1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
392
THE PREDICTION OF SURFACE ROUGHNESS IN FINISH TURNING
OF EN-19 STEEL
Vivek John 1*
, Rahul Davis 2
, Kulan Abel Kandulna 3
, Asian Abhishek Kandulna 4
1
Assistant Professor, Department of Mechanical Engineering, SSET, SHIATS,
Allahabad -211007, Uttar Pradesh, India
2
Assistant Professor, Department of Mechanical Engineering, SSET, SHIATS,
Allahabad -211007, Uttar Pradesh, India
3
B.Tech Mechanical Engineering, Department of Mechanical Engineering, SSET, SHIATS,
Allahabad -211007, Uttar Pradesh, India
4
B.Tech Mechanical Engineering, Department of Mechanical Engineering, SSET, SHIATS,
Allahabad -211007, Uttar Pradesh, India
ABSTRACT
Surface roughness, is an admensuration of the pattern of the surface. The peaks and
valleys are the indicators to determine whether the surface is rough or smooth. Roughness
portrays an extensive role in demonstrating how the object will interface with its
environment. The method we have used here was a turning process in which there are
basically five distinct specifications i.e pressurized coolant jet, rake angle, spindle speed, feed
rate and depth of cut. The Taguchi approach is an adequate channel in which response
variable can be optimized by taking fewer experimental runs. The aim of the paper is to
obtain an optimal setting of these five distinct turning process parameters by using Carbide P-
30 cutting tool in turning En19 steel having carbon percentage 0.39 as specimen. The
Analysis of Variance (ANOVA) and Signal-to-Noise (SN) ratio and were used to analyze the
performance. The results illustrate that Spindle speed followed by pressurized coolant jet,
rake angle, feed rate and depth of cut was the combination of the optimal levels of factors that
affects the surface roughness of the specimen. The results have been cross checked by the
confirmation experiments.
Keywords: EN-19 steel, turning operation, Taguchi Method
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 3, May - June (2013), pp. 392-399
© IAEME: www.iaeme.com/ijmet.asp
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
393
1. INTRODUCTION
Human tendency is to remain one step ahead from others, engineers take keen interest
in inventing new surface finish parameters specific to parts that their organizations
manufacture. Thus, out of something akin to pride of ownership, new surface finish
parameters are born, in response to the existing parameters that may have done the job
satisfactorily. In rapidly changing industries, new applications require surface conditions
unlike those that occur in the traditional metalworking fields for which most existing
parameters were developed1
.During turning operation, cutting tools remove material from the
component to achieve the required shape, dimension and surface roughness (finish).
However, wear occurs during the cutting action. Surface roughness can be one of the factors
in businesses to gain a competitive edge3
. The aim of the industries is focused on low cost
production and high quality products in less time. It is very important now for manufacturers
to enhance the efficiency of product and process, keeping the tolerances of stricter part
maintained, and thus improving the quality of part. Design of experiments via Taguchi
method can be used for attaining high quality at minimum cost. Also the quality obtained by
means of the optimization of the product or process is found to be cost effective4
. EN19 is a
high quality, high tensile, alloy steel and combines high tensile strength, shock resistance,
good ductility and resistance to wear5
. EN19 is most suitable for the manufacture of parts
such as heavy-duty axles and shafts, gears, bolts and studs. EN19 is capable of retaining good
impact values at low temperatures6
. Since Turning is the primary operation in most of the
production process in the industry, surface finish of turned components has greater influence
on the quality of the product7
. Surface roughness in turning has been found to be influenced
in by a number of factors such as spindle speed, pressurized coolant jet etc8
.
2. METHODOLOGY
In this research work L16 Taguchi orthogonal method has been used in order to study
the effect of five different process parameters (spindle speed, pressurized coolant jet, rake
angle, feed rate, Depth of cut) on the surface roughness of EN19 steel in turning operations
by Carbide P-30 cutting tool and surface roughness was measured in each run in Sparko
Engineering Workshop, Allahabad. The length of the work piece was found to be 252 mm.
Therefore for the following research, EN19 steel with carbon (0.39%), silicon (0.24),
Chromium (1%) and Manganese (0.68%) was chosen for specimen material.
Table: 2.1 Details of the Turning Operation
Factors Level 1 Level2
Depth of Cut(mm) 0.5 1.0
Feed Rate (mm/rev) 0.16 0.8
Spindle Speed (rpm) 760 1560
Pressurized Coolant Jet (bar) 0.5 1
Rake angles (degrees) 40
70

3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
394
In this experiment, the assignment of factors was carried out using MINITAB 15
Software. Using the L16 orthogonal array the trial runs have been conducted on Lathe
Machine for turning operations.
Table 2.2: Results of Experimental Trial Runs for Turning Operation
Trial
no.
Feed
Rate
(mm/r
ev)
Spindle
Speed
(rpm)
Depth
of Cut
(mm)
Rake
Angle
(deg)
Pressurized
Coolant
Jet(bar)
Surface
Rough-
ness
(µm)
SN
Ratio
1 0.16 780 0.5 4 0.5 41.5 -32.3610
2 0.16 780 0.5 7 1.0 55.2 -34.8388
3 0.16 780 1.0 4 1.0 41.0 -32.2557
4 0.16 780 1.0 7 0.5 131.0 -42.3454
5 0.16 1560 0.5 4 1.0 25.5 -28.1308
6 0.16 1560 0.5 7 0.5 95.1 -39.5636
7 0.16 1560 1.0 4 0.5 87.2 -38.8103
8 0.16 1560 1.0 7 1.0 47.4 -33.5156
9 0.8 780 0.5 4 1.0 41.0 -32.2557
10 0.8 780 0.5 7 0.5 46.2 -33.2928
11 0.8 780 1.0 4 0.5 25.8 -28.2324
12 0.8 780 1.0 7 1.0 34.8 -30.8316
13 0.8 1560 0.5 4 0.5 127.1 -42.0829
14 0.8 1560 0.5 7 1.0 120.0 -41.5836
15 0.8 1560 1.0 4 1.0 74.3 -37.4198
16 0.8 1560 1.0 7 0.5 102.0 -40.1720
Table 2.3: Analysis of Variance for Surface Roughness
Source DF Seq SS Adj SS Adj MS F P
Feed Rate (mm/rev) 1 140 140 140 0.08 0.787
Spindle speed (rpm) 1 4294 4294 4294 2.46 0.168
Depth of Cut (mm) 1 4 4 4 0.00 0.963
Rake Angle (degrees) 1 1770 1770 1770 1.01 0.353
Pressurized Coolant Jet (bar) 1 2935 2935 2935 1.68 0.243
Spindle speed (rpm)* Pressurized
Coolant Jet (bar)
1 321 321 321 0.18 0.638
Depth of Cut (mm)*
Rake Angle (degrees)
1 2 2 2 0.00 0.975
Depth of Cut (mm)* Pressurized Coolant
Jet (bar)
1 403 403 403 0.23 0.648
Rake Angle (degrees)* Pressurized
Coolant Jet (bar)
1 18 18 18 0.01 0.922
Error 6 10487 10487 1748
Total 15 20374

4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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Table 2.4: Analysis of Variance for SN Ratio
Source DF Seq SS Adq SS Adj
MS
F P
Feed Rate (mm/rev) 1 1.02 1.02 1.02 0.03 0.858
Spindle speed (rpm) 1 75.97 75.97 75.97 2.58 0.159
Depth of Cut (mm) 1 0.02 0.02 0.02 0.00 0.981
Rake Angle (degrees) 1 37.81 37.81 37.81 1.28 0.300
Pressurized Coolant Jet (bar) 1 42.34 42.34 42.34 1.44 0.276
Spindle Speed (rpm)*
Pressurized Coolant Jet (bar)
1 12.13 12.13 12.13 0.41 0.545
Depth of Cut (mm)*
Rake Angle (degrees)
1 1.16 1.16 1.16 0.04 0.849
Depth of Cut (mm)*
Pressurized Coolant Jet (bar)
1 1.59 1.59 1.59 0.05 0.824
Rake Angle (degrees)*
Pressurized Coolant Jet (bar)
1 0.63 0.63 0.63 0.02 0.888
Error 6 176.58 176.58 29.43
Total 15 349.25
Table 2.5: Response Table for Signal to Noise Ratio
Level 1 Feed Rate
(mm/rev)
(A)
Spindle
Speed(rpm)
(B)
Depth of
Cut(mm)
(C)
Rake
Angle(degrees)
(D)
Pressurized
Coolant Jet
(bar) (E)
1 -35.23 -37.66 -35.51 -33.94 -37.11
2 -35.73 -33.30 -35.45 -37.02 -33.85
∆max-min 0.51 4.36 0.07 3.07 3.25
Rank 4 1 5 3 2
Table 2.6: Response Table for Means
Level Feed Rate
(mm/rev)
(A)
Spindle
speed
(rpm) (B)
Depth of
Cut (mm)
(C)
Rake Angle
(degrees)
(D)
Pressurized
Coolant Jet
(bar) (E)
1 65.49 84.82 68.95 57.92 81.99
2 71.40 52.06 67.94 78.96 54.90
∆max-min 5.91 32.76 1.01 21.04 27.09
Rank 4 1 5 3 2
From Table 2.5 and 2.6, Optimal Parameters for Turning Operation were A1, B2, C2,
D1and E2 Signal-to-noise ratio (SN) is utilized to measure the deviation of quality
characteristic from the target. In this experiment, the response is the surface roughness
which should be maximized, so the desired SNR characteristic is in the category of Larger
the better.

5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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Table 2.5 shows the SNR of the surface roughness for each level of the factors. From
Table 2.5 the difference of SN ratio between level 1 and 2 indicates that spindle speed
contributes the highest effect (∆max-min=4.36) on the surface roughness followed by
pressurized coolant jet (∆max-min = 3.25), rake angle (∆max-min= 3.07), feed rate (∆max-
min=0.51) and depth of cut (∆max-min=0.07).
Table 2.6 indicates the same result in terms of the difference of Mean between level 1
and 2 indicates that spindle speed contributes the highest effect (∆max-min=32.76) on the
surface roughness followed by pressurized coolant jet (∆max-min = 27.09), rake angle (∆max-
min = 21.04), feed rate (∆max-min=5.91) and depth of cut (∆max-min=1.01).
Therefore the Predicted optimal value of Means of Surface Roughness
ηp (Surface Roughness)
= 68.44+[65.49-68.44]+[52.06-68.44]+[67.94-68.44]+[57.92-68.44]+[54.9-68.44]
= 24.55
Therefore the optimal Predicted value of average Surface roughness for SN Ratio
µp (SN Ratio) = -35.48+[-35.23+35.48]+33.30+35.48]+[- 35.45+35.48]+[-
33.94+35.48]+[-33.85+35.48]
= -29.85
Thus the optimal predicted value of is µp (Surface Roughness) = 29.85
3. RESULTS AND DISCUSSION
Comparing the F values of ANOVA Table 2.3 and 2.4 of Surface Roughness with the
suitable F values shows that none of the factor was found to be significant moreover none of
the interaction were found to be significant.
0.80.16
80
70
60
50
7801560 1.00.5
74
80
70
60
50
1.00.5
Feed Rate(mm/rev)
MeanofMeans
Spindlespeed (rpm) Depth of Cut (mm)
RakeAngle(degrees) Pressurized Coolant Jet (bar)
MainEffectsPlotfor Means
Data Means
1.00.5
100
75
50
1.00.5
100
75
50
100
75
50
7801560
100
75
50
74
Spindlespeed(rpm)
DepthofCut(mm)
RakeAngle(degrees)
PressurizedCoolantJet(bar)
1560
780
(rpm)
speed
Spindle
0.5
1.0
Cut(mm)
Depth of
4
7
(degrees)
RakeAngle
0.5
1.0
(bar)
CoolantJet
Pressurized
InteractionPlotfor Means
Data Means
Figure 3.1: Main Effects Plot for Means Figure 3.2: Interaction plot for Means

6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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0.80.16
-34
-35
-36
-37
-38
7801560 1.00.5
74
-34
-35
-36
-37
-38
1.00.5
Feed Rate(mm/rev)
MeanofSNratios
Spindlespeed (rpm) Depthof Cut(mm)
RakeAngle(degrees) Pressurized CoolantJet(bar)
MainEffectsPlotforSNratios
DataMeans
Signal-to-noise: Smaller is better
1.00.5
-30
-35
-40
1.00.5
-30
-35
-40
-30
-35
-40
7801560
-30
-35
-40
74
Spindlespeed(rpm)
DepthofCut(mm)
RakeAngle(degrees)
PressurizedCoolantJet(bar)
1560
780
(rpm)
speed
Spindle
0.5
1.0
Cut(mm)
Depth of
4
7
(degrees)
RakeAngle
0.5
1.0
(bar)
CoolantJet
Pressurized
InteractionPlotforSNratios
Data Means
Signal-to-noise: Smaller is better
Figure 3.3: Main Effects Plot for SN
ratio
Figure 3.4: Interaction Plots for SN
ratio
From Table 2.5, Table 2.6 and Fig 3.1 and Fig 3.2, optimal levels of the factors for
surface toughness are first level of Feed Rate (0.16 mm/rev), second level of Spindle Speed
(1560 rpm), second level of Depth of Cut (1 mm), first level of Rake Angle(40
) and second
level of pressurized Coolant Jet (1 bar).
The combination of the optimal levels of the parameters was not found within the
trials of Table no 2.2 (L-16 orthogonal array) but the obtained combination of the optimal
levels of the factors was verified using the confirmation tests.
The results of the confirmation experiments are given as follows:
Table 2.7: Results of the Confirmation Tests for the optimal levels of the factors
Feed
Rate
(mm/rev)
Spindle
Speed
(rpm)
Depth
of Cut
(mm)
Rake
Angle
(degree)
Pressurized
Coolant Jet (bar)
Surface
Roughness
0.16 1560 1.0 4 1.0 24.55
0.16 1560 1.0 4 1.0 24.49
0.16 1560 1.0 4 1.0 24.41
0.16 1560 1.0 4 1.0 24.33
4. Summary and Conclusions
• The optimization of the various levels of the factors followed by confirmation test
confirms that the obtained results were found within the limits.
• The results attained by the above research work can be suggested to get the minimum
surface roughness under the above conditions.
• The current research work comprises of the use of EN19 steel having 0.39% carbon.
The research work can contain the application of other materials having different
chemical compositions.
• More number of interactions of the various levels of the factors can also be included
in order to expand the research.

7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
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