1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
62
CUTTING PARAMETER OPTIMIZATION FOR MINIMIZING MACHINING
DISTORTION OF THIN WALL THIN FLOOR AVIONIC COMPONENTS
USING TAGUCHI TECHNIQUE
Garimella Sridhar #1
, P. Ramesh Babu *2
#
Research Scholar, College of Engineering, OU, Hyderabad, India
*
Associate Professor, College Of Engineering, OU, Hyderabad, India
ABSTRACT
Distortion of thin wall thin floor aluminium components during and after machining is one of
the main challenges faced by aerospace manufacturing industries. These parts have to be machined
from prismatic blanks to features with walls and floors as thin as 1mm. So, in this experimental study
series of machining experiments were carried out using Taguchi design of experiments to find the
effect of important machining parameters (speed, feed, depth of cut, width of cut, tool path layout)
which influence distortion of the parts during machining and optimize them for minimizing
distortion. An L’16 orthogonal array, signal-to- noise (S/N) ratio and ANOVA are utilized in this
study. By this approach both the optimum parameters and main parameters which influence
distortion can be found. Optimum parameters are finally verified with the help of confirmation
experiment.
1. INTRODUCTION
Distortion of thin wall thin floor components is one of the major challenges facing
manufacturing industries. Machining these thin wall thin floor components from prismatic blocks,
removing most of the material, almost to sheet metal configurations, resulting in distorted parts,
leading to rejection and reworks is causing great economic loss to manufacturers. Literature survey
reveals many factors which effect the distortion during manufacture of these thin wall thin floor
parts. Right from design configuration, material to machine, clamping configuration to machining
parameters viz., speed, feed, depth of cut, width of cut, tool path strategy, tool geometry used and
their cumulative effect can cause distortion[1]. Important parameters which are controllable easily by
any machinist during manufacturing of these low rigidity parts are feed, speed, depth of cut, width of
cut and tool path strategy. The related published works on machining of these thin wall thin floor
parts is as under.
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
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ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 4, July - August (2013), pp. 62-69
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
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In depth studies were conducted by Budak on peripheral milling of flexible titanium plates
and cutting force and chatter stability models were developed [2]. Static surface form errors due to
deflection during peripheral milling of low rigidity walls and material removal simulation studies
were carried out by Tsai, et al., Ratchev, et al., and Wan, et al [3-5]. In the last decade focus was
shifted to analyze the distortion during metal removal process in-Toto. The effect of initial residual
stresses on part distortion was studied by Wang & Padmanaban, and Wang et al [6-7]. Optimization
of fixture design for machining thin walled work pieces was studied by Lie, et al.,[8]. Simulation
studies of machining distortion on thin walled aircraft structures and validation by experiments was
done by DONG Hui-yue, et al., Yun-bo BI, et al., and Yong YANG., et al [9-11]. Though simulation
studies and validation experiments were carried to understand distortion during material removal
process, much experimental work was not done to understand the effects of machining parameters
directly on distortion.
Yang and Tarng used Taguchi experimental design to find optimum cutting parameters to
increase tool life and surface finish in turning S45C steel [12]. Ramanujam, et al., optimized multi-
machining parameters during turning of composites using Taguchi and Desirability Function
Analysis [13]. Sanjit, et al., used Principal Component Analysis based Taguchi method in optimizing
the milling process parameters in improving surface finish and increasing the Material Removal Rate
[14]. Kuram, et al., used Taguchi and ANOVA technique in optimizing the cutting fluids and
machining parameters to reduce tool wear and cutting forces [15]. Sadasiva Rao., et al., used Taguchi
based Grey Relational Analysis in optimizing multiple characteristics during Face milling process
[16].
In this present work the effects of controllable machining parameters viz., Speed, Feed,
Depth of Cut, Width of Cut and Tool Path layout on machining distortion are analyzed by way of
machining experiments adopting Taguchi experimental approach (L’16 orthogonal array) and
ANOVA technique and find optimum cutting parameters which minimize distortion for the first
time.
2. EXPERIMENTAL WORK
2.1 Work piece and Work piece material
The work material selected for the study was aluminum alloy 2014A T651. This is alloy with
copper as principle alloying element which is used in avionic structures. A representative thin wall
thin floor work piece as shown in figure1 was used for experiments. The physical & chemical
properties are shown in Table 1 & Table 2 respectively.
Figure 1 Representative part used for experiment
(All Dimensions in mm)

3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
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2.2 Experimental Setup
The machining experiments were carried on CNC 3-axis vertical machining centre (VMC
MICRON VCP 600 Haidenhain controller ITNC 530 as shown in figure 2. The cutting tool used is 2
flutes solid carbide slot drill ø 10mm. New tool is used for machining each experimental work piece.
The work piece was clamped from underneath using a vacuum fixture made for machining
experimental work piece as shown in figure 3.
Table 1 Physical properties of alloy
Table 2 Chemical properties of alloy
Figure 2 Vertical Machining centre used for experiments
Sl.
No
PROPERTY VALUE
1 Yield strength
380 Mpa
(minimum)
2 Tensile strength
405 Mpa
(minimum)
3 Hardness Rockwell B 82
4 Density 2.80 g/cc
5 Poisson’s Ratio 0.2 to 1.2
6 Elongation 4 to 7 %
7 Modulus of Elasticity 72.4 GPa
Sl.
No ELEMENT
PERCENTAGE (%)
1 Copper 3.8 to 4.8
2 Magnesium 0.2 to 0.8
3 Silicon 0.6 to 0.9
4 Iron 0.7 max
5 Manganese 0.2 to 1.2
6 Aluminum Reminder

4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
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Figure 3 Figure 4
Figures showing Vacuum fixture with work piece used for experiments
The machining parameters considered for experiments are 5 factors i.e., Speed, Feed, Depth
Of Cut (DOC), Width Of Cut (WOC) and Tool path layouts as shown in Figures 6 with each
parameter having 4 levels as shown in table 3. The quality characteristic i.e., response which is of
main focus in these experiments is Distortion and. The distortion is measured by using CMM the
distortion taken as quality characteristic is maximum deviation from the flat surface in millimeters as
shown in figure 5.
Figure 5 Maximum Distortion Figure 6 Tool Path Layouts
Table 3 Experimental Design showing factors and levels used in experiments
FACTORS
LEVELS
1 2 3 4
FEED F (mm/TOOTH) 0.05 0.1 0.15 0.2
SPEED V (m/min) 100 150 200 250
DEPTH OF CUT D (mm) 0.4 0.8 1.2 1.4
WIDTH OF CUT Ae (% of D) (mm) 50 60 70 80
TOOL PATH
LAYOUTS
T
ZIGZAG
(Z)
ONE
WAY
(O)
PARALLEL
SPIRAL
[INSIDEOUT]
(P)
CONSTANT
OVERLAP
[INSIDEOUT]
(S)

5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
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3. RESULTS AND DISCUSSIONS
3.1 Analysis of Signal to Noise Ratio
In Taguchi analysis Signal to Noise ratio (S/N) is used to know the deviation of quality
characteristic from desired value. There are four types of characteristics viz., Lower the Better (LB),
Nominal the Best (NB), Higher the better (HB) and Smaller the Better (SB). In this current
experiments Smaller the Better (SB) is used as least distortion is desirable characteristic. The SB is
calculated by the following equation
݊ ൌ െ10log ቂ
ଵ
௡
ሼ∑ ‫ݕ‬௜
ଶ௡
௜ୀଵ ሽቃ (1)
Where n is number of experiments and yi is ith
value measured in a run. The values of
maximum distortion and S/N ratio calculated using equation (1) are listed in Table 4. Figure 7 shows
the main effects plot for S/N ratios. It can be seen from Figure 7 and Table 5 that the optimum
parameters for minimizing the distortion are feed 0.05 feed/ tooth, speed 150 m/min., depth of cut
0.4mm, width of cut is 70% of the diameter of the cutter and Tool path is constant overlap.
Table 4 Values of S/N ratios for distortion
Experiment
No.
FEED
(mm/ tooth)
SPEED
(m/min.)
DEPTH
OF
CUT
(mm)
WIDTH
OF
CUT
(mm)
TOOL
PATH
DISTORTION
(mm)
Signal
to
Noise
ratio
(S/N)
1 0.05 100 0.4 5 ZIG ZAG 0.16 15.92
2 0.05 150 0.8 6 ONE WAY 0.26 11.70
3 0.05 200 1.2 7
PARALLEL
SPIRAL
0.28
11.06
4 0.05 250 1.6 8
CONSTANT
OVER LAP
0.19
14.42
5 0.1 100 0.8 7
CONSTANT
OVER LAP
0.24
12.40
6 0.1 150 0.4 8
PARALLEL
SPIRAL
0.12
18.42
7 0.1 200 1.6 5 ONE WAY 0.39 8.18
8 0.1 250 1.2 6 ZIG ZAG 0.44 7.13
9 0.15 100 1.2 8 ONE WAY 0.41 7.74
10 0.15 150 1.6 7 ZIG ZAG 0.12 18.42
11 0.15 200 0.4 6
CONSTANT
OVER LAP
0.11
19.17
12 0.15 250 0.8 5
PARALLEL
SPIRAL
0.81
1.83
13 0.2 100 1.6 6
PARALLEL
SPIRAL
0.52
5.68
14 0.2 150 1.2 5
CONSTANT
OVER LAP
0.44
7.13
15 0.2 200 0.8 8 ZIG ZAG 0.27 11.37
16 0.2 250 0.4 7 ONE WAY 0.10 20.00

6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
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3.2 Analysis of Variance (ANOVA)
Analysis of variance is used to determine the contribution of each factor under consideration
which influences the distortion due to machining. Table 6 shows the summary of ANOVA results for
distortion. It can be seen from the ANVOA analysis that depth of cut has major influence on
distortion contributing 55.72%. The next factor which has major impact on distortion is Width of cut
contributing 25.31%. The way of cutting i.e., tool path layout has also a contribution of 9.61% to
distortion and planning the tool path is also important in minimizing distortion followed by cutting
speed contributing 6.87%. From these experimental results it is found that feed has negligible effect
on distortion contributing only 2.49%.
Table 5 Importance of parameters with S/N ratio values for distortion
FACTORS
LEVELS
1 2 3 4
A(feed) *13.27 11.53 11.79 11.04
B(speed) 10.43 *13.91 12.44 10.84
C(depth of cut) *18.37 9.32 8.26 11.67
D(width of cut) 8.26 10.92 *15.46 12.98
E(tool path) 13.20 11.90 9.24 *13.28
*indicate optimized parameters to minimize distortion
4321
17.5
15.0
12.5
10.0
4321 4321
4321
17.5
15.0
12.5
10.0
4321
A
MeanofSNratios
B C
D E
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Figure 7 S/N ratio values for distortion
Table 6 ANOVA values for distortion
FACTOR DOF
AVERAGE S/N VALUES
SUM OF
SQUARES
MEAN
SQUARE
PERCENTAGE
OF
CONTRIBUTION
LEVEL
1
LEVEL
2
LEVEL
3
LEVEL
4
FEED 3 13.27 11.53 11.79 11.04 11.072 3.691 2.49
SPEED 3 10.43 13.91 12.44 10.84 30.476 10.159 6.87
DOC 3 18.37 9.32 8.26 11.67 247.339 82.446 55.72
WOC 3 8.26 10.92 15.46 12.98 112.353 37.451 25.31
TOOL
LAYOUT
3 13.20 11.9 9.24 13.28 42.662 14.221 9.61
ERROR 0 0
TOTAL 15 443.903 100

7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
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3.3 Results of Confirmation Experiment
Confirmation experiments were done taking the optimum factors obtained by Taguchi
analysis as per Table 5, the results of the experiment given in Table 7. It can be seen that the
distortion 0.05mm of the component which is very less.
Table 7 Optimum parameters showing distortion
Feed
A1
Speed
B2
Depth
of cut
C1
Width of
cut
D3
Tool Path
E4
Distortion
0.05mm/tooth 150m/min 0.4mm 7mm Constant overlap 0.05mm
3.4 Observations
In the experiments conducted it has been observed that the location of the maximum
distortion is not same in all the experiments and is varying. This is due to redistribution of stresses
while equilibrating after machining. In Experiments 1 and 2 twist was observed in the components.
In Experiments 14 and 15 the distortion was observed only along the direction. In Experiments 8 and
9 distortion was observed only in one area. In Experiment 12 and 13 distortion was observed all over
the component. In Experiments 4, 5 and 6 U shaped distortion was observed.
4. CONCLUSION
Taguchi method has been applied to find significant controllable machining parameters
which influence the distortion during machining and optimum machining parameters to minimize
distortion. Based on results achieved it can be concluded that depth of cut followed by width of cut
main contributing factors influencing distortion.
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