal.2003 , Naher et al.2003 , Hsu and Yu 2006 , Krimpenis et al.2006 ). It also helps verify the die design based on therevealed flow behaviour and solidification phenomena (Ulysse 1999 , Hu et al.2000 , Dai et al.2003 ). From the productquality control and assurance aspect, through revealing the filling and solidification behaviour related to product qualityand defect forming mechanism, the simulation provides physical basis and useful information for product qualityimprovement and defect avoidance (Bird et al.1960 , Guo et al.2005 , Mochnacki et al.2005 , Monroe and Beckermann2005 , Neumann et al.2005 , Peng et al.2005 , Venkatesan et al.2005 , Zhang et al.2006 ). These prior researches,however, are basically monolithic and did not systematically present a complete simulation-enabled product defectprediction paradigm and articulate how the CAE simulation helps defect prediction and avoidance. Furthermore, they alsodid not reveal what information can be revealed through CAE simulation; what kind of information is needed to identify

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the corresponding defects; what defects may occur in filling and solidification processes; and how to come out withremedies for product defect avoidance based on the information provided by simulation. In this paper, the castingprocess is first modelled by numerical methods and the association between the real process, modelling, simulation andoutput variables are presented. The information flow in the simulation process and solution generation cycle isarticulated. A process-based simulation paradigm for prediction of casting defects is proposed. The categorisedinformation revealed by simulation and the defects categories, which can be predicted based on the output information ofsimulation, are presented. Through case studies, the simulation process, procedure and how the defect occurs in thecasting process is analysed based on the identified simulation process are extensively described.

2. Modelling of the casting process

To dynamically simulate the casting process, modelling is the first issue to be addressed. Modelling will represent thecasting processes by models from physical and mathematical perspectives. From the mathematical aspect, models areformulated as governing equations and boundary conditions. For most engineering problems, the models are non-linearin terms of both the geometry and material properties of the casting systems to be modelled. Numerical methods are thusemployed to convert the non-linear equations into simultaneous and algebraic equations. These equations furtherrepresent the physical relationship of the casting system in the form of action-behaviour-property relationship. In theHPDC process, the action is the high pressure generated by the fast movement of plunge in the chamber; the behaviouris the flow of the metal melt. The behaviour is then decided by the reheological properties of the melt. Figure 1 presentsthis relationship from physical and mathematical modelling points of view.

In addition, Figure 2 further illustrates the association between the real process, simulation procedure, physicalphenomena to be modelled, governing equations representing the specific physical behaviour, and the output variables.In real casting processes, materials and material properties, equipment and working parameters are the input to modelthe physical behaviour and phenomena of the casting process. The simulation results, on the other hand, reveal thephysical information related to the casting process and the final microstructures, defects, quality and property of thecasting. From the modelling perspective, there are three phenomena to be considered. They are mould filling,solidification and cooling, and stress and strain distribution of casting after ejection.

Taking the modelling of filling process as an instance, there are three physical phenomena viz., melt momentum balance,mass balance and energy balance, to be represented and modelled.

These phenomena are modelled by the following governing equations:

Continuity equation (when T > Ts):

(1)

Momentum equation (Navier-Stokes equation, when T > Ts):

(2)

Energy Equation:

(3) where:t

–time,

x–space,

ρ–density,

μ–viscosity,

g–gravity,

Cp–heat capacity,

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λ–conductivity,

U–velocity,

T–temperature,

Ts–solidus temperature, and

Q–heat source.

For the open surfaces, a volume of fluid function (VOF), defined as a ratio of metal melt to actual volume, is used to trackthe moving free surface of the metal melt. The VOF function is governed by the equation in the following:

(4)

The governing Equations (1–4 ) are non-linear in terms of the geometry of melt flow path and the material properties ofthe melt. It needs numerical methods to linearise and discretise and then a set of simultaneous and algebraic equationsare obtained. Through solving the linearised equations, the velocity, pressure and temperature of the melt are obtained.

For solidification modelling, the Fourier heat conduction equation is used. Phase transformation enthalpies like melt heatare considered. Through modelling of heat balance in the solidification process, the temperature distribution in casting isdetermined and the solidification behaviour is identified.

To determine the casting stress and strain, the equilibrium equation and Hooke's law are employed for representing therelationship between displacement, stress and strain. Through solving these equations, the displacement, stress andstrain are determined.

3. Simulation of the casting process system

To reveal the casting defects, the simulation of the entire casting system is needed. Figure 3 presents a process-basedsimulation framework for prediction of casting defects. From the figure, it can be seen that the casting product design isbased on the voice of customers (VoC), product functional requirements and design specifications. The product geometryis used as the basis for subsequent casting geometry design, casting process determination and process parameterconfiguration. The whole casting system is then determined after the die design is fixed. Therefore, the whole castingsystem is formed through the product and casting design, process determination, die design, casting equipment selectionand working parameter configuration. In this research, the casting system is first modelled by way of establishment ofphysical, mathematical and numerical models of the system and then input into a CAE simulation system for simulation.In the CAE modelling process, the physical model idealises the real engineering problems and abstracts them to complywith certain physical theory with assumptions. The mathematical model specifies the mathematical equations such as thedifferential equations in FEM analysis the physical model should follow. It also details the boundary and initial conditionsand constraints. The numerical model describes the element types, mesh density and solution parameters. The solutionparameters further provide detailed calculation tolerances, error bounds, iteration specifications and convergence criteria.Currently, most CAE simulation packages have part of the built-in content of these models, but users still need toprepare and input most of the model information into the CAE simulation systems. The information includes CAD STLmodels, element types, mesh density and number, material and the casting process related parameters, includingpouring, liquidus and solidus temperatures etc.

Upon the completion of simulation, the filling-, solidification-, thermal-, and property and quality-related data andinformation are available for analysis and identification of any potential product quality issues and defects in the casting.The defects could be caused by the irrational design of process, tooling, and product/casting. They are thus calledprocess-related, tooling-related, product/casting-related defects. For any product quality and defect issues, the designneeds to be changed. With the new or modified design, new simulation is needed until the defect-free casting is obtained.

4. Behaviour and information revealed via CAE simulation

As stated in the previous section, CAE simulation of the entire casting system reveals filling and solidification behaviourin the casting process and identifies the necessary information related to product quality and defect formation. Since the

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filling, solidification and after-ejection behaviour affect the product quality and defect formation significantly, theinformation revealed by CAE simulation related to this behaviour is summarised in the following subsections.

4.1 Filling process simulation

In the filling process, through modelling and simulation of the momentum, mass and energy balances of the metal melt,the melt velocity, flow direction, pressure and temperature are determined. These parameters reveal the physicalbehaviour and thermal behaviour in the filling process and thus the whole panorama of the filling behaviour is figured out:

Melt-front positions, turbulence in the melt movement and filling smoothness;

Air entrapment in the casting and cavity and identification of whether the venting conditions are needed;

Thermal behaviour and temperature distribution in the casting;

Filling sequence in the casting and determination of overflow and venting locations;

Melt velocity distribution and its relationship to die erosion in the filling process;

Surface finishing prediction based on velocity distribution, especially in HPDC casting process;

Undesirable/irrational filling behaviour such as split melt stream, misruns and cold shut etc. in the filling process.

Through filling process simulation, it is revealed whether the filling pattern is reasonable and the quality casting isensured. In addition, the solutions for design improvement and casting quality enhancement can be developed based onthe simulation results.

4.2 Solidification process simulation

In the solidification process, the physical, thermal and metallurgical phenomena concurrently occur in the process. Theinteraction and interplay of these phenomena are simultaneous. It is thus difficult, if not impossible, to reveal theindividual phenomenon by using traditional technologies. The CAE simulation technology, however, provides anapproach to revealing these phenomena in process conditions and identifying the needed information to evaluate andpredict product quality and product defects in the solidification process. Through modelling and simulation of thesolidification process, the entire solidification behaviour and the following information are presented:

The last solidification area and the location of ingate;

Solidification sequence and the temperature distribution in the casting and die;

Rationality of cooling layout and design;

Runner system design;

Hotspot in the casting;

Shrinkage and porosity distribution in the casting and the solutions for uniform-shrinkage and porosity-free casting;

Feeder size and its location definition.

4.3 Stress analysis in casting

After the solidification of casting, the casting is ejected from die and some physical phenomena related to this processinclude:

Temperature distribution in the casting and die after the ejection of casting;

Distortion of the casting;

Residual stress in the casting and its distribution;

Product quality and defect existing;

Optimised casting system design such as feeding system design;

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Die stress analysis and thermal deformation prediction and optimal design.

The stress and strain in casting and die are important as they affect the casting geometry accuracy, casting productfunction and service, and die fatigue life. The accurate determination of stress and strain helps generate solutions forcasting and tooling quality improvement.

5. Case studies

To illustrate how the filling and solidification process simulation helps identify casting defects and reveal the root-causesof the defects, two industrial cases of HPDC are presented. The two cases are both the four-cavity HPDC process.Figure 4 shows the melt flow path and the layout of the casting system of the first case. The CAD models as shown inFigure 4 are created in Unigraphics, a commercial CAD/CAM system for product design and development, and thenconverted into STL format through CAD model data exchange. The generated STL CAD models are directly importedinto the casting CAE simulation systems for filling and solidification process simulation. In this case, the cast material isAlSi9Cu3, a widely-used die cast material. The die material is X38CrMoV5. The CAE simulation is Magmasoft, which is apopular and commercial casting simulation system in industries and academia. The element type is cuboid and the castis meshed into 1.5 million elements. In this case, the pouring temperature of the melt is 670°C. The liquidus and solidustemperatures are 578°C and 479°C, respectively. All the die components have an initial temperature of 150°C. Fivecycles of simulation are conducted to reach a stable condition of the simulation and in such a way that the simulationoutcomes are reliable. It takes 11 hours to complete the five-cycle simulation.

In the figure, the melt is shot from the chamber driven by the plunge movement and goes into the inlet and biscuit. Themelt flows along the runner and enters into the cavity where the casting is moulded. The ingate shown in the figure is thegate between the runner and casting cavity. The melt flow velocity at the ingate (40–100 m/s) is an important parameteras it significantly affects the filling behaviour and casting quality. In this case, the velocity at the ingate is set to be 40 m/s.After the cavity is filled up, the extra melt, dirty metal and the air in the melt go into the overflow portion. The material inoverflow is trimmed away after the casting is ejected.

Figure 5 presents the filling process and the position of melt-front advancement (MFA) during the filling process in CaseI. The MFA describes the movement status of the melt flow and the arrival sequence in the filling process. It also showsthe melt position for the given percentage of the filling. The MFA reveals the flow phenomenon behaviour and the defectsrelated to the imbalanced MFA can be identified. Through analysis of MFA, the defects can be avoided via the rationaldesign of the MFA in the entire filling process. In addition, the MFA also indicates the last area to be filled up. The lastarea is usually the location of overflow which is the container of dirty melt and air. Figure 5(a) shows the filling status at85% where most of the casing is filled up. But the two boss features behind are not filled. That would mean that they arethe last portions to be filled up and air entrapment could exist there. Figure 5(b) presents 90% filling of the casting. It isfound that the last area to be filled up in the casting is the right-side boss feature. Air entrapment exists in the bossfeature as the air is enclosed already before the melt started to flow into the overflow. The air entrapment could be thecause of not filling the boss feature and further becomes defect. To solve the problems, there are two approaches. Oneis to employ an air venting mechanism on the top of the boss feature. The other is to arrange an overflow there. But thearrangement of overflow there needs to shift the parting line location to this place. For this case, it is not feasible.

Figure 6 presents another case, viz., case II, of the filling and solidification process simulation. The materials of die andcast are the same as the previous case but the mesh density and the simulation time needed are different. In this case,the meshed elements are 1.6 million and it takes about 12 hours to complete the five-cycle of simulation. The simulationreveals the melt flow behaviour and the defects caused by the unreasonable flow pattern and irrational temperaturedistribution during the solidification process. Figure 6 first presents the flow path and the layout of the casting system,which includes inlet, biscuit, runner, ingate, casting and overflow. Similar to Case I, Case II is also a four-cavity HPDCcasting. From the figure, it is found that there are two overflows in each cavity. The design of the overflow is in such away that the two-overflow locations are located at the last filling places, which is consistent with the simulation findings,as shown in Figure 7 . In Figure 7 , there are three last filling areas. Two of them are selected as the overflow location.The third one is not selected as any overflow location or air venting mechanism location. Potential defects could existthere. Figure 8 further articulates the issue for the third last filling place shown in Figure 7 . From the figure, it is foundthat the melt stream is divided into two by the cores for moulding the arc-slot and central hole, as shown in Figure 8(a) .The two melt streams push the air into the centre of the flange and finally there is air entrapment in the centre of theflange. Since there is no efficient venting mechanism and the entrapment air blocks the melt flow and finally there is aconcave feature in the flange, as shown in Figure 8(b) . To avoid the defect, a change of flow pattern is necessary. Themelt flow needs to reverse this direction. The ‘big’ arrow as shown in the figure illustrates that if the melt flow is from thisdirection, there will be no two streams facing each other and no air entrapment in this area. To realise this idea, a ‘bridge’in the ‘big’ arrow location can be created for such a flow pattern. In addition, there is an alternative approach, which is to

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create an air venting hole in the centre of the flange where the concave feature defect is located.

Figure 9(a) presents the solidification result of case II predicted by CAE simulation. It shows the hottest area and thus it isthe last solidification area in the cast. On the other hand, a fracture defect is formed in the real part. In addition, a defectcalled ‘orange skin’ is found in the real part. From the figure, it is seen that the hottest area has more than 500°C. As thesolidus temperature of the material is only 479°C, this would mean that there is no melt feeding for the last solidificationarea when it is solidified. When the melt is fully solidified, the volume contraction from liquid to solid occurs in such a waythat the product surfaces have many small concave features, which are the so-called ‘orange skin’. If the volumecontraction is big, the fracture may happen, as shown in Figure 9(b) . To void this uneven temperature distribution in thesolidification process, a better designed cooling system is needed. In addition, a local cooling mechanism such as spotcooling could absorb the ‘extra heat’ locally and make the melt solidify at the same pace with its surrounding areas. Inthis way, the solidification related defects can be avoided.

6. Conclusions

The design and development of a casting product, process and system is a trial-and-error process based on heuristicknow-how in current casting industries. The solution generated in such a way lacks scientific calculation and analysis.Since the casting process is very complicated in terms of casting geometry and the physical, metallurgical and thermalbehaviour involved in the casting process, this kind of process determination needs a lot of tryout in the workshop. Theproduct quality and defects are difficult to predict in the up-front design process. The simulation-enabled casting qualityprediction and defect evaluation reduces trial-and-error in the workshop as the process is virtually realised and verified bycomputer and any quality issue can be pinpointed and the related solutions can be proposed. In this paper, the panoramaof simulation-enabled casting quality and defect analysis and prediction were articulated. The framework, procedure andprocess of simulation-enabled casting defect prediction were presented. Through industrial case studies, the efficiencywas verified and validated.

Figure 1.Modelling of the casting process from physical and mathematical perspectives: action-behaviour-property relationship.

Figure 2.Association among the process, modelling, simulation and output variables.

Figure 3.The process-based simulation paradigm for prediction of casting defects.

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Figure 4.The layout of the casting filling system in Case I.

Figure 5.The MFA positions in the filling process. (a) Filling at 85% of the whole process; (b) filling at 90% of the whole process.

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Figure 6.The layout of the casting filling system in Case II.

Figure 7.The melt-front advancement position in filling process.

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Figure 8.Unreasonable filling pattern and the defect caused. (a) Simulation predicted irrational flow; (b) the real defect revealed byexperiment.

Figure 9.Defect caused by the unreasonable temperature distribution in the solidification process. (a) Simulation result; (b) realpart.

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for the research grant G-YF67 to support thisresearch.

Reference:

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