DOI : 10.17577/IJERTV2IS60449
- Open Access
- Total Downloads : 499
- Authors : Vishal Francis, Ashutosh Dubey
- Paper ID : IJERTV2IS60449
- Volume & Issue : Volume 02, Issue 06 (June 2013)
- Published (First Online): 18-06-2013
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Assessment of Cutting Parameters for Optimization of Material Removal Rate In Face Milling Operation: Taguchi Method and Regression Analysis
Vishal Francis1, Ashutosh Dubey2
1-2 Assistant Professor, Department of Mechanical Engineering, SHIATS, Allahabad
Abstract
The present research work discusses about the application of Taguchi method and Regression Analysis for optimization of Material Removal Rate in machining of Gun metal with a HSS tool. The experiment was designed using Taguchis experimental design technique. The cutting parameters selected are spindle speed, feed and depth of cut. The effect of cutting parameters on Material Removal Rate is investigated and the optimum cutting conditions for maximizing the Material Removal Rate is determined. Linear regression equation is developed with an objective to establish a correlation between the selected cutting parameters and Material Removal Rate. The predicted values are compared with experimental values and are found to be in good agreement. Depth of cut is found to be the most influencing factor affecting Material Removal Rate followed by spindle speed and feed.
Keywords: Taguchi method; Regression Analysis; Material Removal Rate, Gun Metal.
-
Introduction
Considering the current scenario of manufacturing industries, it has become vital for every firm to meet the demands in minimum span of time. It is worthwhile important to study the rate at which the material is being removed and at the same time achieving the quality requirements. The volume of material removed per minute in machining of a component is termed as material removal rate. The present work investigates the effect of cutting parameters on Material removal rate in face milling operation in a CNC machine, in order to find the optimum combination of the parameters to maximize the Material removal rate. The experiment was designed using Taguchi method. Dr. Taguchi employed design of experiments (DOE), which is one of the most important and efficient tools of total quality management (TQM) for designing high quality systems at reduced cost. Taguchi emphasizes on the fact that Quality provides robustness and immune to the uncontrollable factors in the manufacturing state. This approach helps to reduce the large number of experimental trials when the number
of process parameters increases [1]. A L27 orthogonal array was selected and the three Process parameter viz. depth of cut, feed and spindle speeds were varied to analyze the results obtained. Regression analysis is applied to develop a linear regression equation.
-
Taguchi Methodology
Taguchi method is based on performing evaluation or experiments to test the sensitivity of a set of response variables to a set of control parameters (or independent variables) by considering experiments in orthogonal array with an aim to attain the optimum setting of the control parameters. Orthogonal arrays provide a best set of well balanced (minimum) experiments [2]. These experiments provide full information about all the factors that affect the response parameter [3]. Taguchi method stresses the importance of studying the response variation using the signal to noise (S/N) ratio, resulting in minimization of quality characteristic variation due to uncontrollable parameter. Larger the better characteristic is used for calculation of S/N ratio for Material Removal Rate.
Where n is the number of measurement in a trail/row and Yi is the measured value in the run/row.
-
Materials and Methods
The work material used in the present investigation is a gun metal rectangular block of 80 X 80 X 40 mm. The chemical composition
consists of (88%) copper, (10%) tin and (2%) zinc. Figure 1 shows the experimental set up for face milling operation. A L27 orthogonal array is employed for conducting the experimental runs and MRR is calculated for each run. Taguchis method is used to design the experiment. The three cutting parameters selected for the present research work are spindle speed (S), feed (f) and depth of cut (d), with three level tests for each factor. Table 1 represents the machining parameters used and their levels chosen.
Figure 1: Experimental set up for milling operation
Ex p.
No.
spee d (rp
m)
feed (mm/r ev)
doc (m
m)
MRR
(mm3/m in)
S/N Ratio
1
600
40
0.2
241152
107.6
46
2
600
40
0.4
482304
113.6
66
3
600
40
0.6
723456
117.1
88
4
600
50
0.2
301440
109.5
84
5
600
50
0.4
602880
115.6
05
6
600
50
0.6
904320
119.1
26
7
600
60
0.2
361728
111.1
68
8
600
60
0.4
723456
117.1
88
9
600
60
0.6
1085184
120.7
10
10
800
40
0.2
321536
110.1
45
11
800
40
0.4
643072
116.1
65
12
800
40
0.6
964608
119.6
87
13
800
50
0.2
401920
112.0
83
14
800
50
0.4
803840
118.1
03
15
800
50
0.6
1205760
121.6
25
16
800
60
0.2
482304
113.6
66
17
800
60
0.4
964608
119.6
87
18
800
60
0.6
1446912
123.2
09
19
100
0
60
0.2
602880
115.6
05
20
100
0
60
0.4
1205760
121.6
25
21
100
0
60
0.6
1808640
125.1
47
22
100
50
0.2
502400
114.0
Ex p.
No.
spee d (rp
m)
feed (mm/r ev)
doc (m
m)
MRR
(mm3/m in)
S/N Ratio
1
600
40
0.2
241152
107.6
46
2
600
40
0.4
482304
113.6
66
3
600
40
0.6
723456
117.1
88
4
600
50
0.2
301440
109.5
84
5
600
50
0.4
602880
115.6
05
6
600
50
0.6
904320
119.1
26
7
600
60
0.2
361728
111.1
68
8
600
60
0.4
723456
117.1
88
9
600
60
0.6
1085184
120.7
10
10
800
40
0.2
321536
110.1
45
11
800
40
0.4
643072
116.1
65
12
800
40
0.6
964608
119.6
87
13
800
50
0.2
401920
112.0
83
14
800
50
0.4
803840
118.1
03
15
800
50
0.6
1205760
121.6
25
16
800
60
0.2
482304
113.6
66
17
800
60
0.4
964608
119.6
87
18
800
60
0.6
1446912
123.2
09
19
100
0
60
0.2
602880
115.6
05
20
100
0
60
0.4
1205760
121.6
25
21
100
0
60
0.6
1808640
125.1
47
22
100
50
0.2
502400
114.0
Table 1: Machining parameters and their levels
Parameters
Level1
Level2
Level3
Spindle speed (rpm)
600
800
1000
Feed (mm/rev)
40
50
60
Depth of cut (mm)
0.2
0.4
0.6
All the experiments were done with a HSS tool on a CNC XL mill with following specifications: Machine dimensions L x B x H (1000 x 575 x 650) mm, programmable feed rate
from 0 1200 mm/min, spindle speed 150 4000 rpm, table size 360 x 132mm, and axis motor capacity 0.8 Nm.
-
Results and Discussion
-
Effect of machining parameters on Material removal Rate
Experiments were conducted with three parameters at three different levels. Table 2 shows the results obtained for Material Removal Rate and the corresponding S/N ratios.
Table 2: Experiment results for Surface roughness
0
21
23
100
0
50
0.4
1004800
120.0
42
24
100
0
50
0.6
1507200
123.5
63
25
100
0
40
0.2
401920
112.0
83
26
100
0
40
0.4
803840
118.1
03
27
100
0
40
0.6
1205760
121.6
25
Main Effects Plot for SN ratios
Data Means
speed (rpm) feed (mm/rev)
0
21
23
100
0
50
0.4
1004800
120.0
42
24
100
0
50
0.6
1507200
123.5
63
25
100
0
40
0.2
401920
112.0
83
26
100
0
40
0.4
803840
118.1
03
27
100
0
40
0.6
1205760
121.6
25
Main Effects Plot for SN ratios
Data Means
speed (rpm) feed (mm/rev)
600
800
doc (mm)
1000
40
50
60
600
800
doc (mm)
1000
40
50
60
120
118
116
114
112
120
118
116
114
112
Mean of SN ratios
Mean of SN ratios
Table 3 shows the S/N ratio obtained for different parameter levels. Depth of cut was found to be the most influencing parameter with highest delta value of 9.5 followed by Spindle speed and Feed with 4.4 and 3.5 delta values respectively. Figure 2 shows the main effect plot for S/N ratio. The greatest variation found on Material Removal Rate was due to Depth of cut. The optimum conditions for Material Removal Rate are spindle speed of 1000 rpm, depth of cut of 0.6 mm and feed of 60 mm/rev.
Level
Spindle Speed (rpm)
Feed (mm/rev)
Doc (mm)
1
114.7
115.1
111.8
2
117.2
117.1
117.8
3
119.1
118.7
121.3
Delta
4.4
3.5
9.5
Rank
2
3
1
Level
Spindle Speed (rpm)
Feed (mm/rev)
Doc (mm)
1
114.7
115.1
111.8
2
117.2
117.1
117.8
3
119.1
118.7
121.3
Delta
4.4
3.5
9.5
Rank
2
3
1
Table 3: Response Table for Signal to Noise Ratios (Larger is better)
120
118
116
114
112
120
118
116
114
112
0.2
0.4
0.6
0.2
0.4
0.6
Signal-to-nose: Larger is better
Signal-to-noise: Larger is better
Figure 2: Effect of spindle speed, feed and depth of cut on Material Removal Rate
-
Regression Analysis
The spindle speed, feed and depth of cut are considered in the development of mathematical model for Material Removal Rate. The correlation between the cutting parameters and MRR is obtained by linear regression; equation 1 shows the developed model.
MRR = – 1875627 + 1172 speed (rpm) + 18756
feed (mm/rev) + 2009600 doc (mm) (1)
The predicted and the experimental values of Material Removal Rate are shown in figure 3. It is clear from the figure that most of the predicted values are in close agreement with the experimental values for Material Removal Rate.
Comparision of Experimental Results & Predicted values
Comparision of Experimental Results & Predicted values
2000000
Variable Experimental results Predicted values
2000000
Variable Experimental results Predicted values
1500000
1500000
1000000
1000000
500000
500000
0
0
3 6 9 12 15 18 21 24 27
Experimental runs
3 6 9 12 15 18 21 24 27
Experimental runs
MRR
MRR
Figure 3: Comparison between experimental and predicted values
-
-
Conclusion
The study discusses about the application of Taguchi method and Regression Analysis to investigate the effect of process parameters on Material Removal rate. 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 Material Removal rate.
-
Optimal parameters for Material Removal rate are Depth of cut of 0.6mm, Feed rate of 60mm/rev and spindle speed of 1000 rpm.
-
Linear regression equation is developed to predict the values of Material Removal rate, and the predicted values are compared with the measured value.
References
-
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