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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.
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Fig:2.1. Main procedure of casting [5]
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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.
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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
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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,
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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
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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:
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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.
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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
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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
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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.
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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
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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
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[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
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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.
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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.
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[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]
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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
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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
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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.
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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
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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
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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.