7

CHAPTER 2.

LITERATURE REVIEW

This chapter contains a brief review of literature which exists in

the field of casting and its simulation. Within this broad area, the

present work involves simulations of mould filling and its effect on

solidification behavior of metals/alloys. Accordingly, in line with the

scope of the present study. It also covers the advantages of

simulations in improving the quality of castings, and lists the major

commercial applications currently available.

2.1 THE CASTING PROCESS

The casting process starts from receiving an order from a

customer which may include the design, physical properties, etc.,

then the foundry must plan how to make the castings, what methods

must be used, then produce a prototype of the casting[4], modify the

casting methods to get rid of the defects, produce the product, and

last of all, send the final product to the customer. Fig. 2.1 shows the

main procedure of a casting process, but the procedure in each

casting facility may differ in detail.

8

Fig:2.1. Main procedure of casting [5]

9

2.1.1 New Casting Development

From Fig:2.1, the area in the shaded box could be called the

development of a new casting, which in detail, might be separated into

three stages, product design, tooling development and foundry trials

[8].

2.1.2 Product design

The three important considerations, which effects the techno-

economic value of a cast product, can be stated as:

(i) Functional requirements

(ii) Property requirements and

(iii) Production and quality requirements.

The above requirements are developed through three steps of

the product design, which are conceptual design which focuses on the

geometries of the product to accomplish the required functions,

detailed design, which includes selecting the materials, defining the

geometry and its tolerances and prototyping which basically is

producing a prototype to test the form, fit and function of the product.

Iterations may be done to these design steps to achieve optimality of

techno-economic value of the component.

2.1.3 Tooling Development

This stage involves setting the best orientation of the casting

and the determination of the parting line or parting lines if there has

to be more than two segments of moulds to produce the casting.

10

Also, some castings might have multiple cavities instead of just one. It

also involves the internal cavities such as holes and undercuts which

needs the design and incorporation of cores [6]

The cores or dies must be easy to remove from the part.

additional cavities comprise feeders or risers (number, location, shape,

and dimensions), sprues, runners, the gating system which leads the

molten metal into the mould. Other accessories include cooling,

guiding and ejection systems (for die casting). The method for

manufacturing the tooling depends on its material, complexity, quality

and time/cost considerations.

2.1.4Foundry Trials

Trial castings are made to observe the flaws and defects that

might happen in a casting which may occur from the previous stages.

components must be tested by destructive and non-destructive

techniques for finding surface and sub-surface flaws. Macro porosities

and shrinkages may be seen by the naked eye while micro porosities

and micro structural defects would require seeing through a

microscope. Non-destructive methods include radiography,

ultrasound, magnetic particle, dye penetrant, and eddy current

testing[7]. Using the outcome these tests, the tooling, may be

modified, and the process parameters, may also be modified to

improve the casting quality. If the defects cannot be eliminated by

modifying the process parameters or tooling design, then the product

design may be modified. However, it is very expensive and time

11

consuming. Fig:2.2 shows the relationships of management time

spent, the ability to influence the production process, the cost of

rectifying mistakes in the process and the accumulated costs to each

product development phase

Fig:2.2. Cost and impact of product development phases [8]

2.2 CASTING SIMULATION SOFTWARE

In this day and age, customers, especially in the automotive industry,

would be more likely to request castings with high quality (Q), quick

delivery (D) and at a low cost (C). A tool that foundries may use to

achieve the three goals previously mentioned is to apply Computer

Aided Engineering into their process, in this case is by using

computer simulation software for casting. A generalized procedure for

using casting simulation software may be explained as follows.

1) Build a model of the casting design including the gating system and

all other material used with the casting, such as chills, cores, sleeves,

12

etc. This step may be done by using a CAD (Computer Aided Design)

system.

2) Input required data needed for computation, such as the physical,

mechanical and heat properties of the metal, properties of the mould

or die, pouring temperature, pouring time, pressure, etc.[9]

3) Computation of the simulation, which different casting simulation

programs may have different approaches in simulating the results.

Some well known approaches, for example are, the numerical

simulation approaches (Finite Element and Finite Difference Methods),

the geometrical approach Sarfaraz, et al. [11], the mesh less method

Lewis et al. [10]

4) Simulated results and interpretation of results. The results from the

simulation program may be shown in the form of graphs or colored

figures with numerical results depending on what criterion is used,

such as the temperatures in each section of the casting at a given

time, solidification times, hot spots, material density, etc. These

results must be translated into useful information to evaluate if a

casting is sound or not, or what must be done to improve the casting

design and start from step 1 once again.

2.2.1 The Usefulness of Casting Simulation Software

In the past, the foundry man has strived for ways to improve the

casting process and eliminate the defects that occurred in the castings

by trial and error and past experiences. The time needed to produce a

particular product is a time-consuming process. Problems occurred in

the casting may only be solved through trial and error. Scientists

13

throughout the years have studied the science of casting and

metallurgy and developed theories and mathematical models to

explain the properties of metals while going through the solidification

process. Simulation programs were developed from these methods

which are useful in predicting how the casting will come out. Defects

and problems can be discovered before the actual casting is cast

avoiding costly tests to prevent the problems. The process of

manufacturing a new casting design in a foundry starts from receiving

a design from a customer, which would include all dimensions and

tolerances, what kind of material and additives, and may also include

the strength, hardness or surface finish, etc. Then the foundry man or

the foundry engineer would design the gating and risering system for

the casting. The time used in designing and re-designing the gating

and risering system might take a few days or up to several weeks

before good castings can be made. Depending on the casting

complexity and the skill of the foundry man or the engineer. Casting

simulation software can predict where and what defects might occur

in a casting and the time and material used in the trial stage may be

reduced significantly.

2.2.2 Casting Simulation Methods

The casting simulation programs have different approaches in

calculating and predicting the outcome of a casting. Each method

hold advantages and disadvantages compared to another. Some

casting simulation methods may be shown below:

14

i) Numerical Approach such as : Finite Element Method (FEM), and

Finite Difference Method (FDM)

ii) Geometrical Approach - K-Contour Method.

iii) Computer Wave Front Analysis, generally implemented as: Pour-

out Method - Cubic Spline Functions

iv) Mesh less Method.

v) Grid-based simulation system Pan et al. [12]

2.2.3 Study of Casting Technology and Simulation Softwares for

Castings

Metal casting has evolved throughout the ages. The techniques have

been passed on and improved through generations. Metal casting,

although having a history of thousands of years, still hasnt stopped

evolving. The developments and findings of new casting techniques

and technologies are made every day.

Alloy steel sand casting technology is very unique and has a

very long history. The research on how to simulate the sand casting

process hasnt been found in the literature search during the study.

Because alloy steel casting process experiences the similar mould

filling and solidification as in other casting process, it implies that

many approaches and methods conducted in the designs and

analyses of the conventional casting process might be extended to the

designs and analyses of alloy steel casting process. The methodologies

of other casting process are evaluated to help in the development of an

approach to simulate sand casting process, applied to alloy steels.

15

The casting simulation research has been actively carried out

for around 20 years. During the early stage, the research work was

focused on the thermal analysis of the casting process [13,14]. In the

early 1990s, more and more work dealt with coupled thermal,

radiative view factor and fluid-flow calculation [15,16]. But until very

recent, the research work hasnt been focused on the integrated model

of the whole casting process. The research topics may be categorized

into following interrelated areas:

o Heat Transfer Model

o Mould filling analysis

o Solidification analysis

o Stress modelling

o Casting process control and optimization

2.2.4 Thermal Analysis of Casting Process

Thermal analysis is the first research area of the computer

simulation of the casting process. The early research work included

the heat conduction and molten metal solidification in the casting

process [17]. The radioactive heat transfer is added to the models of

investment casting process [18, 19] since the heat transfer through

radiation is significant in this process.

2.2.5 Mould Filling Analysis of Casting Process

Mould filling has been one of the earliest areas of the casting

simulation. Since the nature of the mould filling is influenced by the

shape of mould cavity, the algorithm is developed based on the

computing techniques for solving transitions in fluid flow. The earliest

16

algorithms used for the mould cavity flow filling are Marker-and-Cell

(MAC) developed by J.E. Welch et al. [20] and the Simplified Marker-

and-Cell (SMAC) algorithm developed by A. A. Amsdem and F. H.

Harlow et al. [21and 22]. Then another algorithm, Volume of Fluid

(VOF) was developed by B. D. Nichols and C.W. Hirt et al. [23] this

approach tracks the free surface by solving a transport equation of the

pseudo-concentration function. Several modifications have been made

to the original VOF algorithm, which were subsequently named as

Donor-Acceptor approximation [24] and Van Leer approximation [25].

The SOLA-VOF [25, 26] which employs the Donor-Acceptor algorithm

became a popular approach in the mould filling simulation research.

In recent years, progress has been made in embedding VOF-

filling type algorithms into FE (Finite Element) codes [27, 28, and 29].

The unstructured mesh facility of FE permits a casting mould to be

represented more accurately using far less elements/cells than in the

control volume case.

2.2.6 Solidification Analysis of Casting Process

The central element in the solidification process model is in

dealing with the nonlinearity associated with the latent heat

release/absorption along with the solid liquid interface. A fully

developed solidification model also needs to integrate the latent heat

algorithms with algorithms dealing with fluid flow. For a pure metal

and eutectic alloy, the solidification temperature is at one point. But

for other alloys, there exists a solid-liquid region, the solidification

starts at the liquidus temperature and ends at the solidus

17

temperature. To handle the latent heat release between the solid and

liquid interface, two major classes of methods have been

developed,viz.,1. Front tracking methods [30, 11] and 2. Fixed grid

methods [31]. The inter-dendrite flow in the mushy region should be

included in the fluid flow model, this is achieve on the addition of

appropriate source terms, e.g., a Darcy like source term [33] to signify

the porous nature of the mushy region. A range of examples of this

approach can be found in the references.

2.2.7 Stress Analysis of Casting Process

In the process of solidification, there is an interaction between

the cooling behavior and the deformation of the solidified component

of the metal in the mould. As the metal cools, it often shrinks and

causes a gap to form at the metal surface-mould interface and this

impact the subsequent heat transfer behavior. The solution of models

of the solidification process based on assuming heat conduction only

and using a FE method has been possible for some years. It is clearly

demonstrated whenever the thermal behavior is independent of the

deformation behavior, then the FE method is a straightforward

approach [34,35and36], given the thermal history to predict the

elastic deformation behavior as the phase change proceeds. Much

work [37] on the stress development in continuously cast metals

during solidification has also been pursued over the past decade. In a

comprehensive review of the thermal stress development in metal

casting processes, Dantzig [38] describes the formulation of elastic,

plastic and creep behavior.

18

The researchers [34, 35] have been using the thermo-elastic FE

code and heat transfer codes to model the impact of the interaction

between heat transfer and deformation. The explicit thermo-elastic FE

code enables the prediction of the separation between the metal and

the mould. The heat transfer FE code is used to simulate the

temperature during solidification. The heat transfer code pumps

through the temperature distribution at every certain thermal time

steps for the metal-mould geometry to thermo-elastic FE code to

evaluate the distortion of the metal and the mould. The impact of this

distortion on the formation of gaps is then fed back to the heat

transfer code. The numerical methods used in computer simulation of

casting process, are presented in detail in [39,33 and 40]

2.2.8 Studies Useful for Casting Simulation Software

In order for a casting simulation software program to predict the

results of a casting, there must be studies and researches about the

characteristics of each component in each process. Liu et al[41].

studied the effect of die pressure, time of loading and piston position

of pressure amplification on the variation of pressure and the quality

of casting because casting pressure conditions in die casting have

immense effect on die casting defects, which may be gas porosity,

shrinkage porosity and gas holes. Normally metals shrink when they

lose heat, which is the same as a casting would shrink when it

solidifies in a mould, but sometimes the casting may start to expand

after it cools down to a certain temperature according to what material

19

it is made of. The heat transfer rate would depend on the heat transfer

coefficient (h) between two types of surface, in the case of casting are

the casting and the mould, but since a casting may shrink in the

solidification process, the h value may change because of the gap of

air formed by the shrinkage of the casting. Wang et al[42]. has

conducted a study to measure the interfacial heat transfer coefficient

(h) between high temperature casting alloys and moulds during the

casting due to gap formation. It was found that a high value of

interfacial heat transfer coefficient is generally obtained at the start of

the casting, then the value drops abruptly and then rises to a certain

value, and then the value gradually decreases. It was also observed

that the heat transfer coefficient (h) value is not considerably affected

by the casting alloys but rather by the mould material; castings with

ceramic moulds would have an h value between 22W/m2K and

350W/m2K while sand moulds are between 40W/m2K and

90W/m2K.DeLooze et al. [43] has studied how the operating

parameters of a low pressure die cast (LPDC) machine and the quality

level of the aluminum melt precious the casting cooling rate and/or

the microstructure of the aluminum. The arrangement and

distribution of micro porosity in the castings was used as an indicator

of casting quality and solidification conditions, and experimental data

for the operation of burst feeding in low pressure die casting was

detected. There were important improvements to the directional

solidification and micro structural refinement were achieve with die

cooling has done research on applying Campbells ten casting rules

20

[44]to develop high quality aluminum castings quick cast process,

Yang et al [45] studied the effect of casting temperature on the

properties of gravity cast and squeeze cast aluminum alloy with 13.5

percent silicon and zinc alloy with 4.6 percent aluminum and found

out that casting temperature had an effect on the mechanical

properties of both the types of casting. Herman et al.[46] has done a

study about implementing an optimization tool consisting in an

optimization algorithm and casting process simulator. It was applied

to an industrial casting machine where spray coolant flows were

optimized.

Casting process simulation has become an industry standard,

casting simulation has helped foundries to point out the factors that

have a significant effect on the quality and price of the casting, as

observed by Jakumeit et al.[47].

Many a casting and casting related simulation software

programs are created in order to achieve the most accurate

predictions. Some may have more strength in some areas than the

other. Sarfaraz, Ahmad Reza [48] have done a study of coupling two

simulation programs, the CFD (Computational Fluid Dynamics)

program FLUENT and casting simulation tool CASTS, for simulating a

mould filling and solidification for an aerospace investment casting.

Also, a paper by Moreira and Ribeiro et al.[49] discusse the

advantages and limitations of the use of two software packages,

FLOW-3D based upon the Finite Element Method and SOLIDCast

21

based upon the Finite Difference Method. The Finite Element and

Finite Difference Methods are the two most well known approaches in

casting simulation software. Both methods are mesh based simulation

programs which may have some disadvantages in predicting hot spots

and simulating the jetting and splashing effects during mould filling.

Alter natively, the experienced foundry men, devised the techniques

for predicting hot spots by using a geometric transformation method

known as the medial axis transformation and a technique based on

mesh-less method for simulating the mould filling process.

2.3 Implemented Casting Simulation Software Case-studies.

Many casting manufacturers have implemented casting simulation

software to their production and have been successful. Wright et al.

[50] conducted two successful case studies of implementing casting

simulation technology within the company, Walker Die Casting, which

is a producer of complex aluminum castings. In 1995, a foundry

named Raahen Tersvalimo Oy in Finland was casting various valve

components for Neles Controls. The foundry was experiencing some

defects in a particular stainless steel that resulted in repair welding.

The foundry considered investing in casting simulation software and

this component was selected as a test case. The foundry used

CastCAE to simulate the test model and the defects were predicted

exactly as what the foundry experienced. Circular chill and insulating

sleeves were added in the system and resulted in a sound casting.

22

Later, the real casting was made according to the new design and

resulted in sound castings.

A research by Alonso and Franco et al. [51] used SOLIDCast

along with OPTICast to raise the yield of vertical gating systems in the

investment casting process of a jewelry workshop. It was found that

these two modules showed a great potential from improving the design

of the filling systems.

2.4 CURRENT STATUS OF REASERCH

So many recent investigators have discussed on a variety of issues in

the terms of yield, shrinkage, and soundness of castings through

simulation. Some of them were discussed here.

2.4.1 Solidification Modelling Review

Kannan et al. [52] acknowledged that the present thrust of

solidification modelling research lies in the enhancement of heat

transfer and fluid flow techniques towards the goal of solving micro

structural modelling problems. The current state of the art, which

takes the form of 3D finite element and finite difference codes, fully

coupled with a computational fluid dynamics simulation. Ghosh et al.

[53] but since to a large extent leftovers unidentified about the process

of nucleation in these materials, some level of ambiguity exists in their

model. Brown and Spittle et al. [54] spot out that while finite element

and finite difference analysis methods can offer commanding solutions

to solidification problems, they should have a physical model of the

solidification process upon which to support their analysis. Tu et al.

23

[56] make use of a typical, state of the art finite element application to

examine the investment casting processes. Beffel et al. [57] their work

reveal the extent of information available from a typical FEM-based

solidification simulator. It also reveal a need for significant computing

time.

The investigations by Pehlke et al. [58] resulted in two

commercially available solidification simulators, the 2D AFS Solid

package and the 3D AFS Solidification System. Further the literature

reveals that Estrin et al.[55] utilized built in mould and material

databases in order to produce quality results. Brown et al. [54]

observed that each FDM model requires complete reworking in order

to accommodate the analysis of a new alloy. Further they realized an

speed improvement over conventional FDM analysis with cellular

automaton software. Hill et al. [60] as well use a cellular method to

arrive at a quick, fairly accurate solidification time plot. The

information gained from this analysis provided a basis for design of

risers and gating using expert system routine, to quickly visualize the

thick regions of a casting in the same way as a casting engineer does.

Among the earliest solidification modelling techniques, Chvorinovs

rule is quite popular [61] however Upadhya and Paul et al. [62] stated

that most applications utilizing Chvorinovs rule used some form of

feature-based modelling or sectioning of a solid model in order to

break a full casting geometry into simple components. While some of

these applications are capable of performing 3D calculations, most of

them only apply to 2D simplifications of 3D models. Pei et al. [63]

24

presented an application to perform most of the work in dealing out a

solid model for modulus calculations. Sirilertworakul et al. [64]

presented solidification modelling details by sectioning a casting

geometry automatically using an AutoCAD function to accurately

predict localized differences in cooling rates for internal and external

corners which influence micro structures and internal stresses in the

castings. while Nieses et al. [65] also computed the section moduli of

2D sections based upon an area/perimeter value for section modulus,

Sirilertworakul et al. [64] proposed a point modulus calculation which

depends upon both the distance of a point to each edge of a section

and a view factor for each edge with respect to that point.

In an attempt to extend Chvorinovs rule to high alloy steels,

cast irons and certain non ferrous alloys DeKalb et al. [66] combined

this global section modulus formula with a considerable amount of

experimental data. This code popularly known as SWIFT, can also

calculate the start and end of the mushy zone of a long solidification

range alloy at a given time. [62 and 67] used a distributed point

modulus calculation to calculate the local section modulus of points

in a finite difference mesh of a casting. A majority of the commercial

packages available today, for solidification modelling, incorporate

concepts discussed above. Such as SWIFT, MAVIS (cellular

automaton), and ProCAST. In addition these commercial packages

have extended capabilities to be run on PCs, and many of them can

be operated on multiple platforms. Further, to facilitate solidification

25

modelling in investment casting ,Sandia National Laboratories has

brought a commercial package, called FASTCAST [68]

P.L Jain et al.[69] suggested the best suitable testing method for

the identification of defects in the casting, as result of improper design

of parting line or due to misplacement of cope and drag.

Viswanathan et al.[70]described the extended utility of ProCAST

software for avoiding shrinkage, improving cast metal yield, optimizing

the gating system, optimizing mould filling, and finding the thermal

stresses, as means to maintain the casting quality.

Maria Jose et al. [71] reported that successfull solidification

modelling of steel sand castings using ProCAST which resulted

reducing the production costs and increasing of profits by improving

yield.

B.Ravi et al. [72] while reviewing CAD/CAM revolution for small

and medium foundries, reported that mould filling has the greatest

influence on casting quality. He substantiated by stating that the flow

of molten metal being poured experiences turbulence for a variety of

reasons. In his paper, he described the systematic procedure for

design, analysis, optimization and validation in steel casting practice

and compared the performance of various commercial packages.

A.K .Tahari et al.[73] simulated the combined effects of the heat

transfer; sand permeability, free surface pattern, and fluid flow during

26

the modelling stage of metal casting. Practically, such routine can be

used to analyze a casting design through simulation.

In an attempt to develop high quality aluminum castings, Wong

et al.[74] had applied Campbells 10 casting rules and the designs

were validated by using CAE software .

B.Ravi et al. [75] elucidated the role of simulation in design and

analysis of casting, the benefits, bottle necks and best practices

involved. It is further concluded that casting simulation tools has

become essential for a better and efficient casting practice.

Guharaja et al. [76] have done a study about obtaining the

optimal settings of green sand casting to yield the optimum quality

characteristics by using Taguchis parameter design approach and

verified by confirming with practical experiments.

A paper by Moreira and Ribeiro et al.[77]discussed the

performance of two software packages, namely FLOW-3D and SOLID

Cast they concluded that SOLIDCast cannot be applied to complex

geometries but gave the results in short span of time for simple

geometries.

Hu Hong-jun et al. [78] researched the influence of casting

process on quality of casting using ProCAST. They reported significant

improvement in casting yield by optimizing the pouring and riser

system.

27

B.Ravi et al.[79] analyzed feedability using solidification

simulation, and found that high temperature signifies fewer

possibilities for solidification shrinkage.

Prabhakar Rao et al. [80] analysed the stresses developed in

straight and flanged bars in sand and die-casting of an aluminium

alloy using X-Ray diffractrometry. It was concluded that due to

hindrance in contraction, the flanged bars developed higher stress

than straight bar.

C.Monroe et al. [81] discussed the effect of mould and cast

metal properties on distortion during casting of steel, and concluded

that they have substantial influence on hot tear formation, during

solidification of steel castings .

2.5 CONCLUDING REMARKS

The research findings presented by each of the authors cited in

the literature review were critically examined to establish a sound

foundation for this research project and to provide a basis for

measuring its contribution to knowledge. Starting from the middle of

1980s, due to the decreasing cost of computers and advances in

computing methods, computer simulation of foundry process has

been developed and improved by both academic and industry. Studies

on casting defects and improvement on yield of castings have then

stepped forward from experiment based investigations to computer

simulation aided research. The advantages of the simulation of the

castings and the factors that can be controlled in the simulations to

28

get the improved quality of the casting. So many researchers

described success in modelling and simulation of solidification. By

implementing the simulation skills to the casting process we can

visualize the process and by giving the proper data to the simulation

we can get the optimum parameters of the casting process. The

results of this review revealed that there have been rapid advances in

many areas of casting solidification simulation technology with regard

to casting quality. This was mainly brought on by the requirements to

deliver defect free castings to customers and increase the quality

standards of casting products. An extensive search of the literature

has also indicated that simulation of non ferrous die castings with

FEM simulation has not been explored to the alloy steel sand castings

to date. This is the topic of research addressed in this thesis. The

above literature review reveals that the primary focus of earlier

researchers was on the mechanism of casting defect formation and

measures to control the same. Further, it can be noticed that the

results of these studies were not confirmed with manufacturing

industry. Added to this, there has not been any significant work

reported in the literature on solidification modelling of alloy steel

castings made by sand casting route. This literature search was

focused on obtaining information on simulation of sand castings with

complex shapes, by varying gating positions, reducing defects and

improving yield of the castings. However, it was found that there was

no published research addressing these issues within an integrated (a

combination of earlier mentioned factors) framework. Hence, there is a

29

need to examine the performance of simulation applied to alloy steel

castings, made by sand casting. The literature review indicated that

simulation techniques had the potential to be used to detect sub

surface defects in castings. It was clear that factors such as mould

material and cast materials are critical variables in the simulation of

castings. However, FEA approach has not been suitably explore for

simulation of alloy steel sand castings. Therefore, the development of

an effective methodology for numerical simulation of alloy steel

castings made by CO2 strengthened sand moulds through a FEA

simulation approach is investigated as part of this research program.