CHAPTER 2 REVIEW OF LITERATURE -...
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CHAPTER 2
REVIEW OF LITERATURE
2.1 INTRODUCTION
This chapter presents a literature review on machinability studies of
metal matrix composites, Taguchi methodology, multi-response optimization,
grey relational analysis, desirability function analysis and principal
component analysis. It mainly focuses on the machinability of MMCs, effect
of machining process parameters on cutting force, power consumption,
surface integrity, tool wear and modeling of cutting mechanism. It also
discusses the usage of single and multi response optimization techniques for
the optimization of machining parameters.
2.2 MACHINABILITY OF METAL MATRIX COMPOSITES
2.2.1 Introduction
The term “Machinability” has traditionally referred to the ease with
which a material can be machined with an acceptable quality under a given set
of conditions. But machinability is a difficult term to define and quantify
because large number of variables are involved in it. Cutting forces, power
consumed, tool life, and surface finish are only some of the factors to be
considered when referring to machinability. The difficulty arises because of
the dependence of these factors on a large number of variables such as work
material, tool geometry, cutting conditions and machine tool rigidity
(Muthukrishnan et al 2008a). Materials with good machinability require less
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power to cut but materials with lower machinability require special
arrangements for machining. So, the machinability of materials has significant
economic impact. On other hand, properties like hardness and stiffness which
make metal matrix composites (MMCs) appealing to industry but can present
major challenges when machining because of the existence of hard abrasive
reinforcement particles are harder than most of the cutting tools. Wide spread
application of MMCs will not possible without the solution for the shortened
tool life and material sub surface damages encountered during cutting
operation. So to minimize the processing cost, it is important to understand
the mechanics of machining MMC.
According to Pramanik et al (2006) the research on machining of
MMCs can be divided in to three categories as given below;
1. Experimental studies that compare different tools and/or
coating for Machining MMCs.
2. Empirical and numerical studies related to tool life.
3. Experimental studies on performance of Polycrystalline
Diamond (PCD) tools, machined surface and optimization of
cutting parameters, tool geometry, and work piece
compositions.
2.2.2 Effect on Cutting Force and Power Consumption
Machining of any material by using conventional method requires
power to drive the main spindle and the power to feed the tool against the
work piece. These powers can be measured to access the machinability of the
material. The quantity of the power required by the main spindle can be
measured by main cutting force or by using appropriate power sensors. Power
consumed and specific cutting force, which is the power consumed per unit
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volume of material removed are considered as measures of machinability
(Muthukrishnan et al 2008c).
Paulo Davim and Baptista (2000) studied the relationship between
cutting forces and tool wear of PCD while machining A356/SiC/20p metal
matrix composite in turning and drilling. In their observation, they had found
that, when the cutting speed was around 250 and 350 m/min with a feed of 0.1
mm/rev and depth of cut 1mm, feed force varied between 100 and 200 N for a
cutting time of 20 and 40 minutes respectively. It was observed that, at higher
cutting speed, wear increased the feed force and depth force also. It was also
observed that the increase of cutting speed made a decrease of the cutting
force. It was reported that the excessive cutting speed made a premature wear
in the tool, leading to an accelerated increase of cutting force. As a
conclusion, they have recommended PCD or diamond coated tools for
reducing the cutting forces while machining MMCs.
Paulo Davim (2002) studied the performance of the PCD tool in
turning MMCs, had measured the power and specific cutting force at various
cutting conditions at various stages of tool wear. It was observed that power
and specific power to increase as tool cutting time and tool wear increased at
all cutting conditions. As cutting speed was increased, the specific power was
observed to decrease up to a cutting speed of 250 – 350 m/ min. But it was
found to increase beyond this cutting speed. The author attributed this to very
rapid tool wear taking place at cutting speeds in the range of 500 – 700
m/min.
Manna and Bhattacharayya (2003) investigated the machinability of
Silicon carbide particulate aluminum metal matrix composite of type LM 6
Mg 15 SiCp of 23 µm during turning using uncoated tungsten carbide fixed
rhombic tools. Experiments were conducted at cutting speeds between 20 -
225 m/min with a feed rate of 0.14 – 1 mm/rev (6 feed rates) and depth of cut
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of 0.5 mm. It was observed that the feed force and main cutting force were
high at low cutting speed and decreased as cutting speed was increased. On
the other hand, they have observed that increasing the feed resulted in
increased feed force and main cutting force.
Muthukrishnan et al (2008b) attempted to study the machinability
issues of aluminium-silicon carbide (15P) metal matrix composites (MMC) in
turning using three different grades of poly crystalline diamond (PCD) inserts.
Experiments were conducted at various cutting speeds, feeds and depth of
cuts and parameters, such as surface roughness, specific power consumed,
and material removal rate were measured. It was observed that the 1600 grade
PCD inserts performed well for the surface finish and specific power
consumption criteria followed by the 1500 grade.
2.2.3 Effect on Surface Quality and Integrity
The machined surface quality of composites is one of the most
important concerns which affect the actual application of the composites. In a
machining operation surface quality depends more on the variables of
processes rather than characteristic features of the material. Hence estimation
of surface roughness in variation to the machining parameters and
minimization of the same has become an essential requirement. Surface finish
and surface integrity are important for surface sensitive parts subjected to
fatigue. Therefore, understanding of surface integrity provides many
opportunities to avoid failures and enhance component integrity and reduce
overall cost (Chandrasekaran et al 1997; Sri Ramakrishna et al 2010).
El-Gallab and Sklad (1998a,b) have emphasized on the surface
roughness in their study on machinability of the 20% of SiCp reinforced Al-
MMC. Performing dry turning tests with different cutting parameters, they
have investigated the effect of processing parameters on the surface
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roughness. They found that large chip depths and high cutting speed reduce
the surface roughness.
Paulo Davim (2002) studied the overall performance of the PCD
tool in machining MMCs. Over the general range of cutting conditions,
particularly over a range of feed values, the surface roughness value, R t
experimentally observed was very much higher than the theoretical surface
roughness values. This is because the analytical expressions for the surface
roughness generally do not take in to account the material in-homogeneity,
which is characteristic of MMCs. The Rt values experimentally observed
varied approximately between 2 µm and 8µm, except the situation with a
smaller cutting speed of 250 m/min. The author anyhow claims that with
suitable fine tuning of cutting conditions, it is possible to obtain Ra less than
0.8 microns.
Manna et al (2002) investigated different tooling system for
effective machining of Al/SiC/MMC. They have investigated the influence of
cutting time and length of machining on the tool wear and the influence of
cutting speed, feed rate, depth of cut, inclination angle of the tool holder on
the surface finish have been established for each of the tooling system. They
have used uncoated and coated tungsten carbide tools. From the investigation,
they suggested that the fixed rhombic tooling and fixed circular tooling are
effective for proper machining at high speed with low depth of cut. Rotary
circular tool (RCT) was found to be superior wear resistance and extended
tool life. But according to the results reported by the authors the surface
roughness produced by the rotary tooling system was unacceptably high, Ra
values in the order of 6 to 13 micron. The Ra values were almost 1.5 to 3
times the Ra value produced when fixed circular tools were used.
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Paulo Davim and Antonio (2001a) conducted drilling tests with the
intention of developing optimal drilling conditions using genetic algorithm
approach. They noticed a predominantly abrasive wear mechanism attributed
to the hard particles in the matrix. The surface finish was found to be affected
by the feed rate and not by the cutting speed.
Yanming Quan et al (2003) investigated the hardness and residual
stress of SiC/Al composites in the surface layer affected by machining. The
structure of SiC/Al composites is composed of a soft matrix and hard
reinforcing particles. Under the cutting force the Al matrix and the SiC
particles do not deform uniformly. Thus, it is expected that there will remain
work-hardening and stress in the machined surface layer.
Ding et al (2005) have studied the evaluation of machining
performance of MMC with PCBN and PCD tools. They observed the Rt and
Ra values of the work piece and morphology of the machined surface to be
essentially the same and invariant with cutting distance. But in their study the
machining experiments at 400 m/min cutting speed were conducted only for a
very short duration of 2.5 minutes. Within this period the Rt and Ra remained
almost constant.
Kilickap et al (2005) reported the effect of machining parameters
such as cutting speed, feed and depth of cut on tool wear and surface
roughness while machining AlSiCp MMC. Two types of K10 cutting tool
(uncoated and TiN-coated) were used at different cutting speeds (50, 100 and
150 m/min), feed rates (0.1, 0.2 and 0.3 mm/rev) and depths of cut (0.5, 1 and
1.5 mm). In dry turning condition, tool wear was mainly affected by cutting
speed, increased with increasing cutting speed. Surface roughness influenced
with cutting speed and feed rate. Based on their results the higher cutting
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speeds and lower feed rates produced better surface quality which is shown in
Figure 2.1.
Figure 2.1 Effects of feed rate and cutting speed on surface roughness
(Kilickap et al 2005)
Ge et al (2008) reported the ultra precision turning of SiCp/2041Al
and SiCp/ZL101A composites to investigate the surface quality when
machined using single point diamond tools and polycrystalline diamond
cutters. It was found that cutting parameters, tool material and geometries,
particle reinforcement size and distribution, reinforcement volume fraction
and cooling conditions all had a significant effect on the surface quality when
ultra-precision turning of this particular class of materials.
2.2.4 Effect on Tool Wear
Tool life is the most important parameter for assessing
machinability. Since tool life is a direct function of cutting speed, a better
machinable metal is one which permits higher cutting speed for a given tool
life. The cutting tool materials normally used in metal cutting are High-Speed
Steel (HSS), carbides, coated carbides, ceramics, Ploy Crystalline Cubic
Boron Nitride (PCBN) and diamonds. An important challenge in developing
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new tool materials is to achieve high wear resistance while retaining the high
toughness (Hung et al 1996a,b; Joshi et al 1999).
Lane (1992) reported that the grain size of the cutting tools has
significant influence on the tool wear during machining of MMC. While a
tool with coarse grain has a high abrasion resistance required for increased
performance, increasing the size of the grains can result in drop in the rupture
strength, which also influences over all tool performance.
Tomac et al (1992) investigated the effect of cutting conditions in
machining Al-SiC MMCs with PCD and coated tungsten carbide tools on the
various aspects of machinability like tool wear, cutting forces and surface
finish. They observed that PCD tools have over 30 times higher tool life than
carbides under similar cutting conditions as shown in Figure 2.2. It was
observed that the primary wear mechanism is due to the abrasion of the SiC
particles. The tool life was found to increase at higher feed rates because of
softening of the matrix at higher temperatures. This was attributed to the
groove marks made by the abrasive particles, which were pulled out of the
soft matrix along the surface of the work piece.
Figure 2.2 Comparison of tool wear for coated carbide and PCD tool
(Tomac et al 1992)
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Weinert et al (1993) observed that the tool wear of the uncoated
cemented carbide tool was very large irrespective of the percentage of
reinforcement in both SiC and B4C reinforced aluminum. He recommended
use of PCD inserts for optimal combination of tool life and performance.
Among PCD inserts, it was observed that, the tool wear rate was lower for the
coarse grained PCD while machining SiC or B4C reinforced aluminum.
Lin et al (1992, 1995) observed the flank wear as a primary mode
of tool failure in machining Al-SiC MMC with two bodies and three-body
abrasion between the tool and workpiece playing a dominant role in causing
the flank wear land. In their experiments at high speed turning with PCD tools
(cutting speeds 300 to 700 m/min) tool wear was found to increase with
increasing cutting speed and feed. Within the cutting conditions of the
experiments, the surface finish was observed to be independent of cutting
speed and a slightly worn tool was observed to give better surface finish. The
presence of uniformly dispersed SiC particles resulted in discontinuous chip
formation.
El-Gallab and Sklad (1998c) studied the performance of PCD tools
and concluded that the main wear mechanisms with these tools were abrasion
and micro-cutting of tool material manifested in the form of grooves on the
tool face parallel to the chip flow direction. The grooves on the rake face were
filled with smeared work materials and form a built-up edge, which seemed to
be beneficial since it protected the tool rake from further abrasion. However,
for all the tested tools the tool life was limited by excessive flank wear due to
abrasion. The authors also noted that the cutting parameters play a
determinant role in the tool flank wear. Tool wear may be minimized by
increasing feed rate and cutting speed. Higher cutting speeds are associated
with an increase of the cutting temperatures which led to the formation of a
protective built-up layer.
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Hooper et al (1999) studied the machinability behavior of MMCs in
which the aluminum matrix was reinforced with SiC particles and / or saffil
fibers while machining with PCD and conventional tungsten carbide tools. It
was concluded that PCD tools provide significant advantages in the
machining of MMC. The wear pattern on the tools was found to be similar to
that produced while machining conventional materials. The wear was also
found to be adversely affected by the adhesion between the tool constituents
and the workpiece.
Quan Yanming et al (1999, 2000) investigated the tool wear in
machining SiC particle reinforced aluminium matrix composites with a
special attention on the effect of material structures on the tool wear
mechanism. It was found that volume fraction and the size of SiC particles
played an important role on tool life. It was concluded that coarser SiC
particle reinforcement and higher volume fractions required harder cutting
tools. Edge and corner breakage of carbide and hard film coated tools were
also reported.
Andrewes et al (2000) investigated the machining behavior of SiC
composite using PCD and Chemical Vapor deposition (CVD) coated tools.
They have found that the initial flank wear on both the PCD and CVD
diamond tools was caused by the abrasion of the very hard SiC particles
present in the work piece material. They have also observed that there was no
significant crater wear formation on the rake face of the tool, because of the
low coefficient of friction and high thermal conductivity of diamond.
In their study, they reported that the worn flank encouraged the
adhesion of the work piece material and was therefore often covered with an
aluminum film due to the high pressure generated at the tertiary cutting zone
(tool – work piece interface). Then this film was scratched away by the hard
SiC particles. Many times along with the aluminum a small part of the tool
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material was also scratched and gouged away leading to tool wear. This
process was observed to take place cyclically leading to the progress of flank
wear. Hence the authors concluded that the wear in the flank was caused by
both the abrasive and adhesion wear mechanisms.
Teti (2002) reported that Metal matrix composites emerged as new
materials for the challenging functional requirements of aircraft components
but are finding increasing applications in automotive industry also. The main
problem in machining MMC is the high tool wear leading to an uneconomical
production process or which makes normal commercial production
impossible.
2.2.5 Effect of Cutting Fluid
Hung et al (1997) reported that the application of water as a cutting
fluid helps to reduce build-up edge formation, but fails to improve tool life.
Narahari et al (1999) reported that the lower tool life was experienced in
machining aluminium MMCs in the presence of cutting fluid, which is 10 to
20% of that under dry condition. Hence, aluminium MMCs with SiCp
reinforcement need to be machined under dry conditions for rough and semi
finished machining.
Raviraj Shetty et al (2008, 2009) discussed the use of Taguchi and
response surface methodologies for minimizing the surface roughness in
turning of discontinuously reinforced aluminum composites (DRACs) under
pressured steam jet approach. The measured results were then collected and
analyzed with the help of the commercial software package MINITAB15. The
matrix of test conditions included cutting speeds of 45, 73 and 101 m/min,
feed rates of 0.11, 0.18 and 0.25 mm/rev and steam pressure 4, 7, 10 bar while
the depth of cut was kept constant at 0.5 mm. The effect of cutting parameters
on surface roughness was evaluated and the optimum cutting condition for
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minimizing the surface roughness was also determined. Finally a second order
model was established between the cutting parameters and surface roughness
using response surface methodology. The experimental results revealed that
the most significant machining parameter for surface roughness was steam
pressure followed by feed. The predicted values and measured values were
fairly close, which indicated that the developed model could be effectively
used to predict the surface roughness in the machining of DRACs.
2.2.6 Modelling
Kannan et al (2006, 2008) have studied the flank wear progression
during machining of Metal Matrix Composites using uncoated tungsten tools
of different geometry and ceramic tool. In their study they proposed a model
for abrasive wear and flank wear rate during machining and validated the
proposed model by conducting turning experiments under wide range of
cutting conditions, tool geometries and composite material composition. They
concluded that cutting test results showed good agreement between predicted
and measured tool wear progression.
Pramanik et al (2006) developed the analytical model extending the
classical Merchants Theory, Slip line theory and Grifith’s theory of brittle
fracture to the machining of ceramic particle reinforced MMCs. The authors
have used the models developed to predict the cutting forces and found the
predicted cutting forces in good agreement with the experimentally observed
values. The authors also observed the force due to chip formation to be
significantly larger than the force due to ploughing and the particle fracture.
The authors also concluded that the classical metal cutting theories are by and
large valid for the machining of MMCs also.
Paulo Davim et al (2007) extended the classical Merchants theory
of metal cutting to machining of MMCs. The shear plane angle, which is the
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most critical parameter in modeling the metal cutting process, was compared
with the shear plane angle predicted by Merchants formulations. The authors
conclude that while machining MMCs the Merchants prediction of shear
plane angle was an overestimate of the observed shear plane angle.
Muthukrishnan et al (2008a) developed two modelling techniques
used to predict the surface roughness namely ANOVA and ANN. In
ANOVA, it is revealed that the feed rate has the highest physical as well as
statistical influence on the surface roughness right after the depth of cut and
the cutting speed. ANN methodology consumes lesser time giving accuracy.
Hence, optimization using ANN is the most effective method compared with
ANOVA.
Basheer et al (2008) developed an ANN based model to predict
surface roughness of machined surface of Al/SiCp composites. The predicted
roughness of machined surfaces was found to be in very good agreement with
the unexposed experimental data set.
Dabade et al (2009) considered chip-tool interface friction to
predict cutting forces in oblique cutting. They provided an analytical model to
compute the machining force components in three directions during oblique
cutting. Unfortunately, the authors did not consider the effect of particle
debonding and ploughing force.
Seeman et al (2010) attempted to model the machinability
evaluation through the response surface methodology in machining 20%SiC
Al-MMC. The combined effect of four machining parameters including
cutting speed, feed rate, depth of cut and machining time on the basis of two
performance characteristics of flank wear and surface roughness were
investigated. It is concluded that the cutting speed and feed rate of the
regression models are found to be more significant when compared to other
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parameters and the optimal cutting conditions are cutting speed 50m/min,
feed rate 0.05mm/rev, depth of cut 0.84mm and machining time 2.4min.
2.3 TAGUCHI METHOD – AN OVERVIEW
The Taguchi method of experimental design is one of the
conventional approaches for producing high quality products at low cost. It is
an efficient and effective method of designing experiments and a fast way of
identifying the parameters which influence the processes. It is a modified
method in design and analysis compared to traditional design and is widely
used in making quality improvements by developing orthogonal array and
simplifying the analysis of variance (ANOVA). This approach is used to
determine the feasible combination of design parameters that reduces
variability in product responses. Taguchi stresses that quality variation is the
main enemy of quality engineering and every effort should be made to reduce
the variations in the quality characteristics.
Taguchi has developed a methodology for the application of
factorial designed experiments that has taken the design of experiments
(DOE), from the exclusive world of statistician and brought it more fully in to
the world of manufacturing. His contributions have also made the
practitioner’s work simpler by advocating the use of fewer experimental
designs, and providing a clearer understanding of the nature of variation and
the economic consequences of quality engineering in the world of
manufacturing (Yang et al 1998; Bhattacharya et al 2009).
The Taguchi method is widely used to find an optimum setting of
manufacturing process parameters. It is one of the most important statistical
tools of TQM for designing high-quality systems at reduced cost. The main
thrust of the Taguchi techniques is the use of parameter design, which is an
engineering method for product or process design. The objective of parameter
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design is to optimize the settings of the process parameter values for
improving the performance characteristics and to identify the product
parameter values under the optimal process parameter values (Montgomery,
2001; Ahmet Hascalik et al 2008). In addition, it is expected that the optimal
process parameter values obtained from the parameter design are insensitive
to the variation of environment conditions and other noise factors. Therefore,
the parameter design is the key step in the Taguchi method to achieve high
quality without increasing cost (Taguchi et al 1989).
Basically, classical parameter design, developed by Fisher (1925),
is complex and not easy to use. Especially, a larger number of experiments
have to be carried out when the number of process parameters increase. In
contrast, the Taguchi method uses a special design of orthogonal arrays to
study the entire parameter space with a small number of experiments only.
Taguchi also defined a loss function to calculate the deviations between the
experimental value and the desired value. He recommends the use of the loss
function to measure the performance characteristic deviating from the desired
value.
The value of the loss function is further transformed into a signal to
noise (S/N) ratio. Usually there are three categories of the performance
characteristic in the analysis of the S/N ratio, that is the Smaller the better,
Larger the best and Nominal the best. The S/N ratio for each level of process
parameter is computed based on the S/N ratio analysis. Regardless of the
category of the performance characteristic, the optimal levels for the process
parameters are selected based on highest S/N ratio. Furthermore, a statistical
analysis of variance ANOVA is performed to see that the process parameters
are statistically significant. With S/N and ANOVA analysis, the optimal
combination of the process parameters can be predicted. Finally, a
confirmation experiment is conducted to verify the optimal process parameter
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obtained from the parameter design (Ross, 1998). There is general agreement
that off-line experiments during product or process design stage are of great
value. Reducing quality loss by designing the products and processes to be
insensitive to variation in noise variables is a novel concept to statisticians
and quality engineers (Aman Aggarwal et al 2005).
Paulo Davim. (2003) studied the influence of cutting conditions and
cutting time on turning metal matrix composites. A plan of experiments,
based on the techniques of Taguchi, was performed. An orthogonal array and
the analysis of variance are employed to investigate the cutting characteristics
of MMC using PCD tools.
Aman Aggarwal et al (2005) and Indrajit Mukherjee et al (2006)
reported a review of literature on optimization of machining techniques. This
review shows that various traditional machining optimization techniques like
Lagrange’s method, geometric programming, goal programming, dynamic
programming etc. have been successfully applied in the past for optimizing
the various turning process variables. Fuzzy logic, genetic algorithm, scatter
search, Taguchi technique and response surface methodology are the latest
optimization techniques that are being applied successfully in industrial
applications for optimal selection of process variables in the area of
machining. A review of literature on optimization techniques has revealed
that there are, in particular, successful industrial applications of design of
experiment-based approaches for optimal settings of process variables.
Palanikumar (2008) studied the use of Taguchi and response
surface methodologies for minimizing the surface roughness in machining
glass fiber reinforced plastics (GFRP) with a polycrystalline diamond (PCD)
tool. The experiments have been conducted using Taguchi’s experimental
design technique. The cutting parameters used are cutting speed, feed and
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depth of cut. The effect of cutting parameters on surface roughness is
evaluated and the optimum cutting condition for minimizing the surface
roughness is determined. A second-order model has been established between
the cutting parameters and surface roughness using response surface
methodology. The experimental results reveal that the most significant
machining parameter for surface roughness is feed followed by cutting speed.
Gul Tosun (2010) reported a statistical analysis of process
parameters for surface roughness in drilling of Al/SiCp metal matrix
composite. The experimental studies were conducted under varying spindle
speed, feed rate, drill type, point angle of drill, and heat treatment. The
settings of drilling parameters were determined by using Taguchi
experimental design method. The level of importance of the drilling
parameters is determined by using analysis of variance. The optimum drilling
parameter combination was obtained by using the analysis of signal-to-noise
ratio. Confirmation tests verified that the selected optimal combination of
process parameter through Taguchi design was able to achieve desired surface
roughness.
Harlal Singh et al (2010) reported the utilization of robust design-
based Taguchi method for optimization of Abrasive Flow Machining (AFM)
parameters. Here, AFM has been used to finish conventionally machined
cylindrical surface of Al/15 wt% SiCp-MMC workpiece. The influences of
AFM process parameters on surface finish and material removal have been
analyzed. Taguchi experimental design concept, L18 (61×37) mixed
orthogonal array is used to determine the S/N ratio and optimize the AFM
process parameters. Analysis of variance and F-test values also indicates the
significant AFM parameters affecting the finishing performance.
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2.4 MULTI-RESPONSE OPTIMIZATION – A REVIEW
The performance of a manufactured product often characterize by a
group of responses. These responses in general are correlated and measured
via a different measurement scale. Consequently, a decision-maker must
resolve the parameter selection problem to optimize each response. This
problem is regarded as a multi-response optimization problem, subject to
different response requirements. Multiple-response design problems have
been widely studied in the quality improvement and quality management
literature.
For such problems, several optimization criteria have been
proposed, including maximization of process yield, maximization of process
capability, minimization of process costs, etc. Most of the common methods
are incomplete in such a way that a response variable is selected as the
primary one and is optimized by adhering to the other constraints set by the
criteria. Many heuristic methodologies have been developed to resolve the
multi-response problem (Gaitonde et al 2008,2009; Raissi et al 2009).
According to Phadke (1989), it is difficult to optimize
simultaneously responses in complex process by single-response method and
engineering judgment is primarily used to resolve such complicated problems.
An engineer’s judgment often increases the degree of uncertainty during
decision making process, making it most critical to the quality of finished
product.
Cornell and Khuri (1987) surveyed the multi-response problem
using a response surface method (RSM). Response surface methodology
consists of a group of techniques used in empirical study of the relationship
between a response and several input variables (Myers, 1995). Most of the
work in RSM has been focused on the case where there is only one response
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of interest. In product or process development, however, it is quite common
that several response variables are of interest. In this case, determination of
optimum conditions on the input variables would require simultaneous
consideration of all the responses.
Logothetis and Haigh (1988) also discussed a manufacturing
process differentiated by five responses. In doing so, they selected one of the
five response variables as primary and optimized the objective function
sequentially while ignoring possible correlations among the responses.
Optimizing the process with respect to any single response leads to non
optimum values for the remaining characteristics.
Tai et al (1992) assigned a weight for each response to resolve the
problem. Pignatiello (1993) utilized a squared deviation-from-target and a
variance to form an expected loss function for optimizing a multiple response
problem. Layne (1995) presented a procedure capable of simultaneously
considering three functions: weighted loss function, desirability function, and
distance function.
Antony (2001) reported that the approach adopted by Taguchi
practitioners to tackle multiple response optimization problems by employing
engineering knowledge together with their experience brings some degree of
uncertainty and, therefore, the validity and robustness of results cannot be
guaranteed. Traditionally, assigning a weight for each response solved this
problem. However, the equation pertaining to summing of weighted S/N ratio
is difficult to explain from the view point of Taguchi’s quality loss function.
To overcome the problem of conflicting responses of single response
optimization, multi-response optimization was used (Vijayan et al 2009).
Lee-Ing Tong et al (2004) proposed procedure used the desirability
function and dual-response-surface method to optimize the multi-response
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problems in a dynamic system. They established a regression model to obtain
the sensitivity and quality variation for each experimental run and the
desirability function is used to obtain a total measurement for the multiple
responses. Next, the dual-response-surface method was used to obtain a set of
possible optimal factor–level combinations. The optimal factor level setting
proposed to maximize total desirability.
Liao and Chen (2002) proposed data envelopment analysis ranking
(DEAR) approach to optimize multi-response problem. The author states that
Taguchi method can only be used to optimize single response problems and
PCA, although considered to solve multi-response problem, itself has
shortcomings. The new approach is capable of decreasing uncertainty caused
by engineering judgment in the Taguchi method and overcoming the
shortcomings of PCA. Two real cases on improving the polysilicon deposition
process and hard disk drives quality process were performed and the result
indicates the feasibility and effectiveness of DEAR approach as compared to
Taguchi method and PCA.
In order to overcome the single response optimization problem of
Taguchi method, Hung-Chang Liao (2003) proposed an effective procedure
called PCR-TOPSIS that is based on process capability ratio (PCR) theory
and on the theory of order preference by similarity to the ideal solution
(TOPSIS) to optimize multi-response problems.
Orthogonal array with grey relational analysis was employed to
optimize the multiresponse characteristics of electric discharge machining of
Al-10%SiCP composites (Narender Singh et al 2004b). The experimental
result for the optimal setting shows that there is considerable improvement in
the process. The application of this technique converts the multi response
variable to a single response grey relational grade and, therefore, simplifies
the optimization procedure. Shibendu Shekar Roy (2006) presents a genetic
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fuzzy expert system for predicting surface finish in turning of metal matrix
composites.
Jayapaul et al (2005) reported a review of literature on solving
multi-response problems in the taguchi method. Twelve unifying approaches
are studied in their work to transform a multi-response design problem into a
single response problem using mathematical transformations. Each of these
methods contains assumptions regarding a risk preference of the user,
response relationship, and the marginal rate of substitution. The user should
understand these assumptions before implementing any of these methods.
Onur Koksoy and Tankut Yalcinoz (2006) presented a
methodology for analyzing several quality characteristics simultaneously
using the mean square error (MSE) criterion when data are collected from a
combined array. They proposed a genetic algorithm based on arithmetic
crossover for the multi-response problem in conjunction with a composite
objective function based on the individual MSE functions of each response.
Jiju Antony et al (2006) used artificial intelligent tool (neuro-fuzzy
model) and Taguchi method of experimental design to tackle problems
involving multiple responses optimization. They proposed a single crisp
performance index called Multi-Response Statistics (MRS) as a combined
response indicator of several responses. MRS is computed for every run by
applying neuro-fuzzy model. ANOVA is carried out on the MRS values to
identify the key factors/interactions having significant effect on the overall
process. Finally, optimal setting of the control factors is decided by selecting
the level having highest value of MRS.
Hari singh et al (2006) proposed a simplified model based on
Taguchi’s approach and utility concept to determine the optimal settings of
the process parameters for turning process to yield optimum quality
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characteristics of EN24 steel turned parts using TiC coated carbide inserts.
The model can be extended to any number of quality characteristics provided
proper utility scales for the characteristics are available from the realistic data.
Shibendu Shekhar Roy (2006) attempted to design an expert system
using two soft computing tools, namely fuzzy logic and genetic algorithm, so
that the surface finish in ultra-precision diamond turning of metal matrix
composite can be modeled for set of given cutting parameters, namely spindle
speed, feed rate and depth of cut. Jayapaul et al (2008) attempted the
simultaneous optimization of multi-response problems in the taguchi method
using genetic algorithm.
Research shows that the multi-response problem is still an issue
with the taguchi method. Researchers have tried to find a series of theories
and methods in seeking a combination of factors/levels to achieve the
situation of optimal multi-response instead of using engineer’s judgement to
make a decision in the taguchi method (Hung-Chang liao 2006).
The following sections discuss the review of literature on the use of
multi-response optimization techniques such as grey relational analysis,
desirability function analysis and principal component analysis.
2.4.1 Grey Relational Analysis
Grey relational analysis (GRA) is based on the grey system theory.
GRA is used to study the relation among various attributes in a system and for
solving the complicated interrelationships among the multiple responses. It is
a kind of measure method focusing on the qualitative description and
comparison of variation. In comparison with the conventional methods which
requires massive amount of samples, typical (e.g. linear exponential or
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logarithmic) distribution of samples and large amount of calculation work,
GRA possesses the following advantages:
Simple and easy calculation.
Reasonable number of samples.
Typical distribution of samples is needless.
No contradictory conclusions against the qualitative analysis.
Suitable and effective in dealing with discrete data. (Deng,
1989)
The methodology uses the simultaneous optimization of the mean
and variance, since it considers S/N ratio values as basis for analysis. To
optimize the parameter conditions for multiple quality characteristics
problems, first the experimental output data are converted into S/N ratio
values. The S/N ratios of each quality characteristics are transformed into
normalized values to avoid the effect of adopting different units for all quality
characteristics. This normalized S/N ratio values are considered for GRA.
Next, the grey relational co-efficient values are calculated corresponding to
each response. Then the grey grade is calculated by taking the average of grey
relational co-efficient corresponding to each experiment. The grey grade
values are treated as the overall evaluation of experimental data for the multi
response process. The optimal level of the process parameters is the level with
the highest grade.
Lin and Lin (2002) have explored the optimization of the
parameters for electrical discharge machining process. The findings are
verified by GRA. The study also analyses the effect of data normalization and
data integrity in GRA to predict the rank of the parameter effect in the case of
insufficient data derived from the Taguchi method.
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Wang et al (2008) have presented a new method that uses GRA and
fuzzy clustering to form part families. The main objective is to identify part
families based on a new similarity coefficient which considers processing
time, lot size, machine usability, etc., by using GRA.
Narender Singh et al (2004,b) reported the use of orthogonal array
with grey relational analysis to optimize the multi-response characteristics of
electrical discharge machining of Al-10%SiCp composites. The experimental
result for the optimal setting shows that there is considerable improvement in
the process. The application of this technique converts the multi-response
variable to a single response grey relational grade and therefore simplifies the
optimization procedure.
Nihat Tosun (2006) used GRA for optimising the drilling process
parameters for the work piece surface roughness and the burr height. Various
drilling parameters, such as feed rate, cutting speed, drill and point angles of
drill were considered. An orthogonal array was used for the experimental
design. Optimal machining parameters were determined by the grey relational
grade obtained from the grey relational analysis for multi-performance
characteristics (the surface roughness and the burr height). Experimental
results have shown that the surface roughness and the burr height in the
drilling process improved effectively.
Lung Kwang Pan (2007) demonstrated the effectiveness of
optimizing multiple quality characteristics of Nd:YAG laser welded titanium
alloy plates via Taguchi method-based Grey analysis. The modified algorithm
adopted here was successfully used for both detraining the optimum settings
of machine parameters and for combining multiple quality characteristics into
one integrated numerical value called Grey relational grade or rank.
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Noorul Haq et al (2008) applied orthogonal array with grey
relational analysis for the optimization of drilling parameters on drilling
Al/SiC metal matrix composite. Based on the grey relational grade, optimum
levels of the parameters have been identified and significant contribution of
parameters is determined by ANOVA.
Tsao (2009) optimized the milling parameters of A6061P-T651
aluminium alloy with multiple performance characteristics using grey-
Taguchi method. Chorng-Jyh Tzeng et al (2009) also applied Taguchi method
and GRA to optimize the dry machining parameters for high-purity graphite
in end milling process. Lu et al (2009) reported the use of grey relational
analysis coupled with principal component analysis for optimization design of
the cutting parameters in high-speed end milling.
Yu-min Chiang et al (2009) reported the use of the taguchi method
with grey relational analysis to optimize the thin-film sputtering process with
multiple quality characteristic in color filter manufacturing. In this work the
weights of the quality characteristics are determined by employing the
entropy measurement method.
Siddhi Jailani et al (2010) attempted to optimise the sintering
process parameters of Al-SiC (12%) alloy/fly ash composite using grey
relational analysis. Experiments have been performed under different
conditions of temperature, fly ash content, and compacting pressure.
Taguchi’s L9 orthogonal array was used to investigate the sintering process
parameters. Optimal levels of parameters were identified using grey relational
analysis, and significant parameter was determined by analysis of variance.
Experimental results indicate that multi-response characteristics such as
density and hardness can be improved effectively through grey relational
analysis.
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2.4.2 Desirability Function Analysis
The desirability function is a useful tool to analyze a multi-response
problem (Derringer and Suich 1980). Therefore, the desirability function is
employed in this study. The desirability function is primarily proposed by
Harrington (Harrington 1965) and is modified to be more flexible in practical
application by Derringer and Suich (Derringer and Suich 1980). The value of
the desirability function, which represents the degree of achieving the target
lies between 0 and 1 and it represents the closeness of a response to its ideal
value. If a response falls within the unacceptable intervals, the desirability is
0, and if a response falls within the ideal intervals or the response reaches its
ideal value, the desirability is 1. Meanwhile, when a response falls within the
tolerance intervals but not the ideal interval, or when it fails to reach its ideal
value, the desirability lies between 0 and 1. The more closely the response
approaches the ideal intervals or ideal values, the closer the desirability is to
1. According to the objective properties of a desirability function, the
desirability function can be categorized into the nominal-the best (NB)
response, the larger-the-better (LB) response and the smaller-the-better (SB)
response.
The proposed desirability function transforms each response to a
corresponding desirability value between 0 and 1. All the desirability can be
combined to form a composite desirability function which converts a multi-
response problem into a single-response one. The desirability function is a
scale invariant index which enables quality characteristics to be compared to
various units. In such method the plant manager can easily determine the
optimal parameters among a group of solutions.
Chao Liu and Lawrence Yao (2002) reported the task of the process
design in the laser forming of sheet metal to determine a set of parameters,
including laser scanning paths, laser power, and scanning speed, given a
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prescribed shape. Response surface methodology is used as an optimization
tool. The propagation of error technique is built into the design process as an
additional response to be optimized via desirability function and hence make
the design robust.
Hsu (2004) presents an integrated optimization approach based on
neural networks, exponential desirability functions and Tabu search to
optimize a fused biconic taper process for a Taiwanese fiber-optic passive
component manufacturer. The confirmation results demonstrated the
practicability and effectiveness of the proposed approach.
Aman Aggarwal (2008b) reported the use of DFA for the
optimization of multiple quality characteristics such as tool life, cutting force,
surface roughness and power consumption in CNC turning of AISI P-20 tool
steel using liquid nitrogen as a coolant. Experimental results show the
improvement of desirability values between single and multi-response
optimization. Finally he concluded that DFA is an attractive method for
industry for optimization of multiple quality characteristic problems.
Naveen sait (2009) presents a use of desirability function analysis
for optimizing the machining parameters on turning glass-fibre reinforced
plastic (GFRP) pipes. In this work, based on Taguchi’s L18 orthogonal array,
turning experiments were conducted for filament wound and hand layup
GFRP pipes using K20 grade cemented carbide cutting tool. The machining
parameters such as cutting velocity, feed rate and depth of cut are optimized
by multi-response considerations namely surface roughness, flank wear, crater
wear and machining force. It is clearly shown that the multi-responses in the
machining process are improved through this approach. Thus, the application
of desirability function analysis in Taguchi technique proves to be an
effective tool for optimizing the machining parameters.
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Ming Der Jean et al (2011) presented the desirability function based
on Taguchi designed experiments to solve multiple responses statistical
optimal problems for the tungsten carbide/cobalt (WC-Co) coatings of high-
velocity-oxygen-fuel (HVOF) processes.
Hsu (2011) studied the use of combined multiple responses with the
multi-criteria optimization design of products and manufacturing processes,
and utilized the principle component analysis to compute the principle score
of five indicators of innovation ability as dependent variables. Utilizing the
factor analysis, the variables were retrenched and the factor scores were
computed as independent variables. Furthermore, this research established the
response surface models by using principle scores as dependent variables and
factor scores as independent variables. Finally, this research analyzed the key
influence factors on innovation ability by desirability function and sensitivity
analysis.
2.4.3 Principal Component Analysis
Principal Component Analysis (PCA) was proposed and evolved as
statistical tool by Hotelling in 1993. Its main advantage is significantly
alleviating loading and complexion of information by simplifying several
correlated variables into fewer uncorrelated and independent principal
components, and preserving as much original information as possible using
linear combination. In recent times, PCA has gradually become an analytical
tool for the optimization of a system with multiple performance
characteristics (Antony et al 2000).
Su and Tong (1997) used the signal to noise (S/N) ratio and system
sensitivity are used to assess the performance of each response. They
performed principal component analysis (PCA) on SN values and system
sensitivity values to obtain a set of uncorrelated principle components, which
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are linear combinations of the original responses. Additionally, they used of
variation mode chart to interpret the variation mode (or principal component
variation) resulting from PCA. They suggested that based on engineering
requirements, engineers can determine the optimization direction for each
principal component using the variation mode chart. Finally, technique for
order preference by similarity to ideal solution (TOPSIS) applied to derive the
overall performance index (OPI) for multiple responses. The optimal
factor/level combination can be determined with the maximum OPI value and
therefore, simultaneously reduces the quality variation and brings the mean to
the target value.
Fung and Kang (2005) used Taguchi method and PCA to optimize
the injection moulding process for friction properties of fibre-reinforced
polybutylene terephthalate (PBT). Initially Taguchi method was used
followed by PCA to correspond to multi-response cases, for transforming the
correlated friction properties to a set of uncorrelated components and
evaluating the principal components. The appropriate number of the principle
components, and the influence of the number on the optimum process
condition, was subsequently studied by extracting more than one principal
component and integrating it into a comprehensive index.
Hung-Chang Liao (2006) reported two shortcomings in the
conventional PCA method. First, when more than one principal component is
selected whose eigenvalue is greater than one, the required trade-off for a
feasible solution is unknown; and second, the multi-response performance
index cannot replace the multi-response solution when the chosen principal
component can only be explained by total variation. In order to overcome
these two main shortcomings in the PCA method, it proposes a weighted
principal component analysis (WPCA) method. In this WPCA method, all
components are taken into consideration in order to completely explain
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variation in all responses. This method uses the explained variation as the
weight to combine all principal components in order to form a multi-response
performance index.
Aman Aggarwal (2008a) used PCA for optimizing multiple
characteristics (tool life, cutting force, surface roughness and power
consumption) in CNC turning of AISI P-20 tool steel. Five controllable
factors of the turning process were studied at three levels each viz cutting
speed, feed, depth of cut, nose radius and cutting environment. L27 Orthogonal
array was used for conducting the experiments. The single response
optimisation was conducted by Taguchi method. PCA was employed to
correspond to multi response cases.
Saurav Datta et al (2010) reported the integrated optimization
approach using principal component analysis, utility concept in combination
of Taguchi’s robust methodology for optimizing multiple surface quality
characteristics of mild steel turned products. In this study, the interaction
effects of process parameters have been neglected. But in practical case, this
assumption may not be valid. Another disadvantage of this approach is the
unrealistic assumption that the responses are treated equally important (equal
priority weight).
2.5 SUMMARY
The following Conclusions were derived from the Review of
Literature,
Metallic matrix composites have found considerable
applications in aerospace, automotive and electronic industries
because of their improved strength, stiffness and increased wear
resistance over unreinforced alloys. However, the final
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conversion of these composites in to engineering products is
always associated with machining.
A continuing problem with MMCs is that they are difficult to
machine, due to the hardness and abrasive nature of the
reinforcing particles.
The particles used in the MMCs are harder than most of the
cutting tool materials. This results in accelerated tool wear and
premature tool failure. Conventional tool materials such as
High-speed steel, coated and uncoated carbide tools sustained
significant levels of tool wear after short period of machining.
Most of the researchers reported diamond is the most preferred
tool material for machining MMCs.
The particulate reinforcement size and volume fraction together
with the cutting parameters are the major factors affecting the
machining performance.
The choice of cutting conditions, proper tool material and
machine tool are essential for successful machining
performance. The process parameter optimization of machining
Al-SiCp MMCs with multiple response consideration is not
reported yet.
Literature shows that the multi-response problem is still an issue
with the taguchi method. Researchers have tried to find a series
of theories and methods in seeking a combination of
factors/levels to achieve the situation of optimal multi-response
instead of using engineer’s judgement to make a decision in the
taguchi method
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The literature reviewed on machining of metal matrix composites
shows that a little research was carried out for the optimization of machining
parameters for Al-SiCp MMCs. Further, most published literature have been
concerned with the optimization of a single performance (or response)
characteristic. But the performance of a machining process often
characterized by a group of responses. If more than one response comes into
consideration it is very difficult to select the optimal setting which can
achieve all quality requirements simultaneously. Otherwise optimizing one
quality feature may lead severe quality loss to other quality characteristics
which may not be accepted by the customers. Handling the more demanding
multiple performance characteristics are seldom considered in the literature.
In order to tackle such a multi-response optimization problem, the present
study applied extended Taguchi methods like Grey Relational Analysis
(GRA), Desirability Function Analysis (DFA) and Weighted Principal
Component Analysis (WPCA) methods for determining optimum machining
parameters of Al-SiCp MMCs.