Fabrication and Turning of Al-sic-b4c

8/9/2019 Fabrication and Turning of Al-sic-b4c

1/12

This article was downloaded by: [UQ Library]On: 02 June 2013, At: 09:11Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of the Chinese Institute of Industrial

EngineersPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tjci20

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

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss, actions,claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or

indirectly in connection with or arising out of the use of this material.
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/tjci20


2/12

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


3/12

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.


4/12

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.

Journal of the Chinese Institute of Industrial Engineers 517


5/12

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



6/12

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



7/12

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



8/12

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



9/12

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.



10/12

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


(2003).

[6] Davim, J.P., Study of drilling metal matrix com-

posites based on the taguchi techniques,Journal of


(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



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


(2003).

[14] Monaghan, J. and P. OReilly, The drilling of an

Al/SiC metal matrix composites, Journal of


(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


(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).



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

* [email protected]


Documents

Fabrication and Turning of Al-sic-b4c