 Open Access
 Total Downloads : 513
 Authors : I Srinivasa Reddy, S. Suresh, F. A. Gurunadham, Anand Raju,
 Paper ID : IJERTV3IS20244
 Volume & Issue : Volume 03, Issue 02 (February 2014)
 Published (First Online): 13022014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Determination of Optimum Parameters in CNC Drilling of Aluminium Alloy Al6463 by Taguchi Method
1I Srinivasa Reddy, 2S. Suresh, 3F. Anand Raju, 4A. Gurunadham,
1. Department of Mechanical Engineering, SIETK, puttur
2,3.Associate professor, M.tech MISTE, SIETK, puttur
4.Assistant professor, M.tech, SEAGI, Thirupathi
Abstract In this paper the drilling of AL6463 aluminium alloy with the help of CNC drilling machine operation with Tool use high speed steel by applying Taguchi methodology has been reported. The purpose of this paper is to investigate the influence of cutting parameters, such as cutting speed and feed rate, and point angle on surface roughness, hole dia error and burr height produced when drilling AL6463 aluminium alloy. A plan of experiments, based on L9 Taguchi design method, was made and drilling was done with the selected cutting parameters. All tests were run at cutting speeds of 1250,1500 and 1750 r.p.m. and feed 25,50, and 75 mm/min and point angle of 90Â°, 118Â°, and 140Â°. The orthogonal array, signaltonoise ratio, and analysis of variance (ANOVA) were employed to investigate the optimal drilling parameters.
Keywords: CNC Drilling, Taguchi Method, Surface Roughness, Hole dia error, Burr Height, S/N Ratio, ANOVA

INTRODUCTION
Amongst traditional machining processes, drilling is one of the most important metal cutting operations, comprising approximately 33% of all metal cutting operations. Drilling is a process of making holes or enlarging a hole in an object by forcing a rotating tool called drill. The same operation can be accomplished in some other machine by holding the drill stationary and rotating the work. The most general example of this class is drilling in lathe.
One of the most significant developments in production engineering over the last 20 years is the application of numerically controlled machine tools in production. No doubt, CNC application first started with AEROSPACE Industries to manufacture highly complex parts made of light alloys, requiring heavy metal removal. The CNC machine tools today have made considerable inroads to medium or large batch production in many metal working industries. The relatively high capital cost of CNC machines, further to be justified only by the AEROSPACE industries, is now being accepted by other industries because of the numerous direct & indirect benefits derived with their use. This writeup presents a brief out line of CNC machines and their inherent advantages in production.

The first benefit offered by all forms of CNC machine tools is improved automation.

The second major benefit of CNC technology is consistent and accurate work pieces.

A third benefit offered by most forms of CNC machine tools is flexibility.

Speed:
In a drilling machine, the cutting speed is the speed at which the material is removed from the work piece due to the rotary motion given to the drill. In particular, this refers to the peripheral speed of a point on the surface of drill in contact with the work. It is expressed in metre per minute and can be calculated by using the following equation.
= m/min
Where, V is the cutting speed in m/min
D is the diameter of the drill in mm, and N is the speed of revolution in r.p.m

Feed:
The feed in drilling machine refers to the axial distance moved by the drill in one revolution. It is expressed in mm per rev. It may also be expressed as m per min.
F = f. N mm/min
Here, F is the feed in mm per minute, f is the feed in mm/rev and N is the spindle speed in r.p.m

Depth of cut:
The depth of cut in drilling refers to the radius of drill being used and is selected on the basis of hole diameter desired or required. It is expressed in mm and can be calculated from the following equation.
d = in mm
2
Where d is the depth of cut in mm, and
D is the diameter of the drill in mm


TAGUCHI METHOD
Competitive crisis in manufacturing during the 1970s and 1980s that gave rise to the modern quality movement, leading to the introduction of Taguchi methods to the U.S. in the 1980s. Taguchis method is a system of design engineering to increase quality. Taguchi Methods refers to a collection of principles which make up the framework of a continually evolving approach to quality. Taguchi Methods of Quality Engineering design is built around three integral
elements, the loss function, signaltonoise ratio, and orthogonal arrays, which are each closely related to the
There are 3 SignaltoNoise ratios of common interest for optimization of Static Problems.
definition of quality.
Taguchi method is a scientifically disciplined

SmallerTheBetter S/N = 10 log (
Â²)
=

LargerTheBetter S/N = 10 log ( )
mechanism for evaluating and implementing improvements in products, processes, materials, equipment, and facilities. These improvements are aimed at improving the desired characteristics and simultaneously reducing the number of defects by studying the key variables controlling the process and optimizing the procedures or design to yield the best results. Taguchi proposed a standard procedure for applying his method for optimizing any process.

Orthogonal Arrays
An orthogonal array is a type of experiment where the columns for the independent variables are orthogonal to one another. Orthogonal arrays are employed to study the effect of several control factors. Orthogonal arrays are used to investigate quality. Orthogonal arrays are not unique to Taguchi. They were discovered considerably earlier (Bendell, 1998). However Taguchi has simplified their use by providing tabulated sets of standard orthogonal arrays and corresponding linear graphs to fit specific projects (ASI, 1989; Taguchi and Kenishi, 1987). A L9 Orthogonal array is shown in the table 2.1

Selection of Orthogonal array
To select an appropriate orthogonal array for the experiments, the total degrees of freedom need to be
computed. The degrees of freedom are defined as the number
= Â²


NominalTheBest S/N= – 10 log (Â²)
Â²

Analysis of variance (ANOVA)
The purpose of the analysis of variance (ANOVA) is to investigate which design parameters significantly affect the quality characteristic. This is to accomplished by separating the total variability of the S/N ratios, which is measured by the sum of the squared deviations from the total mean S/N ratio, into contributions by each of the design parameters and the error.
Various terms in ANOVA are

Sum of squares (SS)

Mean Squares (MS)

Percentage contribution


Sum of squares (SS)
SST = SSFactors + SSerror
T 1
(Total variation) can be written as SS = ( Â²)
Where T= , where y is responses
SSFactors= SSA+ SSB + SSC + SSD
Where A, B, C and D are Process parameters, Sum of squares of individual factors can be calculated as follows
2 + 2 +()Â² Â²
of comparisons between design parameters that need to be
SSA=
3
made to determine which level is better and specifically how much better it is. For example, a threelevel design parameter counts for two degrees of freedom. The degrees of freedom associated with the interaction between two design parameters are given by the poduct of the degrees of freedom for the two design parameters.
Table 2.1: A L9 Orthogonal array
Exp.No.
A
B
C
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
3
5
2
2
1
6
2
3
2
7
3
1
2
8
3
2
3
9
3
3
1
C. SignaltoNoise Ratio
The signaltonoise concept is closely related to the robustness of a product design. A Robust Design or product delivers strong signal. It performs its expected function and can cope with variations (noise), both internal and external. In signaltoNoise Ratio, signal represents the desirable value and noise represents the undesirable value.
Similarly SSB, SSC and SSD can be calculated.

Mean of Squares (Variance)
MS = ( )
( )
Degree of freedom: A degree of freedom in a statistical sense is associated with each piece of information that is estimated from the data.

Percentage Contribution of each parameter
The equation for calculating the % contribution of each factor is
% of Contribution = ( )
( )


DESIGN OF EXPERIMENT
In this study, three machining parameters were selected as control factors, and each parameter was designed to have three levels, denoted 1, 2, and 3 (Table 3.1). The experimental design was according to an L9) array based on Taguchi method, while using the Taguchi orthogonal array would markedly reduce the number of experiments. A set of experiments designed using the Taguchi method was conducted to investigate the relation between the process parameters and Surface roughness, Hole dia error and Burr height. DESIGN EXPERT @ 16 minitab software was used for regression and graphical analysis of the obtained data.
Table3.1 Process parameters and their levels
Symbol
Parameter
Level
1
Level
2
Level
3
A
Spindle speed(rpm)
1250
1500
1750
B
Feed rate
(mm/min)
25
50
75
C
Point angle
90
118
140

EXPERIMENTAL WORK
The work piece material selected for investigation is the aluminium alloy AL6463. The chemical and mechanical properties of the work piece are shown in table 4.1 and 4.2 respectively.
Table 4.1: Chemical composition of Al 6463 alloy
Al
Mg
Si
Fe
Cu
Zn
Mn
Ti
Cr
95.8
98.5
0.45
0.9
0.20
0.6
0.35
0.10
0.25
0.15
0.15
0.04
0.35
Table 4.2: Mechanical properties of Al 6463 alloy
UTS(Mpa)
YS(Mpa)
Density (g/cm3)
Elongation (%)
Hardness (Bhn)
310
276
2.7
12
95
Three HSS twist uncoated drills with 9.5 mm diameter and different point angles 90Â°, 118Â°, and 140Â° are used for the experiments.
Table 4.3: Design of experiments for drilling operations
Figure 1: Work pieces after drilling operation
Table 4.4: Measured response values in drilling of Al 6463
Exp. No.
Surface roughness
(Microns)
Hole diametral error
(mm)
Burr height
(mm)
1
3.47
0.07
1.79
2
2.16
0.03
0.84
3
3.28
0.04
0.92
4
4.12
0.11
0.85
5
3.94
0.14
0.89
6
3.08
0.02
0.62
7
1.33
0.05
0.94
8
1.47
0.04
0.83
9
2.36
0.08
1.92

RESULTS AND DISCUSSION
The response values are measured from the experiments and their corresponding S/N ratio are calculated by applying lower the better type.
Exp.No 
Spindle Speed(rpm) 
Feed Rate (mm/min) 
Point Angle (degree) 
1 
1250 
25 
90 
2 
1250 
50 
118 
3 
1250 
75 
140 
4 
1500 
25 
140 
5 
1500 
50 
90 
6 
1500 
75 
118 
7 
1750 
25 
118 
8 
1750 
50 
140 
9 
1750 
75 
90 
S/N ratio for Smaller the better type Response is
S/N = 10 log (
Â²)
=
Where r= Number of repetitions in a trail
The experiments have conducted using different point angles 90Â°, 118Â°, and 140Â° and the work pieces after drilling operation are as shown in Fig 1. surface roughness, hole diametral error, and burr height were measured with the help of talysurf, digital calliper, and tool makers microscope respectively. The average values of individual response for different point angles are shown in table 4.4

Calculation of S/N ratio for the first experimental trial

S/N ratio for Surface roughness For experiment i =1, = 3.47
Here only one repetition is considered in each experimental trail,
Hence r = 1 throughout all calculations of S/N ratio.
S /N = 10 log (3.47Â²)
= 10 log (12.05)
= 10*1.0809 = 10.81.

S/N ratio for Hole Diametral error
i =1, =0.07 and r =1 S /N = 10 log (0.07Â²)
= 10 log (0.0049)
= 10*(2.309) = 23.09.

S/N ratio for Burr height
i =1, =1.79 and r =1
S /N = 10 log (1.97Â²)
= 10 log (3.2041)
= 10*(.506)
= 5.06.
Similarly S/N ratio for all experimental trails are calculated and shown in table 5.1
Table 5.1: S/N ratio values for Responses
Exp.No
Surface roughness
(microns)
Hole diametral
error (mm)
Burr height (mm)
1
10.81
23.09
5.06
2
6.69
30.45
1.51
3
10.32
27.96
0.72
4
12.30
19.17
1.41
5
11.91
17.08
1.01
6
9.77
33.98
4.15
7
2.48
26.02
0.54
8
3.35
27.96
1.62
9
7.45
21.94
5.67


Determination of Optimal process parameters for surface roughness
Optimal levels of process parameters for individual responses are determined by S/N ratio analysis and the steps in analysis are as follows

Determination of Mean S/N ratio for each level of the parameters.

Draw graph between Mean S/N ratio values and process parameter values.

Determination of Mean S/N ratio for each level of the parameters.
Mean S/N ratio for parameter A at level 1 can be calculated as follow
m= ((10.81) + (6.69) + (10.32)) / 3 = 9.27.
Similarly the Mean S/N ratio values for each level of the parameters were calculated and shown in table 5.2
Table 5.2: Mean S/N Response table of Surface Roughness
Levels
Speed
Feed
Point
angle
1
9.27
8.53
10.06
2
11.33
7.32
6.31
3
4.43
9.18
8.66
Overall mean (dB) = 8.343
Levels
Speed
Feed
Point
angle
1
2.97
2.97
3.25
2
3.71
2.52
2.19
3
1.72
2.91
2.95
Overall mean () = 2.8
Table 5.3: Mean table of Surface Roughness
The response graph between mean S/N ratio and process parameter levels is shown in graph 1
Graph 1:S/N Response Graph of Surface roughness
From the above graph the optimal combination of process parameters is A3 B2 C2



Determination of Optimal process parameters for hole diametral error
Following the above steps The Mean S/N ratio values for each level of the parameters is shown in table 5.4.
Table 5.4: Mean S/N Response table of Hole diametral error
Levels
Speed
Feed
Point
Angle
1
27.16
22.76
20.70
2
23.41
25.16
30.15
3
25.30
27.96
25.03
Overall mean (dB) = 25.29
Table 5.6: Mean table of Hole diametral error
Levels
Speed
Feed
Point
angle
1
0.046
0.076
0.096
2
0.090
0.070
0.033
3
0.056
0.046
0.063
Overall mean () = 0.064
The response graph between mean S/N ratio and process parameter levels is shown in graph 2.
Graph 2: S/N Response Graph of Hole diametral error
From the above graph the optimal combination of process parameters is A1 B3
C2

Determination of Optimal process parameters for burr height
The Mean S/N ratio values for each level of the parameters are shown in table 5.7.
Table 5.7: Mean S/N Response table of Burr height
Levels
Speed
Feed
Point
angle
1
0.94
1.04
3.24
2
2.19
1.38
2.07
3
1.17
0.27
1.25
Overall mean (dB) = 0.08
Table 5.8: Mean table of Burr height
Levels
Speed
Feed
Point
angle
1
1.18
1.19
1.53
2
0.79
0.85
0.80
3
1.23
1.15
0.87
Overall mean () = 1.07
The response graph between mean S/N ratio and process parameter levels is shown in graph 3.
Graph 3: S/N Response Graph of Burr height
From the above graph the optimal combination of process parameters is A2 B2 C2
Optimal process parameter levels for Responses are summarized in the following table 5.9.
Table 5.9: Optimal process parameter levels for Responses
S.No
Response
Optimal machining
parameters
1
Surface
roughness
A3 B2 C2
2
Hole diametral
error
A1 B3 C2
3
Burr height
A2 B2 C2

Contribution of Process parameters on Responses
Contribution of process parameters affecting responses are determined by performing ANOVA in MINITAB software and results are as follows.
It is observed from Table 12 the surface roughness is affected by the Spindle speed(A), Feed rate(B), Point angle(C)
are 73.06%, 4.24%, 21.78% respectively. The percent numbers depict that the Spindle Speed and Point angle have significant effects on the Surface roughness.
Table 5.10: ANOVA of Surface roughness
Parameter
DF
SS
MS
F
P (%)
A
2
6.0884
3.0442
81.173
73.06
B
2
0.3538
0.1769
4.7173
4.24
C
2
1.8155
0.9077
21.205
21.78
Error
2
0.075
0.0375
0.90
Total
8
8.3327

Contribution of Process parameters on Hole diametral error
It is observed from Table 5.11 the hole diametral error is affected by Spindle speed(A), Feed rate(B), Point angle(C) are 23.80%, 11.11% 47.46%, respectively. The percent numbers depict that the Point angle and Spindle speed have significant effects on the Hole diametral error.
Table 5.11: ANOVA of Hole diametral error
23.80
Parameter
DF
SS
MS
F
P (%)
A
2
0.003
0.0015
1.3636
B
2
0.0014
0.0007
0.6363
11.11
C
2
0.006
0.003
2.7272
47.61
Error
2
0.0022
0.001
17.46
Total
8
0.0126

Contribution of Process parameters on Burr height
It is observed from Table 5.12 the burr height is affected by the Spindle speed(A), Feed rate(B), Point angle(C) are 21.26%, 12.37%, 58.93% respectively. The percent numbers depict that the Spindle Speed and Point angle have significant effects on the burr height.
Table 5.12: ANOVA of Burr height
Parameter 
DF 
SS 
MS 
F 
P (%) 
A 
2 
0.3560 
0.1780 
2.8617 
21.26 
B 
2 
0.2071 
0.1035 
1.6639 
12.37 
C 
2 
0.9866 
0.4933 
7.9308 
58.93 
Error 
2 
0.1243 
0.0622 
7.42 

Total 
8 
1.6740 
Percentage contribution of process parameters on responses shown in figure 2
Responses
Spindle
Speed
Feed rate
Point Angle
Surface Roughness Hole dia error, Burr Height
VI. CONCLUSION
80
70
60
50
40
30
20
10
0
Percentage Contribution
Parameters and their levels
Figure 2: Percentage Contribution of Process Parameters on
For achieving minimum surface roughness on the
diametral error and lower burr height always at lower cutting speeds and standard point angles are preferred.
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Aluminum alloy always higher cutting speeds and standard point angles are preferred and for achieving minimum hole