Optimization of process parameters in drilling of aisi 1015 steel for exit burr u

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
327
OPTIMIZATION OF PROCESS PARAMETERS IN DRILLING OF AISI 1015
STEEL FOR EXIT BURR USING RSM AND TAGUCHI
Mr. Dhanke V. D.(1)
Prof. Phafat N.G.(2)
Prof. Dr. Deshmukh R. R.(3)
(1)
Student, M.E. Manufacturing, Mechanical Engineering Department, J.N.E.C. Aurangabad,
Maharashtra, India
(2)(3)
Associate Professor, Mechanical Engineering Department, J.N.E.C. Aurangabad, Maharashtra,
India
ABSTRACT
This paper concentrates on the analysis and optimization of input parameters for minimum
burr size using Response surface methodology and Taguchi method. Speed, feed, drill diameter and
point angle were taken as input machining parameters while two response variables namely burr
height and burr thickness were chosen for the given study. Central composite design (CCD) was used
for the experimental design. Optimum machining parameters were determined using S/N ratio
obtained from Taguchi optimization method for multi response characteristics i.e. burr height and
burr thickness. With this technique minimum burr height were obtained at cutting speed 28 m/min,
feed rate 0.1 mm/rev, drill diameter 20 mm and point angle 135° and minimum burr thickness were
obtained at cutting speed 16 m/min, feed rate 0.05 mm/rev, drill diameter 4 mm and point angle
142°.
Keywords: Burr height, Burr thickness, Grey relational analysis, Drilling, AISI 1015.
1. INTRODUCTION
Drilling is one of the important manufacturing operations that can be carried out on number
of parts for assembly work. Drilling operation is essential for manufacturing industries like
automobile industry, aerospace industry, medical and electrical related industries etc.
Formation of exit burr after drilling operation is one of the important problems that have to be
minimized or control in order to avoid its adverse effect during manufacturing stage or assembly
work. Burr is defined as the projection of material at the end of the hole due to plastic deformation of
the material. Burr has two types’ viz. entrance burr and exit burr. The exit burr is important because
these are larger in size than entrance burr. Exit burr creates several problems like decreasing quality
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of the products, improper seating between mating parts during assembly, jamming and misalignment
of parts etc. Burrs are injurious to workers during machining operations. Especially in precision
industries, burrs are quiet undesirable. Due to all these problems deburring operation should be
carried out on the work piece to remove the burr formed during drilling operation. This will result in
waste of time and money. Almost 30% of the total manufacturing cost will be spending on deburring
and edge finishing of components.
Most of the research work on the burr minimization focused on optimization of parameters
for machining various metals and alloys. Gaitonde and Karnik [1] introduce an optimal parameter
model using artificial neural network and particle swarm optimization. Gaitonde et al [2] used ANN
and genetic algorithm for multi performance optimization. Again Gaitonde et al [3] used Taguchi
method for process optimization for drilling burr. Nihal Tosun [4] investgated the effect of drilling
parameters on burr height and surface roughness.
Erol Kilckap [5] developed a RSM based model in the drilling of Al-7075 for the
minimization of burr height and surface roughness. Erol kilckap et al [6] again produce a model for
burr height and surface roughness in the drilling of AISI 1045 steel. L. Ken Lauderbaugh [7] use the
combined experimental and simulation tool for process optimization.
As drilling burr formation is a very complicated phenomenon affected by many parameters
such as drill geometry, material property and process conditions. Therefore it is imperative to
develop a reliable model that predicts the burr size to reduce deburring cost as burrs cannot be
completely eliminated. “However, no generally accepted analytical model is available in drilling up
to now”. This investigation work differs from other studies carried out on burr formation as it
attempts to develop a prediction model, which determines whether a drill bit consisting of important
process parameters yields a minimum burr or not. The important process parameters are determined
based on the literature review of previous studies carried out on burr formation. An response surface
methodology (RSM) model is developed that predicts burr height and thickness. RSM is selected
because of its capability to learn and simplify from examples and adjust to changing conditions. In
addition they can be applied in manufacturing area as they are an effective tool to model non linear
systems.
2. RESPONSE SURFACE METHODOLOGY
RSM consists of the experimental strategy for exploring the space of the process or input
factors, empirical statistical modeling to develop an appropriate approximating relationship between
the output and the process variables, and optimization methods for finding the levels or values of the
process variables that produce desirable values of the response outputs. Response surface method
designs also help in quantifying the relationships between one or more measured responses and the
vital input factors (MINITAB SOFTWARE version 14).
Process modeling by response surface methodology (RSM) using statistical design of
experiment based on central composite design is proved to be an efficient modeling tool. The
methodology reduces cost of experimentation and provides the information about the main and
interaction effects.
The first step of RSM is to define the limits of the experimental domain to be explored. These
limits are made as wide as possible to obtain a clear response from the model. The cutting speed,
feed rate, drill diameter and point angle are the drilling variables selected for present investigation.
The different levels retained for this study are depicted in Table 1.
In the next step is the planning to accomplish the experiments by means of RSM using a
Central Composite Design. In many engineering fields, there is a relationship between an output

329
variable of interest (y) and a set of controllable variables ),........,( 21 nxxx . The relationship between
the drilling control parameters and the responses is given as:
ε+= ),........,( 21 nxxxfy (1)
where ε represents the noise or error observed in the response (y). If we denote the expected response
to be η== ),........,()( 21 nxxxfyF , then the surface represented by
),........,( 21 nxxxf=η (2)
is called a response surface. The variables nxxx ,........, 21 in Eq. 2 are called natural variables because
they are expressed in natural units of measurement. In most RSM problems, the form of the
relationship between the independent variables and the response is unknown; it is approximated.
Thus, the first step in RSM is to find an appropriate approximation for the true functional
relationship between response and the set of independent variables. Usually, a low-order polynomial
in some region of the independent variables is employed. If the response is well modeled by a linear
function of the independent variables, then the approximating function is the first order model:
εββββ +++++= kkxxxy ..............22110 (3)
If there is curvature in the system, then a polynomial of higher degree must be used, such as the
second-order model:
εββββ +∑∑+∑+∑+=
−
==
jiij
k
j
k
i
jjj
k
j
jj
k
j
xxxxy
1
2
11
0 (4)
Where 1,.....2,1 −= ki and kj ,.........2,1= also ji <
3. EXPERIMENTAL WORK
AISI 1015 steel plates with 10 mm thickness were used as a work piece material for the given
study. AISI 1015 steel is widely used in the applications like Rivets, Screws, panels, ship plates,
boiler plates, fan blades, gear, valves, cam shafts, crank shafts, connecting rods, railway axles, tubes
of bicycles and automobiles, small forgings etc. The composition of AISI Steel is 0.178% C, 0.60%
Mn, 0.04% Cr, 0.03% Ni, 0.002% Mo, 0.019% S, 0.020% P and 0.19% Si. The Hardness is 170
BHN. Experimental studies were performed on CNC Machining centre HASS VF2SS with
Maximum speed, 12000 r.p.m.
In the present study four process parameters (speed, feed, drill diameter and point angle) were
considered. A five level central composite design was used to study linear, quadratic and two factor
interaction effect between the four process variable and responses (Table 1). The upper limit of a
factor was coded as +2 and the lower limit as -2; coded values for intermediate levels were
calculated from the following relationship:
minmax
minmax )](2[2
XX
XXX
Xi
−
+−
= (5)
Where Xi is the required coded values of a variable X, X is any value of the variable from Xmin to
Xmax. Xmin the lower level of the variable and Xmax is the upper level of the variable.

330
Table 1 Factors and their levels
Factors
Levels
-2 -1 0 +1 +2
Speed (V), m/min 12 16 20 24 28
Feed (f), mm/rev 0.05 0.1 0.15 0.2 0.25
Drill dia. (D), mm 04 08 12 16 20
Point angle(PA), degree 110 118 126 134 142
4. RESULTS AND DISCUSSION
4.1 Experimental results and Taguchi analysis
A series of drilling experiments were conducted to investigate the influence of process
parameters on burr height and burr thickness in drilling AISI 1015 steel. Experimental results and
S/N ratio for burr height and burr thickness for drilling AISI 1015 steel with various drilling
parameters are shown in table 1. The S/N ratios for each experiment were calculated by using
equation:
Lower is the best, )(log10 2
10 ijy
N
S
−=
Where, ijy is the response value at the th
i run
Now, by utilizing calculated values of S/N ratios (Table 1), average S/N response ratios were
calculated for burr height and burr thickness. Table 2 shows S/N response table for burr height and
Table 3 shows S/N response table for burr thickness. The S/N ratio response graphs for both
responses are shown in Fig 1 and 2.
Table 2 S/N response table for burr height
Levels
S/N ratio for Burr height
V f D θ
1 10.43 9.9 10.49 9.92
2 9.85 11.03 9.85 9.87
3 9.46 9.83 9.48 9.71
4 10.03 8.85 10.03 10.01
5 11.54 7.29 11.21 8.83
Table 3 S/N response table for burr thickness
Regardless of quality characteristics, a greater S/N ratio corresponds to a better performance.
The level of factor with the highest signal-to-noise ratio is the optimum level.
Levels
S/N ratio for Burr thickness
V f D θ
1 20.45 22.5 21.94 19.58
2 21.44 22.1 20.59 20.34
3 20.07 20.08 19.88 19.98
4 20.19 19.52 21.03 21.29
5 20.26 18.06 21.21 22.27

331
D rilling param eter lev els
11 .5
11 .0
10 .5
10 .0
9 .5
54321
11
10
9
8
7
54321
11 .0
10 .5
10 .0
9 .5
1 0. 00
9. 75
9. 50
9. 25
9. 00
V f
D ?
MeanS/Nratio
D rilling pa ra m e te r le v e l
2 1 .6
2 1 .2
2 0 .8
2 0 .4
2 0 .0
54321
2 2
2 1
2 0
1 9
1 8
54321
2 2 .0
2 1 .5
2 1 .0
2 0 .5
2 0 .0
2 2
2 1
2 0
V f
D ?
MeanS/Nratio
Optimal parameter combination for Burr height:
The optimal parameter combination level for the present research work in the drilling process
is speed at level 5, feed rate at level 2, drill diameter at level 5 and point angle at level 4 for burr
height. That means, the optimal cutting parameters for burr height (Bh) were obtained at 28 m/min
cutting speed (level 5), 0.1 mm/rev feed rate (level 2), 20 mm drill diameter (level 5), and 1340
point
angle (level 4).
Optimal parameter combination for Burr thickness:
The optimal parameter combination level for the present work in the drilling process is speed
at level 2, feed rate at level 1, drill diameter at level 1 and point angle at level 5 for burr thickness.
That means, the optimal cutting parameters for burr thickness (Bt) were obtained at 16 m/min cutting
speed (level 2), 0.05 mm/rev feed rate (level 1), 4 mm drill diameter (level 1), and 1420
point angle
(level 5).
Fig.1 The effect of drilling parameters on burr height
Fig. 2 The effect of drilling parameters on burr thickness

332
Table 4 Experimental design and responses
Run
Levels of factors Expt. results S/N ratio
V F D θ Bh Bt Bh Bt
1 0 0 0 0 0.362 0.110 8.83 19.17
2 1 -1 1 1 0.355 0.097 9.00 20.26
3 0 0 0 0 0.352 0.115 9.07 18.79
4 0 -2 0 0 0.32 0.075 9.90 22.50
5 1 -1 -1 1 0.230 0.077 12.77 22.27
6 -1 1 1 1 0.395 0.092 8.07 20.72
7 1 -1 -1 -1 0.270 0.102 11.37 19.83
8 0 0 0 0 0.362 0.120 8.83 18.42
9 0 2 0 0 0.432 0.125 7.29 18.06
10 0 0 0 -2 0.319 0.105 9.92 19.58
11 -1 1 1 -1 0.300 0.100 10.46 20.00
12 -1 -1 1 -1 0.205 0.075 13.76 22.50
13 0 0 0 0 0.325 0.110 9.76 19.17
14 -1 1 -1 1 0.385 0.090 8.29 20.92
15 1 1 1 -1 0.357 0.097 8.95 20.26
16 0 0 0 0 0.347 0.099 9.19 20.09
17 1 1 -1 -1 0.335 0.125 9.50 18.06
18 1 1 1 1 0.370 0.110 8.64 19.17
19 -2 0 0 0 0.301 0.095 10.43 20.45
20 -1 -1 -1 1 0.270 0.052 11.37 25.68
21 1 1 -1 1 0.305 0.115 10.31 18.79
22 0 0 2 0 0.275 0.087 11.21 21.21
23 1 -1 1 -1 0.327 0.072 9.71 22.85
24 0 0 0 0 0.343 0.098 9.29 20.18
25 0 0 0 2 0.362 0.077 8.83 22.27
26 0 0 -2 0 0.299 0.080 10.49 21.94
27 0 0 0 0 0.302 0.105 10.40 19.58
28 -1 -1 -1 -1 0.370 0.090 8.64 20.92
29 2 0 0 0 0.265 0.097 11.54 20.26
30 -1 1 -1 -1 0.470 0.122 6.56 18.27
31 -1 -1 1 1 0.262 0.075 11.63 22.50

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Table 5 Regression table for burr height
Term Coef SE Coef T P
Constant 0.341857 0.008979 38.073 0.000
V -0.007500 0.004849 -1.547 0.141
f 0.035500 0.004849 7.321 0.000
D -0.004667 0.004849 -0.962 0.350
PA 0.001000 0.004849 0.206 0.839
V*V -0.014089 0.004442 -3.171 0.006
f*f 0.009161 0.004442 2.062 0.056
D*D -0.013089 0.004442 -2.946 0.009
PA*PA 0.000286 0.004442 0.064 0.950
V*f -0.016125 0.005939 -2.715 0.015
V*D 0.037625 0.005939 6.335 0.000
V*PA 0.000250 0.005939 0.042 0.967
f*D -0.005125 0.005939 -0.863 0.401
f*PA 0.003000 0.005939 0.505 0.620
D*PA 0.028000 0.005939 4.715 0.000
S = 0.02376 R-Sq = 90.5% R-Sq(adj) = 82.1%
Table 6 Regression table for burr thickness
Term Coef SE Coef T P
Constant 0.108143 0.002781 38.892 0.000
V 0.004292 0.001502 2.858 0.011
f 0.012958 0.001502 8.629 0.000
D -0.001708 0.001502 -1.138 0.272
PA -0.005458 0.001502 -3.635 0.002
V*V -0.002942 0.001376 -2.138 0.048
f*f -0.001942 0.001376 -1.412 0.177
D*D -0.006067 0.001376 -4.410 0.000
PA*PA -0.004192 0.001376 -3.047 0.008
V*f -0.000812 0.001839 -0.442 0.665
V*D -0.001938 0.001839 -1.053 0.308
V*PA 0.005063 0.001839 2.753 0.014
f*D -0.003188 0.001839 -1.733 0.102
f*PA 0.000063 0.001839 0.034 0.973
D*PA 0.008438 0.001839 4.588 0.000
S = 0.007357 R-Sq = 90.8% R-Sq(adj) = 82.8%

334
Table 7 RSM model adequacy check
Run
Order
V f D PA
Experimental
values (mm)
Predicted values
from RSM model
(mm)
Error
%
Bh Bt Bh Bt Bh Bt
1 20 0.15 12 126 0.362 0.11 0.342 0.108 5.564 1.688
2 24 0.10 16 134 0.355 0.097 0.362 0.093 1.855 4.466
3 20 0.15 12 126 0.352 0.115 0.342 0.108 2.882 5.963
4 20 0.05 12 126 0.32 0.075 0.308 0.074 3.906 0.721
5 24 0.10 8 134 0.23 0.077 0.229 0.077 0.470 0.379
6 16 0.20 16 134 0.395 0.092 0.368 0.098 6.814 5.978
7 24 0.10 8 118 0.27 0.102 0.289 0.095 7.192 7.353
8 20 0.15 12 126 0.362 0.12 0.342 0.108 5.564 9.881
9 20 0.25 12 126 0.432 0.125 0.450 0.126 4.051 1.033
10 20 0.15 12 110 0.319 0.105 0.341 0.102 6.897 2.580
11 16 0.20 16 118 0.3 0.1 0.304 0.102 1.195 1.540
12 16 0.10 16 118 0.205 0.075 0.217 0.081 5.651 7.336
13 20 0.15 12 126 0.325 0.11 0.342 0.108 5.187 1.688
14 16 0.20 8 134 0.385 0.09 0.406 0.087 5.563 3.844
15 24 0.20 16 118 0.357 0.097 0.332 0.094 7.119 2.579
16 20 0.15 12 126 0.347 0.099 0.342 0.108 1.482 9.235
17 24 0.20 8 118 0.335 0.125 0.332 0.125 0.771 0.034
18 24 0.20 16 134 0.37 0.11 0.396 0.111 7.050 0.645
19 12 0.15 12 126 0.301 0.095 0.301 0.088 0.166 7.588
20 16 0.10 8 134 0.27 0.052 0.287 0.052 6.266 0.958
21 24 0.20 8 134 0.305 0.115 0.284 0.108 6.912 6.520
22 20 0.15 20 126 0.275 0.087 0.280 0.080 1.879 7.518
23 24 0.10 16 118 0.327 0.072 0.309 0.077 5.479 6.539
24 20 0.15 12 126 0.343 0.098 0.342 0.108 0.333 10.350
25 20 0.15 12 142 0.362 0.077 0.345 0.080 4.696 4.492
26 20 0.15 4 126 0.299 0.08 0.299 0.087 0.055 9.114
27 20 0.15 12 126 0.302 0.105 0.342 0.108 13.198 2.993
28 16 0.10 8 118 0.37 0.09 0.347 0.091 6.103 0.602
29 28 0.15 12 126 0.265 0.097 0.271 0.105 2.076 8.205
30 16 0.20 8 118 0.47 0.122 0.455 0.124 3.209 1.911
31 16 0.10 16 134 0.262 0.075 0.269 0.076 2.704 1.613

335
4.2 Response surface analysis
For the burr height data, the regression table shows the following:
• Linear effects: The p-value of 0.141, 0.350 and 0.839 for speed, drill diameter and point angle is
not less than 0.05. Therefore, there is no significant effect of these three factors on the model.
The p-value of 0.000 for feed is less than 0.05. Therefore drill diameter has significant effect.
• Squared effect: The p-value of squared effect V*V = 0.006 and D*D = 0.009 which is less than
0.05. Hence these two squared effects are significant.
• Interaction effect: The p-values of V*f= 0.015, V*D = 0.000 and D*θ = 0.000 are less than 0.05.
Therefore, their effect on the model is significant. That means the effect of speed on burr height
depends on the drill diameter and feed rate. The effect of diameter on burr height depends on the
point angle.
For the burr thickness data, the regression table shows the following:
• Linear effects: The p-value of 0.011, 0.000 and 0.002 for speed, feed and point angle is less than
0.05. Therefore, there is significant effect of these three factors on the model.
• Squared effect: The p-value of squared effect V*V = 0.048, D*D = 0.000 and θ*θ = 0.008 is less
than 0.05. Hence these three squared effects are significant.
• Interaction effect: the p-values of V*θ = 0.014 and D*θ = 0.000 are less than 0.05. Therefore,
their effect on the model is significant. That means the effect of speed on burr height depends on
the point angle and the effect of diameter on burr height depends on the point angle.
For each term in the model, there is a coefficient. Using these coefficients we have construct an
equation representing the relationship between the response and the factors.
For the burr height data, regression equation is:
Burr height (Bh) = 0.341857 - 0.007500(V) +0.035500(f) - 0.004667(D) + 0.001000(θ) -
0.014089(V2
) + 0.009161(f2
) - 0.013089(D2
) + 0.000286(θ2
) - 0.016125(V*f) + 0.037625(V*D) +
0.000250(V*θ) - 0.005125(f*D) + 0.003000(f*θ) + 0.028000(D*θ)
(5)
For the burr thickness data, regression equation is:
Burr thickness (Bt) = 0.108143 + 0.004292(V) + 0.012958(f) - 0.001708(D) - 0.005458(θ) -
0.002942(V2
) - 0.001942(f2
) - 0.006067(D2
) - 0.004192(θ2
) - 0.000812(V*f) - 0.001938(V*D) +
0.005063(V*θ) - 0.003188(f*D) + 0.000063(f*θ) + 0.008438(D*θ)
(6)
The normal probability plots of the residuals versus the predicted response for burr height and
burr thickness are shown in Figs. 3 and 4, respectively. Figures 3 and 4 revealed that the residuals
generally fall on a straight line, implying that the errors are normally distributed. This implies that
the models proposed are adequate, and there is no reason to suspect any violation of the
independence or constant variance assumption.

336
R e s id u a l
Percent
0 .0 40 .0 30 .0 20 .0 10 .0 0- 0 .0 1- 0 .0 2- 0 .0 3- 0 .0 4- 0 .0 5
99
95
90
80
70
60
50
40
30
20
10
5
1
N o r m a l P r o b a b i l i ty P l o t o f th e R e s i d u a l s
(r e s p o n s e is Bh )
R e s id u a l
Percent
0 .01 50 .01 00 .00 50.00 0- 0 .0 05- 0 .0 1 0
99
95
90
80
70
60
50
40
30
20
10
5
1
N o r m a l P r o b a b ility P lo t o f th e R e s id u a ls
(re s po n se is Bt)
Fig. 3 Normal plot of the residuals for burr height
Fig. 4 Normal plot of residuals for burr thickness
5. CONCLUSION
This paper has used Taguchi method and response surface methodology for selecting the optimum
combination values of process parameters affecting the burr height and burr thickness in dry drilling
of AISI 1015 steel. The conclusions of this present study were drawn as follows:
• The optimal levels of the controllable factors were cutting speed 28 m/min, feed rate 0.1 mm/rev,
drill diameter 20 mm and point angle 135° for burr height.
• The optimal levels of the controllable factors were cutting speed 16 m/min, feed rate 0.05
mm/rev, drill diameter 4 mm and point angle 142° for burr thickness.
• RSM has been used to determine the burr height and burr thickness attained by various drilling
parameters. The quadratic modes developed using RSM were reasonably accurate and can be
used for prediction within the limits of the factors investigated.
• From RSM model and experiment results, the predicted and measured values are quite close,
which indicates that the developed model can be effectively used to predict the burr height and
thickness. The given models can be utilized to select the level of drilling parameters.

337
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