EXPERIMENTAL STUDY OF TURNING OPERATION AND OPTIMIZATION OF MRR AND SURFACE ROUGHNESS USING TAGUCHI METHOD

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763
Issue 03, Volume 3 (March 2016) www.ijirae.com
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EXPERIMENTAL STUDY OF TURNING OPERATION
AND OPTIMIZATION OF MRR AND SURFACE
ROUGHNESS USING TAGUCHI METHOD
SHIVAM GOYAL VARANPAL SINGH KANDRA PRAKHAR YADAV
Assistant Professor U.G, Student U.G, Student
Mechanical engineering Mechanical engineering Mechanical engineering
SRM University, NCR Campus SRM University, NCR Campus SRM University, NCR Campus
Abstract— In this research work turning operation is performed on AISI 1020 mild steel. Here we conducted
experiments by taking Cutting Speed, Feed Rate & Depth of cut as process parameters and got the optimized value of
MRR & SR. An L9 orthogonal array, the signal-to-noise (S/N) ratio are employed to the study the performance
characteristics in the turning using WNMG332RP carbide insert with a nose radius of 0.8mm. Taguchi method is
used to optimize surface roughness and material removal rate (MRR) during machining operation on CNC turning.
The experimental result shows that on increasing depth of cut and feed the combined S/N ratio increases while on
increasing cutting speed the combined S/N ratio decreases. It results that cutting speed is most significantly influences
the Surface roughness followed by feed and in case of MRR, depth of cut is the most significant parameter followed by
cutting speed .While the combination of both is most significantly affected by the depth of cut followed by the feed.
Keywords— Insert, Taguchi, S/N ratio, Mild steel, MRR, Surface Roughness.
I. INTRODUCTION
Surface roughness and material removal rate prediction plays a significant role in machining industry for proper
planning and control of machining parameters and optimization of cutting conditions. Now-a-days increasing the
productivity and quality of the machined parts are the main challenges of metal cutting industry during turning processes.
Optimization in turning processes is considered a vital role for continual improvement of output quality in product and
processes include modelling of input-output and in process parameters relationship and determination of optimal cutting
conditions. The effects of process parameters on tool life on turning process and the subsequent optimal settings of
parameters are accomplished using Taguchi`s method. Surface roughness has become the most significant technical
requirement and it is an index of product quality. In order to improve the tribological properties, fatigue strength,
corrosion resistance and aesthetic appeal of the product, a reasonably good surface finish is desired.
Several angles are important when introducing the cutting tool's edge into a rotating work piece. These angles include
the angle of inclination, rake angle, effective rake angle, lead or entry angle, and tool nose radius.
As a result of exhaustive review of work done by previous researchers [1-20], it is found that a very little work has
been found in use of AISI 1020 mild steel on the three cutting parameters i.e. speed, feed and depth of cut.
The study demonstrates detailed methodology of the proposed optimization technique which is based on Taguchi
method; and ranks the parameters namely cutting speed, feed & depth of cut through S/N ratio. MRR of a turned product
along with surface finish of work piece have been optimized.
II. MATERIALS AND METHOD
A. CNC Turning Center
ACE Designers Ltd. make CNC turning centre with Fanuc Oi-mate-TD controller is used to carry out the
experimentation.
TABLE I
SPECIFICATIONS OF CNC TURNING CENTER
MAX. TURNING DIAMETER 190 MM
MAX. TURNING LENGTH 200 MM
CHUCK SIZE 135 MM
SPINDLE SPEED 50- 4000 RPM
SPINDLE MOTOR POWER 5.5 KW/ 3.7 KW

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B. Selection of Cutting Tools
The cutting tool selected for present work is carbide inserts. The inserts used in present work is WNMG 332 RP (ANSI
coding).
The tool geometry of the insert is as follows:
Insert WNMG 332 RP – Trigon Shape, Clearance angle 0°, Inscribed Circle size- 9.5mm, Thickness- 5mm, Nose radius-
0.8mm.
C. Selection of Work piece Material
The work piece material used for current work is AISI 1020 Mild Steel circular bars (ϕ 25mm x 110mm).
D. Process Parameters and Levels used in the Experiment
The machining process on CNC lathe is programmed by cutting speed, feed and depth of cut. The parameters and levels
used in the experiment are shown in Table II.
TABLE II
PROCESS PARAMETERS AND LEVELS
LEVELS VARIABLES
FEED MM/REV (A) DEPTH OF CUT MM (B) CUTTING SPEED M/MIN (C)
LEVEL 1 0.1 0.5 75
LEVEL 2 0.2 0.75 125
LEVEL 3 0.3 1.0 175
E. Design Matrix
In the present work there are three levels and three factors. According to Taguchi approach L9 has been selected. So,
according to Taguchi L9 array design matrix of variables are formed. The array has been made with the help of
MINITAB17.
TABLE III
DESIGN MATRIX OF VARIABLES
EXPERIMENT FEED (MM/REV) A DOC (MM) B CUTTING SPEED (M/MIN) C
1 0.1 0.5 75
2 0.1 0.75 125
3 0.1 1.0 175
4 0.2 0.5 125
5 0.2 0.75 175
6 0.2 1.0 75
7 0.3 0.5 175
8 0.3 0.75 75
9 0.3 1.0 125
III. RESULTS AND DISCUSSIONS
A. Material Removal Rate (MRR)
Initial and final weights of work pieces are noted using digital weighing machine. Machining time is also recorded.
Following equations are used to calculate the response Material Removal Rate (MRR):
The density of the mild steel is taken as 7.87 x 10-3
g/mm3
.
B. Surface Roughness (Ra)
Roughness measurement has been done using a stylus type Taylor-Hobson-Talysurf equipment. The evaluation length of
5.6 mm is used to measure response Ra value in µm.
C. Response Table
Response table for the experimental design matrix is shown in table IV.

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TABLE IV
RESPONSE TABLE OF Ra AND MRR
EXP. A B C RA MRR
1 0.1 0.5 75 1.464 1355.358
2 0.1 0.75 125 2.062 3815.76
3 0.1 1.0 175 2.972 6496.546
4 0.2 0.5 125 3.284 3494.282
5 0.2 0.75 175 4.264 6873.998
6 0.2 1.0 75 2.220 7115.629
7 0.3 0.5 175 3.662 5476.931
8 0.3 0.75 75 2.549 7637.775
9 0.3 1.0 125 3.586 12228.79
D. Analysis of Single Response Stage
The optimal settings and the predicted optimal values for surface roughness and MRR are determined individually by
Taguchi’s approach. Table VII shows these individual optimal values and its corresponding settings of the process
parameters for the specified performance characteristics.
TABLE V
MEANS OF Ra AT DIFFERENT LEVELS
LEVELS
MEAN VALUE OF RA
IV.
FEED (MM/REV)A DOC (MM)B CUTTING SPEED (M/MIN)C
LEVEL 1 2.166233 2.8036 2.0775
LEVEL 2 3.2559 2.923667 2.977467
LEVEL 3 3.265933 2.926133 3.598433
TABLE VI
MEANS OF MRR AT DIFFERENT LEVELS
LEVELS MEAN VALUE OF MRR
XI.FEED (MM/REV)A DOC (MM)B CUTTING SPEED (M/MIN)C
LEVEL 1 3889.221 3442.19 5369.587
LEVEL 2 5827.97 6109.177 6512.945
LEVEL 3 8447.833 8613.656 6282.492
TABLE VII
INDIVIDUAL OPTIMAL VALUES AND CORRESPONDING SETTING OF PROCESS PARAMETERS
PERFORMANCE CHARACTERISTICS OPTIMAL PARAMETER LEVEL OPTIMUM LEVEL
RA (µM) A1-B1-C1 1.464
MRR (MM
3
/MIN) A3-B3-C2 12228.79

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Fig. 2 Response Graph for MRR
ANALYSIS OF PLOT FOR MRR:
It is observed that the maximum MRR is obtained at the 125 m/min of cutting speed, 0.3 mm/rev of feed and 1 mm depth
of cut. The plot shows that on increasing the depth of cut and feed, the MRR further increases and on increasing cutting
speed, the MRR increases initially and then decreases.
Fig. 1 Response Graph for Ra
ANALYSIS OF PLOT FOR SURFACE ROUGHNESS:
It is observed that the minimum surface roughness is obtained at the 75 m/min of cutting speed, 0.1 mm/rev of feed and
0.5 mm depth of cut. The plot shows that on increasing the cutting sped, depth of cut and feed, the Surface roughness
further increases.
E. Analysis of Multi- response stage
The S/N ratio considers both the mean and the variability. In the present work, a multi- response methodology based on
Taguchi technique and Utility concept is used for optimizing the multi-responses (Ra and MRR). Taguchi proposed
many different possible S/N ratios to obtain the optimum parameters setting. Two of them are selected for the present
work. Those are, Smaller the better type S/N ratio for Ra

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Larger the better S/N ratio for MRR
From the utility concept, the multi-response S/N ratio of the overall utility value is given by
Where W1 & W2 are the weights assigned to the Ra and MRR. Assignment of weights to the performance
characteristics are based on experience of engineers, customer’s requirements and their priorities. In the present work
equal importance is given for both Ra and MRR. Therefore W1 & W2 = 0.5.
The optimal combination of process parameters (A3-B3-C2) for simultaneous optimization of Surface roughness (Ra) and
material removal rate (MRR) is obtained by the mean values of the multi-response S/N ratio of the overall utility value
are shown in Table IX. According to the Table IX for the results of S/N ratio multiple performance characteristics, depth
of cut is the most significant parameter affecting the performance followed by the cutting speed.
TABLE VIII
DESIGN MATRIX WITH MULTI- RESPONSE S/N RATIO
EXP. A B C η1 FOR RA η2 FOR MRR ηobs
1 0.1 0.5 75 -3.31082 62.64108 29.66513
2 0.1 0.75 125 -6.28577 71.631621 32.67292
3 0.1 1.0 175 -9.46302 76.253651 33.39531
4 0.2 0.5 125 -10.3286 70.867159 30.26928
5 0.2 0.75 175 -12.5963 76.744187 32.07392
6 0.2 1.0 75 -6.9251 77.044266 35.05958
7 0.3 0.5 175 -11.2758 74.770746 31.74748
8 0.3 0.75 75 -8.1274 77.659337 34.76597
9 0.3 1.0 125 -11.0927 81.747672 35.32749
TABLE IX
MEAN VALUE OF ηobs AT DIFFERENT LEVELS
LEVELS MEAN VALUE OF ηobs
XVIII.FEED (MM/REV)A DOC (MM)B CUTTING SPEED (M/MIN)C
LEVEL 1 31.91112 30.56063 33.16356
LEVEL 2 32.4676 33.17094 32.75657
LEVEL 3 33.94698 34.59413 32.40557

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Fig. 3 Multi-Response S/N Ratio Graph
ANALYSIS OF PLOT FOR MULTI RESPONSE:
The graph shows the optimum levels of process parameters for the multi-response optimization are thus determined to be
125 m/min of cutting speed, 0.3 mm/rev of feed and 1 mm depth of cut. The plot shows that on increasing depth of cut
and feed the combined S/N ratio increases while on increasing cutting speed the combined S/N ratio decreases.
IV. CONCLUSIONS
Turning tests were performed on AISI 1020 mild steel work piece using carbide insert 0.8 mm nose radius. The
influences of cutting speed, feed rate and depth of were investigated on the machined surface roughness and Material
Removal Rate (MRR). Based on the results obtained, the following conclusions have been drawn:
 The analysis of the experimental observations highlights that MRR in CNC turning process is greatly influenced
by depth of cut followed by feed.
 It is observed that the cutting speed is most significantly influences the Ra followed by the feed.
 For simultaneous optimization of Surface roughness (Ra) and material removal rate (MRR) depth of cut is the
most significant parameter affecting the performance followed by the feed.
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EXPERIMENTAL STUDY OF TURNING OPERATION AND OPTIMIZATION OF MRR AND SURFACE ROUGHNESS USING TAGUCHI METHOD