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This article was downloaded by: [Aston University] On: 28 August 2014, At: 11:46 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Materials and Manufacturing Processes Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/lmmp20 Experimental Investigation and Optimization of Process Parameters in Milling of Hybrid Metal Matrix Composites A. Arun Premnath a , T. Alwarsamy b & T. Rajmohan a a Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University , Enathur, Kanchipuram , Tamilnadu , India b Government College of Technology , Coimbatore , India Published online: 12 Sep 2012. To cite this article: A. Arun Premnath , T. Alwarsamy & T. Rajmohan (2012) Experimental Investigation and Optimization of Process Parameters in Milling of Hybrid Metal Matrix Composites, Materials and Manufacturing Processes, 27:10, 1035-1044, DOI: 10.1080/10426914.2012.677911 To link to this article: http://dx.doi.org/10.1080/10426914.2012.677911 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

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Page 1: Experimental Investigation and Optimization of Process Parameters in Milling of Hybrid Metal Matrix Composites

This article was downloaded by: [Aston University]On: 28 August 2014, At: 11:46Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Materials and Manufacturing ProcessesPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/lmmp20

Experimental Investigation and Optimization ofProcess Parameters in Milling of Hybrid Metal MatrixCompositesA. Arun Premnath a , T. Alwarsamy b & T. Rajmohan aa Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University , Enathur,Kanchipuram , Tamilnadu , Indiab Government College of Technology , Coimbatore , IndiaPublished online: 12 Sep 2012.

To cite this article: A. Arun Premnath , T. Alwarsamy & T. Rajmohan (2012) Experimental Investigation and Optimization ofProcess Parameters in Milling of Hybrid Metal Matrix Composites, Materials and Manufacturing Processes, 27:10, 1035-1044,DOI: 10.1080/10426914.2012.677911

To link to this article: http://dx.doi.org/10.1080/10426914.2012.677911

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

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 anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: Experimental Investigation and Optimization of Process Parameters in Milling of Hybrid Metal Matrix Composites

Experimental Investigation and Optimization of Process Parameters

in Milling of Hybrid Metal Matrix Composites

A. Arun Premnath1

, T. Alwarsamy2

, and T. Rajmohan1

1Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, Enathur, Kanchipuram, Tamilnadu, India2Government College of Technology, Coimbatore, India

This article presents the experimental investigation and optimization of the machining parameters while milling of hybrid metal matrix

composites (MMCs) using tungsten carbide insert. Materials used for the present investigation are Al 6061-aluminium alloy reinforced with

alumina (Al2O3) of size 45 micron and graphite (Gr) of an average size 60 micron, which are produced through stir casting route. The response

surface methodology (RSM) is used for modeling, optimization, and analysis of dominant machining parameters, namely, spindle speed, feed

rate, depth of cut and weight fraction of Al2O3 particles in terms of their effect on cutting force and surface roughness during milling hybrid

MMCs. The experimental data were collected based on a four factor-three level full central composite design (CCD). The multiple regression

analysis using RSM was conducted to establish input–output relationships of the process. Mathematical models were developed and tested

for adequacy using analysis of variance and other adequacy measures using the developed models. The main and interaction effects of the

input variables on the predicted responses were also investigated. The predicted and measured values are fairly close, which indicates that

the developed models can be effectively used to predict the responses in milling of hybrid MMCs. The optimized milling process parameters

obtained by numerical optimization using RSM, ensure a minimum cutting force of 132.8N and surface roughness of 0.28mm. After the

milling test, a scanning electron microscope (SEM) was used to investigate the machined surface and tool wear.

Keywords Cutting force; Hybrid composites; Milling; Response surface methodology; Surface roughness.

INTRODUCTION

Metal matrix composites (MMC) have become a lead-ing material in composite materials, and particle rein-forced aluminium MMC have received considerableattention due to their excellent engineering properties.These materials are known as the difficult-to-machinematerial, because of the hardness and abrasive natureof reinforcement elements like silicon carbide (SiC)particles [1]. MMCs are gaining increasing attention forapplications in aerospace, defense, and automobileindustries. These materials have been considered foruse in automobile brake rotors and various componentsin internal combustion engines as the material used forsuch applications generally requires lighter weight andgreater wear resistance than those of conventional mate-rials. One factor that prevents more manufacturers fromembracingMMC technology is their difficulty in machin-ing [2]. Hybrid MMCs are obtained by reinforcing thematrix alloy with more than one type of reinforcementshaving different properties [3]. Hybrid MMC has raiseda keen interest in recent times for potential applicationsin aerospace and automotive industries because of theirsuperior strength-to-weight ratio and high temperatureresistance [4]. Graphite aluminiumMMC reinforced withalumina is easier to machine than composites reinforced

with both SiC and graphite, as reported by Songmeneet al. [5]. Graphite is generally greyish black, opaque,and has a lustrous black sheen. It has properties of bothmetals and nonmetals. Due to its interaxial shearingaction, it is used as solid lubricant. It has low value ofcoefficient of thermal expansion when it is used asreinforcement. Developments in reinforcing Al2O3 withaluminium alloy enhances the machinability, thus lower-ing the production cost and widening the industrialapplications, as observed by Al-Qutub et al. [6].The primary objective of a manufacturing operation is

to efficiently produce parts with high quality. Milling is awidely used machining process in manufacturing, inwhich face milling produces flat surfaces. In order toimprove the efficiency of the machining process and toreduce the total machining cost, optimum machiningparameters have to be obtained. The setting up ofmachining parameters relies strongly on the operator’sexperience. Optimum machining parameters are of greatconcern in manufacturing environment, where the econ-omy of machining operation plays a key role in competi-tiveness in the market. Machining damage due toexcessive cutting force results in the rejection of compositecomponents. Therefore, the necessity to predict the cut-ting force is essential in determining the process para-meter which results in machining damage. It is difficultto utilize the highest performance of a machine becausethere are too many adjustable machining parameters [7].Various machining parameters that normally affectmachining are speed, feed, depth of cut, tool geometry,etc. The surface roughness of a machined product plays

Received December 22, 2011; Accepted February 26, 2012

Address correspondence to A. Arun Premnath, Sri Chandrase-kharendra Saraswathi Viswa Maha Vidyalaya University, Enathur,Kanchipuram, Tamilnadu 631561, India; E-mail: [email protected]

Materials and Manufacturing Processes, 27: 1035–1044, 2012Copyright # Taylor & Francis Group, LLCISSN: 1042-6914 print=1532-2475 onlineDOI: 10.1080/10426914.2012.677911

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a significant role in determining the quality of the productin today’s manufacturing industry. Moreover, surfaceroughness is an important factor in determining themachinability of materials [8].Response surface methodology (RSM) provides a

powerful means to achieve breakthrough improvementsin product quality and process efficiency. From the view-point of manufacturing fields, this can reduce the num-ber of required experiments when taking into accountthe numerous factors affecting experimental results.RSM can show how to carry out the fewest experimentswhile maintaining the most important information [9].Optimum machining condition in turning Al356=SiC=20p MMCs for minimizing the surface roughness wasdetermined using the RSM-based desirability functionapproach [10]. Rajmohan and Palanikumar have usedRSM to predict thrust force and surface roughness indrilling hybrid composites using coated carbide drills[11, 12].Hou et al. [13] studied the influence of cutting speed,

feed rate, and depth of cut on ignition of AM50A mag-nesium alloy during high-speed face milling and foundthat ignition could easily be caused under some cuttingspeed and feed rate when the depth of cut was fixed,and ignition was rarely observed when the depth of cutwas 80 micron. Yung et al. [14] studied the influence ofthe machining parameters on the groove width and theaverage surface roughness during the end-milling ofhigh-purity graphite under dry machining conditions.The experiments are based on orthogonal array, and greyrelational analysis method is then applied to determinean optimal machining parameter setting. They concludedthat the feed rate is the most significant factor for themachining process. Junzhan et al. [15] studied the effectof cutting speed on the temperature rise of flank wearduring machining of AM50A magnesium alloy. Theresults show that the temperature first increases and thendecreases as the cutting speed increases. Tosun andHuseyinoglu [16] studied the effect of cutting parametersand cooling technique on the surface roughness duringmilling of 7075-T6 aluminum alloy. They used analysisof variance to statistically analyze the surface roughness.Ozcelik et al. [17] investigated the performance of dryand wet cutting during end milling of AISI 316 stainlesssteel. The milling experiments were carried out in twostages. In the first stage, feed rate was kept constant,and in the second stage, cutting speed was kept constant.The results obtained from the experimental studies con-firmed that the presence of a semisynthetic cutting fluidhas a negative impact in the milling of AISI 316. Theyfurther concluded that the failure of cutting tool in wetmilling is due to the intense thermal stress caused bythe application of semisynthetic cutting fluid. SureshKumar Reddy and Venkateswara Rao reported that bydirecting graphite powder on the tool and workpieceinterface as solid lubricant reduces the heat generatedand cutting force during milling of AISI 1045 steel byusing solid coated carbide end mill cutters [18].Hayajneh and Radaideh have studied the surface finish

of the machined surfaces in end milling by using spindle

speed, feed rate, and depth of cut as input data [19].The model building process by fuzzy subtracting cluster-ing-based system identification is capable of predictingthe surface finish for a given set of inputs. Further, theyverified the model by employing different sets of inputsduring end milling of aluminium alloy 390. Fratila andCaizar reported that speed is the dominant factor com-pared to feed and depth of cut in the face milling process[20]. Ubeyli et al. [21] studied the effect of feed rate on toolwear in milling of B4C reinforced aluminum MMCs pro-duced by liquid phase sintering method. Milling experi-ments were conducted with three different types ofcementide carbide tools: uncoated, double coated(TiNþTiAlN), and triple coated (TiCNþAl2O3þTiN)for three different feed rates. They concluded that higherfeed rates led to lower tool wear for all type of tools andcoated tools exhibited better performance than uncoatedtool with respect to flank wear. Rajesh et al. [22] reportedthat surface roughness of Al alloy is less as compared toAl alloy composite during turning by carbide as well aspolycrystalline diamond (PCD) inserts. Further, theyrecommended carbide insert for lower speed and PCDfor higher speed for low flank wear. Kok et al. [23]observed that the surface roughness value of the K10 toolwas higher than that of the TP30 tool. The surface rough-ness increased with an increase in the cutting speed, whileit decreased with an increase in the size and volume frac-tion of particles for both tools in all cutting conditions.Ding et al. [24] investigated the effects of cutting speed,feed, radial depth of cut, and axial depth of cut on surfaceroughness and residual stress during end-milling of AISIH13 steel with the different geometrical inserts. Theyfound that the best combination of surface roughnessand residual stress of the milled surface are not achievedwith the same geometrical insert and cutting parameters.Optimization of surface roughness during micromilling ofmachined surfaces using various cutting parameters suchas feed rate was studied by Cardoso and Davim [25].Most of the current literatures present experimental

results whenmilling ceramic-reinforcedMMCs. However,limited information is available on the milling of graphiticceramic-reinforced composites. The main objective of thisarticle is to study the influence of cutting speed, feed rate,depth of cut, and weight fraction of Al2O3 on cutting forceand surface roughness in face milling of hybrid compo-sites fabricated by stir castingmethod. These cutting para-meters are significant because these factors play animportant role in the performance of the machinedcomponent. Therefore, this study presents the results ofa detailed experimental investigation to determine theeffect of cutting parameters in milling hybrid composites.

EXPERIMENTAL

Materials and Methods

Three specimens of various weight fractions of Al2O3

(5, 10, 15%) and graphite (Gr) particles of 5%weight rein-forced with Al 6061 are considered. The composites usedin the present work were shaped in the form of plates ofthe size 250mm� 190mm� 12mm and were prepared

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by stir casting route. This process is similar to the fabri-cation method used in earlier research [11]. Stir castingis a liquid state method of composites fabrication, inwhich dispersed phase of Al2O3 and Gr particles aremixed in molten state of Al 6061. The chemical compo-sition of the Al 6061 alloy is shown in Table 1. The rein-forcements (Al2O3 and Gr) in powder form are preheatedin an electric furnace in order to remove the moisture con-tent present in the particles. Al 6061 rods are kept in agraphite crucible and are melted in an electric furnaceat a temperature of 730�C. When the Al 6061 rods aremelted completely, Al2O3 is added first using a spatulacovered with aluminium foil. After homogeneous mixingof alumina powder, graphite powder is added in theAl-Al2O3 mixture. The Al-Al2O3-Gr mixture is stirredusing a Gr agitator at a constant speed of 600 rpm forabout 15–20minutes. Care should be taken to avoid inho-mogeneous mixing of the mixture, as particle agglomer-ation and sedimentation during the melt may occur.The Al-Al2O3-Gr mixture composite is then injected intocast iron mould. The surfaces of moulds are rubbed usingemery. This is done to eliminate the exposure of moltencomposite to pockets of oxidized surface on the cast ironmoulds. Then they are preheated for about 20 minutes bybeing kept over the furnace. The composite mixture isthen allowed to solidify for about 15 minutes. Thus, threegrades of specimens were fabricated.

Experimental Design

RSM is a collection of statistical and mathematicaltechniques useful for developing, improving, and opti-mizing processes. RSM is an important branch of experi-mental design. RSM is a critical technology in developingnew processes and optimizing their performance. Theobjectives of quality improvement, including reductionof variability and improved process and product perfor-mance, can often be accomplished directly using RSM.The test was designed based on a four factor-three levelscentral composite design (CCD) with full replication.Table 2 shows the process variables and design levelsused. In order to optimize machining process parameters,the numerical optimization technique has been used.Using analysis of variance (ANOVA), the significanceof input parameters is evaluated. Design-Expert 8.0was used to establish the design matrix, to analyze theexperimental data, and to fit the experiential data to asecond-order polynomial. Sequential F test, lack-of-fittest, and other adequacy measures were used to checkperformance of the model.

Development of RSM-Based Models

In the present work, a mathematical model has beendeveloped for testing performances, namely, cutting

force and surface roughness, using RSM. RSM requiresexperiments to be conducted according to the design ofexperiments [11]. A three-level second-order face CCDwas adopted to study linear, quadratic, and two-factorinteraction effects. Four parameters, namely, spindlespeed, feed rate, depth of cut, and weight fractions ofAl2O3 were identified, and the ranges of the parameterswere selected based on the preliminary experiments.Table 3 shows 30 sets of coded conditions used to formthe design matrix of CCD. The CCD consists of six cen-ter points, and a star point. The variables at the inter-mediate (0) level constitute the center points, and thecombinations of each of the variables at either its lowest(�1) or highest (þ1) with the other two variables of theintermediate levels constitute the star points. The trialsin the design matrix indicate the sequence run number,and v, f, d, and w represent the notation used for thevariables of the spindle speed, feed rate, depth of cut,and weight fraction of Al2O3. In order to study theeffect of the process parameters, a second-order poly-nomial response surface can be fitted into the followingequation:

y ¼ b0 þXk

i¼1

bi xi þXQ

i¼1

bi j xi2 þX

i

X

j

bijxixj þ e ð1Þ

where ‘Y ’ is the corresponding response, and xi is thevalue of the ith machining process parameter. The termb is the regression co-efficient, and E the residual mea-sure, resulting from an experimental error in the observa-tions. This quadratic model works quite well over theentire factor space.

Experimental Procedure

Face milling is conducted on ARIX VMC 100 CNCvertical machining center. The experimental setup isshown in Fig. 1. A brief summary of the experimentalconditions are shown in Table 4. A tungsten carbideinsert and a cutter of 16mm diameter is employed. Allexperiments are performed under the dry machining con-dition. A three-component (Model: KISTLER 9257B)dynamometer platform was used to measure the cuttingforces (Fz). The force data were recorded by a specificallydesigned, very compact multichannel microprocessorcontrolled data acquisition system with a single A=D

TABLE 1.—Chemical composition of 6061 aluminium alloy (wt%).

Element Si Cu Mg Mn Fe Zn Sn Ti Pb Al

Wt% 0.80 0.35 0.8 0.02 0.01 0.008 0.01 0.01 0.02 97.9

TABLE 2.—Process variables and experimental design levels used.

Limits

Processvariables Notation Unit �1 0 þ1

Feed rate f mm=min 50 100 150

(mm=tooth) (0.016) (0.033) (0.050)

Speed v Rpm 1500 3000 4500

(m=min) (75) (150) (226)

Depth of cut d mm 0.1 0.3 0.5

Weight fraction of

Al2O3

w % 5 10 15

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converter preceded by a multiplexer. The individualanalog signals were first amplified and conditioned bycharge amplifier (Model: KISTLER 5070). After amplifi-cation and conditioning, the output signals were appliedto a multiplexer. Further, they are converted into digitalsignals by the A=D converter sequentially. The systemconsists of a sample-hold circuit, which enables it to hold

the analog signals till conversion of previous analog todigital (A=D) data takes place in the A=D converter.When the conversion is complete, the status line fromthe converter causes S=H to return to the sample modeand acquire signal from the next channel. On completionof acquisition, either immediately or upon receiving acommand, the S=H is switched to hold mode. The con-version begins again, and the multiplexer switches tothe subsequent channel. The data thus obtained can bestored into a memory element for further processing ordisplayed onto a display device. The data can also bestored onto a personal computer after completion ofexperiments. The surface roughness was measured usingthe Surfcoder surface profilometer with a cut off length

TABLE 3.—Central composite design (CCD) matrix, with coded and actual variables independent process variables.

Coded variables Actual variables

Exp. NoRunorder

Feed ratef (mm=min)

Speedv (rpm)

Depth of cutd (mm)

Weight fractionof Al2O3 w (%)

Feed ratef (mm=min)

Speedv (rpm)

Depth of cutd (mm)

Weight fractionof Al2O3 w (%)

1 25 �1 �1 �1 �1 50 1500 0.1 5

2 18 1 �1 �1 �1 150 1500 0.1 5

3 12 �1 1 �1 �1 50 4500 0.1 5

4 29 1 1 �1 �1 150 4500 0.1 5

5 19 �1 �1 1 �1 50 1500 0.5 5

6 15 1 �1 1 �1 150 1500 0.5 5

7 30 �1 1 1 �1 50 4500 0.5 5

8 26 1 1 1 �1 150 4500 0.5 5

9 20 �1 �1 �1 1 50 1500 0.1 15

10 7 1 �1 �1 1 150 1500 0.1 15

11 16 �1 1 �1 1 50 4500 0.1 15

12 5 1 1 �1 1 150 4500 0.1 15

13 13 �1 �1 1 1 50 1500 0.5 15

14 8 1 �1 1 1 150 1500 0.5 15

15 27 �1 1 1 1 50 4500 0.5 15

16 9 1 1 1 1 150 4500 0.5 15

17 22 �1 0 0 0 50 3000 0.3 10

18 3 1 0 0 0 150 3000 0.3 10

19 17 0 �1 0 0 100 1500 0.3 10

20 14 0 1 0 0 100 4500 0.3 10

21 10 0 0 �1 0 100 3000 0.1 10

22 23 0 0 1 0 100 3000 0.5 10

23 4 0 0 0 �1 100 3000 0.3 5

24 28 0 0 0 1 100 3000 0.3 15

25 11 0 0 0 0 100 3000 0.3 10

26 21 0 0 0 0 100 3000 0.3 10

27 1 0 0 0 0 100 3000 0.3 10

28 6 0 0 0 0 100 3000 0.3 10

29 24 0 0 0 0 100 3000 0.3 10

30 2 0 0 0 0 100 3000 0.3 10

FIGURE 1.—Experimental setup.

TABLE 4.—Summary of experimental conditions.

Machine ARIX VMC 100(CNC) Vertical Machining

Center

Tool insert Tungsten carbide(BDMT11T3) BP-KOREA

Nose radius 0.8mm

Tool holder

specification

BD11 D016-S16-Z2 BP-KOREA

I6mm diameter

Environment Dry

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of 2.5mm and sampling length of 0.8mm. An average ofthree measurements was used to characterize the surfaceroughness in each cutting condition.

RESULTS AND DISCUSSIONS

Results of the machining parameters were obtainedaccording to the CCD matrix of 30 experiments withcoded and actual independent process variables. Mea-sured responses are listed in Table 5. Analyzing the mea-sured responses by software, the fit summary outputindicates that the quadratic models are statisticallyrecommended for further response analysis.

Analysis of Developed Quadratic Model

The adequacy of the developed models were tested at95% confidence interval using the ANOVA technique,and the results of the quadratic order response surfacemodel fitting in the form of ANOVA are given inTables 6 and 7. The test for significance of the regressionmodels, the test for significance on individual model coef-ficients, and the lack-of-fit test were performed. Tables 6and 7 also show the other adequacy measures R2,adjusted R2, and predicted R2. The coefficient of deter-mination R2 indicates the goodness of fit for the models,which provides a measure of variability in the observedresponse values and can be explained by the controllablefactors and their interactions. In this case, all the valuesof coefficient of determination R2 are nearly equal to 1.The adjusted coefficient of determination R2 is a vari-ation of the ordinary R2 statistic that reflects the numberof factors in the model. The entire adequacy measures arecloser to 1, which is in reasonable agreement and indicateadequatemodels. Values of ‘‘Probability>F’’ in Tables 6and 7 for all models are less than 0.0500, and these indi-cate that all models are significant. In all cases, the‘‘Lack-of-fit’’ value implies that the ‘‘Lack-of-fit’’ is notsignificant relative to the pure error. In fact, Nonsignifi-cant lack of fit is desirable. The final mathematical mod-els in coded factors=variable forms to predict the cutting

TABLE 5.—Experimental measured responses.

Exp. No Run order Cutting force (N) Surface roughness (mm)

1 25 55.42 1.53

2 18 182.65 1.84

3 12 144.9 0.42

4 29 270.81 0.7

5 19 62.57 1.58

6 15 187.22 1.9

7 30 165.55 0.62

8 26 277.8 0.84

9 20 98.75 1.11

10 7 230.23 1.68

11 16 120.76 0.28

12 5 289.11 0.67

13 13 80.87 1.28

14 8 190.55 1.73

15 27 150.85 0.32

16 9 286.42 0.73

17 22 127.07 0.98

18 3 247.67 1.32

19 17 133.89 1.62

20 14 208.72 0.51

21 10 167.87 1.19

22 23 179.05 1.27

23 4 158.45 1.22

24 28 174.56 1.1

25 11 165.03 1.17

26 21 170.22 1.2

27 1 172.5 1.21

28 6 175.72 1.23

29 24 176.54 1.26

30 2 177.76 1.29

TABLE 6.—ANOVA for response surface reduced quadratic model of cutting force.

Source Sum of squares df Mean square F value p-value prob.>F

Model 104695.1 14 7478.223 112.8122 <0.0001 significant

f-feed 74204.93 1 74204.93 1119.414 <0.0001

v-speed 26662.79 1 26662.79 402.2198 <0.0001

d-depth 23.07469 1 23.07469 0.348092 0.5640

w-Weight 756.994 1 756.994 11.41958 0.0041

fv 150.3076 1 150.3076 2.267455 0.1529

fd 313.467 1 313.467 4.728786 0.0461

fw 189.3376 1 189.3376 2.85624 0.1117

vd 636.0484 1 636.0484 9.595066 0.0074

vw 968.1432 1 968.1432 14.60486 0.0017

dw 302.0644 1 302.0644 4.556773 0.0497

f2 436.2755 1 436.2755 6.581406 0.0215

v2 24.71579 1 24.71579 0.372848 0.5506

d2 2.258243 1 2.258243 0.034067 0.8560

w2 161.2322 1 161.2322 2.432257 0.1397

Residual 994.3367 15 66.28911

Lack of fit 880.2586 10 88.02586 3.858141 0.0746 not significant

Pure error 114.0781 5 22.81562

Cor total 105689.5 29

R2 0.990591903

Adj R2 0.981811012

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force and surface roughness are given below:

Cutting Force ¼ �52:97598þ 0:11861 � f þ 0:033874 � vþ 44:32756 � d þ 10:64688 � þ4:08667e

� 005 � f � v� 0:44262 � f � dþ 0:013760 � f �wþ 0:021017 � v � d� 1:03717e� 003 � v �w� 4:34500 � d �wþ 5:19056e

� 003 � f 2 � 1:37271e� 006 � v2

� 23:33991 � d2 � 0:31554 �w2 ð2Þ

Roughness ¼ þ1:54903þ 5:80961e� 003 � f � 3:94481e

� 005 � v� 0:34711 � d � 0:026779 � w� 2:91667e� 007 � f � v� 9:37500e

� 004 � f � d þ 1:72500e� 004 � f � wþ 2:29167e� 005 � v � d þ 3:91667e

� 006 � v � w� 8:12500e� 003 � d � w� 1:36140e� 005 � f 2 � 5:29045e

� 008 � v2 þ 1:14912 � d2 � 9:61404e

� 004 � w2: ð3Þ

Optimization of Machining Parameters by theNumerical Optimization Method

Speed, feed rate, depth of cut, and weight fraction ofAl2O3 are the major milling parameters that are con-sidered in these experiments. For reducing the cuttingforce and surface roughness, the optimization of process

parameters is required. The optimization analysis iscarried out using RSM-based desirability analysis. Thegoal set, lower and upper limits used, weights used, andimportances of the given factors are presented inTable 8. In the desirability-based approach, different bestsolutions were obtained. The solution with the highestdesirability is preferred. The best three solutions obtainedfor the optimization are presented in Table 9. The optimi-zation is carried out for a combination of goals. The goalsapply to the factors and responses. The goals used forresponses are ‘‘minimize,’’ and the goals used for the fac-tors are ‘‘within range.’’ A weight can be assigned to agoal to adjust the shape of its particular desirability func-tion. The solutions are sorted out with the most desirablebeing the first. By default, the input factors are set inrange and thus prevent extrapolation.Estimated contour plot for Desirability is presented

in Fig. 2. These response contours can help in the predic-tion of desirability at any zone of the experimentaldomain [12]. The interaction effect of machining vari-ables on cutting force is presented as a three-dimensional(3D) graph in Figs. 3–5. These figures reveal that cutting

TABLE 7.—ANOVA for response surface reduced quadratic model of surface roughness.

Source Sum of squares df Mean square F value p-value prob>F

model 5.673319 14 0.405237 171.9652 <0.0001 Significant

f-feed 0.601339 1 0.601339 255.1824 <0.0001

v-speed 4.6818 1 4.6818 1986.755 <0.0001

d-depth 0.040139 1 0.040139 17.03322 0.0009

w-weight 0.170139 1 0.170139 72.19965 <0.0001

fv 0.007656 1 0.007656 3.248984 0.0916

fd 0.001406 1 0.001406 0.596752 0.4518

fw 0.029756 1 0.029756 12.62728 0.0029

vd 0.000756 1 0.000756 0.32092 0.5794

vw 0.013806 1 0.013806 5.85878 0.0287

dw 0.001056 1 0.001056 0.448227 0.5134

f2 0.003001 1 0.003001 1.273613 0.2768

v2 0.036712 1 0.036712 15.57879 0.0013

d2 0.005474 1 0.005474 2.322932 0.1483

w2 0.001497 1 0.001497 0.635148 0.4379

Residual 0.035348 15 0.002357

Lack of fit 0.026014 10 0.002601 1.393621 0.3752 not significant

Pure error 0.009333 5 0.001867

Cor total 5.708667 29

R2 0.993808083

Adj R2 0.988028961

TABLE 8.—Goals set and limits used for the optimization.

Parameter andresponse Goal

Lowerlimit

Upperlimit

Lowerweight

Upperweight Importance

Feed rate Is in range 50 150 1 1 3

Speed Is in range 1500 4500 1 1 3

Depth of cut Is in range 0.1 0.5 1 1 3

Weight fraction

of Al2O3

Is in range 5 15 1 1 3

Cutting force Minimize 55.42 289.11 1 1 3

Surface roughness Minimize 0.28 1.9 1 1 3

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forces become larger at higher feed rates compared tothat at lower feed rates. The feed rate and speed are thepredominant factors influencing cutting force. Theobtained results are in line with those of Ubeyli et al.[26]. From the ANOVA analysis, it is found that feed isthe most significant parameters. It can also be observedthat there is less predominant variation in cutting forceon increasing spindle speed for all feed rates considered.This is because the generation of temperature is high at ahigher cutting speed, which softens the material, and thecutting force thus decreases. The addition of 5% graphitereduces the cutting forces significantly, which is attribu-ted to the solid lubricating property of the graphite par-ticles. Brown and Surappa [27] reported that graphiteparticles reduce the interfacial friction between the tooland the workpiece materials, which leads to the reductionin cutting force. Thus the inclusion of graphite particlesin the composites contributes positively to its perfor-mance with respect to the cutting force.The interaction effect of machining variables on sur-

face roughness is presented as a 3D graph in Figs. 6–8.It is clear from the figures that the surface roughnessdecreases with an increase in the cutting speed and weightfraction of alumina; however, it increases with anincrease in the feed and depth of cut. Similar results arealso obtained by Basavarajappa et al. [28]. The cuttingspeed plays an important role in deciding the surfaceroughness [20]. At high cutting speeds, the surface rough-ness decreases. At low speeds, a built-up edge (BUE) is

formed, and also the chip fracture produces a roughsurface. As the speed increases, the BUE vanishes, chipfracture decreases, and hence the roughness decreases.The increases in feed proportionally increase the surfaceroughness. The increase in the feed rate increases the nor-mal load on the tool and also generates more heat, whichin turn, increases the surface roughness. The weight frac-tion of Al2O3 particles plays an important role in decid-ing the surface roughness [29]. The increase in weightfraction of Al2O3 decreases the surface roughness. Withincrease in weight fraction, the rate of decrease inroughness is reduced due to increased brittleness andsubsequent disappearance of BUE [30]. The presence ofgraphite particles increases the surface roughness, asthe crushed graphite particles form a deep valley and,hence, increase the surface roughness of the material [31].From the ANOVA analysis, it is found that speed is

the most significant parameter. Among the interactions,the interaction between feed rate and weight fraction ofAl2O3 is a more significant factor than other interac-tions. Hence, the overall performance is improved whenspeed of 4,500 rpm, feed rate of 50mm=min, depth of cutof 0.10mm, and 15% Al2O3 which could result in amaximum desirability for the minimum cutting forceof 132.8N and surface roughness of 0.28mm.After machining, scanning electron microscope (SEM)

is used to examine the machined surface, and the micro-graph is presented in Fig. 9. The figure reveals that somemicroscratches and feed marks exist when machining athigher feed rates. This was generally attributed to the

TABLE 9.—Best global solution for optimization.

No. Feed rate f (mm=min) Speed v (rpm) Depth of cut d (mm) Wt fraction of Al2O3 w (%) Cutting force (N) Roughness (mm) Desirability

1 50.04 4406.52 0.10 15.00 132.8 0.280007 0.875

2 50.00 4411.13 0.10 14.96 132.89 0.280834 0.874

3 50.03 4404.70 0.11 15.00 133.105 0.280004 0.874

FIGURE 2.—Estimated contour plot on desirability (color figure available

online).

FIGURE 3.—3D graph shows the interaction effects of cutting force on

speed vs. depth of cut when feed rate¼ 50mm=min and weight fraction

alumina¼ 10% (color figure available online).

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generation of force and high temperatures at the cuttingzone and also to the deformation on the flank surface andadherence on the workpiece material. Figure 10 showsSEM micrograph of the wear patterns of tungstencarbide insert. In machining of conventional metals, thecutting tools are abraded by the strain-hardened chipsgenerated in the machining process. Since the pressurebetween the workpiece and the cutting tool is quite high,much heat is generated. The BUE formed in tungstencarbide insert is clearly evident from Fig. 10. It clearlyshows the adhesion of material on the flank surface. Itcan be seen that the flank wear is caused by the abrasive

nature of the hard Al2O3 particles present in the work-piece materials. Because of the high pressure generatedat the tool-work piece interface, the worn flank encour-aged the adhesion of the workpiece material.

Confirmation Experiment

The mathematical models developed for cutting forceand surface roughness, given by Eqs. (2) and (3), havealready been validated statistically through the F-test.These fitted models were found to be significant. Inaddition to the statistical validation, the developed

FIGURE 5.—3D graph shows the interaction effects of cutting force on

speed vs. depth of cut when feed rate¼ 150mm=min and weight fraction

alumina¼ 10% (color figure available online).

FIGURE 4.—3D graph shows the interaction effects of cutting force on

speed vs. depth of cut when feed rate¼ 100mm=min and weight fraction

alumina¼ 10% (color figure available online).

FIGURE 6.—3D graph shows the interaction effects of surface roughness on

feed vs. weight fraction of alumina when speed¼ 1,500 rpm and depth of

cut¼ 0.3mm (color figure available online).

FIGURE 7.—3D graph shows the interaction effects of surface roughness on

feed vs. weight fraction of alumina when speed¼ 3,000 rpm and depth of

cut¼ 0.3mm (color figure available online).

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Page 10: Experimental Investigation and Optimization of Process Parameters in Milling of Hybrid Metal Matrix Composites

models have also been validated by conductingconfirmation experiments. The plan for the confirmationexperiment and the values of the cutting force and surfaceroughness obtained by the confirmation experiment andthose predicted through the optimization approach aregiven in Table 10. It is clearly indicated that the predictedvalues are very close to those of the experimental results.

CONCLUSION

The following conclusions are drawn from theexperimental results during the milling of Al hybridcomposites using a tungsten carbide insert under differ-ent cutting conditions:

1. The predicted and measured values are fairly close,which indicates that the developed mathematicalmodels can be effectively used to predict the responsein the milling of hybrid MMCs.

2. The ANOVA clearly shows that the feed rate andspeed are the two dominant parameters and, hence,contribute towards improving the surface qualityand reducing the cutting force.

3. The optimization results indicate that a speed of4,500 rpm, feed rate of 50mm=min, depth of cut of0.1mm, and 15% Al2O3 could result in a minimumcutting force of 132.8N and surface roughness of0.28mm within the domain of experiments.

4. An analysis of the machined surface shows the exist-ence of some microscratches and feed marks whilemachining at higher feed rates.

5. SEM is used to investigate the tool wear. Flank wearis caused by the abrasive nature of the hard Al2O3

particles present in the workpiece materials. Becauseof the high pressure generated at the tool-workpieceinterface, the worn flank is encouraged by adhesionof the work piece material.

ACKNOWLEDGMENT

A. A. Premnath gratefully acknowledges the financialsupport of Sri Chandrasekharendra Saraswathi ViswaMahavidyalaya Kancheepuram, India, for carrying outthis research work.

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FIGURE 10.—SEM micrograph of carbide inserts.

FIGURE 9.—SEM image illustrating the machined surface when speed¼1,500 rpm, feed rate¼ 150mm=min.

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