Ie3115371544

Ashish Kabra, Amit Agarwal, Vikas Agarwal, Sanjay Goyal, Ajay Bangar / International
Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 1, January -February 2013, pp.1537-1544
Parametric Optimization & Modeling for Surface Roughness,
Feed and Radial Force of EN-19/ANSI-4140 Steel in CNC Turning
Using Taguchi and Regression Analysis Method
Ashish Kabra*, Amit Agarwal*, Vikas Agarwal* Sanjay Goyal**, Ajay
Bangar**
*
(Department of Mechanical Engineering, F.E.T.R.B.S, Agra, India. Email:ashishkabra@gmail.com)
**
(Department of Mechanical Engineering, M.P.C.T., Gwalior, M.P., India.)

ABSTRACT
Efficient turning of high performance machining parameters of machining parameters by
EN series material can be achieved through trial and error, previous work experiences of the
proper selection of turning process parameters to process planner and machining data hand books are
minimize surface roughness, feed and radial very time consuming and tedious process. The
forces. In this present paper outlines an increasing importance of turning operations is
experimental study to optimize and study the gaining new dimensions in the present industrial
effects of process parameters in CNC turning on age, in which the growing competition calls for all
Surface roughness, feed and radial forces of the efforts to be directed towards the economical
EN19/AISI4140 (medium carbon steel) work manufacture of machined parts. [7]
material in dry environment conditions. The EN19 is a medium-carbon steel and finds its typical
orthogonal array, signal to noise ratio and applications in the manufacturing of automobile and
analysis of variance are employed to study the machine tool parts such as gears, shafts, spindles, etc.
performance characteristics in CNC turning Properties of EN19 steel, like low specific heat, and
operation. Three machining parameters are tendency to strain-harden and diffuse between tool and
chosen as process parameters: Cutting Speed, work material, give rise to certain problems in its
Feed rate and Depth of cut. The experimentation machining such as large forces, high cutting-tool
plan is designed using Taguchi’s L9 Orthogonal temperatures, poor surface finish and built-up-edge
Array (OA) and Minitab-16 statistical software is formation. This material is thus difficult to machine.
used. Optimal values of process parameters for [1].
desired performance characteristics are obtained Taguchi proposes an off-line strategy for
by Taguchi design of experiment. Prediction quality improvement in place of an attempt to
models are developed with the help of regression inspect quality into a product on the production line.
analysis method using Minitab-16 software and He observes that no amount of inspection can put
finally the optimal and predicted results are also quality back into the product; it merely treats a
verified with the help of confirmation symptom. To achieve desirable product quality by
experiments. design, Taguchi recommends a three stage process:
system design, parameter design and tolerance
Keywords- Taguchi method, surface roughness, design. While system design helps to identify the
feed and radial force, CNC turning, regression working levels of the design parameters, parameter
modeling. design seeks to determine the parameter levels that
produce the best performance of the product/process
1. INTRODUCTION under study. The optimal condition is selected so
Increasing the productivity and the quality that the influence of uncontrollable factors (noise
of the machined parts are the main challenges of factors) causes minimum variation to system
metal-based industry; there has been increased performance. The orthogonal arrays, variance and
interest in monitoring all aspects of the machining signal to noise analysis are the essential tools of
process. Turning is the most widely used among all parameter design. Tolerance design is a step to fine
the cutting processes. The finished component is tune the results of parameter design by tightening
subjected to dimensional accuracy, required surface the tolerance of parameters with significant
finish; the tool is subjected to less force, minimum influence on the product. [2-3]
possible temperature and maximum tool life; and The objective of this paper is to obtain
Process is subjected to high material removal rate. optimal settings and study the effect of process
In order to achieve these desired performance parameters-cutting speed, feed and depth of cut,
measures in any machining operation, proper resulting in an optimum value of surface roughness,
selections of machining parameters are very feed and radial forces while turning En19 steel with
essential. The present method of selection of uncoated carbide inserts. The effects of the process

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parameters on desired surface roughness, feed and In this paper our objective is to minimize surface
radial forces and the subsequent optimal settings of roughness, feed and radial forces then we select
the parameters for obtaining optimal value of lower the better criteria for each response to
surface roughness, feed and radial forces have been calculate S/N ratio.
accomplished using Taguchi‟s parameter design
approach. 3. EXPERIMENTAL DETAIL
3.1. WORK MATERIAL
2.BACKGROUND OF TAGUCHI The present work deals with the turning of
METHOD hard material such as EN-19(AISI 4140) steel. This
Dr. Genichi Taguchi is a Japanese material is chosen because of its wide use in
statistician and Deming prize winner who pioneered manufacturing machinery parts, gear wheels, tie
techniques to improve quality through Robust rods, bolts, pins and shaft requiring high resistance.
Design of products and production processes. Dr. Since the present trend in the manufacturing
Taguchi developed fractional factorial experimental industry is high speed dry machining, it is applied to
designs that use a very limited number of evaluate the performance of uncoated tools in
experimental runs. The Taguchi method is a typical manufacturing processes.
systematic application of design and analysis of
experiments for the purpose of designing and Table1: The chemical and mechanical properties of
improving product quality. In recent years, the EN-19/AISI 4140 steel [11]
Taguchi method has become a powerful tool for Elements C Si Mn Cr Mo
improving productivity during research and % % % % %
development so that high quality products can be Weight 0.40 0.25 0.85 1.0 0.2
produced quickly and at low cost [3]. % % % % % 5%
Taguchi asserted that the development of Mech. Yield Tensile Elon Hardness
his methods of experimental design started in Japan Prop. Strength Strength gatio (HB)
about 1948. These methods were then refined over (MPa) (MPa) n (%)
the next several decades. They were introduced in 770 930 17 275
the United States around 1980. Although, Taguchi's
approach was built on traditional concepts of design
of experiments (DOE), such as factorial and
fractional factorial designs and orthogonal arrays, he
created and promoted some new DOE techniques
such as signal-to-noise ratios, robust designs, and
parameter and tolerance designs.[2]
Basically, experimental design method was
developed by Fisher. However, classical
experimental design methods are too complex and
not easy to use. Furthermore, a large number of
experiments have to be carried out when the number
of the process parameters increases. To solve this
problem, the Taguchi method uses a special design
of orthogonal arrays to study the entire parameter Fig. 1: Photogragh of Full length unfinished rod of
space with a small number of experiments only. The EN-19 material, diagram showing dimensions of
experimental results are then transformed into a finished work piece and Photogragh of finished
signal-to-noise (S/N) ratio. Taguchi recommends the work piece after CNC turning operation.
use of S/N ratio to measure the quality
characteristics deviating from the desired values. 3.2. SELECTION OF CUTTING TOOLS AND
Usually, there are three categories of quality TOOL HOLDERS
characteristics in analysis of S/N ratio i.e. lower-the- The cutting tool selected for machining
better, higher-the -better, and nominal-the-better.[4] EN19 steel is uncoated carbide inserts. The
The Signal to noise ratio measures the sensitivity of specifications of tool geometry and tool holder are
the quality characteristic being investigated in as follows:
controlled manner, to those external influencing Cutting Tool Specification:
factors (Noise Factors) not under control. The aim in Tool material-Uncoated carbide tool
any experiment is always to determine the highest Manufactured by-KORLOY Inc.
possible (S/N) ratio for the result irrespective of the Tool Designation-TOEH 060102L ST10
type of quality characteristic. A high value of S/N T - Insert Shape = 60o , C- Clearance Angle = 7 o
implies the signal is much higher than the random Relief angle = 8 o , nose radius=0.2 mm
effect of Noise factor. [9]

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Table 2: Process variables and their levels
S.No Process Process Levels
. Paramet Paramet Lo Mediu Hig
er er w m h
Cutting
1. V Speed 100 150 200
(m/min.)
Fig 2: Uncoated carbide tool Feed
2. f Rate 0.0 0.11 0.17
Cutting Tool Holder Specification: (mm/rev 5
Designation -PTTNR/L 2020-K16 .)
L=125 mm, W=20 mm, H=20 mm Depth
3. d Of Cut 0.4 0.8 1.2
(mm.)
Feed force, Radial force acts in the direction of
radius of w/p and in the direction of feed of tool
both are responsible for causing dimensional
Fig3 cutting tool holder inaccuracy and vibration. In the given fig.5 feed
force is represent by Ft and radial force is represent
3.3. EXPERIMENTAL PLAN AND CUTTING by Ff.
CONDITIONS
The experiments are carried out on a CNC
turning lathe machine (PRAGA-INDIA RTC 600) in
Dry working environment Installed at National Small
Industries Corporation L.T.D, Aligarh (U.P.) India. The
experimental setup is shown in Fig.4. For the present
experimental work the three controllable process
parameters with three levels have been taken [12].
The levels of the individual process
parameters/factors are given in Table 2.Feed and Fig: 5 Forces on single point cutting tool
radial forces are varying over a time span of cutting In this experimental work the Feed and Radial
operation due to the variation of mechanical forces are measured with a piezoelectric 3-
properties of the work piece, vibrations etc. Component Dynamometer (Kistler- 9257B) and
Therefore considering this as a noise factor in this charge amplifiers (Kistler Type 5814B1) available
experimental work, forces are measured three times at National Small Industries Corporation L.T.D,
during the machining process for overcome this Aligarh (U.P.) as shown in Fig.6
variation.

CNC Lathe Machine Specifications:
Type: - CNC Lathe PTC-600
Controller: - Fanuc
Swing Over bed: - 400 mm.
Spindle Power: - 7.5/11 Kw
Turning Length (mm): 300
Rapid Feed (X& Z axis): 24 M/min
Speed Range: 50-4500 rpm
Fig. 6: Photograph of Kistler piezoelectric
dynamometer and Kistler Charge amplifier

Surface roughness or surface finish is an
important measure of product quality and
performance. Surface roughness has an impact on
the mechanical properties like fatigue behavior,
corrosion resistance, creep life, etc. It also affects
other functional attributes of parts like friction,
wear, light reflection, heat transmission, lubrication,
electrical conductivity, etc. In this paper, Average
Surface roughness is measured by direct contact
Fig 4: Photograph of PRAGA-INDIA PTC 600 CNC method using Profilometer as shown in Fig.7
lathe

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Surface roughness ,Feed and radial forces are being
a „lower the better„ type of machining quality
characteristic, the S/N ratio for this type of quality
characteristic is calculated by the following equation
is given below [9 ]:
S/N ratio = - 10 Log10 {(Y12 + Y22 + . . . + YN2)/N}
------------------ equation
Fig 7: Photoghaph of Profilometer (SRT-6210). (1)
Where Y1, Y2. . …., YN are the responses of the
3.3.1 SELECTION OF ORTHOGONAL machining characteristic, for a trial condition
ARRAYS AND EXPERIMENTAL DESIGN repeated n times. The S/N ratios were computed
The DOF is defined as the number of using Eq. 1 for each of the 9 trials and the values are
comparisons between machining parameters that need reported in Table 4 and Table 5.
to be made to determine, which level is better and Table 3: The basic Taguchi L9 Orthogonal array
specifically how much better it is. As per Taguchi Sr.No. Cutting speed Feed Depth of
experimental design philosophy a set of three levels (V) Rate (f) cut (d)
assigned to each process parameter has two degrees (m/min) (mm/rev) (mm.)
of freedom (DOF) and OA (Orthogonal Array) to be 1. 1 1 1
selected must satisfy the following conditions: 2. 1 2 2
D.O.F. of O.A. selected ≥ D.O.F. required. 3. 1 3 3
The experiment under consideration has 6 D.O.F. and 4. 2 1 2
therefore requires an O.A with 8 or more D.O.F. 5. 2 2 3
Hence an O.A. with at least 9 experiments is to be 6. 2 3 1
selected to estimate the effect of each factor and the 7. 3 1 3
desired interaction (if any). 8. 3 2 1
For such kind of situation Taguchi L9 orthogonal 9. 3 3 2
array is used as shown in Table 3.

Table 4: Experimental Data Related and S/N values for Feed (Ft) and Radial Force (Fr).
V F d Feed force Radial force
S.No.
Ft1(N) Ft2 (N) Ft3 (N) Ft (N) S/N Fr1 (N) Fr2 (N) Fr3 (N) Fr (N) S/N
1. 100 0.05 0.4 49.75 52.88 49.34 50.66 -34.10 29.85 31.72 29.60 30.39 -29.66
2. 100 0.11 0.8 67.31 71.10 69.00 69.01 -36.80 40.38 42.66 41.40 41.41 -32.36
3. 100 0.17 1.2 84.39 83.85 86.22 84.82 -38.57 49.63 49.31 50.73 49.89 -33.96
4. 150 0.05 0.8 66.22 64.23 62.22 64.22 -36.16 39.73 38.54 37.33 38.53 -31.72
5. 150 0.11 1.2 76.65 77.13 78.21 77.33 -37.77 45.99 46.27 46.92 46.39 -33.33
6. 150 0.17 0.4 61.01 61.01 64.22 62.08 -35.86 35.60 35.61 37.53 36.25 -31.19
7. 200 0.05 1.2 69.22 69.11 69.02 69.12 -36.79 41.53 41.47 41.41 41.47 -32.35
8. 200 0.11 0.4 54.55 52.25 54.55 53.78 -34.61 32.73 31.35 32.73 32.27 -30.18
9. 200 0.17 0.8 68.47 67.39 67.93 67.93 -36.64 39.08 39.43 39.76 39.76 -31.92
Average Feed force height Y1 = 66.5 N, Average Radial force height Y2 = 39.60 N.

Table 5: Experimental Data & S/N values for Ra 4.ANALYSIS PROCEDURE AND
R1 R2 R3 Ra DISCUSSION
S.No. S/N ratio
(µm) (µm) (µm) (µm) The experiments are conducted to study
1 1.11 1.16 1.33 1.20 -1.61 the effect of process parameters over the output
2 2.21 2.44 2.76 2.47 -7.89 response characteristics and the experimental
3 3.39 3.98 4.29 3.89 -11.83 results for feed and radial forces are given in table
4 1.52 1.42 1.63 1.52 -3.67 4, for surface roughness is given in table 5. For
5 2.82 2.99 2.49 2.77 -8.86 analyzing the results first calculate S/N ratio for all
responses with Lower the better criterion by
6 2.39 2.56 2.41 2.45 -7.80 equation 1, given in table 4 and table 5 and analysis
7 1.45 1.81 2.35 1.87 -5.60 of S/N ratio are done with the help of Taguchi
8 1.80 1.66 1.87 1.78 -5.00 design of experiment using Minitab-16 software.
9 3.42 2.82 2.93 3.05 -9.74 The mean response refers to the average value of
Average surface roughness height Y3 = 2.33 m. the performance characteristic for each parameter
at different levels. The average values of responses
and S/N ratios for each parameter at levels 1, 2 and

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3 are calculated, plotted and tabulated in following Table 8: ANOVA data table for feed force
Figures and Tables. It is clear from mean response, Source DOF SS MS F ratio Cont.
S/N ratio and ANOVA analysis that: %
From fig. 8, feed force is minimum at the
V 2 39.02 19.51 4.82 4.3
3rd level of parameter V, 1st level of parameter f
and 1st level of parameter d. By the table 7 the f 2 158.5 79.26 19.59* 17.5
effect of machining parameters are shown that d 2 699.9 349.9 86.50* 77.3
when the cutting speed is increased from 100 m / Error 2 8.09 4.05 1
min to 200 m / min, feed force is decreased from Total 8 905.5
68.2 N to 63.61 N, when the feed rate is increased
Tabulated F-ratio at 95% confidence level: F0.05;
from 0.05 mm/rev to 0.17 mm/rev, feed force is
2; 2 = 19
increased from 61.33 N to 71.61 N, when the depth
* Significant at 95% confidence level.
of cut is increased from 0.4 mm to 1.2 mm, feed
From fig. 9, radial force is minimum at the
force is increased from 55.51 N to 77.09 N. It is
3rd level of parameter V, 1st level of parameter f
clear from ANOVA data table 8; the contributions
and 1st level of parameter d. By the table 10 the
of cutting speed, feed rate and depth of cut for feed
effect of machining parameters are shown that
force are 4.3%, 17.5%and77.29% respectively.
when the cutting speed is increased from 100 m /
min to 200 m / min, radial force is decreased from
Table 6: Response Table for S/N Ratios of feed
40.59 N to 37.72 N, when the feed rate is increased
force.
from 0.05 mm/rev to 0.17 mm/rev, radial force is
Level V f d increased from 36.80 N to 41.85 N, when the depth
1 -36.49 -35.68 -34.86 of cut is increased from 0.4 mm to 1.2 mm, radial
2 -36.60 -36.39 -36.53 force is increased from 32.97 N to 45.92 N. It is
3 -36.02 -37.02 -37.71 clear from ANOVA data table 11; the contributions
Delta 0.58 1.34 2.85 of cutting speed, feed rate and depth of cut for
radial force are 4.53%, 13.18% and 81.33%
Rank 3 2 1
respectively.
Table 7: Response Table for Means of feed force. Table 9: Response Table for S/N ratio of radial
Level V f d force.
1 68.20 61.33 55.51 Level V f d
2 67.88 66.75 67.10 1 -31.99 -31.24 -30.34
3 63.61 71.61 77.09 2 -32.08 -31.96 -32.00
Delta 4.59 10.28 21.58 3 -31.48 -32.36 -33.22
Rank 3 2 1 Delta 0.60 1.11 2.87
Main Effects Plot for Means
Data Means Rank 3 2 1
cutting speed feed rate

75

70 Table 10: Response Table for Means of radial
65

60
force.
Mean of Means

55
100 150 200 0.05 0.11 0.17
Level V f d
depth of cut

75
1 40.59 36.80 32.97
70 2 40.39 40.05 39.81
65
60
3 37.72 41.85 45.92
55
0.4 0.8 1.2
Delta 2.87 5.06 12.95
Rank 3 2 1
Main Effects Plot for SN ratios
Data Means Main Effects Plot for SN ratios
cutting speed feed rate Data Means
-35
-30
-36
-31
-37
Mean of SN ratios

-32
-38
Mean of SN ratios

100 150 200 0.05 0.11 0.17
-33
depth of cut
-35 100 150 200 0.05 0.11 0.17
depth of cut
-36 -30

-37 -31

-38 -32
0.4 0.8 1.2
Signal-to-noise: Smaller is better -33

0.4 0.8 1.2
Fig 8: Feed force Main effects plot for S/N ratio Signal-to-noise: Smaller is better
and means

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Main Effects Plot for Means Table 13: Response Table for Means of Ra
Data Means
Level V f d
45
1 2.519 1.531 1.810
40
2 2.248 2.338 2.350
Mean of Means

35
3 2.234 3.132 2.841
100 150 200 0.05 0.11 0.17

45
depth of cut
Delta 0.284 1.601 1.031
40
Rank 3 1 2
Main Effects Plot for Means
35
Data Means
0.4 0.8 1.2
3.0

Fig 9: Radial force Main effects plot for S/N ratio 2.5

and means 2.0

Mean of Means
1.5
Table 11: ANOVA data table for radial force 100 150 200 0.05 0.11 0.17
depth of cut
Sourc DO SS MS F ratio Con. 3.0
e F % 2.5

V 2 14.05 7.02 4.82 4.53 2.0

1.5
0.4 0.8 1.2
f 2 40.85 20.43 14.02 13.1 Main Effects Plot for SN ratios
Data Means
8
d 2
251.9 125.9 86.50 81.3 -4
6 8 * 3
-6
Error 2 2.91 1.46 1
Mean of SN ratios

-8
Total 8 309.7
-10
8 100 150 200 0.05 0.11 0.17
depth of cut
Tabulated F-ratio at 95% confidence level: F0.05; -4
2; 2 = 19
-6
-8
From fig. 10, surface roughness is
minimum at the 3rd level of parameter V, 1st level -10
0.4 0.8 1.2
of parameter f and 1st level of parameter d. By the Signal-to-noise: Smaller is better
Table 13 the effect of machining parameters are Fig 10: Surface Roughness Main effects plot for
shown that when the cutting speed is increased S/N ratio and means
from 100 m / min to 200 m / min, surface
roughness is decreased from 2.519µm to 2.234µm, Table 14: ANOVA data table for surface roughness
when the feed rate is increased from 0.05 mm/rev Source D SS MS F ratio Con.
to 0.17 mm/rev, surface roughness is increased F %
from 1.531µm to 3.132µm, when the depth of cut is Cuttin 2 0.1 0.078 5.85 2.8
increased from 0.4 mm to 1.2 mm, surface g 6 5
roughness is increased from 1.810µm to 2.841µm. speed
It is clear from ANOVA data table 14 the Feed 2 3.8 1.920 142.94* 68.2
contributions of cutting speed, feed rate and depth rate 4 1 *
of cut for surface roughness are Depth 2 1.5 0.801 59.65* 26.5
2.8%,68.2%and26.5% respectively. of cut 0 2
Error 2 0.1 0.013 3
Table 12: Response Table for S/N Ratios of Ra 3 4
Level V f d Total 8 5.6
1 -7.111 -3.628 -4.804 2
2 -6.777 -7.252 -7.099 Tabulated F-ratio at 95% confidence level: F0.05;
2; 2 = 19
3 -6.781 -9.789 -8.766
Delta 0.333 6.161 3.962
Rank 3 1 2 4.1 PREDICTED RESULT (BY TAGUCHI
METHOD):
The predicted values of Responses at the optimal
levels are calculated by using the relation:
ñ = nm +

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Where, ñ - Predicted response value after Average value of feed force = 47.83+49.19=48.51
optimization, nm - Total mean value of quality N
characteristic, nim - Mean value of quality
characteristic at optimal level of each parameter, o Table 16: Experimental results of radial force of
- Number of main machining parameters that affect final Confirmation Experiment.
the response parameter. Exp. No. Fr1(N) Fr2(N) Fr3(N) Fr(N)
ñ Ft = 66.5+(63.61-66.5)+(61.33-66.5)+(55.51- 1 29.05 27.35 28.35 28.25
66.5) = 47.45 N 2 28.56 29.95 31.04 29.85
ñ Fr = 39.6+(37.72-39.6)+(36.8-39.6)+(32.97-39.6) Average value of radial force =28.25+29.85=29.05
= 28.29 N N
ñ Ra= 2.33+(2.234-2.33)+(1.531-2.33)+(1.810-
2.33) Table 17: Experimental results of surface
= 0.915 µm roughness of final Confirmation Experiment.
Exp. No. R1(µm) R2(µm) R3(µm) Ra(µm)
5. DEVELOPMENT OF 1 1.05 1.20 1.23 1.16
MATHEMATICAL REGRESSION
2 0.98 1.06 1.08 1.04
MODELS
Average value of surface roughness = 1.1 µm
The experimental results are used to
obtain the mathematical relationship between
process parameters and machine outputs. The 7. COMPARISONS OF THE RESULTS
coefficient of mathematical models is computed The outcome of the calculations and
formulation for the optimization by the methods i.e.
using method of general linear regression. For
Prediction by Taguchi Method and Regression
calculate the linear regression equation minitab-16
software is used. The feed force (Fc), radial force modeling. By using the optimal factor level
and surface roughness are expressed as: combination suggested by Taguchi Methodology
Ra = 0.263333 - 0.00286667 V + 13.3333 f + 1.29167 the experiments are conducted and the results are
d summarized in the table 18.
Ft = 42.3764 - 0.0455333 V + 85.6389 f + 26.9792 d
Table 18: Comparison of the Results of cutting
Fr = 26.0092 - 0.02732 V + 43.05 f + 16.1875 d
performance using the initial & optimal cutting
factors settings.
5.1 PREDICTED RESULT (BY REGRESSION
MODEL): Optimal Cutting Parameters
Ra = 0.263333 - 0.00286667*200 + 13.3333 *0.05 Predicti Predictio
Initial
+ 1.29167 *0.4 =0.87333 µm on n
Cutting
Ft = 42.3764 - 0.0455333 *200 + 85.6389 *0.05 + (Taguch (Regressi Experim
Paramet
26.9792 *0.4 = 48.34 N i on ent
ers
Fr = 26.0092 - 0.02732 *200 + 43.05 *0.05 + 16.1875 Method Modelin
*0.4 =29.17 N ) g)
Lev V3 f1
V1 f1 d1 V3 f1 d1 V3 f1 d1
6 CONFIRMATION & VALIDATION OF el d1
50.66N 48.34 N 48.50 N
OPTIMAL SETTINGS Ft 47.45 N
To validate the optimal cutting Lev V3 f1 V3 f1 d1
V1 f1 d1 V3 f1 d1
conditions suggested by Taguchi methodology the el d1 29.1727
30.39N 29.05 N
planning layout of the confirmation experiments Fr 28.29 N N
verify improvement of the performance V3 f1
Lev V3 f1 d1
characteristic, while considering noise factors V1 f1 d1 d1 V3 f1 d1
el 0.87333
(variation of feed and radial forces during CNC 1.2 µm 0.915 1.1 µm
Ra µm
turning process, variation of surface roughness at µm
different locations); the total two confirmation Improvement in feed force = 50.66-48.50=2.16N
experiments are conducted, measure feed and radial Improvement in radial force = 30.39-29.05=1.34 N
forces at different time intervals, surface roughness Improvement in surface roughness = 1.2 -1.1=0.1
at different location have been taken and tabulated µm
in Table 15,Table 16 and Table 17.
8. CONCLUSION
Table 15: Experimental results of feed force of Experiments conducted according to
final Confirmation Experiment. Taguchi Parameter Design of Minitab-16 software
Exp. No. Ft1(N) Ft2(N) Ft3(N) Ft(N) and subsequently analysis performed by using the
1 46.25 48.55 48.70 47.83 Taguchi Method and Regression Modeling.
Optimal machining conditions are identified for
2 49.25 48.22 50.12 49.19
CNC turning of EN-19 (ANSI 4140 medium

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carbon steel) material. The confirmation “Taguchi method and ANOVA: An
experiments are conducted then the results obtained approach for process parameters
are compared with the above said optimization optimization of hard machining while
methods are discussed as follows: machining hardened steel” Journal of
 Depth of Cut represents the largest influence on Scientific & Industrial Research,Vol. 68, pp.
surface roughness, feed and radial forces followed 686-695.
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 From ANOVA Table 8 and Table 14, it is machining conditions on MRR and surface
apparent that the F- ratio values of Depth of cut and roughness during CNC Turning of different
Feed rate are greater than F0.05, 2, 2 =19.00. While Materials Using TiN Coated Cutting Tools –
Cutting Speed is proved to be a least significant A Taguchi approach”., International Journal
machining factor for feed force and surface of Industrial Engineering Computations,Vol.
roughness. 3,pp.925–930
 From ANOVA Table 11 it is apparent that the F- 8. Kalpakjian S. and Schmid Steven R. (2000),
ratio values of Depth of cut greater than F0.05, 2, 2 “Manufacturing Engineering and
=19.00. While cutting speed and feed rate are Technology”, 4th ed, Pearson Education
proved to be a least significant machining factor for Asia. ISBN 81-7808-157-1.
radial force. 9. Ross,Phillips J.(2005) “Taguchi Techniques
 Final confirmation result shows that at optimal for quality Engineering “second edition,
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