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Journal of the Chinese Institute of Industrial
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Fabrication and turning of Al/SiC/B4C hybrid metal
matrix composites optimization using desirability
analysisN. Muthukrishnan
a, T.S. Mahesh Babu
b& R. Ramanujam
c
aDepartment of Automobile Engineering, Sri Venkateswara College of Engineering,
Pennalur, Sriperumbudur 602 105, Tamil Nadu, IndiabDepartment of Aeronautical Engineering, Sathyabama University, Jeppiaar Nagar, Rajiv
Gandhi Road, Chennai 600 119, Tamil Nadu, IndiacDepartment of Mechanical Engineering, School of Mechanical Engineering, Vellore
Institute of Technology, Vellore, Tamil Nadu, India
Published online: 05 Oct 2012.
To cite this article:N. Muthukrishnan , T.S. Mahesh Babu & R. Ramanujam (2012): Fabrication and turning of Al/SiC/B4C hybrid metal matrix composites optimization using desirability analysis, Journal of the Chinese Institute of Industrial
Engineers, 29:8, 515-525
To link to this article: http://dx.doi.org/10.1080/10170669.2012.728540
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http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditionshttp://dx.doi.org/10.1080/10170669.2012.728540http://www.tandfonline.com/loi/tjci208/9/2019 Fabrication and Turning of Al-sic-b4c
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Journal of the Chinese Institute of Industrial Engineers
Vol. 29, No. 8, December 2012, 515525
Fabrication and turning of Al/SiC/B4C hybrid metal matrix composites
optimization using desirability analysisN. Muthukrishnana*, T.S. Mahesh Babub and R. Ramanujamc
aDepartment of Automobile Engineering, Sri Venkateswara College of Engineering, Pennalur,Sriperumbudur 602 105, Tamil Nadu, India; bDepartment of Aeronautical Engineering, Sathyabama University,
Jeppiaar Nagar, Rajiv Gandhi Road, Chennai 600 119, Tamil Nadu, India; cDepartment of MechanicalEngineering, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
(Received January 2012; revised April 2012; accepted August 2012)
This article presents the detailed discussions on fabrication of aluminumsilicon carbide (10% byweight of particles) and boron carbide (5% by weight of particles) hybrid metal matrix composites(Al/SiC/B4C MMCs) using stir casting method. The cylindrical rods of diameter 65 mm and length200 mm are fabricated and subsequently machined using medium duty lathe to study the machinabilityissues of hybrid MMC using polycrystalline diamond insert of 1600 grade. The optimum machining
parameters have been identified by a composite desirability value obtained from desirability functionanalysis as the performance index, and significant contribution of parameters can then be determinedby analysis of variance. Confirmation test is also conducted to validate the test result. Experimentalresults have shown that machining performance can be improved effectively through this approach.Results show at higher cutting speeds, good surface finish is obtained with faster tool wear. Percentageof error obtained between experimental value and predicted value is within the limit. Using the optimalparameters, tool wear analysis also studied for the duration of 30 min.
Keywords: turning; cutting force; tool wear; PCD; surface roughness; desirability function; ANOVA
1. Introduction
Considerable research work in the field of material
science has been progressed toward the develop-
ment of new light-weight, high performance engi-
neering materials, such as composites. Metallic
matrix hybrid composites are one among them.
Metal matrix composites (MMCs) have become the
necessary materials in various engineering applica-
tions like aerospace, marine, and automobile engi-
neering applications, because of their light-weight,
high-strength, stiffness, and resistance to high
temperature [32]. However, the final conversion of
these composites into engineering products is
always associated with machining, either by turning
or by milling. A continuing problem with hybrid
MMCs is that they are difficult to machine, due tothe hardness and abrasive nature of the reinforcing
particles [26,36]. The presence of hard ceramic
particles in the composites makes them extremely
difficult to machine as they lead to rapid tool wear
[2,14]. The hard SiC particles in Al/SiCMMCs
which intermittently come in contact with the tool
surface and acts as small cutting edges like those of
the grinding wheel. These particles act as an
abrasive between cutting tool and work piece and
resulting in formation of high tool wear and poor
surface finish [46,15,16]. Ramulu et al. [24]
reported that the aluminum particulates caused
extremely rapid flank wear in cutting tools, when
machining Al2O3 particulate reinforced aluminum-
based MMC. Optimum machining condition inturning Al356/SiC/20p MMCs for minimizing the
surface roughness was determined using desirability
function approach [20]. Dabade et al. [3] have
reported an elaborative experimentation with the
help of Taguchi methods on Al/SiC MMC to
analyze the effects of size and volume fraction of
reinforcements in the composites on cutting forces
and surface roughness. Kremer et al. [10] conducted
the experiment to study the effect of SiC percentage
in the Al/SiC particulate MMCs on the machin-
ability studies. Artificial neural network based
model for the prediction of surface roughnessduring turning of composite material by back
propagation algorithm [21]. The effect of machin-
ing parameters on the surface roughness was
evaluated and optimum machining conditions for
maximizing the metal removal rate and minimizing
the surface roughness were determined using
response surface methodology in turning particu-
late MMC [19]. Rajmohan et al. [22,23] have
selected response surface methodology to predict
the thrust force and surface roughness in drilling
hybrid MMC using coated carbide drills. Tool wear
*Corresponding author. Email: [email protected]
ISSN 10170669 print/ISSN 21517606 online
2012 Chinese Institute of Industrial Engineers
http://dx.doi.org/10.1080/10170669.2012.728540
http://www.tandfonline.com
8/9/2019 Fabrication and Turning of Al-sic-b4c
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is excessive when carbide tipped tools were used for
turning Al/SiC/MMCs [13]. Coated carbide tools
perform better than uncoated carbide tools in terms
of tool wear for machining these materials. The
better performance of them can be attributed due
to the coating and larger and more stable built-up-
edge (BUE) on the tool [2,13]. Polycrystallinediamond (PCD) tools are more suitable for
machining Al/SiCMMCs in terms of both tool
wear and surface finish because of the higher
hardness than SiC particles [11,12,28]. More
number of papers published in drilling of hybrid
composites of various reinforcements in polymer,
metals, and ceramics. Only limited number of
papers published in turning of Al/SiC/B4C hybrid
MMCs using multi-response optimization. Hsu [9]
proposed a four-phased procedure based on neural
network and principal component analysis to
resolve the parameter design problem with multipleresponses and concluded that the proposed proce-
dure is relatively simple and could be implemented
easily using readymade statistical software. Chang
[1] reported that lot of skillful techniques parameter
design problems available; however, methods for
tackling the dynamic multi-response problems are
rare. He proposed an approach based on back
propagation neural networks and desirability func-
tions to optimize parameter design of the dynamic
multi-response and concluded that the best param-
eter setting can be obtained by maximizing single
desirability index. Tsai [34] carried out a compar-
ative study of optimizing the reflow thermal pro-
filing parameters using a hybrid artificial
intelligence and desirability function approaches
without/with combining multiple performance
characteristics into a single desirability. He
reported that empirical evaluation results show
that the desirability function approach with com-
bining the multiple performances into a single
desirability is superior to that obtained by the
hybrid artificial intelligence methods. In the view of
above problems, the main objective of this study is
to investigate the influence of different cutting
parameters on surface finish and cutting forcecriterion. The Taguchi L27 orthogonal array is
utilized for experimental planning for turning of
AlSiCB4C hybrid MMC. The results are
analyzed to achieve optimal surface roughness
and cutting force. Desirability function analysis
(DFA) was performed to combine the multiple
performance characteristics into one numerical
score called composite desirability value to deter-
mine the optimal machine parameter settings.
Analysis of variance (ANOVA) is also performed
to investigate the most influencing parameters on
the surface finish and cutting force.
2. Taguchi technique
Taguchi technique is a powerful tool for the design
of high quality systems [25,30,31]. It provides a
simple, efficient, and systematic approach to opti-
mize design for performance, quality, and cost. The
methodology is valuable when design parameters
are qualitative and discrete. Taguchi parameter
design can optimize the performance characteristics
through the setting of design parameters and
reduce the sensitivity of the system performance
to the source of variation [25,27]. This technique isa multi-step process, which follow a certain
sequence for the experiments to yield an improved
understanding of product or process performance.
This design of experiment process made up of three
main phases: the planning, the conducting, and
analysis interpretation. The planning phase is the
most important phase; one must give a maximum
importance to this phase. The data collected from
all the experiments in the set are analyzed to
determine the effect of various design parameters.
This approach is to use a fractional factorial
approach and this may be accomplished with theaid of orthogonal arrays. ANOVA is a mathemat-
ical technique, which is based on least square
approach. The treatment of the experimental
results is based on the analysis of average and
ANOVA [46,35].
3. Fabrication of hybrid MMC
The base metal ingot (Al 356) is cleaned using
acetone. Then, it is melted using electric arc furnace
(capacity 20 kg/melt). Temperature of the melting
process is 710725
C. At this stage, all cover flux isadded in the furnace. Once the base alloy is melted
completely, degassing process is carried out by
adding hexachloroethane tablets. This removes
nitrogen, carbon-dioxide and other gases absorbed
by the melt in the furnace. The silicon carbide and
boron carbide particles (SiC and B4C) ranges from
Table 1. Chemical composition of AlSiC (10%) B4C (5%) hybrid MMC.
Type ofhybrid MMC Reinforcement
SiC(%)
B4C(%)
Si(%)
Mg(%)
Fe(%)
Cu(%)
Mn(%)
Zn(%)
Ti(%)
Al(%)
Particulate MMC SiC and B4C (3065mm) 10.00 5.00 7.85 0.68 0.25 0.14 0.07 0.07 0.16 Balance
516 N. Muthukrishnanet al.
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30 to 65 mm are now preheated to a temperature of
790C. The melted base alloy is stirred for about
56 min at 450 rpm. Silicon carbide, boron carbide,
and magnesium are continuously added to the melt.
The magnesium is added in order to compensate for
its losses during melting and for wetting purposes.
After this stirring process, the molten mixture ispoured into the steel molds of required diameter and
length. Figure 1 shows the stir casting setup and
Figure 2 shows the microstructure of the fabricated
specimen. Table 1 shows the chemical composition
of Al-SiC(10p) B4C (5P)- Hybrid MMC.
4. Experimental procedure
Commercially fabricated cylindrical bars having
10% of SiC particles and 5% of B4C on matrix of
Al 356, using stir casting method of diameter 65 mm
and 200 mm long are turned on self-centered three
jaw chuck, medium duty lathe of spindle power
2 kW. Figure 3 shows the experimental setup with
tool dynamometer integral with it. Parameters such
as surface roughness of machined component were
measured by Mitutoyo surftest (Make-Japan
Model SJ-301) measuring instrument with the
cut-off length 2.5 mm.
Cutting force was measured using Unitech lathe
tool dynamometer with digital indicator. Thecutting tool selected for machining AlSiCB4C
MMCs was PCD insert of fine grade (1600 grade).
The PCD inserts used were of ISO coding CNMA
120408 and tool holder of ISO coding PCLNR
2020M12. The specifications for PCD insert are as
follows: substrate for PCD is tungsten carbide,
nose radius 0.8 mm, shank height 25 mm, shank
width 25 mm, average particle size 4 mm, volume
fraction of diamond 90%, compressive strength
7.5 GPa, and elastic modulus 850 GPa. Table 2
presents the machining parameters and their levels.
Table 3 presents the experimental layout.
5. Desirability function analysis
One useful approach to optimization of multiple
responses is to use the simultaneous optimization
technique popularized by Naveen Sait [17]. Their
procedure introduces the concept of desirability
functions. The method makes use of an objective
function, D(X), called the desirability function and
transforms an estimated response into a scale free
value (di) called desirability. The desirable ranges
are from 0 to 1 (least to most desirable, respec-
tively). The factor settings with maximum total
desirability are considered to be the optimal
parameter conditions.
Optimization steps using DFA
Step 1: Calculate the individual desirability index
(di) for the corresponding response functions
according to the response characteristics using the
formula proposed Naveen Sait [17]. There are three
forms of the desirability functions according to the
response characteristics.
(a) The nominal-the-best: The value ofby isrequired to achieve a particular targetT. when theby
Figure 2. Microstructure of Al 356 reinforced with 10%SiC and 5% B4C.
Figure 1. Stir casting set up.
Figure 3. Experimental set up.
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equals toT, the desirability value equals to 1; if the
departure of
by exceeds a particular range from the
target, the desirability value equals to 0, and suchsituation represents the worst case. The desirability
function of the nominal-the-best can be written as
given in Equation (1):
di
^y ymin
Tymin
s, ymin y T, s 0
^y ymax
Tymax
t, T ^y ymax, t 0
0
0BBBBB@ 1
where, the ymax and ymin represent the upper and
lower tolerance limits ofby, and s, and t representthe weights.
(b) The larger-the-better: The value of
by is
expected to be the larger the better. When the
by
exceeds a particular criteria value, which can beviewed as the requirement, the desirability value
equals to 1; if the byis less than a particular criteriavalue, which is unacceptable, the desirability equals
to 0. The desirability function of the larger-the-
better can be written as given in Equation (2):
di
0,
^y ymin
ymax ymin
r,
1,
0BB@
^y ymin
ymin ^y ymax,
^y yminr 0
2
Table 3. Experimental layout using L27 orthogonal array and corresponding response values.
Machining parameters Response
Group no.Cutting
speed (A)Feed
(B)Depth of
cut (C)Surface roughness
(Ra) (mm)Cutting force
in (F) (N)
1 1 1 1 2.10 39.242 1 1 2 2.15 49.053 1 1 3 2.02 98.10
4 1 2 1 3.73 58.865 1 2 2 3.95 60.166 1 2 3 3.37 68.867 1 3 1 6.53 88.298 1 3 2 6.74 98.489 1 3 3 6.76 102.29
10 2 1 1 1.40 58.8611 2 1 2 2.37 65.8612 2 1 3 2.29 68.8613 2 2 1 3.04 88.2914 2 2 2 4.25 98.4815 2 2 3 4.06 104.4816 2 3 1 7.17 117.7217 2 3 2 6.93 127.5318 2 3 3 6.90 134.72
19 3 1 1 2.24 98.1020 3 1 2 4.99 103.2921 3 1 3 2.38 108.1022 3 2 1 3.58 127.5323 3 2 2 3.95 137.7224 3 2 3 4.95 132.7225 3 3 1 6.88 235.4426 3 3 2 7.36 246.2027 3 3 3 7.22 296.20
Table 2. Machining parameter and their levels.
Symbol Machining parameter Level 1 Level 2 Level 3
A Cutting speed (m/min) 90 140 220B Feed (mm/rev) 0.1 0.2 0.32C Depth of cut (mm) 0.5 0.75 1.0
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where, the ymin represents the lower tolerance limit
ofby, the ymax the upper tolerance limit ofby and rthe weight.
The smaller-the-better: The value ofby isexpected to be the smaller the better. When the
by
is less than a particular criteria value, the desir-
ability value equals to 1; if theby exceeds aparticular criteria value, the desirability value
equals to 0. The desirability function of the
smaller-the-better can be written as given in
Equation (3):
di
1,
^yymaxymin ymax
r,
0,
0B@ ymin y ymax, r 0
^y ymin
r 0
^y ymax
3
where the ymin represents the lower tolerance limit
ofby, the ymax the upper tolerance limit ofby and rthe weight. The s, t, and r in Equations (1)(3)
indicate the weights and are defined according to
the requirement of the user. If the corresponding
response is expected to be closer to the target, the
weight can be set to the larger value; otherwise, the
weight can be set to the smaller value. In this study,
the smaller-the-better characteristic is applied to
determine the individual desirability values for
surface roughness and cutting force since both are
to be minimized.
Step 2: Compute the composite desirability (dG).
The individual desirability index of all the
responses can be combined to form a single value
called composite desirability (dG) by the following
Equation (4):
dG dw11 d
w22 . . . d
wnn
1W 4
where, di is the individual desirability of the
property Yi, wi the weight of the property Yi in
the composite desirability, and W the sum of the
individual weights. In this investigation, weights for
each characteristic (such as surface roughness and
cutting force) are assigned equally as 0.5.
Step 3: Determine the optimal parameter and its
level combination. The higher the composite desir-
ability value implies better product quality.
Therefore, on the basis of the composite desirability
(dG), the parameter effect and the optimum level for
each controllable parameter are estimated.
Step 4: Perform ANOVA for identifying the
significant parameters. ANOVA establishes the
relative significance of parameters. The calculated
total sum of square value is used to measure therelative influence of the parameters.
Table 4 shows the evaluated individual desir-
ability and composite desirability for each experi-
ment using L27 orthogonal array. The higher
composite desirability value represents that the
corresponding experimental result is closer to the
ideally normalized value. Since the experimental
design is orthogonal, it is then possible to separate
out the effect of each machining parameter on the
composite desirability values at different levels. The
response mean of the composite desirability for
each level of the machining parameter is summa-
rized in Table 5. In addition, the total mean of the
composite desirability for 27 trials is also calculated
and listed in Table 5. Figure 4 shows the factor
effects for the composite desirability value for the
levels of the machining parameters.
Basically, the larger the composite desirability,
the better is the multiple performance characteris-
tics. However, relative importance among the
machining parameters for the multiple performance
characteristics is still need to be known so that the
optimal combinations of the machining parameterlevels can be determined more accurately [33].
Table 4. Evaluated individual and compositedesirability.
Exp.no.
Individualdesirability (di)
Composite
desirability(dG)
Surface
roughness(Ra) (mm)
Cutting
force(N)
1 0.88255 1 0.9394422 0.874161 0.961823 0.9169453 0.895973 0.770937 0.8311074 0.60906 0.923646 0.7500375 0.572148 0.918587 0.724966 0.669463 0.884729 0.7696067 0.139262 0.809114 0.3356768 0.104027 0.769458 0.2829219 0.100671 0.754631 0.275626
10 1 0.923646 0.96106511 0.837248 0.896404 0.86632112 0.850671 0.884729 0.867533
13 0.724832 0.809114 0.76581514 0.521812 0.769458 0.6336515 0.553691 0.746108 0.64273916 0.031879 0.694583 0.14880417 0.072148 0.656406 0.21761918 0.077181 0.628425 0.22023319 0.85906 0.770937 0.81380720 0.397651 0.750739 0.54638121 0.83557 0.732021 0.78208422 0.634228 0.656406 0.64522223 0.572148 0.61675 0.5940324 0.404362 0.636208 0.50720725 0.080537 0.236457 0.13799826 0 0.194583 027 0.02349 0 0
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Step 5: Calculate the predicted optimum condi-
tion. Once the optimal level of the design param-
eters has been selected, the final step is to predict
and verify the quality characteristics using the
optimal level of the design parameters.
6. Implementation of the methodologyStep 1: The individual desirability (di) is calcu-
lated for all the responses depending upon the type
of quality characteristics. Since all the responses are
possessing minimization objective, the equation
corresponding to smaller the better type is selected.
The computed individual desirability for each
quality characteristics using Equation (3) are
presented in Table 4.
Step 2: The composite desirability values (dG) are
calculated using Equation (4). The weightage for
responses are based on assumed weightage of 1:1
for surface roughness and machining force. Finally,these values are considered for optimizing the
multi-response parameter design problem. The
results are presented in Table 4.
Step 3: From the value of composite desirability
in Table 4, the parameter effect and the optimal
level are estimated. The results are tabulated in
Table 5 and parameter effects are plotted in
Figure 4.
Step 4: Using the composite desirability value,ANOVA is formulated for identifying the
significant parameters. The result of ANOVA is
presented in Table 6.
Step 5: Prediction of optimum condition: Using
the identified optimal parameter condition, the
quality characteristics are verified by conducting
confirmation experiments.
7. Analysis of variance
ANOVA is a method of apportioning variability of
an output to various inputs. Table 6 presents theresults of ANOVA analysis. The purpose of the
MeanofCompositeDesirability
321
0. 8
0. 6
0. 4
0. 2
321
321
0. 8
0. 6
0. 4
0. 2
cut t ing spe e d Fe e d
Depth of cut
Main Effects Plot for Composite Des irabil i ty
Figure 4. Response graph for composite desirability.
Table 5. Response table for the composite desirability.
Machining parameter
Average composite desirability
Level 1 Level 2 Level 3MaximumMinimum
Cutting speed (A) 0.6473 0.5915 0.4474 0.1999Feed rate (B) 0.8360 0.6703 0.1798 0.6562Depth of cut (C) 0.6108 0.5314 0.5440 0.0794Total mean of composite
desirability 0.5621
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ANOVA is to investigate which machining param-
eters significantly affect the performance charac-
teristics. This is accomplished by separating the
total variability of the composite desirability value,
which is measured by the sum of the squareddeviations from the total mean of the composite
desirability value, into contributions by each
machining parameter and the error. First, the
total sum of the squared deviations SST from the
total mean of the composite desirability value mcan be calculated as:
SSTXpj1
j m2 5
where p is the number of experiments in the
orthogonal array and j the mean composite
desirability value for the jth experiment. The totalsum of the squared deviations SSTis decomposed
in to two sources: the sum of the squared deviations
SSd due to each machining parameter and its
interaction effects and the sum of the squared error
SSe. The percentage contribution by each of the
machining parameter in the total sum of the
squared deviations SST can be used to evaluate
the importance of the machining parameter change
on the performance characteristic. In addition, the
Fishers F-test can also be used to determine which
machining parameters have a significant effect on
the performance characteristic. Usually, the change
of the machining parameters has a significant effecton performance characteristic whenFis large.
Results of ANOVA for composite desirability
value (Table 6) indicate that feed rate is the most
significant machining parameter for affecting the
multiple performance characteristics.
Based on the above discussion, the optimal
machining parameters are the cutting speed at
level 1, feed at level 1, and depth of cut at level 1.
8. Confirmation experiment
Once the optimal level of machining parameters isselected the final step is to predict and verify the
improvement of the performance characteristics
using the optimal level of the machining parame-
ters. The estimated composite desirability value
using the optimum level of the machining param-
eters can be calculated as
m Xqi1
j m 6
where m is the total mean of the composite
desirability value, j the mean of the composite
desirability value at the optimum level, and q the
number of machining parameters that significantly
affects the multiple performance characteristics.
Based on Equation (6) [33], the estimated
composite desirability value using the optimal
machining parameters can then be obtained.
Table 7 presents the results of the confirmation
experiment.
Using the optimal machining parameters, sur-
face roughness Ra is improved from 6.88 to
2.10 mm in experimentation and 1.83 mm in predic-
tion, similarly the cutting force is greatly reduced
from 235.44 to 39.24 N in experimentation and
28.47 N in prediction. It is clearly shown that
multiple performance characteristics in the AlSiC
B4C machining process are greatly improved
through this study. From this analysis, it is found
that the percentage of error for surface roughness is
found (using Equation (7)) to be 12.85%, where s
the percentage of error for cutting force is found tobe 27.44%.
Percentage of error
Experimental Value Predicted value
Experimental value 100
7
9. Tool wear
From the above observations, best machining
parameter was determined as cutting speed
90 m/min, feed rate 0.1 mm/rev, and depth of cut0.5 mm (experimental reading number 1). Now
Table 6. ANOVA table for the composite desirability.
SourceDegrees
of freedom SS MS FCAL P (%)
A 2 0.1916 0.0958 17.95 7.99B 2 2.0959 1.0479 196.40 87.48
C 2 0.0328 0.0164 3.08 1.37A B 4 0.0136 0.0034 0.64 0.56A C 4 0.0123 0.0030 0.58 0.53B C 4 0.0070 0.0017 0.33 0.29Error 8 0.0426 0.0053 1.78
Total 26 2.3960 100.00
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setting this cutting condition as a constant param-eter and machined the samples for a time duration of
30 min and the tool flank wear study was carried out
(Figure 5).
From Figure 5, it is clearly understood that, the
tool flank wear is increasing linearly and reaches
approximately 0.2 mm after 30 min duration. At
low cutting speed, worn flank encourages the
adhesion of work piece material on the tool insert
and formed BUE [7,8,15,32,35].
At lower cutting speed, formation of BUE
forms a protective cap and protects the cutting edge
from abrading [5,32]. Main wear pattern observed
on the cutting insert was the flank wear in the nose
region [29] two bodies and three body abrasive
wear are also observed. Three body abrasive wear is
caused by the released hard particles, entrapped
between the tool and the work piece [12,18,37]. The
BUE formation in aluminum machining in general
and in machining AlSiCB4C hybrid MMC in
particular adversely affects the surface formation.
Devoid of any fixed geometry, these BUEs result in
unacceptable surface finishes. During experiments,
the BUE formed at the cutting speed of 90 m/min
was dissolved using boiling concentrated NaOH
solution. This was carried out to continue themachining process and to measure flank wear.
Tool was monitored for normal types of wear
namely flank, crater, and nose using a tool makers
microscope. Tool flank wear was caused by abra-
sive nature of the hard silicon and boron carbide
particles presented in the work piece. Figure 6
shows the scanning electron microscope (SEM)
image of fresh insert. Figure 7 shows SEM image of
PCD 1600 grade insert after machining the work
piece for 30 min duration. It is proved that hard
silicon and boron carbide particles which have
higher hardness than diamond abrading the cuttingtool [5,8]. It is observed that the tool life of PCD
Table 7. Results of confirmation experiment.
Initialmachiningparameters
Optimal machining parametersPercentage
of errorPrediction Experiment
Setting level A3B3C1 A1B1C1 A1B1C1
Surface roughness (Ra) (mm) 6.88 1.83 2.10 12.85Cutting force (N) 235.44 28.47 39.24 27.44
0.1379 0.9699 0.9394 3.24
Improvement in composite desirability value 0.8015
y = 0.007x-0.017
R = 0.960
-0.05
0
0.05
0.1
0.15
0.2
0.25
0 5 10 15 20 25 30 35
Toolwear(mm)
Time duration (sec)
PCD 1600 Grade
Linear (PCD 1600
Grade)
Figure 5. Time duration versus tool flank wear (30 minduration).
250X
Figure 6. SEM image of fresh PCD 1600 grade.
Al
Nose wear
Figure 7. SEM image of worn out insert after 30 min
duration.
522 N. Muthukrishnanet al.
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1600 grade is performing well in the chosen cutting
condition
10. Conclusion
(a) The use of orthogonal array with DFA to
optimize the AlSiC(10%)B4C(5%)
hybrid composites machining process with
multiple performance characteristics has
been reported in this article.
(b) The DFA of the experimental results of
surface roughness and cutting force can
convert optimization of the multiple per-
formance characteristics into optimization
of the single performance characteristic
called the composite desirability value.
(c) As a result, optimization of the complicated
multiple performance characteristics can begreatly simplified through this approach. It
is shown that the performance characteris-
tics of the turning process of AlSiC(10%)
B4C(5%) hybrid composites such as surface
roughness and cutting force are improved
together using the proposed method in this
study.
(d) Confirmatory experiment proves that pre-
dicted and experimental values are very
close to each other.
(e) Percentage of error in predicted and exper-
imental value was found to be less than28%.
(f) The primary wear mode is in the nose
region of the flank. The wear is believed to
be the abrasive action of hard SiC and
Boron particles on the tool flank.
(g) It is also observed that two and three bodies
wear mechanisms play a major role in the
tool failure.
Notes on contributors
N. Muthukrishnan is a Professor and Head of Automobile Engineering in Sri Venkateswara Collegeof Engineering, Sriperumbudur, Chennai, India. He hasmore than 20 years of experience in academics and 7years of research experience in Mechanical engineering.His research interest is in Machining/Manufacturing. Heis acting as reviewer for Springer, Elsevier, Inderscience,and Taylor & Francis Journals. He has published morethan 15 papers in the National and International peerreviewed journals and more than 50 papers in National/International Conference proceedings and has publisheda number of papers in the areas of Materials,manufacturing, and management. He is also acting asEditorial Board Member of two International Journals.His biography is listed in Marquis who is who in theworld and also in Marquis who is who in Science andengineering. He is also listed in top 100 educators for the
year 2011, by International biographical centerCambridge, England.
T.S. Mahesh Babu is working as an Associate Professorin Aeronautical Engineering Department, SathyabamaUniversity. Currently, he is doing his Doctoral programunder the guidance of Dr N. Muthukrishnan in the areaof composite machining. His area of interest is metalcutting/machining. He is having more than 10 years ofteaching experience.
R. Ramanujam is an Associate Professor in MechanicalEngineering, Vellore Institute of Technology,Tamilnadu, India. He has 10 years of experience inteaching and 4 years in research. His current researchinterests are in the field of quality engineering andmachining process optimization. He has published 15papers in National and International Journals andConferences.
References
[1] Chang, H.-H., Dynamic multi-response experi-
ments by back propagation networks and desirabil-
ity functions, Journal of the Chinese Institute of
Industrial Engineers, 23(4), 280288 (2006).
[2] Ciftci, I., M. Turker and U. Sekar, Evaluation of
tool wear when machining SiC reinforced Al-2014
alloy matrix composites, Materials and Design, 25,
251255 (2004).
[3] Dabade, U.A., H.A. Sonawane and S. Joshi,
Cutting forces and surface roughness in machining
Al/SICP composites of varying composition,
Machining Science and Technology, 14, 258279
(2010).
[4] Davim, J.P., An experimental study of tribological
behaviour of the brass/steel pair, Journal of
Materials Processing Technology, 100, 273279
(2000).
[5] Davim, J.P., Design optimization of cutting
parameters for turning metal matrix composites
based on the orthogonal arrays, Journal of
Materials Processing Technology, 132, 340344
(2003).
[6] Davim, J.P., Study of drilling metal matrix com-
posites based on the taguchi techniques,Journal of
Materials Processing Technology, 132, 250254
(2003).
[7] Deonath and P.K. Rohatgi, Cast aluminium alloycomposites containing copper-coated ground mica
particles, Journal of Materials Science, 16,
15991606 (1981).
[8] Gallab, M. and M. Sklad, Machining of Al/SiCp
metal matrix composites. Part II: workpiece integ-
rity, Journal of Materials Processing Technology,
83, 277283 (1998).
[9] Hsu, C.-M., Solving multi-response problems
through neural networks and principal component
analysis, Journal of the Chinese Institute of
Industrial Engineers, 18, 4754 (2001).
[10] Kremer, A., S. Devillez, D. Dominiak, M.
Dudzinski and E.I. Mansori, Machinability of
Al/Sic particulate metal-matrix composites under
dry conditions with cvd diamond-coated carbide
Journal of the Chinese Institute of Industrial Engineers 523
8/9/2019 Fabrication and Turning of Al-sic-b4c
11/12
tools, Machining Science and Technology, 12,
214233 (2010).
[11] Lin, J.T., D. Bhattacharya and V. Kecman,
Multiple regression and network analysis in com-
posite machining, Composites Science and
Technology, 63(34), 539548 (2003).
[12] Lin, J., D. Bhattacharyya and C. Lane,
Machinability of a silicon carbide reinforced alu-
minium metal matrix composite, Wear, 181182,
883888 (1995).
[13] Manna, A. and B. Bhattacharya, A study of
machinability of Al-SiC-MMC, Journal of
Materials Processing Technology, 140, 711716
(2003).
[14] Monaghan, J. and P. OReilly, The drilling of an
Al/SiC metal matrix composites, Journal of
Materials Processing Technology, 33, 469480
(1992).
[15] Morscher, G.N., G. Ojard, R. Miller, Y. Gowayed,
U. Santhosh and J. Ahmad, Tensile creep and
fatigue of Sylramic-iBN melt-infiltrated SiC matrixcomposites: retained properties, damage develop-
ment, and failure mechanisms, Composites Science
and Technology, 68, 33053313 (2008).
[16] Mubaraki, B., S. Bandyopadhyay, R.F. Fowle,
P. Mathew and P.J. Health, Drilling studies of
an Al203Al metal matrix composite. Part I.
Drill wears characteristics, Journal of Materials
Science, 30, 62736280 (1995).
[17] Naveen Sait, A., S. Aravindan and A. Noorul Hag,
Optimisation of machining parameters of glass-
fibre reinforced plastic (GFRP) pipes by desir-
ability function analysis using Taguchi technique,
International Journal of Advanced Manufacturing
Technology, 43, 581589 (2009).
[18] Ozben, T., E. Kilickap and O. Cakir, Investigation
of mechanical and machinability properties of SiC
particle reinforced Al-MMC, Journal of Materials
Processing Technology, 198(13), 220225 (2008).
[19] Palanikumar, K. and R. Karthikeyan, Assessment
of factors influencing surface roughness on the
machining of Al/SiC particulate composites,
Materials and Design, 28, 15841591 (2007).
[20] Palanikumar, K., N. Muthukrishnan and K.S.
Hariprasad, Surface roughness parameters optimi-
zation in machining A356/sic/20p metal matrix
composites by PCD tool using response surface
methodology and desirability function, MachiningScience and Technology, 12, 529545 (2008).
[21] Pendse, D.M. and S.S. Joshi, Modeling and
optimization of machining process in discontinu-
ously reinforced aluminium matrix composites,
Machining Science and Technology,8, 85102 (2004).
[22] Rajmohan, T. and K. Palanikumar, Experimental
investigation and analysis of thrust force in drilling
hybrid metal matrix composites by coated carbide
drills, Materials and Manufacturing Processes, 26,
961968 (2011).
[23] Rajmohan, T. and K. Palanikumar, Optimization
of machining parameters for surface roughness and
burr height in drilling hybrid composites,Materials
and Manufacturing Processes, 27(3), 320328,
(2012), doi:10.1080/10426914.2011.58549.
[24] Ramulu, M., P.N. Rao and H. Kao, Drilling of
Al203 p /6061 metal matrix composite, Journal of
Materials Processing Technology, 124, 244254
(2002).
[25] Ross, P.J., Taguchi Techniques for Quality
Engineering, McGraw-Hill, NY (1998).
[26] Rouby, D. and P. Reynaud, Fatigue behaviour
related to interface modification during load cycling
in ceramic-matrix fibre composites, Composites
Science and Technology, 48, 109118 (1993).
[27] Roy, K.R., A Primer on Taguchi Method, Van
Nostrad Reinhold, NY (1990).
[28] Sahin, Y., M. Kok and H. Celik, Tool wear and
surface roughness of Al2O3 particlereinforced
aluminium alloy composites, Journal of Materials
Processing Technology, 128, 280291 (2002).[29] Seeman, M., G. Ganesan, R. Karthikeyan and
A. Velayudam, Study on tool wear and
surface roughness in machining of particulate
aluminium metal matrix composite response
surface methodology approach, International
Journal of Advanced Manufacturing Technology,
48(58), 613624 (2010).
[30] Taguchi, G., Taguchi on Robust Technology
Development Methods, ASME Press, NY (1993).
[31] Taguchi, G. and S. Konishi, Taguchi methods,
orthogonal arrays and linear graphs. In: Tools for
Quality Engineering, American Supplier Institute
(1987).
[32] Tomac, N. and K. Tonnessen, Machinability of
particulate aluminum metal matrix composites,
Annals of the CIRP, 41, 5558 (1992).
[33] Tosun, N., Determination of optimum parameters
for multi-performance characteristics in drilling
using grey relational analysis, International
Journal of Advanced Manufacturing Technology,
28, 450455 (2006).
[34] Tsai, T.-N., Modeling and optimization of heat
flow thermal profiling operation: A comparative
study, Journal of the Chinese Institute of Industrial
Engineers, 26(6), 480492 (2009).
[35] Tsao, C.C. and H. Hocheng, Taguchi analysis of
delamination associated with various drill bits indrilling of composite material, International
Journal of Machine Tools and Manufacture, 44,
10851090 (2004).
[36] Weinert, K., A consideration of tool wear mechan-
ism when machining metal matrix composites
(MMC), CIRP Annals, 42, 9598 (1993).
[37] Yaming, Q. and Z. Zehua, Tool wear and its
mechanism for cutting SiC particle reinforced alu-
minum matrix composites, Journal of Materials
Processing Technology, 100(13), 194199 (2000).
524 N. Muthukrishnanet al.
8/9/2019 Fabrication and Turning of Al-sic-b4c
12/12
AL/SIC/B4C
N. MuthukrishnanProfessor and Head, Department of Automobile Engineering, Sri Venkateswara College of Engineering,
Pennalur, Sriperumbudur 602 105, Tamil Nadu, India
T.S. Mahesh Babu
Associate Professor, Department of Aeronautical Engineering, Sathyabama University, Jeppiaar
Nagar, Rajiv Gandhi Road, Chennai 600 119, Tamil Nadu, India
R. Ramanujam
Associate Professor, Department of Mechanical Engineering, School of Mechanical Engineering,
Vellore Institute of Technology, Tamil Nadu, India
- 10% 5%
Al/SiC/B4C - MMC 65 200
MMC 1600
PCD
ANOVA 1
30
PCD ANOVA
Journal of the Chinese Institute of Industrial Engineers 525