Er.Ankush Aggarwal et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.144-150
RESEARCH ARTICLE
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OPEN ACCESS
Multi-Objective Optimization ( Surface Roughness & Material
Removal Rate) of Aisi 202 Grade Stainless Steel in Cnc Turning
Using Extended Taguchi Method And Grey Analysis
Er.Ankush Aggarwal, Er. Shanti Prakash, Er. Brijbhushan
(M.tech-2nd year, HEC, Jagadhri)
(Sr. Lect., HEC, Jagadhri)
(Lect., HEC, Jagadhri)
ABSTRACT
The present study applied Taguchi method through a case study in straight turning of AISI 202 stainless steel
bar on CNC Machine ( Mfd by ACE DESIGNERS) using Titanium Carbide tool for the optimization of
Material removal rate, Surface Roughness and tool wear process parameter.The study aimed at evaluating the
best process environment which could simultaneously satisfy requirements of both quality as well as
productivity with special emphasis on maximizing material removal rate and minimizing surface roughness and
tool flank wear at various combination of cutting speed, feed, depth of cut. The predicted optimal setting
ensured maximum MRR and minimum surface roughness and tool wear. Since optimum material removal rate
is desired, so higher the better criteria of Taguchi signal to noise ratio is used for MRR –
SNs = -10 log(Sy2/n)
For surface roughness and tool wear –
SNL = -10 log(S(1/y2)/n)
The results have been verified with the help of S/N Ratios calculation and various graphs have been plotted to
show the below mentioned observations.
a) MRR first increases with increase in cutting speed and then decreases.
b) With the increase in feed, MRR increases.
c) With the increase in depth of cut, MRR first increases and then decreases.
d) With the increase in cutting speed, Surface Roughness first decreases and then increases.
e) With the increase in feed, Surface Roughness increases.
f) With the increase in depth of cut, Surface Roughness first increases and then
decreases.
Keywords: CNC turning machine, Grey relational analysis, Material removal rate, Surface roughness.
I. INTRODUCTION
AISI 202 stainless steel belonging to the low
nickel and high manganese stainless steel, the nickel
content is generally below 4%, 8% of the manganese
content is a section of nickel stainless steel. AISI 202
stainless steel is 200 series stainless steel. AISI 202
stainless steel is a section with good mechanical
properties of corrosion resistance, AISI 202 stainless
steel high temperature strength than steel, 18-8, at
800 ℃, the following has good oxidation resistance,
and maintain a high intensity, can replace SUS302
steel. AISI 202 stainless steel is widely used in
architectural decoration, municipal engineering,
guardrail, hotel facilities, shopping mall, vitreous
armrest, public facilities etc. The three primary
factors in any basic turning operation are speed, feed,
and depth of cut. Other factors such as kind of
material and type of tool have a large influence, of
course, but these three are the ones the operator can
change by adjusting the controls, right at the
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machine. M.Kladhar [1] from the analysis, observed
that the feed is the most significant factor that
influences the surface roughness followed by nose
radius. he attempted to generate prediction models
for surface roughness. The predicted values are
confirmed by using validation experiments.[1]
It was reported that austenitic stainless steels
come under the category of difficult to machine
materials [1]. Little work has been reported on the
determination of optimum machining parameters
when machining austenitic stainless steels. Lin [8]
investigated surface roughness variations of different
grades of austenitic stainless steel under different
cutting conditions in high speed fine turning.
Ranganathan and Senthilvalen [9] developed a
mathematical model for process parameters on hard
turning of AISI 316 stainless steel. Surface roughness
and tool wear was predicted by Regression analysis
and ANOVA theory. Anthony xavior and Adithan
[10] determined the influence of different cutting
144 | P a g e
Er.Ankush Aggarwal et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.144-150
fluids on wear and surface roughness in turning of
AISI304 austenitic stainless steel. Ibrahim Ciftci [10]
conducted the experiments to Machine AISI 304 and
AISI 316 austenitic stainless steels using CVD multilayer coated cemented carbide tools. The results
showed that cutting speed significantly affected the
machined surface roughness values. The goal of the
modern industry is to manufacture high quality
products in a short time. Computer Numerical
Control (CNC) machines are capable of achieving
high accuracy with very low processing time [1,2].
During machining, surface quality is one of the most
specified customer requirements.. In the present
study the multi-objective optimization of surface
roughness and material removal rate of AISI 202 has
-1
0
1
Sample
No
been done using Taguchi method and Grey analysis
[6].
II. DESIGN OF EXPERIMENT
Experiments have been carried out using
Taguchi’s
L9
Orthogonal
Array
(OA)
experimental design which consists of 9
combinations of spindle speed, longitudinal feed
rate and depth of cut. According to the design
catalogue [Peace, G., S., (1993)] prepared by
Taguchi, L9 Orthogonal Array design of
experiment has been found suitable in the present
work.
Table 1: Process variables and their limits
Cutting Speed
Feed (F)
M/Min
Mm/Rev
Values In Coded
Form
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115
130
145
Depth Of Cut (D)
Mm
0.07
0.14
0.21
0.6
1.2
1.8
Table 2: Taguchi’s L9 Orthogonal Array for MRR S/N Ratio
Cutting
Feed
Depth of cut
MRR
speed
(mm/rev)
(mm)
(Gm/Sec)
(m/min)
S/N Ratio
1
115
.07
.6
0.94
-0.54
2
3
4
5
6
7
8
9
115
115
130
130
130
145
145
145
.14
.21
.07
.14
.21
.07
.14
.21
1.2
1.8
1.2
1.8
.6
1.8
.6
1.2
2.91
2.75
1.31
2.65
5.28
2.38
1.25
3.67
9.24
8.76
2.34
8.48
14.45
7.53
1.93
11.27
Table 3:
Sample
No
1
2
3
4
5
6
7
8
9
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Cutting
Speed
(M/Min)
115
115
115
130
130
130
145
145
145
S/N Ratio Calculation for Surface Roughness
Feed
Depth Of Cut
MRR
(Mm/Rev)
(Mm)
(Gm/Sec)
.07
.14
.21
.07
.14
.21
.07
.14
.21
.6
1.2
1.8
1.2
1.8
.6
1.8
.6
1.2
0.94
2.91
2.75
1.31
2.65
5.28
2.38
1.25
3.67
Surface
Roughness
Mean (Ra)
2.11
2.61
2.353
1.89
2.3
2.29
1.923
2
2.673
S/N
Ratio
6.48
8.33
7.43
5.52
7.20
7.19
5.66
6.02
8.53
145 | P a g e
Er.Ankush Aggarwal et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.144-150
III. EXPERIMENTAL SET UP
Nine nos samples of Material AISI 202 were
taken for machining and their weight before
machining and after machining were precisely
recorded and time taken using a calibrated watch and
following observations were recorded.
S.S. bars of diameter 31mm and length 41mm
required for conducting the experiment have been
prepared first. Nine no of samples of same material
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and same dimensions have been made. After that the
weight of each sample has been measured accurately
with the help of digital balance meter. Then using
different levels of the process parameters nine
specimens have been turned on CNC lathe machine
accordingly. Machining time for each sample has
been calculated accordingly. The surface roughness
of the work pieces have been measured by stylus type
surface roughness tester Mitutoyo SJ-211.
WORK PIECE MATERIAL:
Stainless Steel AISI 202.Dimension for material is Ø31 X 41 mm.
CHEMICAL COMPOSITION
AISI
C
Mn
202
<=0.15
7.50-10.0
P
<=0.06
S
<=0.03
Si
<=0.75
Cr
17.0-19.0
Ni
4.0-6.0
SELECTION OF CUTTING TOOL:
The cutting tool selected for present work is Titanium carbide.
IV. GREY RELATIONAL ANALYSIS
Data Normalization It is the first step in the grey relational analysis.
Where xi (k) is the sequence of the surface
roughness and material removal rate after data
normalization, max yi(k) and min yi(k) are the largest
value and smallest value of original sequence of
surface roughness and MRR respectively.
V. GREY RELATIONAL
COEFFICIENT CALCULATION
VI. GREY RELATIONAL GRADE
The grey relational grade can be calculated by
using the below mentioned formula—
After normalization of original sequence, Grey
relational coefficient is calculated
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146 | P a g e
Er.Ankush Aggarwal et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.144-150
Table-4:
S.NO
X0
1
2
3
4
5
6
7
8
9
GRGC RSDC GRCC -
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OPTIMUM PROCESS PARAMETERS FOR MULTI OBJECTIVE OPTIMIZATION USING
GREY ANALYSIS
GRGC
RSDC
GRCC
MRR
Ra
MRR
Ra
MRR
Ra
1.000
1.000
1.000
1.000
1.000
1.000
0.000
0.719
1.000
0.281
0.333
0.640
0.450
0.080
0.550
0.920
0.476
0.352
0.416
0.408
0.584
0.592
0.461
0.457
0.084
1.000
0.916
0.000
0.353
1.000
0.396
0.476
0.604
0.524
0.452
0.488
1.000
0.489
0.000
0.511
1.000
0.494
0.331
0.957
0.669
0.043
0.427
0.920
0.070
0.859
0.930
0.141
0.349
0.780
0.627
0.000
0.373
1.000
0.572
0.333
Grey Relational Generation calculation
Reference Sequence Definition calculation
Grey Relational Coefficient Calculation
S.No
Table -5:
Grey Relational Grade (GRG)
Rank
1
2
3
4
5
6
7
8
9
0.486
0.414
0.459
0.676
0.470
0.747
0.673
0.564
0.452
5
9
7
2
6
1
3
4
8
Higher GRG means that the corresponding parameter combination is the optimal.
VII. RESULT AND CONCLUSION
The optimum values of Process variables for
MRR areCutting speed = 130 m/min
Feed
= .21mm/rev
Depth of cut = .6 mm
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The results have been verified with the help of S/N
Ratios calculation and various graphs have been
plotted to show the below mentioned observations.
a) MRR first increases with increase in cutting
speed and then decreases.
b) With the increase in feed, MRR increases.
c) With the increase in depth of cut MRR first
increases
and
then
decreases.
147 | P a g e
Er.Ankush Aggarwal et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.144-150
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MRR (gm/sec)
MRR VS CUTTING SPEED
3.5
3
2.5
2
1.5
1
0.5
0
3.08
2.43
2.21
115
130
145
Fig:1
MRR VS DEPTH OF CUT
2.65
MRR (gm/sec)
2.62
2.6
2.59
2.55
2.5
2.49
2.45
2.4
…
0.6
1.2
1.8
Fig 2
MRR (gm/sec)
PLOT OF MRR VS FEED
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
3.89
2.27
1.54
0.07
0.14
Feed (mm/rev)
0.21
Fig 3
The optimum value of Process variables for Surface Roughness are--Cutting speed = 130 m/min
Feed
= .07 mm/rev
Depth of cut = 1.2 mm
Conclusions for Surface Roughness
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a) With the increase in cutting speed Surface
Roughness first decreases and then increases.
b) With the increase in feed, Surface Roughness
increases.
148 | P a g e
Er.Ankush Aggarwal et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.144-150
c) With the increase in depth of cut Surface
Roughness first increases and then decreases.
Surface Roughness
2.4
2.35
2.3
2.25
2.2
2.15
2.1
2.05
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Surface Roughness Vs Cutting Speed
2.36
2.2
2.16
…
115
130
145
Fig 4.7
Plot of Surface Roughness Vs Feed
3
Surface Roughness
2.5
2.43
2.3
2
1.97
1.5
1
0.5
0
…
0.07
0.14
0.21
Surface Roughness
Surface Roughness Vs Depth of Cut
2.45
2.4
2.35
2.3
2.25
2.2
2.15
2.1
2.05
2
2.39
2.19
2.13
Depth of Cut (mm)
1.2
0.6
1.8
Fig-4.9
Grey analysis shows that optimum value of
process variable for multi-objective optimization
of MRR and Surface Roughness areHigher GRG means that the corresponding
parameter combination is the optimal.Grey analysis
shows that optimum value of process variable for
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multi-objective optimization of MRR and Surface
Roughness :
Cutting Speed -- 130 m/sec
Feed
-- .21mm/rev
Depth of Cut -- .6 mm
149 | P a g e
Er.Ankush Aggarwal et al Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.144-150
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
Optimization of process parameter of AISI
202 austenetic stainless steel in CNC
Turning. ARPN Journal of engineering and
applied sciences.Vol 5, No 9, sept 2010.M.
Kaladhar,
dept
of
Mech
engineering, Neerukonda Institute of
Technology, email-kaladhar2k@gmail.com.
Computer Numerical Control Turning on
AISI410 with Single and Nano Multilayered
Coated Carbide Tools under Dry
Conditions.
Kamaraj
Chandrasekaran,
Perumal
Marimuthu1,-K.
Raja,
Department of Mechanical Engineering,
Anna University of Technology Madurai,
and Syed Ammal Engineering College.
International
Journal
of
Scientific
Engineering and Technology (ISSN : 22771581) Volume 2 Issue 4, PP : 263-267,1
April IJSET@2013 Page 263-Optimization
of Turning Process Parameters Using
Multivariate
Statistical
MethodAnanthakumar.P1,
Dr.Ramesh.M2,
Parameshwari3Department of Mechanical
Engineering, SACS MAVMM Engineering
College,
Madurai-625301.Email
address: 1ananthresearch@yahoo.co.in,
2 mramesh.sacs@gmail.com.
Journal of Engineering, Computers &
Applied Sciences (JEC&AS) ISSN No:
2319
Volume 1, No.1, October 2012
Optimization of Process Parameters of
Turning Parts:A Taguchi Approach-Neeraj
Sharma, Renu Sharma.
International Journal of
Engineering
Research and applications-IJERA (ISSN :
2248-9622) Volume 2 Issue 5, PP : 15941602, Optimization of Turning Process
Parameters of H13 using Taguchi method
and grey analysis – Pankaj Sharma,
Kamaljeet
Bhambri
Department
of
Mechanical Engineering, MM Engineering
College, Mullana.
D. Philip Selvaraji, P. Chandramohan, ―
Optimization of surface roughness of AISI
304 austenitic stainless steel in dry turning
operation using Taguchi design methodJournal of Engineering Science and
Technology. Vol. 5, No. 3 (2010) 293 – 301.
Optimization of Process Parameters for
Surface Roughness and Material Removal
Rate for SS 316 on CNC Turning MachineNavneet K. Prajapati / International Journal
of Research in Modern Engineering and
Emerging Technology Vol. 1, Issue: 3,
April-2013 (IJRMEET) ISSN: 2320-6586
Lin. W.S. 2008. The study of high speed fine
turning of austenitic stainless steel. Journal
www.ijera.com
[9]
[10]
www.ijera.com
of Achievements in Materials and
Manufacturing. 27(2): 10-12.
Ranganathan. S and senthilvalen. 2009.
Mathematic modeling of process parameters
on hard turning of AISI316 SS by WC insert.
Journal of scientific Industrial and research.
68: 592-599.
Anthony xavior. M and Adithan. 2009.
Determining the influence of cutting fluids
on tool wear and surface roughness during
turning of AISI 304 austenitic stainless steel.
Journal of Material processing Technology.
209: 900-909
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