Optimization of process parameter for maximizing Material removal rate in turning of EN8 (45C8) material on CNC Lathe machine using Taguchi method

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 435
Optimization of process parameter for maximizing Material removal
rate in turning of EN8 (45C8) material on CNC Lathe machine using
Taguchi method
Sachin goyal1, Pavan Agrawal2, Anurag Singh jadon3, Mukesh hemnani4, Promise Mittal5
1,3,4Research Scholar, Dept. Of Mechanical Engineering, Vikrant Institute of Technology & Management, Gwalior
2, 5 Assistant Professor, Dept. Of Mechanical Engineering, Vikrant Institute of Technology & Management, Gwalior
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In today’s era of mass manufacturing, MRR
(material removal rate) is of primary concern using CNC
(computer numerical controlled) machines. In this paper
Taguchi method has been employed with L9 Orthogonal
Array for three parameters RPM, Feed and Depth of cut. For
each of these parameters three different levels have been
identified and used to perform the turning operation for the
maximization of material removal rate on 2D CNC lathe. The
material selected for machining was EN 8 Bright bar with
carbide cutting tool. The MRR is observed as the objectiveto
develop the combination of optimum cutting parameters.
This research proposes an optimization approach using
orthogonal array(L9) for the maximizingMRR andtheresult
from this study confirms the same. This study also produced
a predictive equation for Determining MRR with a given set
of parameters in CNC turning operation. Thus, with the
proposed optimal parameters it is possible to increase the
efficiency of machining process and decrease production
cost in an automated manufacturing environment.
Key Words: Material removal rate(MRR),CNC Lathe,
Turning operation, Design of experiments (DOE),Orthogonal
array(L9) Taguchi method, S/N ratio.
1.INTRODUCTON
Today, CNC machining has grown to beanindispensible part
of machining industry. The accuracy, precision achieved
through CNC could not be achieved by the conventional
manufacturing machines. But still there is a room for errors
in a CNC machine and it basically depends on the skill and
experience of the worker to get the dimensions
right.[1] Metal cutting is one of the most important
processes and broadly used manufacturing processes in
mechanical industries [2] the material removal rate (MRR)
was considered for the present work as the factor directly
affects the cost of machining . The machining parameters
namely cutting speed, feed rate and depth of cut were
considered. The objective was to find the optimized set of
values for maximizing the MRR using Taguchi method. In
order to find the optimized set of input parameters and also
to identify the effect of each towards a particular output,
researchers have been trying for years together. Meng [3]
tried to calculate optimum cutting condition for turning
operation using a machining theory. Researchers [4-7] have
tried to optimize the machining parameters using various
methods like Genetic Algorithm, simulated annealing
method, Multi-Objective Evolutionary Algorithm etc. The
Taguchi method emphasizes over the selection of the most
optimal solution (i.e. MRR) over the set of given inputs (i.e.
cutting speed, feed rate and depth ofcut)witha reducedcost
and increased quality. The optimal solution so obtained is
least affected by any outside disturbances like the noise or
any other environmental conditions [10] Thus, the modern
day approach to find the optimal output over a set of given
input can be easily carried out by the use of Taguchi method
rather than using any other conventional methods. This
method has a wide scope of usevaryingfromtheagricultural
field to medical field and various fields of Engineering
sciences. It has been used by many agricultural scientists to
evaluate the production of their crops based on the different
sets of inputs like the waterfall level, fertility of the land,

types of seeds used and many other factors. It is also usedby
the weather department in forecasting of the various
environmental conditions. And, in the field of Science and
Engineering, it is used for obtaining optimal resultsbasedon
the various engineering inputs. Thus, the Taguchi approach
has a wide variety of applications in the different fields
because of its easiness and optimized results [11] However,
even being having a lot of advantages ,itsuffersfroma major
disadvantage that the results obtained are only relative to
the levels selected and do not exactly indicate the effect of
entire range of each parameter on the performance
characteristic value. They also do not test the output for all
the combinations of input variables as the combination
selected here is limited. Moreover, major disadvantage of
the Taguchi method is that it is not a dynamic processi.e.itis
not concerned with those variables which discretely or
continuously change their values from time to time. Thus, it
deals with mostly the static variables i.e. variables whose
values are not time dependent. Moreover, it deals with
designing of the quality rather than improving the quality.
Because of this, they are applied in the industriesatthe early
stages only [12] However, despite of all thesedisadvantages,
it is used widely in the different areas very effectively. To
reduce the error while using an increased number of inputs,
the orthogonal matrix of Taguchi can be used, which is
selected as per the requirements of the person. In this work
L9 Orthogonal Array was used to obtain optimized
economical results. The Taguchi method for the Design of
Experiments also emphasizes over the use of Loss function,
which is the deviation from the desired value of the quality
characteristics. Based on these Loss functions, the S/N ratio
for each experimental set is evaluated and accordingly the
optimal results are derived. Minitabsoftwareisthenusedfor
the results and finally, theconfirmations resultsareobtained
between the experimental valuesandthedeterminedvalues.
2. TAGUCHI METHOD
Basically, traditional experimental designproceduresaretoo
complex and not easy to use. A largenumberofexperimental
works have to be carried out when the number of the
process parameters increases with their levels. To solvethis
problem, the Taguchi method uses a special design of
orthogonal arrays to study the entire parameter space with
only a small number of experiments. The greatestadvantage
of this method is to save the effort in performing
Experiments: to save the experimental time, to reduce the
cost, and to find out significant factors fast. Taguchi robust
design method is a most powerful tool for the design of a
high-quality system. He consideredthreestepsina process’s
and product’s development: system design, parameter
design, and tolerance design. In system design, the engineer
uses scientific and engineering principles to determine the
fundamental configuration.Intheparameterdesignstep,the
specific values for system parameters are determined.
Tolerance design is used to determinethe besttolerancesfor
the parameters[14]
FIG. Flow chart of Taguchi method [14]
Determining of Quality Characteristics which
required for optimization
Identify the noise factor and test condition
Identify the control factors and their different
levels
Develop the design matrix
Conduction of experiments
Analyze the data and determining the
optimum levels of the control factors
Predict the performance

Taguchi method is a powerful design of experiments (DOE)
tool for optimization of engineering processes, in which the
concept of S/N ratio is used for the improvement of quality.
3. EXPERIMENTAL DETAILS
The experiments are performed on CNC lathe MCL 12
selected. The tool and material selected were carbide tool
and mild steel EN8 Bright bar respectively. Three process
parameters, as, Cutting speed (A), Feed rate(B)andDepthof
cut (C) were considered in the study. Equally spaced three
levels within the operating range of the input parameters
were selected for each of the process parameters. Based on
Taguchi method, an L9 orthogonal array (OA) which has 9
different experiments at three levels was developed.
Table3.1 shows the design factors along with their levels.
Table3.1 Parameters and Their Levels for Experiment
S.No Parameter L1 L2 L3
1 Cutting
speed(A)
600 900 1200
2 Feed Rate(B) 0.15 0.20 0.25
3 Depth of
Cut(C)
0.1 0.2 0.3
Table3.2 Orthogonal Array[L9]
Experiment no Level 1 Level 2 Level 3
1 1 1 1
2 1 2 2
3 1 3 3
4 2 1 2
5 2 2 3
6 2 3 1
7 3 1 3
8 3 2 1
9 3 3 2
Larger is better S/N =-10 log [1/n (Σ1/yi2)]
Table 3.3 Experimental values
Exp
no.
A(RPM
)
B(m
m/re
v)
C(mm
)
MRR
(mm3/min)
S/N Ratio
1 600 0.15 0.1 562.659 55.00490
5
2 600 0.20 0.2 1492.88 63.48049
8
3 600 0.25 0.3 2785.02 68.89656
6
4 900 0.15 0.2 1679.49 64.50354
8
5 900 0.20 0.3 3342.02 70.48018
0
6 900 0.25 0.1 1406.64 62.96365
9
7 1200 0.15 0.3 3342.02 70.48021
0
8 1200 0.20 0.1 1500.42 63.52425
6
9 1200 0.25 0.2 3732.21 71.43932
1
Table3.4 Mean response table for material
removal rate(MRR)
1 Cutting
speed(A)
1613.51 2142.71 2858.21
2 Feed
Rate(B)
3722.77 2111.773 2641.29
3 Depth of
Cut(C)
1156.57 2301.52 3156.35

Table 3.5 S/N ratio table for material removal
rate(MRR)
1 Cutting
speed(A)
62.46 65.98 68.48
2 Feed Rate(B) 63.32 65.82 67.76
3 Depth of
Cut(C)
60.49 66.47 69.95
1200900600
3000
2500
2000
1500
1000
0.250.200.15
0.30.20.1
3000
2500
2000
1500
1000
A
MeanofMeans
B
C
Main Effects Plot for Means
Data Means
Graph 1: means of Responce
1200900600
70.0
67.5
65.0
62.5
60.0
0.250.200.15
0.30.20.1
70.0
67.5
65.0
62.5
60.0
A
MeanofSNratios
B
C
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
Graph 2: Means of S/N ratio
4. Development Mathematical Modeling
Regression analysis is performed to find outtherelationship
between factors and Material removal rate. In conducting
regression analysis, it is assumed that factors and the
response are linearly related to each other. A multiple
regression technique was used to formulate the Cutting
speed, feed rate and depth of cut to the Material removal
rate. The response function representing the material
removal rate can be expressed as: Material removal rate = f
(Cutting speed, feed rate and depth ofcut).Thisequationcan
also be written as:
The regression equation is
MRR = - 3222 + 2.07 A + 7799 B + 9999 C
Predictor Coef SE Coef T-factor P-factor
Constant -3221.8 791.7 -4.07 0.010
A 2.0745 0.4845 4.28 0.008
B 7799 2907 2.68 0.044
C 9999 1454 6.88 0.001
S = 356.043 R-Sq = 93.6% R-Sq(adj) = 89.7%
5. Response Graphs for Means
5.1 Level [3] for cutting speed A3 = 2858.21mm3/min
indicated as the optimum situation in terms of Material
removal rate(MRR) values.
5.2 Level [1] for feed rate B1 = 3722.77mm3/min indicated
as the optimum situation in terms of material removal
rate(MRR) values.
5.3 Level [3] for depth of cut C1 = 3156.35 mm3/min
indicated as the optimum situation in terms of material
removal rate (MRR)values.
6. CONCLUSIONS AND SCOPE OF FUTURE WORK
For main effects depth of cut and Cutting speed have
significant effect on the Material removal rate. Whereas
cutting speed have increasing effect on MRR. This is
consistent with the conclusions from the study of other
investigators.

References
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Optimization of Material Removal Rate During Turning of
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CNC-Review, Vol 3 issue 2 ISSN 2319-8753

Optimization of process parameter for maximizing Material removal rate in turning of EN8 (45C8) material on CNC Lathe machine using Taguchi method

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