manufacturers are facing the challenges of higher Quality and productivity are two important . Productivity can be interpreted in terms of material removal rate in the machining operation and quality represents satisfactory yield in terms of product characteristics as desired by the customers. but conflicting criteria in any machining operations. In order to ensure high productivity, extent of quality is to be compromised.

1. International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
ISSN: 2395-1303 http://www.ijetjournal.org Page 17
Modeling and Analysis of Machining Characteristics of Metal
Matrix Composite in Milling Process
N.Keerthi1, N.Deepthi2,N.Jaya Krishna3
1, 2, 3
Mechanical Engineering, Annamacharya Institute of Technology and sciences Autonomous and Rajampet
I.INTRODUCTION
In the area of globalization
manufacturers are facing the challenges of
higher Quality and productivity are two
important . Productivity can be interpreted
in terms of material removal rate in the
machining operation and quality represents
satisfactory yield in terms of product
characteristics as desired by the customers.
but conflicting criteria in any machining
operations. In order to ensure high
productivity, extent of quality is to be
compromised. It is, therefore, essential to
optimize quality and productivity
simultaneously. Dimensional accuracy, form
stability, surface smoothness, fulfillment of
functional requirements in prescribed area of
application etc. are important quality
attributes of the product. Increase in
productivity results in reduction in
machining time which may result in quality
loss. On the contrary, an improvement in
quality results in increasing machining time
thereby, reducing productivity. Therefore,
there is a need to optimize quality as well as
productivity. Optimizing a single response
may yield positively in some aspects but it
may affect adversely in other aspects. The
problem can be overcome if multiple
objectives are optimized simultaneously. It
is, therefore, required to maximize material
removal rate (MRR), and to improve
product quality simultaneously by selecting
an appropriate (optimal) process
environment. To this end, the present work
deals with multi-objective optimization
philosophy based on Taguchi-Grey
relational analysis method applied in CNC
end milling operation.
II. STIR CASTING PROCESS:
In a stir casting process, the
reinforcing phases are distributed into
molten matrix by mechanical stirring. Stir
casting of metal matrix composites was
initiated in 1968, hen S. Ray introduced
alumina particles into aluminum melt by
stirring molten aluminum alloys containing
the ceramic powders. Mechanical stirring in
the furnace is a key element of this process.
The resultant molten alloy, with ceramic
particles, can then be used for die casting,
permanent mold casting, or sand casting.
Stir casting is suitable for manufacturing
composites with up to 30% volume fractions
of reinforcement. The cast composites are
sometimes further extruded to reduce
porosity, refine the microstructure, and
homogenize the distribution of the
reinforcement. A major concern associated
with the stir casting process is the
segregation of reinforcing particles which is
caused by the surfacing or settling of the
reinforcement particles during the melting
and casting processes.The final distribution
of the particles in the solid depends on
material properties and process parameters
such as the wetting condition of the particles
with the melt, strength of mixing, relative
density, and rate of solidification.The
distribution of the particles in the molten
matrix depends on the geometry of the
mechanical stirrer, stirring parameters,
placement of the mechanical stirrer in the
melt, melting temperature, and the
characteristics of the particles added.
RESEARCH ARTICLE OPEN ACCESS

2. International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
ISSN: 2395-1303 http://www.ijetjournal.org Page 18
III. COMPOSITE MATERIAL
PREPARATION:
For composite material selection of
Matrix and reinforcement are of prime
importance. For this research work we had
selected material as follows.
Matrix
Aluminium alloy 2000, 6000 and
7000 series are used for fabrication of the
automotive parts. PAMC under study consist
of matrix material of aluminium alloy
Al6082 whose chemical composition is
shown in the Table. An advantage of using
aluminium as matrix material is casting
technology is well established, and most
important it is light weight material.
Aluminium alloy is associated with some
disadvantages such as bonding is more
challenging than steel, low strength than
steel and price is 200% of that of steel. But
with proper reinforcement and treatment the
strength can be increased to required level.
Reinforcement
Particles of Al2O3, magnesium and zinc
are used as reinforcement.
Table 1.Specifications Of Cnc Milling
Machine
Fig 1.Expermential set up ( CNC Machnie)
IV. WORK MATERIALPREPARATION
The work material is cut as required sizes of
90x90x12 mm from Al6082-Mg-Zn alloy
matrix raw stock to perform milling
operation on them. These work materials are
prepared by using the stir casting process.
Technical specifications
Travels
X axis 225 mm
Y axis 150 mm
Z axis 115 mm
Distance between Table top and
spindle nose
70-185 mm
Table size 360mm*132 mm
Spindle
Spindle motor capacity 0.4 kw
Programmable spindle speed 150-3000rpm
Spindle nose taper BT 30
Accuracy
Positioning 0.010 mm
Repeatability +_0.005 mm
Feed Rate
Programmable feed rate X Y Z
axis
0-1.2 mm/min
CNC controller
Control system PC based 3 Axis
continuous path
Power source 230V, single phase, 50 Hz

3. International Journal of Engineering and Techniques
ISSN: 2395-1303
Fig 3 Strining of metals
Fig 4 Melting of alloys
Fig .5Pouring of molten metal into mould
The required work materials are prepared by
using the stir casting process with three
different compositions of aluminum
zinc alloy matrix.
Fig 6 Talysurf meter
V. EXPERIMENTAL PROCEDURE
 The Input parameters of the milling
process and their levels (each input
parameter has three levels) are listed
based on previous works (Table 1.2).
International Journal of Engineering and Techniques - Volume 2 Issue 4, July
1303 http://www.ijetjournal.org
Melting of alloys
Pouring of molten metal into mould
The required work materials are prepared by
using the stir casting process with three
different compositions of aluminum-copper-
EXPERIMENTAL PROCEDURE
The Input parameters of the milling
process and their levels (each input
parameter has three levels) are listed
based on previous works (Table 1.2).
 Milling operation is performed on Al
6082-Cu-Zn alloy work material
according to full factorial design
using CNC milling machine.
 The surface roughness values are
measured using Talysurf meter .
 The Metal removal rate is calculated
by means of formula is given by
Table 2. Process parameters and their
levels
Symbol
Machining
parameter Unit
A Spindle speed rpm
B Feed Mm/min
C Depth of cut mm
VI. Results from ANN
Table 3. Experimental data
Speed Feed Depth of cut
1800 75 0.75
1400 75 0.5
1400 100 0.75
1600 75 1
1600 100 0.5
1400 50 0.5
1400 50 0.75
1400 75 1
1600 75 0.5
July – Aug 2016
Page 19
Milling operation is performed on Al
Zn alloy work material
ull factorial design
using CNC milling machine.
The surface roughness values are
measured using Talysurf meter .
The Metal removal rate is calculated
by means of formula is given by
Process parameters and their
Level1 Level2 Level3
1400 1600 1800
Mm/min 50 75 100
0.5 0.75 1
Experimental data
Depth of cut MRR Ra
0.75 557.413 2.494
0.5 369.003 2.325
0.75 744.909 1.469
757.95 2.774
0.5 502.26 1.399
0.5 249.79 0.866
0.75 377.99 2.46445
738.91 4.1435
0.5 376.175 1.0125

4. International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
ISSN: 2395-1303 http://www.ijetjournal.org Page 20
Table 4. Comparison between
Experimental and values
VI.RESULTS FROM TAGUCHI:
From the graph the results predicted are
Graph for MRR
1600 75 0.5 376.175 1.0125
1800 50 0.75 373.567 0.912
1400 100 1 1013.34 2.304
1600 50 0.75 375 .8245
1600 50 1 499.583 1.88
1400 50 1 499 1.85
1800 50 0.5 246.79 0.9055
1800 100 0.75 749.375 1.405
1600 50 0.5 248.18 0.9975
1400 100 0.5 503.94 1.435
1800 75 1 750.469 2.858
1600 100 0.75 751.252 1.169
1800 50 1 508.345 2.6935
1800 100 1 998.17 1.441
1800 75 0.5 370.461 1.3735
1800 100 0.5 492.935 1.3645
1600 75 0.75 562.5 1.368
1600 100 1 1021.27 1.6585
1400 75 0.75 565.82 2.5195
Actual
MRR
Predicted
MRR
Actual
Ra
Predicted
Ra
557.413 510.65 2.494 2.152
369.003 323.63 2.325 2.048
744.909 706.395 1.469 1.568
757.95 710.652 2.774 2.568
502.26 461.857 1.399 1.5144
249.79 220.36 0.866 1.095
377.99 312.265 2.46445 2.124
738.91 685.32 2.1435 2.895
376.175 325.822 1.0125 1.231
373.567 315.236 0.912 1.125
1013.34 995.495 2.304 2.0135
375 304.23 1.8245 1.645
499.583 436.87 1.88 1.624
499.375 425.963 2.888 2.235
246.79 213.262 0.9055 1.236
749.375 702.965 1.405 1.0312
248.18 224.586 0.9975 1.321
503.94 449.56 1.435 1.125
750.469 706.95 2.858 2.452
751.252 680.569 1.169 1.523
508.345 487.95 2.6935 2.158
998.17 945.562 1.441 1.875
370.461 335.26 1.3735 1.468
492.935 482.62 1.3645 1.568
562.5 521.354 1.368 1.647
1021.27 978065 1.6585 1.425
565.82 524.52 2.5195 2.145

5. International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
ISSN: 2395-1303 http://www.ijetjournal.org Page 21
Optimum input parameters are
Speed;1400rpm
Feed:100mm/min
Doc:1mm
Graph for Ra
The optimum set of input parameters are:
Speed;1400rpm
Feed: 50mm/min
Doc:0.5mm
RESULTS FROM ANOVA:
Anova method is used to find the effect of
input parameters on output parameters. The
effect is individually find out are
Table 5.Anova For MRR
Source DF SS MS VARIEN
CE
St.Dev %
TOTAL
Speed 2 125.3480 62.67
40
1882.367 10.253 2.10
Feed 6 642023.8
445
10700
3.974
1
22652.22
0
150.507 35.71
Doc 18 702851.6
382
39047
.3132
39047.31
3
197.604 62.29
From the table it is found that
 The MRR is mostly influenced by
DOC about 62.29 % of MRR is
influenced by DOC
 This is because by increasing the
DOC the volume of material
removed is increased.
Table 6. ANOVA For surface
roughness:
Source DF SS MS VARI
ENCE
St.ev %
TOTAL
Speed 2 2.46
75
1.338 0.064 0.253 9.54
Feed 6 3.95
46
0.6591 0.07 0.163 3.99
Doc 18 10.4
205
0.5789 0.579 0.761 86.47
 Ra is mostly effected by Depth of cut
.it is almost effected by 87%
 We already know that surface
roughness is more if we remove
more amount of material in single
cut.
VII. CONCLUSIONS
 In the present work an Artificial
Neural Network (ANN) model has
been developed to predict the
response (output) parameters
surface roughness, and material
removal rate in Milling process.
 The controllable parameters such as
cutting speeds, feed rate and depth
of cut which influence the responses
are identified and analyzed.
 The optimum combinations of
(input) process parameters are
determined by Taguchi method.
 For producing low value of surface
roughness, the optimum parameter
values are spindle speed (V) 1400,
feed (f) 50, Depth of cut (t)0.5.
321
-2
-4
-6
-8
321
321
-2
-4
-6
-8
A
MeanofSNratios
B
C
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better

6. International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
ISSN: 2395-1303 http://www.ijetjournal.org Page 22
 For high value of material removal
rate, the optimum parameter values
are spindle speed (V) 1400, feed (f)
1, depth of cut (t) 1.
 The analysis of variance (ANOVA)
is also employed to find the
contribution of input parameters on
output parameters.
 Surface roughness is mostly
affected by Depth of cut.
 Material removal rate is mostly
affected by Depth of cut.
VIII. FUTURE SCOPE
 Similar type of techniques is used
for engineering materials like
different processes.
 The Artificial Intelligence Fuzzy
logic can also be used for
prediction of machining responses.
 ANFIS can also be used for
prediction of machining responses.
REFERENCES
1. G.Vijaya Kumar and P.Venkataramaiah-
In This paper is focused on selection of
optimal parameters in drilling of Aluminum
Metal Matrix Composites (AMMC) using
“Grey Relational Analysis”, Volume 3,
Issue 2, May-August (2012), pp. 462-469
2. Ghani J.A., Choudhury I.A. and Hassan
H.H. (2004) ‘Application of Taguchi
method in the optimization of end milling
parameters’, Journal of Materials Processing
Technology, Vol. 145, No. 1, pp. 84–92
3.A. Riaz Ahamed, Paravasu Asokan ,
Sivanandam Aravindan and M. K. Prakash
– performed a drilling of hybrid Al-5%SiCp-
5%B4Cp metal matrix composites with HSS
drills is possible with lower speed and feed
combination, volume 2, pp. 324-345
4. Yang and Chen (2001) attempted to
determine optimal machining parameters for
improving surface roughness performance of
machined Al 6061 in end-milling operation,
Ann CIRP , 1993 42(1):107–109.
5. Kadirgama- Optimization of surface
roughness in aluminum alloys uing RSM
and RBFN. J Mater Process Techno, 1995
48:291–297.
6. N.Deepthi, P.Sivaiah, K.Nagamani -
Optimization and analysis of parameters for
multi-performance characteristics in drilling
of Al6061 by using Taguchi grey relational
analysis and ANOVA analysis, volume 1,
issue 4, July 2013
7. A. Al-Refaie, L. Al-Durgham, and N.
Bata-optimizing the proposes of an approach
for Optimizing multiple responses in the
Taguchi method using regression models
and grey relational analysis.
8. S. R. Karnik, V. N. Gaitonde and J. P.
Davim [12] - performs a comparative study
of the Artificial Neural Network (ANN) and
Response Surface Methodology (RSM)
modeling approaches for predicting burr size
in drilling
9. Ashok Kr. Mishra, Rakesh Sheokand and
Dr. R K Srivastava-optimized the
Tribological behavior of aluminum alloy Al-
6061 reinforced with silicon carbide
particles (10% & 15%weight percentage of
SiCp) fabricated by stir casting process was
investigated.
10. Oktem H., Erzurumlu T. and Kurtaran
H., 2005. Application of response surface
methodology in the optimization of cutting
conditions for surface roughness, Journal of
Material Processing Technology, Vol. 170,
No. 1-2, pp. 11-16.

7. International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
ISSN: 2395-1303 http://www.ijetjournal.org Page 23
11. Reddy sreenivasulu and ch. Srinivasarao,
Tool Wear and Surface Roughness of Al2O3
Particle-Reinforced Aluminum Alloy
Composites, J. Mater Process. Technol.,
2002, 128(1), p 280–291
12. Kopac J. and Krajnik P., 2007. Robust
design of flank milling parameters based on
grey-Taguchi method, Journal of Material
Processing Technology, Vol. 191, No. 1-3,
pp. 400-403.
13.Nihat Tosun, “Determination of optimum
parameters for multi-performance
characteristics in drilling by using grey
relational analysis,” International J. Advance
Manufacturing Technology., 28: 450-455.
102,2006.
14. Noordin M.Y., “Performance Evaluation
of Coated Carbide Cermet Tools When
Turning Hardened Tool Steel,” PhD
Thesis. University Teknologi Malaysia.,
2003.
15.SeropeKalpakjian and Steven R. Schmid,
“Manufacturing Engineering and
Technology,”4th edition. Upper Saddle
River, New Jersey: Prentice Hall, 2001.
16. Noorul Haq A., Marimuthu P. and
Jeyapaul J, “Multi response optimization of
machining parameters of drilling Al/SiC
metal matrix composite using grey relational
analysis in the Taguchi method,”
International J. Advance Manufacturing
Technology., 37:250-255,2008.
17. Nouari M., List G., Girot F. and Ge´hin
D, “Effect of machining parameters and
coating on wear mechanisms in dry drilling
of aluminum alloys,” International Journal
of Machine Tools & Manufacture..45:
1436–1442, 2005.