Modeling Of

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MMMSMultidiscipline Modeling in Mat. and Str.4(2008) 345-358 www.brill.nl/mmms

C Koninklijke Brill NV, Leiden, 2008

MODELING OF MACHINING PARAMETERS TO

PREDICT SURFACE ROUGHNESS IN MACHINING

AL/SIC PARTICULATE COMPOSITES BY CARBIDE

INSERT

K.Palanikumar1

and R.Karthikeyan2

1Department of Mechanical & Production Engineering,Sathyabama University,Chennai-119, INDIA2Department of Manufacturing Engineering,Annamalai University, Chidambaram- 608001, India.

[email protected] , [email protected]

Received 6 April 2007; accepted 9 July 2007

Abstract

Aluminium silicon carbide reinforced metal matrix composite (Al/SiC-MMC) materials are rapidlyreplacing conventional materials in various automotive, aerospace and other industries. Accordingly,

the need for accurate machining of composites has increased enormously. The present work analyzesthe machining of Al/SiC composites for surface roughness. An empirical model has been developed tocorrelate the machining parameters and their interactions with surface roughness. Response surface

regression and analysis of variance are used for making the model. The developed model can beeffectively used to predict the surface roughness in machining Al/SiC-MMC composites. Theinfluences of different parameters in machining Al/SiC particulate composites have been analyzed

through contour graphs and 3D plots.

Keywords

Empirical model, Turning, Al/SiC particulate composites, Surface roughness

1. Introduction

Metal matrix composites (MMC) are attractive materials because of their superiority inmechanical and physical properties. These materials play a significant role in

applications for aerospace and automotive engineering components, and other diverse

industries [1]. Even though many engineering components made from aluminium matrix

composites are manufactured through casting, forging and extrusion, they frequently

require machining to achieve the desired dimensions and surface finish [2]. The issues of

machining MMCs have been addressed from various aspects. From the available

literature on particulate metal matrix composites, it is clear that the morphology,distribution of hard abrasive particles in aluminium matrix, volume fraction of the


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346 K.Palanikumar et al

reinforcement and the properties of matrix materials are all factors that affect the overallcutting process.

The machined surface quality of composite is one of the most important concernswhich affect the actual application of the composites [3]. Surface finish is an important

parameter in machining process and has serious attentions for many years. It has

formulated an important design feature in many situations such as parts subject tofatigue loads, precision fits, fastener holes and aesthetic requirements. In addition to

tolerances, surface roughness imposes one of the most critical constraints for selection

of machines and cutting parameters in process planning [4]. The surface roughness on

machining of Al/SiC MMC has been studied by several researchers. Yusuf Ozcatalbas

[5] have investigated the machinability behaviour of Al4C3 reinforced Al-basedcomposite produced by mechanical alloying technique. Kilickap et al. [6] have noticed

that the surface roughness increased with using higher feed rates in all machining

conditions. The effect of machining on the surface properties of SiC/Al composite isstudied by Yanming Quan and Bangyan Ye [3]. Manna and Bhattacharayya [7] have

observed abrupt change in surface roughness height (Rt). They have reported that theabrupt irregularity in the values of surface roughness heights may be due to the presence

of hard abrasive reinforced SiC particles which are rolling over the machined surface

during turning and ploughing on the surface finish. Published literature on machining ofAl/SiC indicates that only polycrystalline diamond (PCD) tools provide good tool life.

However, due to the high cost of PCD tools, ceramic and cemented carbide tools have

been proposed; ceramic tools are found to be unsatisfactory and the cemented carbide

tools have proved to be accepted tools [8-10]In the present work, machinability parameters which affect the surface roughness in

machining Al/SiC particulate composites using carbide tool has been modeled.Regression and analysis of variance (ANOVA) are used in order to study the main and

interaction effects of machining parameters, viz., % volume fraction of SiC, cuttingspeed, depth of cut and feed rate. The adequacy of the developed model is verified by

using coefficient of determination and residual analysis. The analysis of machiningparameters on surface roughness has been carried out through contour graphs and 3D

graphs.

2. Scheme of Investigation

For achieving the desired surface finish on the machined Al/SiC composite work piece,

the present investigation has been planned in the following steps:1) Identifying the predominant factors, which are affecting the surface roughness of

the machined Al/SiC composites and finding the upper and lower limits of the

factors identified;

2) Developing the experimental design matrix using design of experiments;

3) Performing the experiments as per design matrix;

4) Developing the model for surface roughness in machining of Al/SiC composites;

5) Checking the adequacy of the developed model using coefficient of determination

and residual analysis.


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Table 1 Control parameters and their levels

LevelsS.No Parameter Notation Unit

Low (-1) High (+1)

Cutting speed A m/min 50 150

% volume fraction of SiC B % 10 25

Depth of cut C mm 0.50 1.50

Feed rate D mm/rev 0.10 0.50

2.3 Performing the experiments

LM 25 aluminium alloy (7 Si 0.33 Mg 0.3 Mn 0.5 Fe 0.1 Cu 0.1Ni 0.2Ti) reinforced

with green bonded silicon carbide particles of size 25 m is used with different volume

fractions manufactured through stir casting route. The turning tests have been performed

on PSG 141 lathe. The tool holder used in the present study is PTCNR 2525 M16. The

cutting tool used is carbide tool TNMG 160404 IC 428. The design matrix used and thecorresponding responses are given in Table 2. In the previous study by the authors [9],

the average of three values is taken up for analysis. In the present study, the experiments

have been repeated for three times and the experimental results are presented in Table 2.

The average surface roughness (Ra), which is mostly used in industries, has been taken-up for this present study. The surface roughness has been measured by using

MITUTOYO SURF III surface tester. The specifications of the tester are as follows:

speed of traverse 2-5 mm/sec., range of traverse 2.5 mm, driving power 2VA, measuringrange 0.3-100 m.

Table 2 Design Matrix and Corresponding Output Response

Coded values Original valuesExpt.

No.A B C D A B C D

Surface roughness (Ra),

m

1 -1 -1 -1 -1 50 10 0.50 0.10 3.08 3.25 3.12

2 +1 -1 -1 -1 150 10 0.50 0.10 2.39 2.34 2.32

3 -1 +1 -1 -1 50 25 0.50 0.10 2.39 2.56 2.79

4 +1 +1 -1 -1 150 25 0.50 0.10 1.80 1.72 1.76

5 -1 -1 +1 -1 50 10 1.50 0.10 3.26 3.35 3.35

6 +1 -1 +1 -1 150 10 1.50 0.10 2.13 1.98 2.25

7 -1 +1 +1 -1 50 25 1.50 0.10 3.23 3.12 2.928 +1 +1 +1 -1 150 25 1.50 0.10 2.24 2.18 2.03

9 -1 -1 -1 +1 50 10 0.50 0.50 2.83 3.05 3.06

10 +1 -1 -1 +1 150 10 0.50 0.50 3.25 2.95 3.16

11 -1 +1 -1 +1 50 25 0.50 0.50 2.65 2.65 2.56

12 +1 +1 -1 +1 150 25 0.50 0.50 2.94 2.76 2.82

13 -1 -1 +1 +1 50 10 1.50 0.50 3.35 3.44 3.56

14 +1 -1 +1 +1 150 10 1.50 0.50 3.20 3.43 3.12

15 -1 +1 +1 +1 50 25 1.50 0.50 3.06 3.35 3.04

16 +1 +1 +1 +1 150 25 1.50 0.50 3.02 3.15 2.98


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Modeling of machining parameters to predict 349

2.4 Developing the model for surface roughness in machining of Al/SiCcomposites

For modeling of surface roughness with machining parameters regression has been used.

The regression model approach is more natural and intuitive [13]. For the machining

experiment, representing the surface roughness of the Al/SiC-MMC composite Y, theregression model can be expressed as

0 1 2( ) ( )Y A B = + + + + (2)

Where A, B is coded variables represent different machining parameters, thes areregression coefficients and represents error associated with the model. In the present

case the model chosen includes the effects of main and the interaction effect of all

factors. The model selected is expressed as follows.

0 1 2 3 4 5

6 7 8 9 10

11 12 13 14

15

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

( )

Y A B C D AB

AC AD BC BD CD

ABC ABD ACD BCD

ABCD

= + + + + +

+ + + + +

+ + + +

+

(3)

Where0 constant = .

1 , 2 ,, 15 - co-efficients that depends on main effects and interaction effects.

Normally in engineering problems, the higher order interactions (three factorinteractions and four factor interactions) are practically insignificant and hence they are

not considered in this present work. After removing the three and four factor interactions,

the eqs. (3) can be rewritten as

0 1 2 3 4 5 6

7 8 9 10

( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

Y A B C D AB AC

AD BC BD CD

= + + + + + +

+ + + +(4)

In eqn. (4), for finding the significant factors and their interactions analysis of variance

(ANOVA) has been used. Consequently, taking cognizance of the findings from theANOVA analysis which is presented in Table 3, the above equation has been modified

by removing the insignificant effects on the response function. The interaction between

factors AB, BD and CD (P, probability value is greater than 0.05, i.e, insignificant at

95% confidence level) have no effect on surface roughness, Eq.(4) can be rewritten as:

0 1 2 3 4 5 6 7( ) ( ) ( ) ( ) ( ) ( ) ( )Y A B C D AC AD BC = + + + + + + + (5)

The coefficients for the model are calculated and the final model developed

in coded unit is:Surface roughness,

2.8113 0.2313( ) 0.1562( ) 0.1363( ) 0.2462( )

0.0738( ) 0.2388( ) 0.0678( )

aR A B C D

AC AD BC

= + +

+ +(6)


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Table 3 ANOVA test results

Factors Sum of

Squares

Degrees of freedom

(DOF)

Adj.

Mean

Square

Fratio P

A 2.5669 1 2.5669 150.66 0.000

B 1.1719 1 1.1719 68.78 0.000

C 0.8911 1 0.8911 52.30 0.000

D 2.9107 1 2.9107 170.84 0.000

AB 0.0331 1 0.0331 1.94 0.172

AC 0.2611 1 0.2611 15.32 0.000

AD 2.7361 1 2.7361 160.59 0.000BC 0.2269 1 0.2269 13.3.2 0.001

BD 0.0091 1 0.0091 0.53 0.470

CD 0.0469 1 0.0469 2.75 0.106Error 0.6304 37 0.0170

Total 11.4839 47

AB, BD and CD are insignificant effects at 95% confidence level.

2.5 Checking the adequacy of the developed model using coefficient ofdetermination and residual analysis

The quantity R2

called as coefficient of determination is used to judge the adequacy of

regression models developed. 10 2 R . The R2 value is the variability in the dataaccounted for by the model in percentage [13].

SStotal

SSErrorR = 12 (7)

The coefficient of determination is 94.51% using the above expression for the present

investigation, which shows that good correlation exists between the experimental and

predicted values. Further more, Fig.1 shows the comparisons of values of surfaceroughness obtained from prediction and from actual experiment.

Fig.1 Correlation graph


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Diagnostic checking of the developed model has been performed by using residualanalysis. Residual is the value which is the difference between fitted values and

experimental values. The normal probability of residuals is presented in Fig.2(a).Normal probability plot is used to verify the normality assumption. As shown in figure,

the data are spread roughly along the straight line. It can be concluded that the data are

normally distributed [14]. Fig.2 (b) is residual versus fitted values. In the present model,

the residuals are within 0.2, which shows that good correlation exists between the

observed values and predicted values. Fig 2(c) shows histogram of the residuals. The

histogram gives the statistics about the residuals. Fig.2 (d) shows the residuals with

respect to the experimental runs. From the figure it can be asserted that a tendency to

have positive and negative residuals, which indicates the existence of a certaincorrelation. Also, the plot shows that the residuals are distributed evenly in both

positive and negative along the run. Hence, the data can be said to be independent. From

the residual plots, it has been observed that there is no abnormal condition existing

between the observed results and predicted results and hence, the developed model can

be highly significant and can be used for predicting the surface roughness of machined

Al/SiC components. For confirmation of the model developed, verification tests are

conducted at three selected conditions. Table 4 shows the test results. As shown in Table

4, the difference between the predicted surface roughnesses by the developed model is

small and hence, the model can be used for predicting surface roughness in machining

Al/SiC-MMC composites.

Residual

Percent

0.300.150.00-0.15-0.30

99

90

50

10

1

Fitted Valu e

Residual

3.63.22.82.42.0

0.2

0.1

0.0

-0.1

-0.2

Residual

Frequ

ency

0.20.10.0-0.1-0.2

8

6

4

2

0

Observation Order

Residual

454035302520151051

0.2

0.1

0.0

-0.1

-0.2

(a) Normal Probabi l ity Plot of the Residuals (b) Residuals Versus the Fitted Values

(c) Histogram of the Re siduals (d) Re sidual s Versus the Orde r of the Data

Fig.2 Plot of Residuals


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Table 4 Confirmation of trials and their comparison with the results

S.No Trial No. with

respect to Table 2

Experimental

values

Predicted surface

roughness by

Model

Error

%

1 5 3.31 3.33 0.60

2 10 3.10 3.22 4.19

3 15 3.24 3.17 2.16

3. Results and discussion

Study of the machining characteristics of Al/SiC particulate composites is of currentinterest due to the presence of an abrasive phase in the metal matrix. The presence of

SiC in metal matrix increases hardness, tensile strength and heat resistance [8]. During

machining, the hard abrasive SiC particles intermittently come into contact with cutting

tool and produces rapid tool wear which in turn affects the surface roughness of the

workpiece. In application areas, surface roughness plays a leading role. In Al/SiC

composite machining, many factors affect the surface finish of a machined part. Theparameters such as % volume of SiC, cutting speed, feed rate and depth of cut have good

influences on the surface finish of a machined component. The effect of different

parameters on surface roughness can be analyzed through standardized Pareto chart.

Fig.3 shows Pareto chart for surface roughness in machining of Al/SiC composites. ThePareto chart indicates the effect of different parameters and its interactions. This chart

displays the magnitude and its values. There is a reference line indicated in this chart.

The effect of any parameter which extends more than the reference line indicates the

significance of the variables used [15]. From figure, it can be asserted that the

parameters A, B, C, D, AC, AD and BC bars are extended beyond the 2.03 line and are

considered to be significant. Fig.4 and Fig.5 shows the microstructure of specimens used

for experimentation with 10% volume fraction and 25% volume fraction of SiC particles.

Figure clearly indicates the distribution of SiC particles in the aluminium matrix. The

distribution of SiC in the matrix greatly affects the surface roughness in machining ofAl/SiC composites [10].The micrograph of machined composite topography is presented

in Fig.6 and Fig.7. Fig.6 shows the profile of smooth surface in machining of Al/SiC

composites. In some places of the specimen, tiny surface cracks and very small pit holes

are formed and is shown in Fig.7. This is due to the insufficient penetration ofaluminium matrix in the fabrication of SiC composites through stir casting route. In the

figure, the white layer shows the transformation of aluminium powder during themachining of Al/SiC particulate composites.

The effects of different parameters on machining have been analyzed through contour

graph and surface plots. Fig. 8 shows the contour plots for different parameters. The

hold values for all the contour plots are middle values. This plots shows how a response

variable relates to two factors based on a model equation [15]. Normally, contour plot

shows only two factors at a time, while keeping the remaining factors at constant level.

These contours graphs can help in the prediction of the surface roughness at any zone ofthe experimental domain. It is clear from these figures that the surface roughness

reduces with the increase of cutting speed (A) and % volume fraction of SiC (B).

However, it increases with the increase of depth of cut (C) and feed (D).


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Fig.6 Micrograph of machined specimen

Fig.7 Tiny cracks and pit holes observed on the machined specimen

3.11

2.99

2.87

2.75

2.63

2.51

B*A

10-1

1.0

0.5

0.0

-0.5

-1.0

3.11

2.99

2.87

2.75

2.63

C*A

10-1

1.0

0.5

0.0

-0.5

-1.0

2.99

2.87

2.75

2.63

2.51

2.39

2.27

D*A

10-1

1.0

0.5

0.0

-0.5

-1.0

2.99

2.87

2.75

2.63

C*B

10-1

1.0

0.5

0.0

-0.5

-1.0

3.112.99

2.87

2.75

2.63

2.51

D*B

10-1

1.0

0.5

0.0

-0.5

-1.0

3.11

2.99

2.87

2.75

2.632.51

D*C

10-1

1.0

0.5

0.0

-0.5

-1.0

A, Cutting speed, m/min B, % volume fraction of SiC

C, Depth of cut , mm D, feed , mm/revHold values: Middle values.

Fig.8 Contour graphs for surface roughness


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The contour plots do not give the clear picture about the response surfaces. For clearunderstanding the effect of different parameters on three dimensional surface plots are

used. Fig. 9 shows the 3D response surfaces for surface roughness in the case of twovarying parameters by keeping the other parameters at middle level.

Fig. 9(a) shows the effects of cutting speed at different % volume fraction of SiC.

With a fixed value of % volume fraction of SiC, the surface roughness decreases withincrease in cutting speed. The relation between cutting speed and depth of cut is

presented in Fig. 9(b). The result shows that the increase in depth of cut at constant

cutting speed increases the surface roughness. The reason being, at high depth of cut, the

hard particles in the SiC have more contact with tool and this leads to more wear on the

cutting tool which in turn increases the surface roughness. Fig. 9(c) shows the effect ofcutting speed at different feed rate. From the figure, it can be asserted that the increaseof cutting speed reduces the surface roughness. On the contrary to the cutting speed, the

increase of feed rate increases the surface roughness. The relation between % volumefraction of SiC and depth of cut is presented in Fig. 9(d). The test results show that the

surface roughness increases with increase in depth of cut. The angle of increase is not assteep as that of feed. Because the effect of depth of cut is small than other main

parameters which is evident from the ANOVA analysis. Fig. 9(e) shows the effects of %

volume of SiC with respect to feed. With a fixed value of % volume fraction of SiC, thesurface roughness increases with increase in feed rate. The increase in feed increases the

heat generation during turning which in turn produces high surface roughness. From Fig.

9(f), it can be seen that the surface roughness increases with increase in both feed and

depth of cut. The increase in depth of cut increases the chatter on machine tool, which inturn produces high surface roughness.

(a)

% volume fraction of

Surfacerouhness

m

Cutting speed,


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(b)

(c)

(d)

Surfacerouhness,m

Depth of cut, mm

Cutting speed,

Cutting speed,

Feed rate,Surfacerou

ghness,m


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(e)

(f)Fig.9 Surface plots

From the ANOVA analysis, it is found that feed rate and cutting speed are the

parameters which affect the surface roughness in machining Al/SiC composites, while

the depth of cut is the least significant parameter. Further more, the surface roughnessdecreases with increase in cutting speed and % volume fraction of SiC and it increases

with increase in feed and depth of cut. The surface roughness produced on the Al/SiC -

MMC work piece is mainly due to the feed and cutting speed. The interaction between

the parameters also plays a vital role in machining Al/SiC composites. Among the 6-two

factor interactions considered, AC, AD and BC only have significant effect on the

machining of Al/SiC composites.

4. Conclusions

Using the experimental design, an empirical model has been developed to study the

factors which are having influence on the machining of Al/SiC composites.


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An empirical model has been developed to predict the surface roughness formachining of Al/SiC composites. The technique used is convenient to analyse the

different machining parameters which affect the surface of Al/SiC composites. This method is also useful in studying the main effects and interaction effects of

different influential combinations of machining parameters.

Feed is the factor, which has more influence on surface roughness in machiningAl/SiC- MMC composites

The analysis of the effect of machining parameters on surface roughness has been

performed by using contour graphs and 3D surface plots.

The interaction between cutting speed and feed rate has more influence compared

to other interactions on surface roughness in machining of Al/SiC-MMCcomposites.

The developed model can be used to predict the surface roughness in turning

Al/SiC-MMC composites at 95% confidence level. But the validity of the model islimited to the range of parameters considered for experimentation

The accuracy of the developed model is further improved by accommodating moreparameters and levels

References:

[1]. Abu Abdullah,Proceedings of Advances in Material Processing Technologies, (1998)374-384.[2]. M.El-Gallab, and M.Sklad,J.Mat. Proces. Tech, 83(1998)151-158.[3]. Yan`ming Quan, and Bangyan Ye,J.Mat.Proces.Tech, 138(2003)464-467.

[4]. X. Wang, and C.X, Feng,Int. J. Adv. Manuf. Tech., 20(5) (2002)348-356.[5]. Yuzuf Ozcatalbas, Mater. Design, 24(2003)215-221.[6]. E.Kilickap, O. Cakir, M.Aksoy and A.Inan,Journal of Mat. Proces. Tech., 164-165(2005)862-

867.[7]. A.Manna, and B.Bhattacharayya,J.Maert. Process. Tech., 140(2003)711-716.[8]. R.Karthikeyan Analysis and optimization of machining characteristics of Al/SiC particulate

composites, Ph.D. Thesis, Annamalai University, Chidambaram:2000.

[9]. K.Palanikumar, and R.Karthikeyan, Mater. Design, 28(2007)1584-1591.[10]. K.Palanikumar, and R.Karthikeyan, Mach. Sci. Technol, 10(2006)417-433.[11]. Alakesh Manna, and B.Bhattacharayya,Int. J. Adv. Manuf. Technol, 25(2005)850-856.[12]. S.Ravi, V.Balasubramanian, S.Babu and S.Nemat Nasser, Mater. Design, 25(2004)125-135.[13]. D.C.Montgomery,Design and analysis of experiments, John Wiley and Sons, NewYork; 1991.

[14]. Y.W.Shew and C.K. Kwong,Int. J. Adv. Manuf Technol., 20(2002)758-764.[15]. Meet MINITAB, Release 14 for Windows, Mini Tab Inc, USA, September, 2003.

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