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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

25

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

27

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

31

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

32

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

33

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

39

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.

40

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

44

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.