38166783 casting-simulation-1

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Casting Simulation For Your Foundry’s Profitability Using Hybrid Method Software.

ABSTRACT:

The present work aims at introducing simulation at all stages of casting to reduce defects and increasing the productivity and profitability of the foundry. The work presents the simulation of a casting using FDM software and various plots of the casting. The work describes the various stages and predictions involved with a complex casting simulation. The journal also presents the detail about mathematics involved in it.

KEYWORDS: Casting, Casting simulation, FDM simulation, SOLIDCast.

INTRODUCTION:

On estimating the defects in the casting components major portion is because of the design problems and minor portion is caused by manufacturing. The cost involved is also very high. Casting process simulation and analysis for various defects is considered to be one of the major productivity tools.

Considering the conventional approach

followed in foundries, i.e. trial and error method, lots of money, energy and time are wasted. Even then process is not controlled accurately. Foundries mostly follow lot of heuristics which they come out with their experience in that casting.

Process operations and casting are to be controlled in a very accurate fashion. One of the approaches that can be adopted is simulation, which is now becoming a part of every industry. Computer aided casting simulation helps us in visualizing the real world environment casting process in a mere few steps of inputs.

Simulation has become an important tool in almost in all foundries. Simulation plays a major role in all casting stages. The main aim of all the foundry makers will be to produce profitable and high quality components to survive in this competitive era. This may be one of the reasons “why now a day’s simulation has become an unavoidable part of casting production”.

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The Shortening of lead times, producing higher quality and improving yield of the casting, Simulation can be used. Casting simulation eliminates shop-floor trials and achieving of desired quality is made easier. Casting simulation requires domain knowledge, and must be fast, powerful, easy to use and accurate.

And also quite often it is required to design to risering and gating designs which were not originally part of casting design. If the ability of the product to be cast is already checked and optimized already in the design stage, a lot of useless works can be avoided.

SIMULATION:

The computer aided analysis is carried out by using a Finite Difference and vector modulus based Software, “SOLIDCast”. SOLIDCast is a PC based tool that is used for simulating the pouring of hot metal of virtually and casting alloy into the sand, shell, investment, or permanent molds, and the subsequent solidification and cooling process.

The analysis is preceded in three stages.

• Solidification Simulation

• FlowCast simulation

• Opticast Simulation.

Solidification simulation uses FDM based method of heat transfer calculation combined with a unique tracking of volumetric changes in the metal, to predict the temperature and volume changes in a casting as it is poured, solidified and cooled.

FlowCast is a full featured CFD simulation, based in the navier stokes equations for fluid flow.

OptiCast is a optimizing methodology followed to optimize the considered casting process with considering the parameters of design variables, constraints and objective function.

The following flow chart gives

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3D CAD MODEL

STL FILE

IMPORT TO CAE

ENVIRONMENT

RUNNER DESIGN

REDESIGN

FINITE ELEMENT

MESH

MATERIAL MODEL SYSTEM

INPUTS

MESH GENERATION

WEIGHTS CALCULATION

VIEW FACTOR CALCULATION

MOLD DATA INPUT

START SIMULATION

SINGLE CYCLE100% CASTING

&RISER

FLOWCAST SIMULATION

CASTPIC PLOTS

CHECKING FOR DEFECTS

NO! ACCEPT

YES! REJECT

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the steps followed in the experimental simulation of a casting using SOLIDCast.

MODEL DETAILS TAKEN FOR STUDY:

Component for study: Clutch housing of 407 tractor

Pattern material: Aluminum

Type of pattern: Match plate

Type of mold: Silica sand.

Pouring temperature: 1400-1450°C

Theoretical Pouring Time: 6.67 s.

Table-1: Metal composition

S. No.

Element Percentage

1 Carbon 3.32 Silicon 1.93 Manganes

e0.7

4 Sulphur 0.15 Phosphoru

s0.15

6 Chromium 0.47 Copper 0.4

PARAMETERS CONSIDERED:

The analysis is governed by set of equations for continuum of mass and energy. Fluid flow is governed by Navier stokes Equation.

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…

(1)

P= RT …… (2)

R + 2 = (ρ v –ρ)/ρL

……(3)

Name of the alloy, thermal conductivity, specific heat, density, initial temperature, solidification temperature, freezing range, latent heat of fusion are all the MATERIAL PARAMETERS to be specified.

Types of mold, initial temperature, thermal conductivity, specific heat, density are the MOLD PARAMETERS need to be specified.

SOLIDIFICATION POINT AND NIYAMA CRITERION is to be specified.

Values for HEAT TRANSFER COEFFICIENTS are also to be specified.

The flow chart given in fig.1 gives the steps involved in simulation.

EXPERIMENTAL SIMULATION:

A complex 3D dimensional model is considered for simulation and to plot the required results. STL file of the model

is imported to the SOLIDCast environment. System and required parameters are to be specified.

DISCRETISATION:

Finite difference method of discretisation is followed over a complex physical domain to form a

computational domain. The discretised model may have millions of cubes, and the heat transfer equations are applied to each cube, over and over. Heat Transfer Equations applied and iterations are carried out over the domain till the solution converges.

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PHYSICAL DOMAIN:

COMPUTATIONAL DOMAIN:

The equations given below gives the applied heat transfer equations and the equation for temperature prediction at the final node. The accuracy of the results of numerical simulation depends upon the size of the mesh, material property data, and the heat transfer coefficients specified for the mold interface.

Q = [ KA(Tn1-T n2)(∆t/x) ] …….(4)

Q = hA(Tn1-T n2) ∆t …….(5)

Tf = Ti + ∑Q/Vρc …….(6)

VIEWFACTOR CALCULATION:

The variations in radiant heat loss can be simulated

by a process of applying “View Factor” calculations to the mesh. The View Factor Calculation takes into account the visibility of all mold surfaces to all other mold surfaces as well as the surrounding environment, and adjusts the conditions at each surface accordingly. View factors are applied to every surface in contact with ambient conditions, so it doesn’t matter if the mold is created as a part of the model, or by meshing.

SOLIDIFICATION SIMULATION:

SOLIDCast runs the filling analysis and followed by solidification analysis. (Fig.3 and Fig.4). Solidification simulation enables visualization of the last freezing regions or hot spots. This facilitates the placement and design of risers and risering aids in order to increase yield while ensuring casting soundness without expensive and time consuming trial runs.

FLOWCAST SIMULATION:

FLOWCast allows visualizing the flow of molten metal through gating systems and filling the mold. FLOWCast, Models conduction, convection and radiation in the mold cavity,

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allowing to analyze the casting model and gating design to predict and minimize flow related defects such as misruns due to premature solidification, or oxide formation, or mold erosion due to excessive velocities during filling . FLOWCast enables to view progressive temperature, fluid velocity, and fluid pressure during the fill, from any angle of view.

_ …….

(7)

ρ((∂v/∂t)+v. ∆v) = -∆p+µ∆2v+f …….(8)

..(9)

..

(10)

..

(11)

SOLIDIFICATION TIME

Solidification time shows the time, for each part of the casting to become completely solid, i.e., to cool

to the Solidus Point. This can help to locate isolated areas of molten metal within the casting and to get a general idea of progressive solidification in various areas of the casting. The isolated area is the area that is prone to shrinkage. (Fig. 11).

CRITICALFRACTION SOLIDIFICATION TIME:

Critical Fraction Solid Time records the time, for each part of the casting to reach the Critical Fraction Solid Point. This is the point at which the alloy is solid enough that liquid feed metal can no longer flow. Critical Fraction Solid Time is generally a better indication than Solidification Time. This plot gives a good indication of whether any contraction that forms will be able to be fed by liquid feed metal within the risers or feeders. The result critical fraction solid time plot noted that there are few isolated pools of molten metal. (Fig. 13).

TEMPERATURE GRADIENT

Temperature Gradient is a measure of variation in temperature within a casting. Temperature Gradient is calculated at each node within the casting as that point hits the Niyama Point on the cooling curve.

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Temperature Gradient can be used to get an idea of whether there was good or poor directional solidification at various points within the casting. Higher temperature gradients are good, as steeper temperature gradients mean a greater driving force for solidification. The brightest areas indicate those areas with the lowest temperature gradients, and the poorest directional solidification. (Fig. 14)

COOLING CURVES

These curves describe how a single point in a casting behaves as it cools, when its temperature is plotted against time. As the casting loses heat (superheat) to the mold, it cools down, remaining a liquid until it begins to solidify. The point that signifies the onset of solidification is called the liquidus point. Once the alloy is completely solid, we say that it has reached the Solidus Point. After reaching this point, the metal begins to cool more rapidly as a solid. As the casting solidifies, it gradually changes from a fully liquid material to a fully solid material. We depend on the flow of liquid feed metal to

feed any area which is prone to contraction, to avoid shrinkage porosity in the casting. (Fig. 16)

Cast Iron is one of the most complex alloys in terms of how it solidifies and how volume changes affect the likelihood of shrinkage porosity.

The example showed a hypereutectic cast iron. In this case, expansion starts immediately upon solidification.(Fig. 12)

SOLIDCast predicts well the volume changes based on theoretical calculations for the behavior of iron and graphite in the solidification process.

NIYAMA CRITERION

Niyama has been used extensively for shrinkage prediction and directional solidification in castings, until the use of more advanced calculations such as the Material Density Function. Lower the value, higher the probability of shrinkage. Niyama criterion plot (Fig. 12) shows little shrinkage porosity in the castings.

COOLING RATE

Cooling Rate is a measure of how quickly a

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casting is cooling down measured at each point in the casting as that point hits the Niyama Point on the cooling curve. Cooling Rate can be an indication of material quality. Areas of the casting that cool rapidly generally have a more favorable grain structure, with less deposition of partially-soluble compounds at the grain boundaries. The plot (Fig. 15) shows most of the sections have the lowest cooling rates.

HOT SPOT SOLIDIFICATION

Hot Spot plotting is a function that locates thermal centers or hot spots within the casting by comparing solidification times or critical fraction solid times of points within local areas. The range of values is always 0 to 10, and generally the value plotted is around 1.1 or 1.2.

The hot spot plot (Fig. 10) does not give an indication of the severity of the defect, as it does not take contraction/expansion into account. But it gives a good indication of areas which may have problems.

FLOW PATH LINES

FLOWCast releases a group of particles from the

fill material / casting interface cells at the start of the filling simulation, and then at regular intervals during the simulation. Each one of the particles released from each fill material / casting interface cell is tracked in time while the filling simulation is executed. The particles can be watched while it moves during the simulation, and also display the particle movement after a simulation is complete. The plot (Fig. 9) shows the fluid particle flow with respect to time governed by navier stokes equation (Eqn.8).

MODULUS VECTOR METHOD:

The method is useful for the identification of hot spots and the simulation of feeding paths accurately. This approach uses the direction of the largest thermal gradient at any point inside a casting to move along a path which leads to a hot spot.

Consider a section of casting showing iso-solidification time contours

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When the temperature Ti of molten metal at a location Pi

reaches the solidus value, the nearest location Pi+1

along the temperature gradient is the one most likely to supply Pi with liquid metal compensate for solidification shrinkage.

Pi , Pi+1 , Pi+2, ……………… Ph

represents the feeding path in reverse.

The approach to locating hot spots and tracing fluid metal flow paths reduces the complexity of computation by at least an order magnitude as there is no longer the need to determine temperature exhaustively at all points inside a casting.

For determining the largest temperature gradient at any point Pi inside the casting, the vector modulus method is followed. Fig. 17 & Fig.18 shows the values computed at two points Pi(x,y,z).

CONCLUSION:

Casting simulation is the mathematical way of predicting a casting process. The objective function of maximizing the yield, minimizing shrinkage and minimizing solidification time are all found to be greatly achieved using Hybrid method software. Simulation should become an indispensable tool in all foundries, minimizing time, energy spent and money, thus maximizing profit. The plot for various parameters and defects very well gives a good idea for redesign and re-simulation done with no cost of time. Casting process simulation has become an industry standard. No foundry that produces high quality castings can consider simulation as unnecessary.

REFERENCES:

1. Ravi.B, Srinivasan.M.N (1990), Hot Spots in castings: Computer aided location and experimental validation

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2. Campbell, John, (2003), The new metallurgy of cast metals, CASTINGS (2ND

Edition), Butterworth-Hienemann, Burlington-MA 01803.

3. Ravi B,(2008),Casting Simulation and optimization;Benefits,Bottlenecks, and Best practices.

4. ASM Handbook (1992), ASM International, the Materials Information Company.

5. Joshi D, Ravi B (2008), Classification and simulation based design of 3D junctions in castings.

6. Louvo Arno, M.Sc, CT-Castech Inc. O.Y(1997), Casting simulation as a trool in concurrent engineering, International ADI and simulation conference.

7. Durgesh Joshi, Ravi B(2007), Feedability Analysis and optimization driven by casting simulation, Indian foundry journal.

8. Rundman B. Karl, Metal Casting, Reference for MY4130.

9. Ravi B, Srinivasan M.N,(1990)Casting solidification analysis by vector modulus method, International Journal of Cast Metals.

10. Heine, Loper & Rosenthal (2005), Principles of Metal Casting, Tata McGraw Hill, New Delhi.

11. Anderson D. John, (1995), Computational Fluid Dynamics, The Basics With Applications, Tata McGraw Hill Series.

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Fig.1 Meshed Model

Fig.2 Material properties

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Fig. 3 Mold Properties

Fig. 4 Weights calculation Fig. 5 Simulation Setup

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Fig. 6 Filling simulation Fig. 7 Solidification simulation

Fig. 8 FlowCast simulation

Fig. 9 Flow path lines

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Fig. 10 Hot spot plot Fig.11 Solidification time

Fig. 12 Niyama criterion Fig. 13 Critical fraction solid point

Fig.14 Temperature Gradient Fig. 15 Cooling rate

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Fig. 16 Cooling curve

Fig.17&18 point values using vector modulus method

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