ABSTRACT The intend of this research work is to employ Taguchi method and Analysis of Variance to find out the influences of cutting parameters such as spindle speed, depth of cut and feed on material removal rate and surface roughness for their optimization. The experimental results obtained were analyzed to find out the most significant factor effecting MRR and surface roughness. Spindle speed was found to be the most significant parameter influencing material removal rate followed by feed and depth of cut in turning of mild steel (0.18% C). Feed rate was found to be the most influencing parameter in case of surface roughness

Ali R Rizvi

Nikita Singh

Ravi S Singh

Santosh Kumar

Vishal Francis

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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 
47 
 
 
 
 
 
 
 
 
 
 
 
APPLICATION OF TAGUCHI METHOD AND ANOVA IN 
OPTIMIZATION OF CUTTING PARAMETERS FOR MATERIAL 
REMOVAL RATE AND SURFACE ROUGHNESS IN TURNING 
OPERATION 
 
 
Vishal Francis*, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi, Santosh Kumar 
 
Department of Mechanical Engineering, SSET, SHIATS, Allahabad – 211007, Uttar Pradesh, 
INDIA 
*Corresponding Author- Assistant Professor, Dept. of Mechanical Engineering, SSET, 
SHIATS, Allahabad – 211007, Uttar Pradesh, INDIA 
 
 
 
ABSTRACT 
  
The intend of this research work is to employ Taguchi method and Analysis of 
Variance to find out the influences of cutting parameters such as spindle speed, depth of cut 
and feed on material removal rate and surface roughness for their optimization. The 
experimental results obtained were analyzed to find out the most significant factor effecting 
MRR and surface roughness. Spindle speed was found to be the most significant parameter 
influencing material removal rate followed by feed and depth of cut in turning of mild steel 
(0.18% C). Feed rate was found to be the most influencing parameter in case of surface 
roughness. 
 
Keywords: Taguchi Method, ANOVA, Mild Steel, Material Removal Rate, Surface 
Roughness.  
 
INTRODUCTION 
  
Turning is one of the common metal cutting operations used for manufacturing of 
finished parts. The surface texture of the produced parts must fulfill the specified limitations 
so it is vitally important to produce parts with adequate surface finish in order to ensure 
product performance and reliability. The present work investigates the effect of cutting 
parameters on surface roughness and material removal rate, experiment was conducted with a 
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING 
AND TECHNOLOGY (IJMET) 
 
 
ISSN 0976 – 6340 (Print) 
ISSN 0976 – 6359 (Online)  
Volume 4, Issue 3, May - June (2013), pp. 47-53 
© IAEME: www.iaeme.com/ijmet.asp 
Journal Impact Factor (2013): 5.7731 (Calculated by GISI)  
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IJMET 
© I A E M E 
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 
48 
 
HSS tool for turning of mild steel. Mild steel is a malleable and ductile material with a low 
tensile strength. The surface hardness can be increased through carburizing.    
The experiment was designed using Taguchi method, an L27 orthogonal array was 
selected and the three process parameter, depth of cut, feed and spindle speeds were varied to 
analyze the results obtained in order to find the optimal levels of the parameters for 
maximum material removal rate and minimum surface roughness.  
ANOVA was employed to find out significance of the different parameters on MRR 
and surface roughness.  
 
METHODOLOGY 
  
Essentially, traditional experimental design procedures are too complicated and not 
easy to use. A large number of experimental works have to be carried out when the number of 
process parameters increases. To solve this problem, the Taguchi method uses a special 
design of orthogonal arrays to study the entire parameter space with only a small number of 
experiments. This method uses a special set of arrays called orthogonal array. This standard 
array gives a way of conducting the minimum number of experiments which could give the 
full information of all the factors that affect the response parameter instead of doing all 
experiments. 
 Analysis of variance was invented by Sir Ronald Fisher, who was a Statistician and 
Geneticist. He coined the phrase "analysis of variance," defined as "the separation of variance 
ascribable to one group of causes from the variance ascribable to the other groups. It is a 
statistical method of comparing means between groups; it computes the F-statistics, named 
after the inventor Fisher. The computed F value is compared with the critical F value to 
determine the significance of the parameters on the response. Variation observed (total) in an 
experimental attributed to each significant factor or interaction is reflected in percent 
contribution (P), which shows relative power of factor or interaction to reduce variation.   
 
MATERIALS AND METHOD 
  
A Mild steel rod of 150mm length and 32mm diameter was used for the experimental work. 
The chemical composition consists of (0.18%) carbon and (0.68%) manganese. The 
experiment was performed by high speed steel tool in dry cutting condition. 
 Turning operation was performed 27 times in Sparko Engineering Workshop, 
Allahabad (U.P.) India. The total length of the work piece was divided into 4 equal parts and 
the surface roughness was measured for each 37.5mm length across the lay. Correspondingly 
MRR was calculated using standard relation: 
 
 MRR= VFD mm
3
/min     (1) 
 
Where, 
MRR= Material Removal Rate, V= cutting velocity in mm/min, F= feed rate in mm/rev, D= 
depth of cut in mm. 
 The three process parameters, viz. depth of cut, feed and spindle speeds were varied 
as shown in table 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 
49 
 
Table 1: Process parameters and their levels 
 
Process 
Parameters 
Level 1 Level 2 Level 3 
Depth of cut (mm) 0.5 1.0 1.5 
Feed (mm/rev) 0.08 0.20 0.32 
Spindle speed 
(rpm) 
780 1560 2340 
 
REULTS AND ANALYSIS OF EXPERIMENTS 
 
Table 2 shows the values of the responses obtained from the experimental runs, 
designed by Taguchi method, the corresponding value of S/N Ratio is mentioned for each 
run. 
 
Table 2: Results for Experimental Trial Runs 
 
Exp. 
No. 
Depth 
of cut 
(mm) 
Feed 
(mm/rev) 
spindle 
speed 
(rpm) 
Surface 
Roughness 
(microns) 
Material 
Removal 
Rate 
(mm3/min) 
S/N Ratio for 
Surface 
Roughness 
S/N Ratio for 
Material 
Removal 
Rate 
1 0.5 0.08 780 97.5 3134976 -39.7801 129.925 
2 0.5 0.08 1560 22.5 6269952 -27.0437 135.945 
3 0.5 0.08 2340 100.0 9404928 -40.0000 139.467 
4 0.5 0.20 780 95.0 783744 -39.5545 117.883 
5 0.5 0.20 1560 105.0 15674880 -40.4238 143.904 
6 0.5 0.20 2340 137.5 23512320 -42.7661 147.426 
7 0.5 0.32 780 150.0 12539904 -43.5218 141.966 
8 0.5 0.32 1560 110.0 25079808 -40.8279 147.986 
9 0.5 0.32 2340 55.0 37619712 -34.8073 151.508 
10 1.0 0.08 780 87.0 6269952 -38.7904 135.945 
11 1.0 0.08 1560 95.0 12539904 -39.5545 141.966 
12 1.0 0.08 2340 96.0 15674880 -39.6454 143.904 
13 1.0 0.20 780 90.0 31349760 -39.0849 149.925 
14 1.0 0.20 1560 96.0 12539904 -39.6454 141.966 
15 1.0 0.20 2340 86.0 47024640 -38.6900 153.447 
16 1.0 0.32 780 98.0 25079808 -39.8245 147.986 
17 1.0 0.32 1560 100.0 50159616 -40.0000 154.007 
18 1.0 0.32 2340 98.0 75239424 -39.8245 157.529 
19 1.5 0.08 780 85.0 9404928 -38.5884 139.467 
20 1.5 0.08 1560 96.7 18809856 -39.7085 145.488 
21 1.5 0.08 2340 97.0 28214784 -39.7354 149.010 
22 1.5 0.20 780 85.0 2351232 -38.5884 127.426 
23 1.5 0.20 1560 93.0 47024640 -39.3697 153.447 
24 1.5 0.20 2340 89.0 70536960 -38.9878 156.968 
25 1.5 0.32 780 95.0 37619712 -39.5545 151.508 
26 1.5 0.32 1560 100.0 37619712 -40.0000 151.508 
27 1.5 0.32 2340 89.0 112859136 -38.9878 161.051 
 
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May 
A. Analysis of the S/N Ratio 
 Taguchi method stresses the importance of 
signal – to – noise (S/N) ratio, resulting in minimization of quality characteristic variation 
due to uncontrollable parameter. 
The S/N Ratio is calculated using larger the better characteristics
 
And the S/N Ratio for Surface roughness is calculated using smaller the better 
characteristics.  
 
Where n is the number of measurement in a trail/row and Yi is 
run/row. 
Table 3 and 4 shows the 
smaller the better for each level of the parameters
 
Table 3: Response Table for Signal to Noise Ratios Larger is better
Level Depth of cut (mm)
1 139.6 
2 147.4 
3 148.4 
Delta 8.9 
Rank 3 
 
Table 4: Response Table for Signal to Noise Ratios Smaller is better
Level Depth of cut (mm)
1 -38.75 
2 -39.45 
3 -39.28 
Delta 0.70 
Rank 3 
 
A greater value of S/N ratio is always considered for better performance regardless of 
the category of the performance characteristics.
The difference of S/N Ratio between level 1 and level 3 indicates the significance of the 
process parameters, greater the difference, greater will be the significance.
Table 3 shows that the spindle speed contributes most significantly towards 
the delta valve (difference between maximum and minimum values) is highest (13.1)
followed by feed (11.5) and depth of cut (8.9)
Table 4 indicates that for surface roughness the parameter that had the most influence 
is feed with delta value of (1.61), followed by spindle speed (1.19) and depth of cut (0.70)
- June (2013) © IAEM
50 
studying the response variation using the 
 for MRR.
 
 
the measured value in the 
Responses for Signal to Noise ratios of larger the better and 
. 
 Feed (mm/rev) Spindle speed
140.1 138.0
143.6 146.2
151.7 151.1
11.5 13.1 
2 1 
 Feed (mm/rev) Spindle speed
-38.09 -39.70
-39.68 -38.51
-39.71 -39.27
1.61 1.19 
1 2 
 
 
. 
– 
E 
 
 
(rpm) 
 
 
 
 
(rpm) 
 
 
 
MRR as 
, 
. 
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 
51 
 
Figure 1 and 2 shows the main effect plot for S/N ratio for MRR and surface 
roughness. It is clear from figure 1 that MRR increases on increasing the values of spindle 
speed, feed and depth of cut. The greatest variation found was due to spindle speed, MRR 
increases on increasing the spindle speed. In case of surface roughness, feed has the greatest 
variation; as there is a considerable increase of surface roughness on increasing the feed. 
 
1 . 51 . 00 . 5
1 5 2
1 4 8
1 4 4
1 4 0
0 . 3 20 . 2 00 . 0 8
2 3 4 01 5 6 07 8 0
1 5 2
1 4 8
1 4 4
1 4 0
DEP TH  O F  C U T (mm)
M
e
a
n
 o
f 
S
N
 r
a
t
io
s
F E E D  R A TE  (mm/re v )
S P IND LE  S P E E D  (rpm)
M a in  E f fe c ts  P lo t  f o r  S N  r a t io s
Da ta  M e a n s
S ig n a l- to - n o is e :  La r g e r  is  b e tte r
 
 
Figure 1 Effect of depth of cut, feed and spindle speed on MRR 
 
1 . 51 . 00 . 5
-3 8 . 0
-3 8 . 4
-3 8 . 8
-3 9 . 2
-3 9 . 6
0 . 3 20 . 2 00 . 0 8
2 3 4 01 5 6 07 8 0
-3 8 . 0
-3 8 . 4
-3 8 . 8
-3 9 . 2
-3 9 . 6
D E P TH  O F  C U T (mm)
M
e
a
n
 o
f
 S
N
 r
a
t
io
s
F E E D  R A TE  (mm/re v )
S P IN D LE  S P E E D  (rp m)
M a in  E f f e c ts  P lo t  f o r  S N  r a t io s
Da ta  M e a n s
S ig n a l- to - n o is e :  S m a lle r  is  b e tte r
 
 
Figure 2 Effect of depth of cut, feed and spindle speed on MRR 
 
B. Analysis of Variance (ANOVA) 
 The response data obtained via experimental runs for MRR and surface roughness 
were subjected to ANOVA for finding out the significant parameters, at above 95% 
confidence level and the results of ANOVA thus obtained for the response parameters are 
illustrated in table 5 and 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 
52 
 
Table 5: ANOVA table for S/N Ratios for MRR 
Source DF SS MS F P 
Depth of 
cut(mm) 
2 424.29 212.14 6.54 0.007 
Feed 
(mm/rev) 
2 631.84 315.92 9.73 0.001 
Spindle speed 
(rpm) 
2 793.97 396.98 12.23 0.000 
Error 20 649.09 32.45   
Total 26 2499.19    
 
 
Table 6: ANOVA table for S/N Ratios for Surface Roughness 
Source DF SS MS F P 
Depth of 
cut(mm) 
2 2.426 1.213 0.13 0.877 
Feed 
(mm/rev) 
2 15.327 7.663 0.83 0.449 
Spindle speed 
(rpm) 
2 6.547 3.273 0.36 0.705 
Error 20 183.935 9.197   
Total 26 208.235    
 
From Table 5, one can observe that the spindle speed (p=0.000) is the most significant 
parameter having 31.77% effect on MRR followed by feed (p= 0.001) having 25.28% 
influence on the response whereas depth of cut (p=0.007) influences the response only by 
16.98%.  
On comparing the percentage contribution of the process parameters on response from 
table 6 it was found that feed have 7.36% influence on surface roughness whereas spindle 
speed and depth of cut contributes less towards surface roughness. 
 
CONCLUSION 
  
The study discusses about the application of Taguchi method and ANOVA to 
investigate the effect of process parameters on MRR and surface roughness. From the 
analysis of the results obtained following conclusion can be drawn: - 
• Statistically designed experiments based on Taguchi method are performed using L27 
orthogonal array to analyze MRR and surface roughness. The results obtained from 
analysis of S/N Ratio and ANOVA were in close agreement. 
•   Optimal parameters for MRR are spindle speed 2340rpm; feed 0.32mm/rev and 
depth of cut 1.5mm. Whereas for surface roughness the optimal parameters found 
were spindle speed 1560rpm; feed 0.08mm/rev and depth of cut 0.5mm.     
• Spindle peed was found to be most significant parameter for MRR. 
 
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 
53 
 
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APPLICATION OF TAGUCHI METHOD AND ANOVA IN OPTIMIZATION OF CUTTING PARAMETERS FOR MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS IN TURNING OPERATION

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APPLICATION OF TAGUCHI METHOD AND ANOVA IN OPTIMIZATION OF CUTTING PARAMETERS FOR MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS IN TURNING OPERATION

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