A STUDY OF THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS USING RESPONSE SURFACE METHOD ON EN11 ALLOY STEEL IN THE END-MILLING PROCESS

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 11, November (2014), pp. 47-58 © IAEME
47
A STUDY OF THE EFFECTS OF MACHINING
PARAMETERS ON SURFACE ROUGHNESS USING
RESPONSE SURFACE METHOD ON EN11 ALLOY STEEL
IN THE END-MILLING PROCESS
Vishesh Ranglani1
, Saurabh Pratap Singh2
, Shashi Kant Tripathi3
, Rahul Davis4
1, 2, 3, 4
(Department of Mechanical Engineering, Shepherd School of Engineering and Technology,
SHIATS, Allahabad, U.P, India)
ABSTRACT
A series of experiments to determine the character of surface of the alloy steel have been
conducted. The main objective of this work is to develop a holistic understanding of the effects of
feed rate, spindle speed, depth of cut and type of coolant on the surface roughness and to create a
model for the conducted study. Such an understanding can provide sapience about the shortcomings
of controlling the finish of machined surfaces when the process parameters are adjusted to obtain a
certain surface finish. The model, which includes the effect of spindle speed, cutting feed rate and
depth of cut, and any three variable interactions, predicted the surface roughness values.
Keywords: Surface Roughness, Milling, ANOVA, EN11.
1. INTRODUCTION
The evaluation of surface roughness of machined parts using a direct contact method has
limitations in handling the different geometrical parts to be measured. Surface roughness affects
many functional parameters, such as friction, wear and tear, light reflection, heat transmission,
ability of distributing and holding a lubricant, coating etc. Therefore, the desired surface finish is
usually specified and appropriate processes are required to maintain the quality. Hence, the
inspection of surface roughness of the work piece is very important to assess the quality of a
component. Alternately, optical measuring methods are applied to overcome the limitations of stylus
method, but, they are also sensitive to lighting conditions and noise. The technique proposed in this
work, requires no apriority information about the lighting conditions and source of noise. Metal
cutting is one of the most significant manufacturing processes in the area of material removal [1].
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND
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ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 11, November (2014), pp. 47-58
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Black [2] defined metal cutting as the removal of metal chips from a work piece in order to obtain a
finished product with desired attributes of size, shape, and surface roughness. The imperative
objective of the science of metal cutting is the solution of practical problems associated with the
efficient and precise removal of metal from work piece. It has been recognized that the reliable
quantitative predictions of the various technological performance measures, preferably in the form of
equations, are essential to develop optimization strategies for selecting cutting conditions in process
planning [3-5].
Milling is a machining process in which the removal of metal takes place due to the cutting
action of a revolving cutter when the work is fed through it. Milling refers to the process of breaking
down, separating, sizing, or classifying aggregate material. For instance rock crushing or grinding to
produce uniform aggregate size for construction purposes, or separation of rock, soil or aggregate
material for the purposes of structural fill or land reclamation activities. Aggregate milling processes
are also used to remove or separate contamination or moisture from aggregate or soil and to produce
"dry fills" prior to transport or structural filling.
2. MATERIALS AND METHODS
2.1. RESPONSE SURFACE METHODOLOGY (RSM)
It is a collection of mathematical and statistical techniques for empirical model building. By
careful design of experiments, the objective is to optimize a response (output variable) which is
influenced by several independent variables (input variables).
Originally, RSM was developed to model experimental responses (Box and Draper, 1987),
and then migrated into the modeling of numerical experiments. The difference is in the type of error
generated by the response. In physical experiments, inaccuracy can be due, for example, to
measurement errors while, in computer experiments, numerical noise is a result of incomplete
convergence of iterative processes, round-off errors or the discrete representation of continuous
physical phenomena[6]. In RSM, the errors are assumed to be random.
The application of RSM to design optimization is aimed at reducing the cost of expensive
analysis methods (e.g. finite element method or CFD analysis) and their associated numerical noise.
The problem can be approximated with smooth functions that improve the convergence of the
optimization process because they reduce the effects of noise and they allow for the use of
derivative-based algorithms. Venter et al. (1996) have discussed the advantages of using RSM for
design optimization applications.
2.2. METHODOLOGY ADOPTED FOR THE PROPOSED DESIGN
1. To design the experiment using Design of Experiment techniques.
2. To obtain a combination of the optimal levels of the parameters in order to minimize surface
roughness with the application of response surface method (RSM).
EN 11(Fig. 10) was chosen to be the specimen material in the proposed work in order to
study the effect of four different parameters (Depth of cut, feed, spindle speed & different coolants)
on the Surface Roughness of the finished specimens using L18 orthogonal design. Therefore the
milling operations and measurements of surface roughness have been done 18 times on the work
pieces for each of the following cases. The work piece were machined by HSS cutting tool wet the
cutting conditions respectively.

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Table 1: Material Composition
Material Carbon (%) Nickel (%) Chromium (%) Molybdenum (%)
EN11 0.4 1.5 1.0 0.23
The Rockwell hardness number was 84 HRC for the EN11 work piece material.
EN11 is a high quality, high tensile, alloy steel. It combines high tensile strength, shock
resistance, good ductility and resistance to wear. EN11 is available from stock in round bar, flat bar
and plate.
EN11 is most suitable for the manufacture of parts such as roller bearing components such as
brake, cylindrical, conical & needle rollers, producing components with enhanced wear resistance.
EN11 is capable of retaining good impact values at low temperatures; hence it is frequently specified
for harsh offshore applications such as hydraulic bolt tensioners and ship borne mechanical handling
equipment. EN11 is a high carbon alloy which is widely used in roller component such as brake,
cylindrical, conical & needle rollers due to their exception thermal resistance and ability to retain
mechanical properties at elevated service temperatures over 1000 °C. However a high carbon alloy is
cut material due to their high degree of hardening and compressive strength and abrasion resistance.
The difficulty of machining EN11 results in to shorter tool life and severe surface abuse to machined
surface.
The Initial dimensions of the specimen for Milling Operation:
Length (mm) = 11±0.5
Breath (mm) = 2±0.5
Height (mm) = 2±0.5
In this experiment four different control factors have been taken into consideration to find out their
influence on surface roughness. All the four parameters are at three levels each. Values of variables
at different level for Milling Operation is as shown in the Table 2.
Table 2: Factors at different levels for Milling Operation
Factors Level 1 Level 2 Level 3
Depth of cut (A) D1 D2 D3
Feed (B) F1 F2 F3
Spindle Speed (C) S1 S2 S3
Coolant s(D) C1 C2
The degree of freedom (DF) of a three level parameter is 2 (number of levels-1) and two level
parameter is 1. The minimum required degree of freedom in the experiment is the sum of all factors.
Table 3: Degrees of Freedom
Factors A B C D Total
Degree of Freedom 2 2 2 1 7
The selection of which orthogonal array to use depends upon:
i. The number of factors.
ii. The number of levels for the factors of interest.

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Total DF for this experiment is 7 as shown in Table 3. As the degree of freedom required for
the experiment is 7 so the orthogonal array that is to be selected should have degree of freedom
higher than 7. The most suitable orthogonal array that can be used for this experiment is L18.
In this experiment, the assignment of factors was carried out using MINITAB 17 Software.
The standard L18 orthogonal array Table 4 as suggested by MINITAB using Taguchi for the
particular experiment are listed in Table 7.
Table 4: Standard L18 Orthogonal Array
Experiment
No.
Depth of Cut
A
Feed rate
B
Spindle Speed
C
Coolant
D
1. 1 1 1 C1
2. 1 2 2 C1
3. 1 3 3 C1
4. 2 1 1 C1
5. 2 2 2 C1
6. 2 3 3 C1
7. 3 1 1 C1
8. 3 2 2 C1
9. 3 3 3 C1
10 1 1 1 C2
11 1 2 2 C2
12 1 3 3 C2
13 2 1 1 C2
14 2 2 2 C2
15 2 3 3 C2
16 3 1 1 C2
17 3 2 2 C2
18 3 3 3 C2
Numerous investigators have conducted experiments to determine the effect of parameters
such as feed rate, spindle speed, depth of cut, Coolant on surface roughness in milling operation.
The values of the input process parameters for the Milling Operation Table 5 are as under:

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Table 5: Details of the Milling Operation
Using the L18 orthogonal array the trial runs have been the conducted on Milling Machine
for milling operations.
Table 6: List of Hardware
S.No.
Item Specifications
1. Milling machine
Size – 165 cm.
Motor -Three Phase motor.
It is shown in Fig. 1.
2.
Cutting Tool
Material of the cutting tool
Multipoint
HSS It is show in Fig. 2.
3. Depth of Cut Measurement Venire Caliper
It is show in Fig. 3.
4. Surface Roughness
Measurement Device
Model No. TR 110 P
Which are used to measure the job in the surface
roughness by the surface roughness tester
It is shown in Fig. 4.
Fig.1: Milling Machine Fig. 2: High Speed Steel(HSS) Cutting tool
Factors Level 1 Level 2 Level 3
Depth of cut (mm) 0.5 1.0 1.5
Feed Rate (mm/rev) 0.000025 0.000125 0.000375
Spindle Speed (rpm) 250 330 510
Coolant C1 C2

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Fig. 3: Venire Calipers Fig. 4: Roughness tester machine
2.2.1 Surface Roughness Terminology
Ra- Arithmetic means value of the deviation of the profile within sampling length
Rz- The maximum height of irregularities is the distance b/w maximum depth of the profile
peaks and profile valley within of sampling length
Rq- Square root of the arithmetic mean of the square of profile deviation (Yi) from mean within
sampling length.
Rt- Total peak-to-valley height .It is the sum of the height of highest peak and the depth of
deepest valley over the evaluation length.
The work piece can be safely turned in the three jaw chuck without supporting the free end work.
Fig. 5: Work Piece Mounted On Vice during Milling Operation
The work pieces were fixed in accordance with the experimental design, and each measured
for surface roughness around the part. Surface roughness was measured with the work piece fixture
and the measurements were taken across the lay. The total length of the work piece (44 mm) was
divided into 2 parts and the surface roughness measurements were taken of each 22 mm around each
work piece.

53
The factors (Depth of cut, Feed rate and Spindle Speed, Different Coolants) were varied at
three levels for both milling operations. The measured response was impact surface roughness.
Analysis of the results was carried out analytically as well as graphically. All the statistical
calculations and plots were generated by MINITAB 17 software.
ANOVA plots of the experimental data have been created to calculate the significance of
each factor for each response. Often, researchers choose 90%, 95%, or 99% Confidence Levels; but
since most of the researchers have chosen 95% Confidence Level, so for this research work also 95%
Confidence Level has been chosen. Thus ᾱ = 0.05 was selected for all statistical calculations. The
response surface method uses the Signal-to-Noise ratio (S/N) to express the scatter around a target
value. A high value of S/N implies that the signal is much higher than the random effects of the noise
factors.
Table 7: Results of Experimental Trial Runs for Milling Operation
The response variables measured were surface roughness, surface roughness tester TR 110P is
used to measure the surface roughness for end milling operation. The single generated value is
measure after working. Surface roughness tester TR 110P is used to measure average surface
roughness.
The experimental come out result for surface roughness (Ra) are given in the Table 7. Values
of Ra are desirable. Thus the data sequences have the smaller-the-better characteristic, the “smaller-
the-best” methodology by using MINITABE 17 to find the result.
Experiment
No.
Depth of Cut
A
Feed Rate
B
Spindle
Speed C
Coolant
D
Ra
1. 0.5 0.000125 250 C1 0.67
2. 0.5 0.00025 330 C1 0.2
3. 0.5 0.000375 250 C1 0.41
4. 1 0.000125 330 C1 0.46
5. 1 0.00025 510 C1 0.52
6. 1 0.000375 250 C1 0.37
7. 1.5 0.000125 330 C1 0.53
8. 1.5 0.00025 510 C1 0.5
9. 1.5 0.000375 250 C1 0.47
10 0.5 0.000125 330 c2 0.96
11 0.5 0.00025 510 c2 0.28
12 0.5 0.000125 250 c2 0.35
13 1 0.00025 330 c2 0.72
14 1 0.000375 510 c2 0.73
15 1 0.000125 250 c2 0.33
16 1.5 0.00025 330 c2 0.75
17 1.5 0.000375 510 c2 0.34
18 1.5 0.00025 250 c2 0.39

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Table 8: Response Table for Signal to Noise Ratios (Smaller is better)
Level Coolant Depth of Cut
(mm)
Feed Rate (mm/rev) Spindle Speed (rpm)
1 7.151 7.622 5.807 7.621
2 6.199 6.046 7.181 5.324
3 6.357 7.008 6.972
Delta 0.951 1.576 1.374 2.296
Rank 4 2 3 1
Table 9: Response Table for Means
Level Coolant Depth of Cut
(mm)
Feed Rate (mm/rev) Spindle Speed (rpm)
1 0.4589 0.4783 0.5500 0.4271
2 0.5389 0.5217 0.4800 0.6033
3 0.4967 0.4640 0.4740
Delta 0.0800 0.0433 0.0860 0.1762
Rank 3 4 2 1
Table 10: Analysis of Variance
Source DF Adj SS Adj MS F-
Value
P-Value
Model 9 0.174778 0.019420 0.3 0.945
Linear 3 0.017613 0.005871 0.10 0.960
Depth of Cut (mm) 1 0.003922 0.003922 0.06 0.806
Feed Rate (mm/rev) 1 0.001090 0.001090 0.02 0.897
Speed (rpm) 1 0.011150 0.011150 0.18 0.680
Square 3 0.123873 0.041291 0.68 0.589
Depth of Cut (mm)Depth of Cut (mm) 1 0.008579 0.008579 0.14 0.717
Feed Rate (mm/rev)Feed Rate (mm/rev) 1 0.010444 0.010444 0.17 0.689
Speed (rpm)*Speed (rpm) 1 0.106344 0.106344 1.75 0.222
2-Way Interaction 3 0.040513 0.013504 0.22 0.878
Depth of Cut (mm)*Feed Rate (mm/rev) 1 0.038733 0.038733 0.64 0.448
Depth of Cut (mm)*Speed (rpm) 1 0.000926 0.000926 0.02 0.905
Feed Rate (mm/rev)*Speed (rpm) 1 0.001428 0.001428 0.02 0.882
Error 8 0.485800 0.060725
Lack-of-Fit 7 0.434600 0.062086 1.2 0.606
Pure Error 1 0.051200 0.051200
Total 17 0.660578

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Fig. 6: Versur order between Residual and observation
Fig. 7: Histogram graph between frequency and Residual (response surface roughness)
In Fig. 6-7, the signal to noise ratio select for the current work was “smaller to better’’
According to Fig. 6, at the first level of depth of cut (0.5), first level of feed rate (0.000375)
mm/rev, first spindle speed (250 rpm) and first level of different Coolants (C1 type) respectively.
The surface roughness on the machined surface was found to be minimum. At Main effects versus
order between Residual and observation.

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Fig. 8: Versus Fits between Residual and Fitted value
Fig. 9: Normal Probability plot
According to Fig. 8 and Fig. 9, the Fits graph and Normal probability plot show the affect on
surface roughness as the result of the analysis. Response Surface method has been successfully used
to show the affect of the various parameters on the surface roughness and probability graph confirms
the same. This comparative study utilized an efficient method for determining the optimum milling
operation parameters in the four different cases for surface finish under varying noise conditions,
through the use of the Response process. Conclusions can be summed up with the following:

57
i. The use of a standard L18 orthogonal array, with four control parameters required 2 work
pieces to conduct the experimental portion in each case.
ii. In milling of EN11 (0.4% C) by High Speed Steel tool, the cutting the combination obtained
for the optimal levels of the parameters was spindle speed (250 rpm) followed by feed
(0.000375 mm/rev) and depth of cut (0.5 mm) and Different Coolant.
Fig. 10: EN11 Specimen Fig. 11: Milling operation on EN11 Specimen
CONCLUSION
The present work has successfully demonstrated the application of Response surface method
for multi objective optimization of process parameters in end milling EN11 metal based alloy. The
conclusions can be drawn from the present work are as follows
i. The highest response surface result 0.73 was observed for the experimental Process, shown in
experiment result (Table 7).
ii. The order of importance for the controllable factors to the minimum force, in sequence, is the
spindle speed, depth of cut, feed rate and different Coolant; the order to minimum surface
roughness, in sequence, is the spindle speed, feed rate, depth of cut and different coolants.
iii. However, it is observed through ANOVA that the spindle speed is the most influential
control factor among the four end milling process parameters investigated in the present
work, when minimization of cutting forces, minimization of surface roughness are
simultaneously considered.
In this research work, the material used is EN11 with 0.4% carbon. The experimentation can
also be done for other materials having more hardness to see the effect of parameters on
Surface Roughness. In each case interaction of the different levels of the factors can be
included and study can be extended. In DOE the number of trials can be repeated with the
same combinations of factors and their interactions to obtain more than one response
(Surface Roughness).
3. ACKNOWLEDGEMENTS
Student’s Workshop, Department of Mechanical Engineering, Shepherd School of
Engineering and Technology, SHIATS, Allahabad. New Metal Testing Laboratory, Allahabad.

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A STUDY OF THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS USING RESPONSE SURFACE METHOD ON EN11 ALLOY STEEL IN THE END-MILLING PROCESS

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