The international journal of advanced manufacturing technology, tập 58, số 1 4, 2012

Int J Adv Manuf Technol (2012) 58:1–8
DOI 10.1007/s00170-011-3388-1

ORIGINAL ARTICLE

Comparison of cranioplasty implants produced
by machining and by casting in a gypsum mold
Dalberto Dias da Costa & Sérgio Fernando Lajarin

Received: 12 August 2009 / Accepted: 12 May 2011 / Published online: 24 May 2011
# Springer-Verlag London Limited 2011

Abstract Cranioplasty is a medical technique used to
correct craniofacial defects. Depending on the size and
location of the defect, a bone substitute to replace the
deformed or missing tissue can be manufactured. With the
advances in computer-based systems and the invention of
new biomaterials, the production of customized implants
with good cosmetic and functional results has now become
widespread. However, little research has been undertaken
into the quality of prefabricated specimens in terms of
dimensional and form errors. Because of the geometric
complexity involved, measurement of this kind of object is
a complicated process. The aim of this paper is to describe
two different manufacturing processes used to produce a
large polymethylmethacrylate (PMMA) implant for use in
cranioplastic surgery and to discuss the results of the
evaluation of the dimensional errors and lead times
associated with these methods. In the first method, the
specimen was directly machined from an acrylic block. In

the second, the implant was cast in a machined gypsum
mold. Both processes were based on a digital model of a
dried human skull scanned by computer tomography.
Dimensional errors were evaluated with a coordinated
measurement machine. Despite their complexity, the
PMMA specimens produced were measured and their
dimensional differences established. Compared with direct
D. D. da Costa (*)
Mechanical Engineering Department,
Universidade Federal do Paraná,
Curitiba, PR, Brazil
e-mail:
S. F. Lajarin
Postgraduate Program in Mechanical Engineering,
Universidade Federal do Paraná,
Paraná,
Curitiba, PR, Brazil

machining, casting results in a longer lead time and,
because of shrinkage, a larger dimensional deviation.
Keywords Cranioplasty . Cast implants . Direct machining

1 Introduction
The use of prefabricated alloplastic implants for cranioplasty applications has grown in recent years, mainly
because such implants help reduce surgical time and allow
a satisfactory esthetic restoration, as has been described by
several researchers [1–5].
Firstly, the injured region, or in some cases the whole
skull, is scanned by computer tomography (CT). The
acquired image set is then processed to separate the region

of interest and the edges making up the bone contours. A
number of commercial packages are currently available for
this kind of application, and some are able to produce a 3D
reconstruction of the scanned volume and export it in Initial
Graphics Exchange Specification or Standard Tessellation
Language (STL) format.
Once a computer-aided design (CAD) model has been
produced, it can be adjusted digitally to facilitate attachment of the prosthesis as proposed by Weihe et al. [6].
Depending on the geometric complexity and biomaterial
chosen, one or more manufacturing processes can be
selected. In most cases, more than one manufacturing
process is usually required to produce the implant.
A number of different manufacturing alternatives have
been studied and recommended for the prefabrication of
cranioplasty implants. Casting, machining, forming, and
layer manufacturing-based processes are the most significant examples. As described by Giannatsis and Dedoussis
[7], Leong et al. [8] and Yang et al. [9], the last of these


2

techniques allows very intricate geometric forms to be
produced and is used to produce biomodels and scaffolds,
which have been the object of much attention from the
research community in recent years.
Direct machining is a very flexible process and has been
used in the production of titanium [6] and acrylic implants
[10]. Its most important limitation is the interaction
(gouging) between the cutting tool and the blind, or even
small, cavities found in the surface of the implant.

However, as described next, the adoption of smaller cutting
tools in the finishing phase can minimize this kind of
geometric constraint, particularly when such cavities do not
represent important anatomical features. In addition to the
problems, they pose in terms of gouging, the free-form
surfaces found in implants pose serious difficulties for setup
planning insofar as determining datum and fixtures is
concerned. Nevertheless, if satisfactory fixture planning
can be developed, machining can be considered an
alternative.
The use of casts offers one significant advantage over
other techniques, namely, the possibility of producing
composite materials in the same mold, as pointed out by
Schiller et al. [11]. The cost of making the mold, however,
is one of the shortcomings of this approach. A combination
of machining and casting, as proposed by Hieu et al. [12]
and Weihe et al. [6] represents a valuable alternative, since
a cheaper free-machining material could be used to build
the mold cavities.
As well as increasing the lead time, a combination of
different manufacturing techniques in the production chain
of a cranioplasty implant affects the final quality of the
implant, in particular its shape and dimensional deviation.
A further, significant problem associated with dimensional
error assessment in implants arises as a result of their
geometric complexity, which makes traditional linear
measurements more difficult to apply [13].
The aim of this paper is to describe two different
manufacturing processes that were used to produce a large
polymethylmethacrylate (PMMA) implant for use in cranioplastic surgery and to discuss the results of the evaluation

of the dimensional errors and lead times associated with
these methods. The longer manufacturing chain involves
mold machining and casting; and the shorter chain, direct
machining of the region modeled.

2 Materials and methods
The starting point for this work was a dried human skull
that had been tomographed and modeled by Bazan [10] and
had its calvarial region digitally extracted as shown in
Fig. 1. Despite the fact that it is unique and does not
correspond to a real cranial defect, the digitally extracted

Int J Adv Manuf Technol (2012) 58:1–8

Fig. 1 Dried skull and the digital model of the region extracted

region represents a substantial challenge both to measure
and manufacture, as it contains two highly curved surfaces
(the internal one and the external one). The third surface
was defined by an arbitrary intersection of the digital model
of the skull with a plane parallel to the scanning plane,
which is roughly parallel to the occlusal plane. The details
of the CT and CAD modeling can be seen in reference [10]
and are not repeated here.
PMMA was chosen in this study because it is extensively
used as biomaterial, is cheap, and can be easily manufactured [3, 12, 14]. The choice of autopolymerizing rather
than heat-polymerizing material was influenced by the
design of the mold and is explained in the next section.
2.1 Direct machining
A prepolymerized powder was hand mixed with the liquid

monomer (Classico São Paulo, Brazil) inside an open box.
Based on the results reported by Jasper et al. [15], the
liquid-to-powder ratio adopted was 0.5 mL/g. The box was
then kept inside an autoclave with a positive pressure of
300 kPa, and the rectangular block formed (57×146×
175 mm) was removed from the box 2 h later.
Most of the machining conditions were very similar to
those used by Bazan [10], the main difference being the use
of smaller cutting tools and the addition of a parallel lace
milling strategy during the finishing of both the concave


Int J Adv Manuf Technol (2012) 58:1–8

3

and convex surfaces. The setup was the same as that used
by Bazan [10] and involved the use of a sacrificial material
for the second fixturing and localization. Schematic
representations of the machining strategies for both the
surfaces are shown in Figs. 2 and 3, and the main cutting
conditions are given in Table 1. All the machining was
planned with Edgecam software (Pathtrace Ltd., Reading,
UK) and executed in a Discovery 4022 three-axis vertical
machining center (Romi, São Paulo, Brazil).
2.2 Casting

Fig. 3 Milling operation at the convex surface

In most of the literature about acrylic castings for

cranioplasty applications, the molds are produced by hand
after the cranial defect has been copied using alginate or
similar material [16] or by layer manufacture of an implant
model [7]. In both cases, the models are then used to create
a gypsum-filled mold.
The use of mold machining is rare. Hieu et al. [12]
proposed this technique as a way of achieving greater
quality and reducing cost compared with layer
manufacturing-based processes. All the parts of the molds
(cores and cavities) they used in their study were machined
in hardwood resins and plastics.
In this work, we propose a different approach (see
Fig. 4) involving the design of a mold based on an
aluminum flask that can be reused and filled up with
gypsum as necessary. The top of the flask can be moved
along a two pin guide so that external pressure can be
applied with a press.
The first gypsum block was cast in the flask and on the
top plate. After it had set, it was removed from the flask
while being kept anchored to the top plate by means of
machined grooves as shown in Fig. 4. After the flask had
been emptied, the second block was cast and kept there
until the end of the whole process. The gypsum casts were
made from type IV dental stone, and the water-to-powder
ratio was 0.2 mL/g in accordance with the manufacturer’s
instructions (Zhermack SpA, Badia Polesine, Italy).
The machining planning and conditions were the same
as those used for the PMMA milling, which are shown in

Figs. 2 and 3 and Table 1. A circular groove was milled

along the upper surface of the gypsum and a rubber O-ring
was inserted in the groove to provide a mechanical seal
during the pressing phase.
A sufficient volume of PMMA mixture was prepared to
provide the 150 mL required for the casting itself and the
excess portion that flows out of the mold, thus guaranteeing
that the mold would be completely filled. Five minutes after
the PMMA was prepared, the mold was closed and secured
to the table of a hydraulic press. An axial load of 2 kN was
applied for 2 h to guarantee complete polymerization.
The setup and run times for each task were recorded to
enable the process times for both manufacturing processes
to be compared.

Fig. 2 Milling operation at the concave surface

2.3 Dimensional error assessment
After the manufacturing phases had been completed, the
skull, gypsum mold (core and die) and directly machined
and molded acrylic implants were all measured against the
digital STL model.
Because the skull was a complete piece, its inner surface
was not inspected. All measurements were carried out with
a coordinated measuring machine (CMM) (Discovery II
from Sheffield, WI, USA) equipped with a 2-mm spherical
touch tip and an accuracy of 5+L/200 μm. PC-DMIS™
CAD++ software (Wilcox, UT, USA) was used for the
localization procedure and measurement analysis. This
package has a special “best-fit” resource based on the
least-squares method that allows the automatic localization

of complex parts. The Design Coordinate System (DCS)
was based on the STL model and used to determine the
Measurement Coordinate System (MCS) for all the
inspected parts.
The procedure to localize the MCS consisted of three
steps, which were applied to the seven surfaces. Firstly,
based on the “3-2-1 principle”, six points were manually
defined to achieve rough localization. In the second stage, a
grid composed of 160 points was created in the DCS and
used by the PC-DMIS localization algorithm. This second
stage was applied iteratively until the system reported


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Int J Adv Manuf Technol (2012) 58:1–8

Table 1 Machining conditions
Cutting conditions
Surface

Operation

Strategy

Cutting speed (m/min)

Concave

End milling the sacrificial material

Roughing
First finishing pass at Z=32 mm
Second finishing pass at the end
Roughing

Z constant
Z constant
Z constant
Parallel lace
Z constant

157
157
44
44
157

0.3
0.3
0.22
0.22
0.3

20 mm end mill
20 mm end mill
4 mm ball nose
4 mm ball nose
20 mm end mill

First finishing pass at Z=6 mm

Second finishing pass at Z=40 mm
End milling to cut off the sacrificial material

Parallel lace
Z constant
Z constant

44
44
44

0.22
0.22
0.22

4 mm ball nose
4 mm ball nose
4 mm end mill

Convex

convergence. In the last stage, a regular grid with 5,000
points was defined to cover the visible surfaces. For every
digitized point, the difference (T) between the point in the
MCS and the corresponding point on the digital surface in
the DCS was computed according to the following
equation:

T ¼ ið x m À x d Þ þ j ð y m À y d Þ þ k ð z m À z d Þ

ð1Þ

where the unit vector “i, j, k” defines the direction in which
the touch trigger approaches the surface,
xm, ym, and zm are the Cartesian coordinates of the point
measured in the MCS, and
Xd, yd, and zd are the Cartesian coordinates of the digital
model in the DCS.
The root mean square (RMS) of the measurements was
used to estimate the dimensional deviation for the inspected
surfaces.
In addition, to improve the RMS-based analysis, the
bounding boxes were calculated for the manufactured
specimens using the digitized points. Their dimensions
were defined by the differences between the largest and
smallest values in the X, Y, and Z directions.

Fig. 4 Mold design

Feed per tooth (mm/rev)

Cutting tool

3 Results
The machined surfaces resemble the surfaces in the digital
model very closely. As shown in Fig. 5, even small
anatomical marks, such as those in the calcified sutures,
were reproduced.
As aluminum alloys have good mechanical strength, the
reusable mold case could be easily referenced and fixed to

the machine table. The case also increased the stiffness of
the gypsum, thus helping reproduce the small details found
in the STL model. The machined core and cavity can be
seen in Fig. 6.
As the core moves into the cavity, the excess PMMA
flows out of the mold; once the core is fully inserted into
the cavity, the rubber O-ring forms a seal between the two
parts of the mold allowing a positive pressure to be
maintained during polymerization. After the setting time,
the casting was easily removed without damaging the
machined gypsum. As shown in Fig. 7, minor flash
formation alternating with small unfilled regions occurred
at the mold parting line. The flashes were manually cut off
before any measurements were taken.


Int J Adv Manuf Technol (2012) 58:1–8

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Fig. 5 Comparison of the
digital STL model and
the machined specimen

Figure 8 shows a histogram of the T values and a
graphical representation of the distribution of these values
over the skull surface generated with the PC-DMIS
software. The darker areas (red and dark blue) indicate the
values outside a range of ±0.3 mm. The maximum values
for T+ and T− are also identified. A similar analysis was

conducted for all the surfaces inspected. The results are
summarized in Table 2, which gives the RMS values and
the amplitude of the points measured.
Table 3 gives the results of the bounding box calculations for the external surfaces of both the cast and the
machined specimen. The values for the STL model were
used for comparative purposes.
The elapsed times for each task in both processes are
shown in Tables 4 and 5. The setup time includes planning,
machine preparation, and material handling. The process
times are the sum of the setup or run times for each task in
the sequence in which they are carried out to produce a
single PMMA specimen.

4 Discussion and conclusion
Starting from a digital STL model produced by Bazan [10] of
a large hypothetical cranioplasty implant, two specimens
Fig. 6 Visual comparison of the
digital model (convex surface)
with the machined (concave)
gypsum cavity

were produced using two different manufacturing processes.
The specimens, which were made of PMMA, were
evaluated with a CMM. Dimensional error assessment was
based on a comparison of the surfaces of the specimens
with the surfaces of the STL model. Because of the high
degree of geometric complexity imposed by this kind of
surface, PC-DMIS software was used to run an automatic
localization procedure.
As a real clinical case was not available for study, a large

region of the skull corresponding to the top of the
calvarium was analyzed. It is reasonable to suppose that
such a specimen is representative, from the point of view of
the geometry, of a great number of the skull defects
reported in the specialized literature [17–19]. Of course,
in the case of smaller implants, especially those with small
cavities, more effort is needed to design and machine the
gypsum molds. However, as pointed out by Hue et al. [12],
correct planning of the parting line and selection of small
cutting tools can minimize the problem, allowing the molds
to be satisfactorily milled in a three-axis machine tool.
The largest RMS value (0.169 mm), which coincided
with the second largest amplitude (T+ =0.816 mm and
T− =−0.669 mm), was observed when the digital model
(STL) was compared with the original dried skull. This
difference can be attributed to three sources of error. The


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Int J Adv Manuf Technol (2012) 58:1–8

Fig. 7 Cast implant with minor
flash formation at the mold
parting line

first, which is known as the partial-volume effect, is a
consequence of the use of computed tomography. As
pointed out by Mazzoli et al. [13] and Bouyssié et al.
[20], this kind of deviation depends on the scanning

parameters adopted, such as section thickness, pitch, tube
current, and voltage. The second source is related to the 3D
reconstruction and factors such as the bone segmentation,
contour vectorization, tessellation, and interpolation methods. The millimeter-to-pixel ratio adopted in the model
evaluated was 250/512, as reported by Bazan [10]. Despite
the facilities available in the software for image segmentation, contour interpolation and tessellation, a certain amount
of error, albeit small, can be expected from this kind of
processing. Mazzoli et al. [13] and Choi et al. [21]
Fig. 8 Histogram of T values
and a graphical representation
of these on the digital STL
model after measurements
of the skull were taken

highlighted the importance of the threshold value adopted
during image segmentation as a factor that has a significant
effect on the quality of the digital model. The third source
of error can be attributed to the localization procedure.
Despite the large point set adopted here, a certain amount of
error should be expected, which, as pointed out by Lai and
Chen [22], depends mainly on the quality of the points
measured.
The analysis in Fig. 8 helps to corroborate the last error
source discussed above. The histogram indicates a wellcentered distribution of the T values, i.e., roughly 50% of
the points are positive. However, their spatial distribution
over the skull surface reveals two patterns. The first,
corresponding to the dark blue area, is mainly composed


Int J Adv Manuf Technol (2012) 58:1–8

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Table 2 Results of the dimensional error assessment of the different
surfaces inspected
Surface inspected

T (amplitude) (mm)
T+

Skull
External (cast)
Internal (cast)
Gypsum mold (cavity)
Gypsum mold (core)
External (machined)
Internal (machined)

0.816
0.516
0.517
0.314
0.462
0.986
0.637

T−

0.161
0.121

0.117
0.022
0.028
0.043
0.045

of negative values less than −0.30 mm. The second, which
follows the calcified sutures, contains positive values
greater than 0.30 mm (red area) and is a result of a
discontinuity in the modeled surface.
The dimensional error found in the cast implant was less
for both surfaces (RMS=0.121 and 0.117 mm for the
external and internal surfaces, respectively) than that
observed for the skull, but with a larger amplitude (T+ =
0.517 and T− =−1.373 mm for the internal surface). As with
the skull, a similar pattern for the more negative T values
was observed, but this is largely explained by the shrinkage
that occurs after the setting and curing time for the PMMA.
This shrinkage can be confirmed by analysis of the bounding
box values given in Table 3. The mean value of the difference
between the cast and the digital model was estimated to be
−0.63%. However, this cannot be entirely attributed to
shrinkage alone as other sources of error are present, such
as the localization procedure, the machined gypsum mold,
and the distortion caused by demolding. The value observed
lies in the linear shrinkage range reported by Keenan et al.
[23] during injection molding of PMMA dentures.
While according to Silikas et al. [24], the estimated
theoretical value for the proportion of monomer used could
be expected to be larger, the smaller shrinkage observed in

the present study can be explained by the fact that positive
pressure was maintained throughout the polymerization
phase and, as reported by Gilbert el al. [25], by the mixing

Table 3 The bounding box dimensions for the manufactured
specimens and the STL model
External surface

Digital model
Cast
Machined

Task

Setup time (min)

Run time (min)

Preparing the gypsum
Machining the mold cavity
Machining the core
Machining the circular groove
Casting the PMMA
Process time

15
40
35
10
30

130

200
100
95
5
135
535

T
(RMS) (mm)

−0.669
−0.706
−1.373
−0.141
−0.090
−0.373
−0.175

Table 4 Setup and run times for the main casting tasks

procedure adopted here, i.e., hand mixing instead of
vacuum mixing.
As the PMMA specimen was cooled inside the mold, the
expansion and contraction caused by the exothermic
reaction during polymerization were constrained by the
mold walls suggesting that residual stress may be present in
the cast specimen.
Both the machined surfaces were found to have low RMS

values, with more than 98% of all the points measured lying
within a range of ±0.1 mm. Larger values were considered to
be outliers, particularly those occurring at the calcified sutures.
This close visual and dimensional resemblance to the digital
model agrees with the results reported by Da Costa [26].
The RMS T value measured for the gypsum mold (core
and cavity) was slightly lower than that observed for the
machined PMMA surfaces. The small difference can be
attributed to the greater stiffness afforded by the gypsum
and the aluminum flask. As milling was done after the
setting time had elapsed, no significant expansion of the
gypsum was expected, contrary to what is observed when
an implant is molded in gypsum slurry [27].
The casting time was longer than that recorded for direct
machining, as it includes the time required for tasks related
to the production of the mold as well as the molding phase
itself. The process times shown in Tables 4 and 5 are the
sum of the setup or run times for each task in the sequence
in which they are carried out. However, as preparation of
the gypsum and casting of the PMMA block can be carried
out beforehand, both the cast and direct-machining process
times can be shortened to 7.5 and 4.7 h, respectively.
Layer-manufactured patterns are extensively used to
produce molds for cast PMMA implants [1, 7]. D´Urso et
Table 5 Setup and run times for the main direct-machining tasks

Directions
X (mm)

Y (mm)

Z (mm)

133.077
131.704
132.688

163.588
161.885
163.297

44.680
44.756
44.446

Task

Setup time (min) Run time (min)

Casting the PMMA block
Machining the sacrificial material
Machining the concave surface
Machining the convex surface
Process time

15
30
15
70
130

120
25
65
80
290


8

al. [1] reported an average time of 10 h to produce casting
patterns and 4 h to cast the PMMA implant.
However, despite the time saving afforded by the
processes investigated here, these cannot compete with
layer-based technology when the implant geometry is
highly complex, particularly when the implants contains
hollow regions and small cavities.
The main goal of this work was achieved. Despite their
complexity, the PMMA specimens produced were measured and the dimensional differences for each specimen
were determined. Compared with direct machining, casting
implies a longer lead time and larger dimensional deviation.
However, if a proper offset value is adopted in the molddesign phase, shrinkage can be minimized. Accordingly, the
inherent advantages of casting, such as the possibility of
producing implants made of composite materials, as
proposed by Schiller et al. [11], can compensate for the
longer lead time associated with this technique.

Acknowledgments The authors would like to express their gratitude
to CAPES, the Brazilian Agency for Postgraduate Education.

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Int J Adv Manuf Technol (2012) 58:9–17
DOI 10.1007/s00170-011-3365-8

ORIGINAL ARTICLE

Multi-objective optimization of green sand mould system
using evolutionary algorithms
B. Surekha & Lalith K. Kaushik & Abhishek K. Panduy &
Pandu R. Vundavilli & Mahesh B. Parappagoudar

Received: 2 May 2010 / Accepted: 25 April 2011 / Published online: 7 May 2011
# Springer-Verlag London Limited 2011

Abstract The quality of cast products in green sand
moulds is largely influenced by the mould properties, such
as green compression strength, permeability, hardness and
others, which depend on the input (process) parameters
(that is, grain fineness number, percentage of clay,
percentage of water and number of strokes). This paper
presents multi-objective optimization of green sand mould
system using evolutionary algorithms, such as genetic
algorithm (GA) and particle swarm optimization (PSO). In
this study, non-linear regression equations developed
between the control factors (process parameters) and
responses like green compression strength, permeability,
hardness and bulk density have been considered for
optimization utilizing GA and PSO. As the green sand
mould system contains four objectives, an attempt is being
B. Surekha : P. R. Vundavilli (*)
Department of Mechanical Engineering,
DVR & Dr. HS MIC College of Technology,
Kanchikacherla, Andhra Pradesh 521180, India

e-mail:
B. Surekha
e-mail:
L. K. Kaushik : A. K. Panduy
Department of Mechanical Engineering,
Rungta College of Engineering & Technology,
Bhilai, Chattisgarh 490024, India
L. K. Kaushik
e-mail:
A. K. Panduy
e-mail:
M. B. Parappagoudar
Department of Mechanical Engineering,
Chhatrapati Shivaji Institute of Technology,
Durg, Chattisgarh 491001, India
e-mail:

made to form a single objective, after considering all the
four individual objectives, to obtain a compromise solution,
which satisfies all the four objectives. The results of this
study show a good agreement with the experimental results.
Keywords Green sand mould system . Optimization .
Genetic algorithm . Particle swarm optimization

1 Introduction
During moulding process, the quality of the parts produced
depends on the properties (that is, green compression
strength, permeability, hardness and bulk density) of
moulding sand. It is important to note that improper levels
of these properties leads to common casting defects, such as

blow holes, pinhole porosity, poor surface finish, dimensional variation, scabs and rat tails, misruns, etc. It is also
important to note that the mould properties are influenced
by a large number of controllable parameters (that is, grain
fineness number, percentage of clay, percentage of water
and number of strokes). Hence, it is important to identify
the levels of the input variables that provide required mould
properties, which improves the quality of the parts
produced by this mould.
Most of the research work on moulding sand during
1960s and 1970s was based on experimental and theoretical
approaches. The relationship between permeability and
transformation zones, mould pressure, void space control,
etc., was developed by Marek [1] through substantial
mathematical equations. In addition to this, Frost and Hiller
[2] established the pressure and hardness distributions in
sand moulds. Later on, Wenninger [3] utilized the rigid
water theory to explain sand–clay–water relationships. This
approach was completely theoretical and not supported by a


10

large number of experiments. Moreover, statistical design
of experiments (DOE) had been used by various investigators to study the effects of different variables on the
green sand mould properties. In [4], DOE technique was
applied to study the effect of process variables on bulk
density and green compression strength. In addition to these
approaches, Casalino et al. [5] utilized Taguchi technique to
establish third order model for permeability and compression strength in laser sintered sand moulds. Moreover,
Parappagoudar et al. [6, 7] developed linear and non-linear

statistical models utilizing full factorial DOE, central
composite design (CCD) and Box-Bhenken design. In the
above work, the authors had considered grain fineness
number, percentage of clay, percentage of water and
number of strokes as input parameters and green compression strength, permeability, hardness and bulk density as the
responses. Among the non-linear regression equations
developed by the abovementioned three approaches,
CCD-based model was found to be the more accurate
model for prediction of the responses. Later on, the
optimization of process parameters of green sand casting
was established in [8] utilizing Taguchi parameter design.
The process parameters such as green compression
strength, moisture content, pouring temperature and mould
hardness vertical and horizontal were considered to identify
the effect of these parameters on casting defects. As the
above developed method involved a traditional optimization method, the solutions obtained were not global optimal
in nature. Therefore, a global optimization method is
required to identify the optimal combination of parameters
for achieving the desired performance of the green sand
mould system. In single objective optimization, one
attempts to obtain the best design or decision, which is
usually the global maximum or minimum depending on the
optimization problem. In green sand mould system, it is
difficult to find a single optimal combination of parameters
for green compression strength, permeability, hardness and
bulk density. Hence, there is a need for a multi-objective
optimization method to arrive at the solutions to this
problem. This multi-objective optimization problem can
be converted to a single objective problem after applying a
suitable method.

This type of problems can be best solved by utilizing
evolutionary algorithms, such as genetic algorithms (GA),
particle swarm optimization (PSO), etc. The early use of
evolutionary search was first reported in the 1960s by
Rosenberg [9]. Since then, there had been a growing
interest in devising different evolutionary algorithms for
multi-objective optimization. There exist two general
approaches to solve the multi-objective optimization problem. The first approach deals with combining individual
objective functions into a single composite objective
function after assigning weight to each objective. The

Int J Adv Manuf Technol (2012) 58:9–17

second approach is based on generating Pareto optimal
solution sets that are non-dominated with respect to each
other. The present research falls in to the first category.
Evolutionary optimization approaches, such as GA and
PSO, have attracted a great deal of attention in recent
times. With their better global search abilities, these
optimization approaches can find global optima more
quickly through cooperation and competition among the
population of potential solutions. It is important to note
that GA was used to solve multi-objective optimization
problems related to grinding [10], turning [11, 12],
abrasive flow machining [13], wire electric discharge
machining [14], drilling and riveting sequence planning
[15], etc. Similarly, PSO was also used to solve the
problems related to grinding [10], electrochemical machining
[16], steam temperature control [17] and some other
engineering problems [18]. To the best of the authors’

knowledge, not much work had been reported in the field of
multi-objective optimization of process parameters of green
sand mould systems.
In the present paper, the non-linear regression equations
developed in [7] has been considered for multi-objective
optimization, utilizing the most popular evolutionary
algorithms, such as GA and PSO. Green compression
strength, permeability, hardness and bulk density are
considered as responses (that is, objectives) and grain
fineness number, percentage of clay, percentage of water
and number of strokes are treated as inputs (that is, process
variables). A single objective has been formed after
combining the four responses. Both the GA and PSO
algorithms are used to optimize this single objective to
obtain a solution. It is interesting to note that the results
obtained by these algorithms are comparable.
The rest of the manuscript is organized as follows.
Section 2 deals with the formulation of the problem. Tools
and techniques used in this study are explained in Section 3.
The results are presented and discussed in Section 4. Some
concluding remarks are made in Section 5.

2 Formulation of the problem
The quality of the parts produced in green sand mould
system mainly depends on the properties (responses) of
the mould, such as green compression strength (GCS),
permeability (P), hardness (H) and bulk density (BD),
which in turn depends on the process variables (that is,
grain fineness number, percentage of clay, percentage of
water and number of strokes). Figure 1 shows the

schematic diagram of green sand mould system as an
input–output model.
The ranges of the process variables used in this study are
given in Table 1.


Int J Adv Manuf Technol (2012) 58:9–17
Fig. 1 Input and output
variables of green sand
moulding system

11

AFS grain fineness no. (GFN)
% of clay (% C)
% of water (% W)
No.of strokes (NS)

While conducting the experiments [7], sieve analysis test
was conducted to determine the grain fineness number and
size distributions of the silica sand. Moreover the strength
of clay was obtained by performing gelling index test.
Then, the experiments were conducted with different
combinations of parameters using central composite design.
Finally, the responses, such as permeability, green compression strength, hardness and bulk density, were measured. The relationship between the responses and the
process variables available in the abovementioned literature
were as given below:
GCS ¼ 17:2527 À 1:7384A À 2:7463B þ 32:3203C
þ 6:575D þ 0:014A2 þ 0:0945B2
À 7:7857C 2 À 1:2079D2 þ 0:0468AB

À 0:1215AC À 0:0451AD þ 0:5516BC
þ 0:6378BD þ 2:689CD:

ð1Þ

P ¼ 1192:51 À 15:98A À 35:66B þ 9:51C
À 105:66D þ 0:07A2 þ 0:45B2 À 4:13C 2
þ 4:22D2 þ 0:11AB þ 0:2AC þ 0:52AD
þ 1:19BC þ 1:99BD À 3:1CD:

ð2Þ

H ¼ 38:2843 À 0:0494A þ 2:4746B þ 7:8434C
þ 7:774D þ 0:001A2 À 0:00389B2 À 1:6988C 2
À 0:6556D2 À 0:0015AB À 0:0151AC
À 0:0006AD À 0:075BC À 0:1938BD
þ 0:65CD:

ð3Þ

Green sand
mould system

Permeability (P)
Green compression strength (GCS)
Hardness (H)
Bulk density (BD)

BD ¼ 1:02616 þ 0:01316A À 0:00052B À 0:06845C
þ 0:0083D À 0:00008A2 þ 0:0009B2

þ 0:0239C 2 À 0:00107D2 À 0:00004AB
À 0:00018AC þ 0:00029AD À 0:00302BC
À 0:00019BD À 0:00186CD:

ð4Þ

The coefficient of correlation (R) is found to be equal to
0.9818, and the ANOVA test (refer to Table 2) was conducted
to check the adequacy of the model is shown below.
The coefficient of correlation for permeability is found to
be equal to 0.964. The combinations of all the linear terms,
that of the square terms and that of the interaction terms, have
significant contribution towards this response, as their p
values are seen to be less than 0.05 (corresponding to 95%
confidence level). It is important to mention that there is a
significant amount of lack of fit of the model after removing
the in-significant terms. Thus, the in-significant terms need
not be removed from the model. As the calculated F values
are found to be more than their respective values calculated
from the table, the model is found to be statistically adequate
for making predictions. The results of the significance tests
for other responses also follow similar trends.
In the present research, an attempt is being made to
optimize the process with multiple mould performance
outputs utilizing evolutionary algorithms. A weighted
method is used for the said purpose. Since the GCS, P, H
and BD are the four different objectives, in order to
overcome the large differences in numerical values between
the objectives, the function corresponds to every mould
performance output is normalized. Then weighted method

is adapted to the normalized performance outputs to form a
single objective function. Hence, the resultant weighted
objective function to be maximized is:
Maximize Z ¼ ðw1 Â f1 þ w2 Â f2 þ w3 Â f3 þ w4 Â f4 Þ

ð5Þ

Subjected to constraints:
Table 1 Process parameters and their ranges
Number

1
2
3
4

Parameters

Grain fineness number
% clay content
% water content
Number of strokes

Symbol

A
B
C
D

52

A

94

ð6Þ

Range
High

Low

94
12
3
5

52
8
1.5
3

8

B

1:5
3

12

ð7Þ

3

ð8Þ

C
D

5

ð9Þ


12

Int J Adv Manuf Technol (2012) 58:9–17

Table 2 Results of ANOVA—
permeability

Source

DF

Sequenced SS

Adjusted SS

Adjusted MS

Regression
Linear
Square
Interaction
Residual error
Lack of fit
Pure error
Total

14
4
4
6
120
10
110
134

308,592
261,699
32,671
14,222
11,683
10,815
868
320,275

308,592.4
48,629.5
32,671.2
14,222.3
11,683.1
10,815.4
867.7

22,042.3
12,157.4
8,167.8
2,370.4
97.4
1,081.5
7.9

where f1, f2, f3 and f4 are the normalized functions for
GCS, P, H and BD, respectively. Moreover, w1, w2, w3 and w4
are the weighted factors for the normalized GCS, P, H and
BD, respectively, and A, B, C and D are the process variables.
It is important to note that the weighted factors are selected in
such a way that their sum will be equal to one. A higher
weighing factor for an objective indicates more importance to
that particular objective. Five different cases have been
considered after choosing different values for the weights: case
1: w1 =0.25, w2 =0.25, w3 =0.25 and w4 =0.25; case 2: w1 =
0.70, w2 =0.10, w3 =0.10 and w4 =0.10; case 3: w1 =0.10, w2 =
0.70, w3 =0.10 and w4 =0.10; case 4: w1 =0.10, w2 =0.10, w3 =
0.70 and w4 =0.10 and case 5: w1 =0.10, w2 =0.10, w3 =0.10
and w4 =0.70. These values are selected randomly in such a

way that the sum of the weights will be equal to one.

3 Tools and techniques used
In the present work, evolutionary algorithms, such as a
binary coded GA and PSO, have been employed to
optimize the single objective function (refer to Eq. 5) of
the green sand mould system. The descriptions of these
algorithms are provided in the subsequent subsections.
3.1 Genetic algorithms
Genetic algorithms are population-based search and optimization procedures, extensively used in the search and optimization of various problem domains [10–15]. The block diagram
showing the working cycle of a GA is shown in Fig. 2. The
selection criterion used in the present study is tournament
selection, and uniform crossover is being used as crossover
mechanism. Finally, bit-wise mutation is used to avoid the
local minima if any. As there are four process variables, each
variable is represented with the help of ten bits. Therefore,
40 bits are used to represent a GA string as shown below.

F

p

Ftable

226.4
124.9
83.89
24.35

0

0
0
0

1.78
2.45
2.45
2.18

137.1

0

3.2 Particle swarm optimization
PSO is a population-based stochastic optimization technique. Due to its easy implementation and quick convergence, PSO has gained much attention in solving many
complex problems [9, 13, 14]. PSO algorithm is a model
that mimics the movement of individuals in a group. In the
present study, MOPSO-CD [19], a variant of PSO has been
utilized for the selection of optimum process parameters of
green sand mould system. The schematic diagram showing
the working cycle of the PSO is shown in the Fig. 3. The
present approach incorporates the crowding distance (that
is, the average distance of its two neighbouring solutions)
and mutation operators into the simple PSO algorithm. This
feature enhances the exploring capability of the algorithm
by preventing the premature convergence problem of PSO
algorithm. Instead of using evolutionary operators, such as
selection and crossover, each particle in the population
moves with velocity which is dynamically adjusted. The
new position and velocity of the particles have been

Start

Randomly initialize
population of solutions
Gen = 0

Is
No
Gen > max gen
?
Gen=gen+1

Assign fitness to
all solutions

Yes
Reproduction
Stop
Crossover

Mutation

1
. .ffl1} 0
. .ffl1} 1
. .ffl0} 0|fflffl.{zffl
. .ffl0}
|fflffl.{zffl
|fflffl.{zffl
|fflffl.{zffl

A

B

C

D

Fig. 2 Flow chart of the genetic algorithm


Int J Adv Manuf Technol (2012) 58:9–17

13

4.1 Genetic algorithms

Start

Initialize population

Generate initial population

Evaluate fitness

Evaluate statistics

Compute crowding distance

Update population

Update position & velocity

Evaluate swarm

Is
optimal solution
obtained

No

Yes
Terminate

End

Fig. 3 Flowchart of the particle swarm optimization

calculated using the formulation given below:
The new velocity :V ½iŠ ¼ W  V ½iŠ þ R1 ½Pbest ½iŠ À P½iŠŠ
þ R2  ½AðGbest Þ À p½iŠŠ

ð10Þ

The new position : P½Š ¼ P½iŠ þ V ½iŠ

ð11Þ

where W is the inertia weight, which is equal to 0.4, R1 and
R2 are the random numbers in the range of [0, −1], Pbest[i]

is the best population that the particle I reached and A(Gbest)
is the global best guide for each dominated solution. The
parameters, namely swarm size, number of generations,
inertia weight (W), social components R1 and R2 and
repository size, play an important role in the present
approach.

4 Results and discussion
The results of computer simulations carried out using GA
and PSO are discussed below.

A parametric study (that is, by varying one parameter of
GA, namely probability of crossover (pc), probability of
mutation (pm), population size and maximum number of
generations at a time) has been conducted to determine the
combination of GA parameters that are responsible for the
optimal mould performance. It is also important to note that
the selection of the weighting factor is also important, and it
should be selected based on the requirement of the decision
maker. In this study, five different cases (refer to Table 2)
have been considered after varying the weighing factors of
the objectives. The results of the parametric study are
shown in Fig. 4, and the procedure for conducting the
systematic study is as follows.
Figure 4a shows the variation of fitness with change in the
probability of crossover (Pc) after keeping the probability of
mutation, population size and number of generations at a
fixed level. As the problem to be solved is a maximization
problem, the probability of crossover value (Pc*) which
produced the maximum fitness has been chosen from this

study. The variation of fitness with probability of mutation
(Pm) is shown in Fig. 4b. In this study, the probability of
crossover is set at Pc* and population size and number of
generations have been kept at the same level as given in
Fig. 4a. The probability of mutation (Pm*), which is
responsible for maximum fitness is identified. Figure 4c
shows the variation of fitness with population size (pop). The
probability of crossover and mutation are kept at Pc* and
Pm*, respectively. Moreover, the number of generations is
kept at the same level as discussed in Fig. 4a, b. In this case
also, the population size (pop*) which is responsible for
maximum fitness has also been identified. Finally, the study
has been conducted to determine the maximum number of
generations (gen*) that maximized the fitness (refer to
Fig. 4d), after fixing the other parameters, such as probability
of crossover, probability of mutation and population size at
Pc*, Pm* and pop*, respectively. The values of the GA
parameters obtained by this study are as given below:
probability of crossover (pc*)=0.85
probability of mutation (pm*)=0.18
population size (pop*)=130
max. number of generations (gen*)=100
Table 3 shows the optimum conditions of the mould
parameters for multiple performance outputs with different
combinations of the weight factors. Moreover, the maximum fitness values for cases 1 to 5 are found to be equal to
0.6654, 0.7620, 0.8522, 0.7629, and 0.6072, respectively.
Case 3 is recommended because it gives maximum green
compression strength, moderate permeability, maximum
hardness and maximum bulk density.

14

Int J Adv Manuf Technol (2012) 58:9–17

Fig. 4 GA parametric study: a
Pc vs fitness, b Pm vs fitness, c
population size vs fitness and d
maximum generations vs fitness

(a)

(b)

(c)

(d)

4.2 Particle swarm optimization
Here also, a systematic study has been conducted to
determine the swarm size, inertia weight and maximum
number of generations. The study is conducted by varying
one parameter at a time. It is important to note that small
swarm size may result in local convergence; large size will

increase computational efforts and may lead to slow
convergence. The results of this study are shown in
Fig. 5. The method of conducting the systematic study is
as follows:
Figure 5a shows the variation of fitness with the change

in the values of inertia weight. During this process, the
other two parameters, such as swarm size and generations

Table 3 Optimum mould parameters for multiple responses with different weighing factors using GA
Process parameters and responses

A: GFN
B: %C
C: %W
D: NS
GCS
P
H
BD

Optimum values of mould parameters and responses
Case 1 (w1 =0.25,
w2 =0.25, w3 =0.25,
w4 =0.25)

Case 2 (w1 =0.70,
w2 =0.10, w3 =0.10,
w4 =0.10)

Case 3 (w1 =0.10,
w2 =0.70, w3 =0.10,
w4 =0.10)

Case 4 (w1 =0.10,
w2 =0.10, w3 =0.70,

w4 =0.10)

Case 5 (w1 =0.10,
w2 =0.10, w3 =0.10,
w4 =0.70)

52.0007
11.9997
2.9886
4.9999
55.5022
107.1197
84.9847
1.5044

52.0012
8.0001
2.4883
3.0000
21.2056
214.501
77.3861
1.4726

93.9998
11.9999
2.6546
4.9998
54.9377
53.6790

89.4473
1.5888

93.9982
11.9997
2.9999
4.9994
53.8732
54.9672
86.9985
1.5799

93.9999
11.9998
2.4601
4.9999
54.7159
52.5185
86.1361
1.5938


Int J Adv Manuf Technol (2012) 58:9–17

15

Fig. 5 PSO parametric study: a
inertia weight vs fitness, b
swarm size vs fitness and c
maximum generations vs fitness

(a)

(b)

(c)
are kept at the fixed level. In this case, the inertia weight
value (W*), corresponding to the maximum fitness is
identified. Figure 5b shows the study related to the swarm
size. In this case, inertia weight is set at W* and maximum
generations are set at the same level as discussed in Fig. 5a.

In this study, the swarm size (SS*) that is responsible for
maximum fitness has been found. Figure 5c shows the
convergence of the solution over number of generations.
The number of generations (G*) that are responsible for
maximum fitness has been obtained in this study. Thus, the

Table 4 Optimum mould parameters for multiple responses with different weighing factors using PSO
Process parameters and responses

A: GFN
B: %C
C: %W
D: NS
GCS
P
H
BD

Optimum values of mould parameters and responses
Case 1 (w1 =0.25,
w2 =0.25, w3 =0.25,
w4 =0.25)

Case 2 (w1 =0.70,
w2 =0.10, w3 =0.10,
w4 =0.10)

Case 3 (w1 =0.10,
w2 =0.70, w3 =0.10,
w4 =0.10)

Case 4 (w1 =0.10,
w2 =0.10, w3 =0.70,
w4 =0.10)

Case 5 (w1 =0.10,
w2 =0.10, w3 =0.10,
w4 =0.70)

52.0024
11.9999
2.9042
4.9937
55.4251
107.7204
84.8603
1.5065

52.0158
8.0002
2.6698
3.0000
20.8928
214.2018
77.6046
1.4701

52.0001
11.9998
2.8452
4.9999
55.4112
107.8949
84.7936
1.5079

93.9935
11.9987
2.9999
5.0000
53.8727
54.9601
86.9991
1.5799

93.9994
11.9977
2.3084

4.9999
54.1248
51.4005
85.8925
1.5976


16

Int J Adv Manuf Technol (2012) 58:9–17

parameters of PSO that are responsible for the better
performance are as follows:
inertia weight (W*)=0.2
swarm size (SS*)=50
number of generations (G*)=30
In this case also, five different cases as mentioned in the
above approach have been considered after varying the
weight factors of the objectives. Table 4 shows the
optimum conditions of the mould parameters for multiple
performance outputs with different combinations of the
weight factors. It is interesting to note that the maximum
fitness values for cases 1 to 5 are found to be equal to
0.6647, 0.7609, 0.8719, 0.7630 and 0.6066, respectively. In
this method also, case 3 is recommended, as it has produced
maximum fitness.
4.3 Comparison between GA and PSO
While applying the evolutionary algorithms, such as GA
and PSO, to optimize a particular system, a number of
parameters are required to be specified. The speed of

convergence of the algorithm depends on an appropriate
choice of the parameters. The optimal parameters of GA are
found to be equal to probability of crossover 0.85,
probability of mutation 0.18, population size 130 and
maximum number of generations 100. In the case of PSO,
the optimal parameters of inertia weight, swarm size and
number of generations are found to be equal to 0.2, 30 and
50, respectively. It is clear that the convergence rate of PSO
is faster than that of the GA. Moreover, confirmation
experiments were conducted for the case 3 of both the GA
and PSO. The percentage errors associated with GCS, P, H
and BD are found to be equal to 3.25%, 8.46%, 5.35% and
2.45%, respectively, for GA, and 3.22%, 5.78%, 4.95% and
2.25%, respectively, for PSO. Moreover, the CPU times
required by GA and PSO, for a population size of 50 and
number of generations equal to 30, are found to be equal to
0.021 and 0.013 s, respectively, on a P-IV machine. It is
interesting to note that PSO has performed better than GA
for all the responses. This may be due to the simple
structure and minimal parameter tuning of PSO compared
to GA.

5 Conclusions
In the present work, an attempt has been made to search for
the optimum process parameter values for the multiple
objectives, namely green compression strength, permeability, hardness and bulk density, utilizing evolutionary
algorithms, such as GA and PSO. It is interesting to note

that PSO has performed better than GA in terms of
computational efficiency. Moreover, the percent deviation

with the experimental results for all the responses for PSO
is less than that of the GA. The simple structure associated
with minimal parameter tuning helps the PSO in outperforming the GA.

References
1. Marek CT (1966) Green sand permeability—its significance and
control. AFS Trans 74:70–81
2. Frost J, Hiller JM (1966) The mechanics of green sand moulding.
AFS Trans 74:177–186
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current fundamental thinking. AFS Trans 76:101–111
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Int J Adv Manuf Technol (2012) 58:19–28
DOI 10.1007/s00170-011-3379-2

ORIGINAL ARTICLE

Cutting performance and wear mechanisms of Sialon–Si3N4
graded nano-composite ceramic cutting tools
Guangming Zheng & Jun Zhao & Zhongjun Gao &
Qingyuan Cao

Received: 12 February 2011 / Accepted: 3 May 2011 / Published online: 15 May 2011
# Springer-Verlag London Limited 2011

Abstract Sialon–Si3N4 graded nano-composite ceramic
tool materials were fabricated by using hot-pressing
technique. The residual stresses in the surface layer of the
graded ceramic tool materials were calculated by the
indentation method. The cutting performance and wear

mechanisms of the graded tools were investigated via
turning of Inconel 718 alloy in comparison with common
reference tools. The surface roughness of the finish hard
turning of Inconel 718 and the microstructures of the chips
were also examined. Worn and fractured surfaces of the
cutting tools were characterized by scanning electron
microscopy and energy-dispersive X-ray spectroscopy.
The results showed that graded structure in Sialon–Si3N4
graded ceramic tool materials can induce residual compressive stresses in the surface layer during fabrication process.
Tool lifetime of graded ceramic tool was higher than that of
the common reference tool. The longer tool life of the
graded nano-composite ceramic tool was attributed to its
synergistic strengthening and toughening mechanisms
induced by the optimum graded compositional structure of
the tool and the addition of nano-sized particles. Wear
mechanisms identified in the machining tests involved
adhesive wear and abrasive wear. The mechanisms responsible for the higher tool life were determined to be the
formation of compressive residual stress in the surface layer
of the graded tools, which led to an increase in the
resistance to fracture.
G. Zheng (*) : J. Zhao : Z. Gao : Q. Cao
Key Laboratory of High Efficiency
and Clean Mechanical Manufacture of MOE,
School of Mechanical Engineering, Shandong University,
17923 Jingshi Road,
Jinan 250061, People’s Republic of China
e-mail:

Keywords Graded ceramic cutting tools . Wear mechanisms .
Residual stress . Surface roughness . Inconel 718

1 Introduction
Si3N4 ceramics are currently the premier ceramic materials
for cutting inserts as a result of their higher strength,
thermal conductivity, lower thermal expansion coefficient,
and higher thermal shock resistance. Sialon ceramics have
excellent thermal shock resistance due to their low
coefficient of thermal expansion. Si3N4 ceramics and Sialon
ceramics are considered to be the ideal tool materials for
machining of nickel-based super alloys at higher speed
conditions [1, 2]. Whereas, the characteristics of Si3N4
ceramics and Sialon ceramics limiting their applications are
their relatively lower hardness and wear resistance [3, 4].
Conventionally, Si3N4 and Sialon ceramic materials were
strengthened and toughened by the addition of particles like
SiC, WC, TiCN, TiC, etc. to improve the mechanical
properties [5–7]. The matrix grains of the composite can
also be refined by adding nano-Si3N4 particles, because the
addition of nano-Si3N4 particles could promote the formation of the duplex distribution characteristic. The optimum
flexural strength and fracture toughness were obtained
when the volume fraction ratio of nano-sized Si3N4 to
micro-sized Si3N4 is fixed at 1:3 [8]. The introduction of
the concept of functionally graded material (FGM) into the
fabrication of ceramic cutting tool materials provided a new
approach to improve their thermal and mechanical properties [9, 10]. Functionally graded cutting ceramics with
symmetrical structure have been developed to perform
high-speed intermittent machining of hard-to-cut materials,
with higher tool lives than those of homogeneous cutting
ceramics [11].

20

Int J Adv Manuf Technol (2012) 58:19–28
Table 2 Composite of each layer of five-layered FGM
Specimen code

1st (5th) layer

2nd (4th) layer

3rd layer

GSS1
GSS2

ST10
SAAT10

ST15
ST15

ST20
ST20

roughness of the finish hard turning of Inconel 718 and
microstructures of the chips were also analyzed.
Fig. 1 Cube-shaped model of five-layered graded material with
symmetrical structure

The understanding of the wear mechanisms in cutting
processes is the prerequisite for not only the proper
application but also the development of graded ceramic
tool materials. In turning of Inconel 718, the typical wear
types of ceramic tools are crater wear, flank wear, and depth
of cut notch wear which are sometimes accompanied by
chipping [12–14]. The typical wear mechanisms of ceramic
tools in turning of Inconel 718 are adhesive wear, abrasive
wear, plastic deformation, diffusion wear, and microbreakout [1, 14, 15]. The wear mechanisms vary with the
location of worn area. For example, adhesive wear is the
main wear mechanism in the rake face, while abrasive wear
is the main wear mechanism in the flank face. The depth of
cut notch wear is very severe when machining Inconel 718
with ceramic tools [2, 16]. Additionally, the diffusion
elements of Inconel 718 alloy to the tool rake face might
accelerate the tool wear rate [17].
In the present paper, Sialon–Si 3N 4 graded nanocomposite ceramic tool materials were fabricated by means
of the optimization of graded compositional structure of the
tool and the addition of nano-sized particles. The cutting
performance and wear mechanisms of graded ceramic tools
were investigated via turning of Inconel 718 alloy, with
common reference tools used as competitors. Worn surfaces
of the tools were characterized by scanning electron
microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to reveal the wear mechanisms. Surface

2 Experimental method
2.1 Preparation of Sialon–Si3N4 graded nano-composite
ceramic tool materials
Figure 1 shows the cube-shaped model of five-layered
graded material with symmetrical structure. The compositional distribution changes along the Z-axis. The thickness

of the surface layer, the second layer, and the third layer
were a, b, and c, respectively. And H is the total thickness
of the material, where H ¼ 2a þ 2b þ c. Thickness ratio
e ¼ a=b ¼ b=c, a structural parameter, was defined to
determine the thickness of each layer. The thickness ratio
is fixed at e=0.3 in the present work.
Graded structures can be properly designed to induce a
surface compressive residual stress. The basic idea is to
model material layers with different thermal expansion
coefficients so that residual stresses arise during the
fabrication process. Compressive residual stresses can be
induced in a layer with lower thermal expansion coefficients.
In virtue of the thermal mismatch effect between the matrix
Si3N4 (thermal expansion coefficient αmatrix =3.2×10−6 K−1)
and the TiC0.7N0.3 (αparticle =8.6×10−6 K−1), the layer with
the highest volume fraction of TiC0.7N0.3 was put in the
middle with the compositional distribution changing from
the middle layer to the two surface layers. Both the two
opposite surfaces of an insert made by this means could be
used as rake face.
The starting materials were α-Si3N4 powders with
average grain size of approximately 0.02 and 0.5 μm,

Table 1 Composition (vol.%) of different composites
Composites
SAAT10
ST10
ST15
ST20

Si3N4 (0.5 μm)

Si3N4 (0.02 μm)

Al2O3 (0.5 μm)

Al2O3 (0.1 μm)

AlN (0.5 μm)

TiC0.7N0.3 (0.5 μm)

Y2O3

53.25
61.50
57.75
54.00

17.75
20.50
19.25
18.00

0
3.2
3.2
3.2

10

0
0
0

5
0
0
0

10
10
15
20

4.0
4.8
4.8
4.8


Int J Adv Manuf Technol (2012) 58:19–28

21

Table 3 Averages and standard deviations of mechanical properties of the tool materials
Tools

Flexural strength (σf, MPa)

Fracture toughness (KIC, MPa m1/2)

Vicker’s hardness (HV, GPa)

980±60
810±30
860±90
645±95

9.54±0.52a
9.33±0.46a
8.19±0.91
7.80±0.55
7.45±0.61

16.91±0.30a
16.98±0.24a
16.29±0.23
16.59±0.31
18.24±0.25

GSS1
GSS2
ST10
SAAT10
KY1540
a

The property of the surface layer

purity 99% (Hefei Kai Nanometer Energy and Technology

Co., Ltd., China), TiC0.7N0.3 particles with average grain
size of approximately 0.5 μm, purity 99% (Beijing
Xingrongyuan Technology Co., Ltd., China), α-Al2O3
powders with average grain size of approximately 0.1 μm,
purity 99.6% (Shanghai TeamShare Nanotechnology Co.,
Ltd., China) and AlN powders with average grain size of
approximately 0.5 μm, purity 99% (Hefei MoK Advanced
Material Technology Co., Ltd., China). α-Al2O3 powders
with average grain size of approximately 0.5 μm, purity
99.9% (Zibo Xinmeiyu Alumina Co., Ltd., China) and
Y2O3 (Sinopharm Chemical Reagent Co., Ltd., Shanghai,
China) were used as sintering additives to promote the
densification of Si3N4 ceramics during the sintering
process. The composition of different composites is shown
in Table 1. For the composite SAAT10, β-Sialon phase
could be produced by chemical reaction between the major
phases Si3N4, Al2O3, and AlN [18].
The surfactant polyethylene glycol (Sinopharm Chemical
Reagent Co., Ltd., Shanghai, China) and ethanol were
used as dispersant and dispersing medium, respectively,
to obtain well-reagglomerated and uniform suspension of
Al2O3 nano-particles and Si3N4 nano-particles. The suspensions were then mixed with micro powders of the same
composite. The mixed slurries were ball-milled for 48 h
and then dried at 100°C in vacuum (Model ZK-40, China).
The powder mixtures were sieved through a 120 mesh sieve.
The composite powders with different mixture ratios were
layered into the graphite mold one layer after another with a
predetermined thickness ratio, according to the material
design results listed in Table 2. The specimens were then
sintered by a multifunctional hot-pressing sintering furnace

(Model ZRC85-25T, China) in a vacuum environment
(the working vacuity is 6.75×10−2 Pa), at temperature of

1,700–1,750°C for 60 min under a fixed uniaxial pressure of
P=35 MPa. For the purpose of comparison, homogeneous
reference ceramic materials (SAAT10 and ST10) were also
manufactured by hot pressing.
Flexural strength was measured by using a three-point
bending tester (Model WDW-50E, China) per international
standard ISO/DIS 14704: 2000. Fracture toughness measurement was performed using indentation method [19].
Samples for fracture toughness and hardness testing were
indented with a Vicker’s hardness tester (Model HV-120,
China) with a load of 196 N and a holding time of 15 s. A
minimum number of five specimens were tested for each
condition. Indentation tests were performed on the top
surface of the outer layer and the residual stresses were
calculated [20, 21]. The calculation results of residual stress
in the material surface layer of GSS1 and GSS2 are −492
and −442 MPa, respectively.
2.2 Cutting experiments
The cutting performance of the Sialon–Si3N4 graded nanocomposite ceramic tools (GSS1 and GSS2) were tested in
comparison with that of the reference ceramic tools
(SAAT10 and ST10) and a commercially available Sialon
ceramic tool (Kennametal, type is SNGN120408T01020,
grade is KY1540). The mechanical properties of the five
tool materials are listed in Table 3. The tools have the
following geometry parameters: rake angle γ0 =−5°, clearance
angle α0 =5°, inclination angle λs =0°, and side cutting edge
angle κr =45°.
A computer numerically controlled center lathe

PUMA200MA with maximum spindle speed of 6,000 r/min
was used for the machining trials. Machining trials were
conducted using 120 mm diameter×380 mm long Inconel

Table 4 Chemical composition of Inconel 718 (wt.%)
C

Si

Mn

Ni

Co

B

Cr

Cu

Al

Ti

Mo

Fe

S

P

Nb

0.031

0.18

0.05

51.50

<1.0

0.0032

19.16

0.05

0.58

0.97

3.07

Bal

0.0055

0.0057

5.06


22

Int J Adv Manuf Technol (2012) 58:19–28

Table 5 Mechanical properties of Inconel 718
Yield strength (MPa)

Tensile strength (MPa)

Elongation (%)

Reduction of area (%)

Hardness HRC

1,060

1,390

21.5

36.0

42

718 alloy bars. The chemical compositions and mechanical
properties of Inconel 718 workpiece materials used in the
experiments are given in Tables 4 and 5, respectively. All
tests were carried out with the following parameters: depth of

cut ap =0.1 mm; feed rate f=0.1 mm/rev; and cutting
speed v=80, 120, and 200 m/min.
Average flank wear VBave =0.30 mm was used as the
tool life criterion. The wear condition of the cutting edge
was examined periodically, and any apparent change in the
edge surface was closely examined with an optical
microscope. A scanning electron microscope (JSM6380LA, Japan) equipped with an energy-dispersive Xray spectrometer was used to examine the nature of the
worn tools and observe morphological features of chips.
The surface roughness of the Inconel 718 was measured

Fig. 2 Average flank wear curves of the five tools at f=0.1 mm/r and
ap =0.1 mm, a v=80 m/min, b v=120 m/min, c v=200 m/min

Fig. 3 SEM micrographs of etched fracture surface, a the first layer
of GSS1, b the first layer of GSS2


Int J Adv Manuf Technol (2012) 58:19–28

Fig. 4 Effect of cutting speed on surface roughness at f=0.1 mm/r
and ap =0.1 mm

by a portable surface roughness tester (Model TR200,
China). The surface roughness measures used in the

paper is the arithmetic mean value of the surface
roughness of profile, Ra.

3 Results and discussion
3.1 Cutting performance of Sialon–Si3N4 graded
nano-composite ceramic tools
Figure 2 shows the variation of average flank wear width
with cutting time of the five tools in turning of Inconel 718,
tested at different speeds with 0.1 mm/r feed rate and
0.1 mm depth of cut. As can be seen from Fig. 2a–c, among
the five cutting tools, graded nano-composite ceramic tools
(GSS1 and GSS2) showed better performance than that of
homogeneous reference tools (ST10 and SAAT10), especially at lower cutting speeds 80 and 120 m/min.
Furthermore, the graded ceramic tool GSS1 showed better
cutting performance than that of the commercially available
Sialon ceramic tool KY1540 at lower cutting speeds 80 and
120 m/min. The tool life of SAAT10 was the shortest
because of its relatively low flexural strength and fracture
toughness (see Table 3).
The cutting experiment results showed that tool life was
affected by cutting speeds significantly. The ceramic tools
exhibited a relative long cutting life at cutting speed
120 m/min. As can be seen from Fig. 2a, the ceramic
tools were not suitable for low speed cutting of nickelbased alloys because of the higher cutting force of the five
tools at cutting speed 80 m/min, at least under this
experiment conditions. The increase in cutting speed
caused a larger increment in cutting temperature at the
cutting edge of the tools. The higher temperature caused
the tools to lose their strength. Therefore, the tool lives
were shortest at cutting speed 200 m/min (Fig. 2c).

23

Figure 3a and b show the SEM micrographs of etched
fracture surface of the first layer of GSS1 and GSS2. The
fractured specimen surfaces for SEM observations were
eroded in melting NaOH at 400°C for 1 min. As shown in
Fig. 3, the matrix grains of the composite were refined by
the addition of nano-Si3N4 particles. In virtue of the
difference in Si3N4 grain size, there existed a dissolution–
transport–reprecipitation gradient in the material after the
formation of the liquid phase. This led to the formation of
the interlocked duplex microstructure of β-Si3N4 grains
(Fig. 3a) and β-Sialon grains (Fig. 3b). The interlocked
duplex microstructure contributed much to the improvement of flexural strength and fracture toughness. In
addition, the graded structure in ceramic tool can also lead
to a little increase in fracture toughness and hardness at the
surface layer of graded tools (see Table 3). The higher
fracture toughness and hardness of graded tool can result in
the decrease in the tool wear rate. This effect may be
another reason for the increase in flank wear resistance of
graded tools over the homogeneous ones. Therefore, the
longer tool life of the graded nano-composite ceramic tool
should be attributed to its synergistic strengthening and
toughening mechanisms induced by the optimum graded
compositional structure of the tool and the addition of
nano-sized particles.
The surface roughness is one of the measures for
evaluation of the product accuracy and plays an important
role in predicting the capability of machining performance.

It is a significant surface quality index that is known to
have considerable influence on properties such as wear
resistance and fatigue strength of a component. The cutting
speed is one of the influencing factors on the surface
roughness [22, 23]. Figure 4 shows the relation between the
surface roughness Ra and the cutting speed. In the range of
cutting speed from 80 to 200 m/min, the value of surface
roughness Ra decreased with the increase of cutting speed.
The cutting force reduced with the increase of cutting speed

Fig. 5 Effect of average flank wear on surface roughness at v=120 m/
min, f=0.1 mm/r, and ap =0.1 mm (GSS1)


24

Int J Adv Manuf Technol (2012) 58:19–28

Fig. 6 Wear patterns of the five tools in turning of Inconel 718 tested at v=120 m/min, f=0.1 mm/r, and ap =0.1 mm, a ST10 tool (after 6.5 min),
b GSS1 tool (after 7.8 min), c SAAT10 tool (after 4.0 min), d GSS2 (after 4.7 min), e KY1540 tool (after 6.4 min)

in high-speed machining process. According to dynamics
theory, the cutting force is the main source of vibration
excitation in cutting process. As the cutting speed increases,
the operating frequency of the cutting system is away from

the low-level natural frequency of the machine. Furthermore,
surface roughness is greatly sensitive to the low-level natural
frequency. So high-speed machining can significantly reduce
the surface roughness.

Int J Adv Manuf Technol (2012) 58:19–28

Fig. 7 SEM images of worn faces of GSS2 tool in turning of Inconel
718 tested at v=120 m/min, f=0.1 mm/r, and ap =0.1 mm (after
4.7 min), a rake face, b flank face

25

shown in Fig. 6a–e. It can be clearly seen that besides crater
wear and flank wear, the depth of cut notch wear for ST10,
GSS1, and SAAT10 took place. However, tool wear
occurred only in the area of the tool nose radius corner
for KY1540 due to its larger tool nose radius (0.8 mm) and
the selection of a small depth of cut of 0.1 mm (the ratio of
the tool nose radius to the depth of cut rε/ap =8). And
chipping was found for KY1540 tool, which might be
attributed to its relatively lower fracture toughness than that
of other tools (see Table 3). As can be seen from Fig. 6c
and d, significant notch wear can be observed for SAAT10
tool, while the GSS2 graded tool was less sensitive to this
wear mode.
The wear characteristics of the rake and flank faces of
GSS2 after machining at 120 m/min for 4.7 min are shown
in Fig. 7. High stresses generated at the tool–chip interface
during machining may also cause plastic deformation along
the chip flow direction (Fig. 7a). It also can be seen that
there are some abrasive traces (point 1 in Fig. 7a and point
3 in Fig. 7b) and built-up layer (point 2 in Fig. 7a and point

4 in Fig. 7b) on both the rake and flank faces. This wear
behavior is typical for abrasion and adhesion.
On the one hand, at the initial wear stage and the steady
wear stage, the built-up layers deposited on the tool face
and could reduce the deterioration of some grooves or pits
during cutting, hence they had a protective action, which
inhibited tool wear and improved tool life. On the other
hand, at the rapid wear stage, some elements of the
workpiece which might spread to the ceramic tool increased
the affinity between the chip and tool material and thus
accelerated the tool wear rate. EDS analyses of point 2 in
Fig. 7a is shown in Fig. 8. Point 2 is very enriched in Ni,
Cr, and Fe, which are the elements of Inconel 718 alloy (see

As can be seen from Fig. 5, the tool wear has also an
effect on the surface roughness in the cutting process. At
the initial wear stage of cutting process, there was a wear-in
process between the insert and the workpiece. And there
may be some burrs and unstable factors (e.g., micro-cracks)
at the tool surface. So the surface roughness was higher
during the initial cutting stage. At the steady wear stage, the
fluctuation of cutting force was small. The relative stable
cutting process contributed to the lower surface roughness.
At the rapid wear stage, the higher surface roughness was
attributed to the rapid flank wear which led to the increase
of cutting force.
3.2 Wear mechanisms of Sialon–Si3N4 graded
nano-composite ceramic tool
The SEM observations of the five tool faces after the
turning of Inconel 718 at 120 m/min cutting speed are

Fig. 8 EDS analyses of the point 2 in Fig. 7a