International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
Optimization of Machining Parameters in Turning Operation
of EN13 Steel using Response Surface Methodology
Balbir Singh,
Deepak Byotra,
Assistant Professor
Workshop Superintendent
School of Mechanical Engineering,
Shri Mata Vaishno Devi University, Katra, Jammu & Kashmir, India
Akshay Bhat, Ashutosh Jha
Student,
Abstract
In today’s manufacturing unit, the most important factors in production are low cost, high quality
product in short time. These can be achieved by selecting optimum level of machining parameters for
machining of any material in mass production. Our paper will showcase the experimental
investigation of EN13 steel for turning operation. The processing parameters that are chosen are
cutting speed, feed rate and depth of cut and these are used to inquire about the material removal rate
(MRR) of EN13 steel. Experiment is designed on the basis of Central Composite Rotatable Design
(CCRD) using Response surface methodology (RSM). Mathematical model is developed between
process parameters and MRR. The significance of processes parameters and adequacy of model are
analyzed using analysis of variance (ANOVA). Interaction effects between the parameters and MRR
are analyzed by various three dimensional graphical representation. Further optimization of
machining parameters for turning operation is carried out. Confirmatory experiment is performed on
the optimal values. Thus, the results obtained are very near to optimal value for maximum value of
MRR.
Keywords:EN13 steel, cutting speed, feed rate, Depth of cut, MRR, ANOVA, RSM.
OVERVIEW:
Carbon steels are by far the most frequently used industrial steels because of their high production
volume,good formability as well as weldability properties. Steels are used increasingly in most
industrial/commercial applications such as in automobile-(body panels;crankshafts;gears;axles)
construction industries -(structural steels ;hammer; seamless tubes ) and machine tools
industries.Nevertheless,because of carbon steels wider area of applications coupled with its low cost
and availability,machining characteristics need to be optimized to further increase its area of
application as well as achieving high quality products [1].In present quick altering scenario in
production industries, uses of optimization methods in metal cutting operations are important for a
production unit to react operatively to severe the competitiveness as well as incrementing requisition
of characteristic yield in the market. This probe aids in assessing optimum machining factor like tool
geometry, tool material, cutting speed, feed rate and depth of cut for cutting force in turning of EN
13 steel on Lathe machine.Turning is most broadly used amongst all the cutting operations that
subsist in manufacturing world. The accretive significanceof turning operations is acquiring modern
aspects in the current industrial period, in which the expanding competition calls for all the efforts to
be channelized towards the economical manufacturing of machined components and surface finish is
one of the most critical quality measures in mechanical products. The turning operation requires a
turning or a lathe machine, work piece, fixture, and a cutting tool.The cutter that isusually a singlepoint cutting tool that is held in the machine.This steel is frequently applied in pressure vessels and
nuclear plant [2]. Response Surface Parametric optimization method is applied for evaluating best
plausible amalgam for maximum material removal rate during machinability. This investigation
showcases an experimental investigatory process into the effect of multifarious process parameters
and tool dependent parameters on material removal rate.
648 Akshay Bhat, Ashutosh Jha, Balbir Singh, Deepak Byotra
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
EXPERIMENTATION:
Table1: Chemical composition of EN13 steel in weight %:
Element
C
Si
Mn
S
P
Ni
Cr
Mo
Percentage of 0.19 0.14
1.37
0.012
0.026
0.56
0.2
0.31
composition
other
97.192
EN 13 steel material is selected for experimentation analysis. EN 13 come under the group of MnNi-Mo steel.The composition of steel is presented in table 1. En 13 bars larger than 250 mm may be
available in quenched and tempered condition but it must be noted that fall off on mechanical
properties is apparent at centre of the core; hence it is recommended to take significantly larger
diameter of the work piece for better experimental readings. The end quenching temperature is
8750C[4]. The cutting tool used for the experiment was HSS tool which is a single point cutting tool.
Following table shows the tool signature of the tool.
TOOL SIGNATURES:
S.NO
Angle
Value
1
Side Rake Angle
7°
2
Back Rake Angle
9°
3
Side Cutting Edge Angle
13°
4
End Cutting Edge Angle
12°
5
Side Relief Angle
6°
6
End Relief Angle
8°
An understanding of what the process parameters are necessary in order to design and operate
machining processes, to specify machine tools and tooling, mathematical model is developed using
RSM approach.Process models can be used to predict the effects of process parameter changes on
process performance. So they are useful for process design and process improvement. An important
aspect of using process models is to understand the relationship between the process parameters and
developed model. This model can be used for predicting MRR while turning EN 13 at set values of
process parameters. The real physical process being modeled [5]. In present work, three process
parameters are chosen namely cutting speed, feed rate and depth of cut for conduct of experiments.
Table 2: Process Parameters and their levels:
Factors/Levels
Unit
-2
-1
0
A:Cutting Speed
(m/min) 14
17.81 20.67
B:Feed Rate
(mm/rev) 0.1
0.225 0.45
C:Depth Of Cut
(mm)
0.1
0.18
0.3
1
26.75
0.67
0.42
2
30
0.8
0.5
Experiment is carried out by first taking the initial weight of the steel on the weighing machine.
Experiment is performed as per run as shown in table 2. After machining, again weight of workpiece
is measured and time of machining is noted.MRR is calculated using equation 1.
MRR= [Initial weight(gm) -Final weight (gm)]/Time taken (sec) ……………. Eq. (1)
Every experiment is performed two times. Averages of two results are taken as final MRR as shown
in table 2. Similarly other runs also carried out in the same way. Material being machined is shown
in Illustration -1.
649
Akshay Bhat, Ashutosh Jha, Balbir Singh, Deepak Byotra
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
Fig.(2) Illustration 1: EN 13 steel shaft during turning on HMT lathe machine
2.1 Response Surface Methodology
Response Surface Methodology (RSM) is a assemblage of mathematical and statistical methodology
that aids in modelling and investigating the issuesin which output or response is effected by various
self-reliant measures. This avenue is used to detect the correlation between the response and the
parameters. It can be used for optimizing the responses. The mannerism of the system is elucidated
by the ensuing second order polynomial regression model also called quadratic model [22]. A general
second order polynomial response surface mathematical model is by equation as below:
n
n
n
i 1
i 1
i j
y  a0   ai xi   aii xi2   aij xi x j ........
wherey is the response under research e.g. MRR produced by the various process factors. xi(i=1,2,
…n) are quantitative process parameters, a0 , ai aii & aij are second order regression coefficients. The
second term of polynomial equation (2) shows a linear change between the two coordinates, third
term exhibit higher order effects and fourth term of the equation represents the interactive effects of
the process parameters.
TABLE 2: Design Layout with actual factors and experimental results for MRR
Factor 1
Factor 2
Factor 3
Response 1
Std Run A:cutting speed B:feed rate C:depth of cut MRR
(m/min)
(mm/rev)
(mm)
(gm/sec)
1
6
10
15
3
8
9
20
12
11
13
650
1
2
3
4
5
6
7
8
9
10
11
17.81
26.75
30
20.67
17.81
26.75
14
20.67
20.67
20.67
20.67
0.225
0.225
0.45
0.45
0.67
0.67
0.45
0.45
0.8
0.1
0.45
0.18
0.42
0.3
0.3
0.18
0.42
0.3
0.3
0.3
0.3
0.1
Akshay Bhat, Ashutosh Jha, Balbir Singh, Deepak Byotra
0.05
0.28
0.29
0.151
0.1505
0.36
0.115
0.17
0.257
0.1325
0.07
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
19
18
14
16
2
4
7
5
17
12
13
14
15
16
17
18
19
20
20.67
20.67
20.67
20.67
26.75
26.75
17.81
17.81
20.67
0.45
0.45
0.45
0.45
0.225
0.67
0.67
0.225
0.45
0.3
0.3
0.5
0.3
0.18
0.18
0.42
0.42
0.3
0.18
0.1425
0.28
0.163
0.19
0.048
0.31
0.12
0.142
RESULTS AND DISCUSSIONS:
Machining characteristics and its optimization play an important role in evaluating quality of
machined products.In order to achieve optimization, a tradeoff between the factors that affect
optimization is always made.
TABLE 3: Analysis of Variance for model of MRR
Sum of
Mean
F
p-value
Source
Squares
Df
Square
Value
Prob> F
0.14
6
0.024
38.92
< 0.0001
significant
Model
0.024
1
0.024
39.04
<
0.0001
A-cutting speed
0.011
1
0.011
18.52
0.0009
B-feed rate
1
0.075
123.47 < 0.0001
C-depth of cut 0.075
0.016
1
0.016
26.11
0.0002
AB
4.368E-003
1
4.368E-003
7.23
0.0186
AC
0.012
1
0.012
20.05
0.0006
BC
7.850E-003
13
6.039E-004
Residual
6.652E-003
8
8.315E-004
3.47
0.0933
not significant
Lack of Fit
1.198E-003
5
2.396E-004
Pure Error
0.15
19
Cor Total
0.025
R-Squared
0.9473
Standard
Deviation
0.18
Adj-R Squared
0.9229
Mean
Pred-R Squared
0.772
Coefficient of 13.65
Variation
Predicted
residual error
0.034
Adeq Precision
19.8
Illustration 2 gives the straight line showing that residuals lie on the linear line which intends that the
lapses are invariantly distributed. Figure 3 shows relation between the actual and predicted value
which are near to each other and that corresponding values lie closer to each other. Thus, this helps to
clarify us that we have got the well fitted 2FI mathematical model from the design expert software.
For the same model we have ANOVA as depicted in Table 3.
651
Akshay Bhat, Ashutosh Jha, Balbir Singh, Deepak Byotra
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
Illustration 2: Normal probability graph of residual
Illustration 3: Predicted vs actual graph of
residual for MRR
for MRR
Figure 4 shows the perturbation graph which gives the deviation of each machining parameter with
MRR. In the below demonstrated graph we have three process parameter where rate of increase of
MRR is more with depth of cut (C) then cutting speed (A) and then the effect more or less remains
same with the feed rate or there is slight variation with slope of line near to parallel to deviation axis
or x-axis.
Illustration 4 perturbation graph for MRR
The conclusions from the response surface model suitable in the form of ANOVA after dilapidating
the insignificant parameters for EN13 steel for optimum MRR value Tables 3. Values of ‘‘p-value>F’’
less than 0.0500 shows model terms are statistically important at 95% confidence level. The Model
F-value of 38.92 implies that model is significant. The ‘‘Lack of Fit F-value’’ in the ANOVA tables is
not significant relative to the pure error.The "Lack of Fit F-value" of 3.47 implies there is a 9.33%
chance that a "Lack of Fit F-value" this large could occur due to noise.
The R2 is the ratio of variability explained by the model to the total variability in the actual data.
This is used to measure goodness of fit. If the value of R2 is unity, then it shows the best result in
terms of model. The calculated value of 0.9473 verify that the relationships between the selected
process parameters and response (MRR) can adequately be described by model.
The value of predicted R2 (0.772) are in good agreement with that of adjusted R2 value
652
Akshay Bhat, Ashutosh Jha, Balbir Singh, Deepak Byotra
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
(0.9229). Adequate Precision checks the S/N ratio. Ratio greater than 4 indicate adequate model
discrimination. The ratios of 19.8 indicate an adequate signal in our machining processes. At the
same condition, a relatively lower value of coefficient of variation 13.65% indicates better precision
and reliability of the conducted experiments.
Through all the above discussion values of one of the optimized value from the DOE which
exactly lies on the normal plot graph for MRR having run number 6 is cutting speed =26.75m/min,
feed rate=0.67mm/rev, depth of cut=0.42mm and MRR=0.36gm/sec.
Mathematical model for MRR in coded form:
On the basis of Observation from the experiment, it can be predicted that MRR is
performanceof cutting speed, feed rate and depth of cut as shown in definitive equation for material
removal rate (MRR) which is given in coded form by equation 3. The equation gives the best
optimized value of material removal rate with respect to optimized parameters
In the same manner, cutting speed (A), feed rate (B), depth of cut (C), combined influence of cutting
force and feed rate, cutting force and depth of cut and feed rate and depth of cut have
noticeableimpact on MRR for machining processes.Each intake factorand its interaction has been
observed to be statisticallycrucial for its effect on material removal rate at a whopping percentage of
95 in terms of confidence level.
MRR= +0.19+0.042*A+0.028*B+0.074*C-0.043*AB+0.024*AC+0.036*BC Eq. (3) The 3D
figures given below show diversified effect when interaction is considered between different
parameters. If we consider figure 5(a) then that MRR remains constant with cutting speed and
decrease slightly with feed rate. In accordance withillustration 5(b) ,MRR augments with the
increment in feed rate and has the same relation with the depth of cut. What needs to be considered is
that the rate of increase is more in depth of cut vis-a –vis the feed rate interaction due to the fact that
beginning value is small and in the same time it attains the maximum MRR value. Whereas in
illustration 5(c), it shows the same effect as in illustration 5(a) but the rate of increment is more in
cutting speed when interaction in regarded between depth of cut and cutting speed.
Illustration 5(a)
653
Akshay Bhat, Ashutosh Jha, Balbir Singh, Deepak Byotra
Illustration 5(b)
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
Illustration 5(c)
Illustration 5(a, b &c): 3D graphs of 2 factor interaction between factors cutting speed, feed rate and
depth of cut on MRR.
CONCLUSIONS:
Thus we get the required optimum result of MRR through this experiment using HSS (high speed
steel) cutting tool on EN13 steel. All choosen parameters have significant effect on MRR values.
Design expert software gave the model as 2FI which has 95% confidence level.
1.
After analysis in ANOVA we got the significant factors along with the combined influential
factors which is coded as A,B,C,AB,AC & BC which gives optimum value of MRR when feeded in
the MRR model way coded equation.
2.
MRR value is also dependent upon the machining time. Lesser the time more is the MRR
value and vice versa.
3.
From figure 4 above we have depth of cut as the most significant factor which is responsible
for higher rate of increase in MRR values compared to cutting speed and feed rate.
ACKNWOLEDGEMENT:
First of all main author would like to thanks Shri Mata Vaishno Devi University for letting us
perform this experiment in the workshop. The main author would also like to thanks our workshop
staff to help us taking various measures in each run.
REFERENCES:
[1]- Rajput, R.K., (2007) Material science and engineering. USA, Kataria& sons, 480 p.
[2]-Steel Castings Handbook edited by Malcolm Blair, Thomas L. Steven pg 18-19.
[3] & [4]-Hardenability diagram of steel, EN 13 steel, database is maintained by US copyright law
and European copyright law.
[5]-Materials and Processes in Manufacturing by E. Paul Degarmo, J T. Black, Ronald A. Kohser ,
chapter 21 Fundamentals of Machining.
[6]-Optimization of Different Machining Parameters of En24 Alloy Steel In CNC Turning by Use of
Taguchi Method, Mahendra Korat, Neeraj Agarwal / International Journal of Engineering Research
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Akshay Bhat, Ashutosh Jha, Balbir Singh, Deepak Byotra
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com March 2015, Volume 3 Special Issue, ISSN 2349-4476
[7]-Optimizing Machining Parameters during Turning Process Suleiman Abdulkareem1*, Usman
Jibrin Rumah1 and Apasi Adaokoma1,International Journal of Integrated Engineering, Vol. 3 No. 1
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[8]-International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May
2013 Page 1564. Evaluation and Optimization of Machining Parameter for turning of EN 8 steel,
Vikas B. Magdum#1, Vinayak R. Naik*2.
[9]- IJDMT-Investigation Of Turning Process to improve productivity (mrr) for better surface finish
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[10]- International Journal of Modern Engineering Research (IJMER) Vol. 3, Issue. 4, Jul - Aug.
2013 pp-2154-2156 Parametric Analysis and Optimization of Turning Operation by Using Taguchi
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[11]- International Review of Applied Engineering Research. Volume 4, Number 3 (2014), pp. 251256 Optimization of Cutting Parameters in Turning Operation of Mild Steel
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