222
Int. J. Materials Engineering Innovation, Vol. 8, Nos. 3/4, 2017
Multi-response optimisation in drilling of
CFRP/Ti6Al4V stacks: a degree of similarity approach
M. Senthilkumar
Department of Production Engineering,
PSG College of Technology,
Coimbatore-641004, Tamil Nadu, India
Email: msenthil_kumar@hotmail.com
A. Prabukarthi*
Department of Mechanical Engineering,
PSG College of Technology,
Coimbatore-641004, Tamil Nadu, India
Email: prabukarthi.arumugam@gmail.com
*Corresponding author
V. Krishnaraj
Department of Production Engineering,
PSG College of Technology,
Coimbatore-641004, Tamil Nadu, India
Email: vkrishnaraj@hotmail.com
Abstract: Carbon fibre reinforced plastic (CFRP) composite with aluminium
or titanium alloy has been widely used by aerospace industries. It is still a
challenge to make holes on composite/metal stack with high quality due to their
dissimilarity in properties. It is vital to have a good insight of the machining
conditions on the quality of drilled holes. This paper aims to investigate
the influence of parameters (spindle speed, feed rate) and tool geometry
on drilling of CFRP/Ti6Al4V stacks with respect to drilling force and hole
quality (roundness, delamination and burr height). The experiments were
carried out using three modified twist drill (K20). Multi-response optimisation
of performance measures (drilling force and hole quality) carried out using
Taguchi integrated TOPSIS and Deng’s similarity-based approach revealed that
spindle speed (895 rpm), feed rate (0.05 mm/rev) and drill tool with point angle
of 130º gave satisfactory performance measures for drilling CFRP/Ti6Al4V
stacks.
Keywords: drilling; carbon fibre reinforced plastic; CFRP; Ti6Al4V; TOPSIS;
Deng’s similarity-based method; Taguchi’s optimisation philosophy.
Reference to this paper should be made as follows: Senthilkumar, M.,
Prabukarthi, A. and Krishnaraj, V. (2017) ‘Multi-response optimisation in
drilling of CFRP/Ti6Al4V stacks: a degree of similarity approach’, Int. J.
Materials Engineering Innovation, Vol. 8, Nos. 3/4, pp.222–249.
Copyright © 2017 Inderscience Enterprises Ltd.
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
223
Biographical notes: M. Senthilkumar is currently working as an Associate
Professor in Department of Production Engineering at the PSG College of
Technology, India. He obtained his BE in Mechanical Engineering in 1994, ME
in Engineering Design in 1996 and PhD in Active Suspension System in 2008.
He has about 15 years of teaching experience and one year of industrial
experience. He has authored about 50 journal papers and about 50 conference
papers. He received ISTE Award in 2006 and AICTE Career Award in 2009
and Outstanding Academician Award in 2011. He has successfully completed
many sponsored research projects and consultancy works. His fields of interest
include vibration control, composites and smart structures.
A. Prabukarthi is currently working as an Assistant Professor in Department of
Mechanical Engineering at the PSG College of Technology, India. He obtained
his BE in Mechanical Engineering in 2004, ME in Manufacturing System and
Management in 2008 and currently pursuing his PhD in Drilling of Stacked
Materials. He has about seven years of teaching experience and two years of
industrial experience. He has authored 11 journal papers and about 20
conference papers.
V. Krishnaraj is currently working as an Assistant Professor in Department of
Mechanical Engineering at the PSG College of Technology, India. He obtained
his BE in Mechanical Engineering in 1994, ME in Production Engineering
in 1999 and PhD in Machining of Composite Materials in 2007. He has about
13 years of teaching experience and ten years of industrial experience. He
has authored about 20 journal papers, about 50 conferences papers and a book
chapter. He received a post-doctoral fellowship from the University of Paul
Sabatier, Toulouse in 2008–2009. He is also in-charge for the Advanced Tool
and Die Centre of PSG College of Technology. He has successfully completed
many sponsored research projects funded by DST, ISRO, ARDB and AICTE.
His fields of interest include CNC, tool design, machining and composite
materials, etc.
1
Introduction
In recent years, the application of composite materials in the field of aerospace and other
types of industries is increasing due to its distinctive properties such as high specific
strength and light weight. Often the composite materials are used in combination with
another material (usually metal) to form a hybrid structure, to obtain greater strength to
weight ratio than conventional materials. The combination of CFRP with titanium alloy
(Ti6Al4V) to form multi layered stacks has gained prominence in recent years, especially
in applications involving aerospace structures subjected to extreme loads. To improve
productivity, fastener holes are drilled through composite/metal stacks, instead of drilling
separately through composite and metal. Drilling becomes a challenging task, when it
comes to machining of dissimilar materials like CFRP and Ti6Al4V, because of different
machining properties.
Several researches were carried out on machining of CFRP and Ti6Al4V separately
and also as a stack. A large amount of researches on drilling of CFRP mainly focuses on
the effect of machining parameters, tool geometry, hole quality [delamination, (peel-up
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M. Senthilkumar et al.
and push-out), roundness and diameter variation] (Liu et al., 2012; Gaitonde et al., 2008;
Karnik et al., 2008; Tsao and Hocheng, 2004). Experimental and statistical study with the
objective to establish, a correlation between cutting velocity and feed rate with the power
(Pc), specific cutting pressure (Ks) and delamination factor (Fd) in a CFRP material was
done. Finally, this correlation was obtained by multiple linear regressions (Davim and
Reis, 2003). Cutting force play a significant role in achieving the better hole quality, by
analysing the thrust force in drilling CFRP by various twist drills and found that feed rate
and drill type are the main parameters that influence the thrust force. The effect of spindle
speed is insignificant. Carefully selected drill geometry and small feed rate produces low
thrust force in drilling (Tsao, 2008). Research were carried out drilling of thin
carbon/epoxy laminates with two types of drills, a helical drill and a drill of special
geometry and concluded that both drills lead to damage at the entrance in wall and exit of
the hole, with the exception of special geometry drill which is possible to cause a
significant reduction in the final damage (Piquet et al., 2000). Study on effect of laminate
configuration and feed rate on cutting performance when drilling holes in carbon fibre
reinforced plastic (CFRP) composites and inferred that best results were obtained with
woven MTM44-1/HTS oven cured material (3,750 holes) while the effect of prepreg
form on tool life was evident only when operating at the higher level of feed rate. Study
on drill geometry and operating effects when cutting small diameter holes in CFRP and
the outcome was concluded that the drill type and feed rate were the main contributing
factors for tool life and thrust force, while cutting speed and feed rate had the most
significant effect on torque (Shyha et al., 2011).
While researches on Ti6Al4V machining focuses mainly on hole quality (burr height,
surface roughness and diameter deviation) (Zhang et al., 2013; Prabukarthi et al., 2013;
Celik, 2014). Once of the major limitation of Ti6Al4V is that it has poor conductivity
which results in the concentration of high temperatures at the tool workpiece and the toolchip interfaces, which accelerates tool wear and consequently increases manufacturing
cost (Rahman et al., 2006). Study of the evolution of tool wear, quality of machined holes
and surface integrity of work-piece, in the dry drilling of alloy Ti6Al4V was observed
even near tool catastrophic failure, evaluated from the point of view of dimensions,
surface roughness and burr height. Focus on high-throughput drilling of titanium alloys,
demonstrated that using proper drilling process parameters, spiral point drill geometry,
and fine-grained WC-Co tool material, the high-throughput drilling of Ti alloy is
technically feasible. The balance of cutting speed and feed rate is essential to achieve
long drill life and good holes surface roughness. Coating plays a key role in extending the
tool life while drilling Ti alloy, straight grade (WC/Co) cemented carbides are regarded
as the most suitable tool material available commercially for the machining of Ti alloys
as the continuous operation (Ezugwu and Wang, 1997).
Drilling process of graphite/bismaleimide-titanium alloy (Gr/Bi-Ti) stacks was
optimised in terms of machined hole quality and machining cost. The drilling
experiments were conducted by using HSS-Co and carbide cutter materials. Drilled hole
quality parameters studied include surface texture, titanium burrs, hole diameter,
cylindricity and roundness deviation. Machining cost was estimated through drill wear
experimentations. Optimum process conditions for achieving desired hole quality and
process cost were found to be a combination of low feed rate and low speed when using
carbide drills, and high feed rate and low speed in drilling with HSS-Co drills (Kim and
Ramalu, 2004).
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
225
Drilling of CFRP/Ti stacks having two flute and three flute drills and also varying
helix angle of 20° and 40°. Drills with higher helix angle suffered from chipping of
primary cutting edges when used at higher feed rate. But drill with lower helix angle has
stronger cutting edge and is less prone to chipping however resulting in higher cutting
forces and temperatures. The failure of the tool edge by adhesion occurs very often when
drilling titanium alloy because of its low thermal conductivity and high affinity to tool
materials. It is difficult to determine the proper cutting conditions. It is also a problem
that the titanium alloy chips damage the inner surface of a CFRP hole when drilling a
CFRP and titanium alloy stack board (Junsuke et al., 2013).
Drilling forces of both the WC and PCD drills gradually increases in both CFRP/Ti
due to tool wear. For the WC drills, the wear pattern is generally smooth and uniform
along the cutting edges. The flank wear was predominant at the higher spindle speed due
to the higher cutting temperature in Ti drilling, while the edge wear and flank wear
showed a similar amount of wear length at the lower spindle speed. Hard carbon fibres
abraded more on the cutting edge, while caused edge wear while the hard phase in Ti
extended the flank wear land in addition to carbide grain pullout when Ti adhesion was
removed (Kyung et al., 2010).
While drilling of CFRP/Ti stacks it was found that both thrust force and torque
increase linearly with feed rate; peel up and push down delamination is minimum at low
feed rate; hole size variation is minimum at 0.1 mm/rev feed rate; optimised machining
parameter for drilling of CFRP/Ti stacks as 1,000 rpm spindle speed and 0.1 mm/rev feed
rate (Krishnaraj et al., 2012).
Taguchi’s philosophy was originally developed to solve single criterion problem, but
most of the problem in machining have multiple quality characteristic. In order to
transform multiple characteristic into single measure of performance multiple criterion
decision making (MCDM) techniques have been used and the performance measure
obtained by MCDM techniques is combined with Taguchi’s philosophy for machining
parameter optimisation (Kumar et al., 2014, Sonkar et al., 2014).
Literatures depicts that considerable amount of work has been carried out by the
pioneers in the area of drilling of composite/metal stacks, however it is felt that little
more work has to be done addressing the effects of tool geometry on the machining
performances. Therefore, the present work considers the effects of tool geometry along
with process parameters. The paper details the effect of machining parameters (spindle
speed and feed rate) and tool geometry on various process performance measure like
thrust force, roundness deviation, delamination factor (at entry and exit) and burr height
in drilling of CFRP/Ti6Al4V stacks. Based on the experimental results, an attempt has
been made to determine the optimal parametric combination using TOPSIS and Deng’s
similarity-based approach in combination with Taguchi optimisation module.
2
Drilling of stacks
2.1 Material details
CFRP composite, with a thickness of 4.2 mm (16 layers) was used for conducting drilling
studies. The laminate was made out of 16 unidirectional plies of 0.26 mm thickness each.
The 16 unidirectional plies are made of carbon/epoxy prepreg and manufactured by
226
M. Senthilkumar et al.
Hexcel Composite Company with the reference Hexply T700-M21. The following was
the staking sequence [90/45/0/-45]2s. These materials were compacted using a vacuum
pump in a controlled atmosphere. A mold for the laminate was prepared and placed in a
vacuum bagging and evacuated to 0.7 bar (ref. Figure 1). Curing was then carried out at
180°C for 120 min during which the pressure was maintained at seven bars in an
autoclave. The nominal fibre volume fraction is 0.59. The material properties of CFRP
are shown in Table 1.
Table 1
Material properties of CFRP
Young’s modulus in the L direction Ell (GPa)
142
Young’s modulus in the T direction Ett(GPa)
8.4
Shear’s modulus Glt(GPa)
4.5
Poisson’s ratio (νlt)
0.33
Thermal expansion co-efficient(K–1)
4.9 E-6
Thermal conductivity (W/mK)
1
A titanium sheet of 3 mm thickness was used for the present study and was place below
the CFRP to form a stack. The material properties of Ti6Al4V are presented in Table 2.
Table 2
Material properties of Ti
Density, ρ (kg/m3)
4430
Modulus of elasticity, E (MPa)
113.8
Ultimate tensile strength, σ (MPa)
950
Thermal conductivity, k (W/mK)
6.7
Heat capacity, C (J/kgK)
586
Thermal expansion co-efficient(K–1)
8.7
2.2 Experimental details
CFRP/Ti stack was mounted on the drill tool dynamometer (syscon SL-674) on the table
of CNC vertical machining centre (Makino S33), and the experimental trials were carried
out using three ф 5mm Ti-Al-N coated solid carbide drills with modified geometry and
schematics experimental setup presented in Figure 1. The axial thrust force and torque
was continuously recorded using dynamometer; roundness (circularity) and exit burr
height in titanium alloy were respectively measured using CMM (Carl Zeiss Contura G2)
and digital height master and delamination was measured using image J software.
Drill geometry plays an important role in drilling of CFRP/Ti stacks. Standard drill
tool manufactures and innovators form their experience suggested the range of values that
gives better result with respect to various tool geometry parameters. The
recommendations are, helix angle should be in the range of 25° and 35° with respect to
axis, margin width should be maintained between 5% to 10% of drill diameter, body
clearance diameter should be maintained between 92% to 96 % of the drill diameter, web
thickness should be maintained between 20% to 30% of drill diameter and chisel edge
angle should be maintained between 105° and 120°.Developer of drill tool for one shot
machining of Ti-Al-CFRP suggested that the helix angle should be in the range of 30° to
35° and point angle be 130° (Sampath and Wangyang, 2013; Capone, 2011; Prabukarthi
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
227
et al., 2016). Hence three tool geometry [TG1–TG3] have been ground with various drill
tool design parameters and shown in Table 3.
Table 3
Modified drill tool parameters (see online version for colours)
1
2
3
TG1
TG2
TG3
Drill point angle
(degree)
130
140
134
Helix angle (degree)
35
30
34
Point clearance angle
(degree)
7
7
8
Parameter
Core diameter (mm)
2.1
1.6
2
Margin (mm)
0.23
0.25
0.25
Body clearance
diameter (mm)
4.8
4.6
4.7
Web thickness (mm)
0.8
0.9
1
Figure 1
Experimental setup
2.3 Design of experiment
The set of experiments for determining the response measurements were developed using
Taguchi’s method of design experiments as it examines the effects of entire machining
process parameters with limited number of experiments in comparison with full factorial
228
M. Senthilkumar et al.
design of experiments. In this study the process parameters focused are spindle speed,
feed rate and tool geometry each varied at three levels shown in Table 4. In this
experimentation module, L27 orthogonal array(OA) has been used.
Table 4
Domain of experiments
Factors
Symbol
Level 1
Level 2
Level 3
N
895
1000
1800
Feed (mm/rev)
F
0.05
0.1
0.08
Tool geometry
TG
1
2
3
Spindle speed (rpm)
2.4 Drilling performance assessment characteristics
Drilling operation has been carried out on CFRP/Ti6Al4V stacks to determine the
performance measures such as thrust force, roundness and delamination (at entrance and
exit), exit burr height. Thrust force is the major cause for damages induced during drilling
and may lead to delamination in composite material and poor quality of machined
surface; increased thrust force is one of the causes for premature failure of drills.
Delamination is a failure mechanism observed in fibre reinforced composites and this
damage occurs around the hole both at entrance and exit. The damage around the hole
(entrance and exit) in CFRP was measured using a scanning technique. Delamination
factor (Fd) values were determined after measuring the maximum area (Amax) in the
delamination zone, i.e., around each hole. This factor was calculated using the ratio the
maximum area (Amax) of the delamination zone to the hole area (A) as shown in Figure 2
(Prasanna et al., 2014). Image J software was used to calculate the delamination zone. In
order to obtain an image with an acceptable quality, a series of parameters, such as
brightness intensity, noise suppression , image enhancement and edge detection must be
appropriately selected (Schulze et al., 2011). The value of delamination factor (Fd) was
obtained by the following equation (1),
Figure 2
Delamination factor
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
Fd =
3
229
Amax
A
(1)
Results and discussions
In order to ascertain the significant factors, Taguchi’s method of analysing factors based
on signal-to-noise (S/N ratio) has been employed. For each performance measure
lower-is-better (LB) criterion has been selected and analysis of variance (ANOVA) has
been performed to investigate the main effects of process parameters on performance
measure characteristics.
3.1 Effect of process parameters on thrust force
During drilling of CFRP/Ti6Al4V stacks it was observed that, thrust force values varied
based on the material being cut by the drill. Figure 3 shows the variation of thrust force at
various spindle speed, feed rate and tool geometry in CFRP and Ti alloy.
It has been observed that the thrust force increased with increase in feed rate while
drilling CFRP and Ti. This characteristic is mainly due to the increase in cross-section of
chip with increase in feed rate. In addition to feed rate the point angle of drill
significantly contributes to thrust force values, it has been experimentally found that
thrust force values recorded with 140° drill is almost twice, compared to other two drills
when drilling CFRP. Overall performance of tool geometry 1 (TG1) is good comparing
the other tool geometries. As per researchers, titanium alloy shows better results at low
speed and high feed rate rates. The thrust force values are three times higher than the
CFRP grades. In case of TG2, the values are four times higher than the CFRP values at
1,000 rpm. There might be a possibility of high tool wear leads to high thrust force. From
experimental results, there is a gradually increase in the thrust force value due to
adhesion of chip to the drill surface. ANOVA table for thrust force in CFRP and Ti6Al4V
are presented in Table 5.
Table 5
Source
ANOVA for thrust force-CFRP and Ti6Al4V
DF
SS
CFRP
F
Ti6Al4V
P
CFRP
Ti6Al4V
CFRP
Ti6Al4V
N
2
415.6
47,314
1.49
N
2
415.6
F
2
2,944.3
61,383
10.55
F
2
2,944.3
TG
2
24,601.4
179,339
88.19
TG
2
24,601.4
N*F
4
944.8
22,517
1.69
N*F
4
944.8
F*TG
4
357.7
14,811
0.64
F*TG
4
357.7
N*TG
4
2,158.4
26,976
3.87
N*TG
4
2,158.4
From Taguchi method of analysing factors based on S/N ratio the optimal condition to
minimise thrust force was found to be combination of (895 rpm, 0.05 mm/rev and TG1)
in CFRP and (1,800 rpm, 0.05 mm/rev and TG1) in Ti alloy as shown in Figure 4.
230
Figure 3
M. Senthilkumar et al.
Variation of thrust force, (a) CFRP (b) Ti6Al4V (see online version for colours)
(a)
(b)
3.2 Effect of process parameter on roundness deviation
The roundness deviation of the machined hole is forth put in terms of circularity. The
error of circularity is defined as the distance between the minimum circumscribing circle
diameter and the maximum inscribing circle diameter (Shyha et al., 2011). Roundness
was measured at the middle of the CFRP laminate and titanium alloy.
During drilling composite/metal stacks it is difficult to obtain close diameter
tolerances due to variation in material properties. Variation of roundness at various
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
231
spindle speed, feed rate and tool geometry while drilling CFRP and Ti alloy are presented
in Figure 5. It has been observed that the roundness increased with increase in spindle
speed and feed rate while drilling CFRP and Ti. This may be due to modulus of elasticity
of materials causing different elastic deformation.
Figure 4
Analysis for thrust force, (a) CFRP (b) Ti6Al4V (see online version for colours)
MainEffects Plot for SN ratios
Data Means
N
f
-39
-40
Mean of SNratios
-41
-42
-43
895
1000
TG
1800
1
2
3
0.05
0.08
0.10
-39
-40
-41
-42
-43
Signal-to-noise: Smaller is better
(a)
MainEffects Plot for SN ratios
Data Means
N
f
-51
Mean of SNratios
-52
-53
-54
-55
895
1000
TG
1800
1
2
3
0.05
0.08
0.10
-51
-52
-53
-54
-55
Signal-to-noise: Smaller is better
(b)
Additionally, the hot chip transported through the hole as well as built-up-edge formed
around cutting edges during drilling of titanium alloy has a direct effect on roundness.
ANOVA table for roundness in CFRP and Ti6Al4V are shown in Table 6.
232
Table 6
M. Senthilkumar et al.
ANOVA for roundness deviation-CFRP and Ti6Al4V
Source
DF
N
F
TG
N*F
F*TG
N*TG
Error
Total
2
2
2
4
4
4
8
26
Figure 5
SS
F
P
CFRP
Ti6Al4V
CFRP
Ti6Al4V
CFRP
Ti6Al4V
0.19106
0.05603
0.00771
0.09875
0.02248
0.00753
0.02829
0.41184
0.00298
0.00159
0.00035
0.00369
0.00049
0.00174
0.00221
0.01304
27.02
7.92
1.09
6.98
1.59
0.53
5.4
2.88
0.63
3.34
0.44
1.57
0
0.013
0.382
0.01
0.267
0.716
0.033
0.114
0.556
0.069
0.777
0.271
Variation of roundness, (a) CFRP (b) Ti6Al4V (see online version for colours)
(a)
(b)
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
233
The same trend of maximum percentage of contribution was observed between tool
geometry and feed rate while analysing it with the roundness deviation of CFRP and
Ti6Al4V. From Taguchi method of analysing factors based on S/N ratio the optimal
condition to minimise roundness deviation was found to be combination of (895 rpm,
0.05 mm/rev and TG1) in CFRP and (1,000 rpm, 0.05 mm/rev and TG1) in Ti alloy as
shown in Figure 6.
Figure 6
Analysis for roundness deviation, (a) CFRP (b) Ti6Al4V (see online version
for colours)
MainEffects Plot for SN ratios
Data Means
N
f
20
Mean of SNratios
18
16
14
12
895
1000
TG
1800
1
2
3
0.05
0.08
0.10
20
18
16
14
12
Signal-to-noise: Smaller is better
(a)
MainEffects Plot for SN ratios
Data Means
N
29
f
28
Mean of SNratios
27
26
25
895
1000
1800
TG
29
28
27
26
25
1
2
3
Signal-to-noise: Smaller is better
(b)
0.05
0.08
0.10
234
M. Senthilkumar et al.
3.3 Effect of process parameter on delamination in CFRP
During drilling of CFRP composite, delamination occurs both at the entrance and exit
point of drill. The first phase is concerned with delamination at entrance (peel up) which
occurs when the cutting force pushes the abraded and cut materials upwards that spiral up
around the flute space. The second phase of delamination is at exit (push down) which is
due to compressive thrust force exerted by the drill tip on the uncut laminate. Figure 7
shows the variation of delamination factor at various spindle speed, feed rate and tool
geometry in CFRP.
Figure 7
Variation of delamination factor, (a) at entrance (b) at exit (see online version
for colours)
(a)
(b)
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
235
It has been observed that the delamination increased with increase in spindle speed and
feed rate irrespective of the tool geometry used, while drilling CFRP. It was observed that
interaction between feed rate and speed percentage of contribution was higher when
compared to interaction between feed rate and tool geometry based on ANOVA table for
delamination factor in CFRP is shown in Table 7.
Table 7
Source
ANOVA for delamination-CFRP
DF
SS
Peel up
F
P
Push down
Peel up
Push down
Peel up
Push down
N
2
0.09054
0.0842
136.06
71.49
0
0
F
2
0.01056
0.00863
15.87
7.33
0.002
0.016
TG
2
0.00323
0.03722
4.86
31.6
0.042
0
N*F
4
0.0006
0.00161
0.45
0.68
0.77
0.622
F*TG
4
0.00126
0.00246
0.95
1.04
0.484
0.442
N*TG
4
0.02094
0.02088
15.73
8.86
0.001
0.005
Error
8
0.00266
0.00471
Total
26
0.1298
0.15972
From Taguchi method of analysing factors based on S/N ratio the optimal condition to
minimise delamination to be combination of (895 rpm, 0.05 mm/rev and TG3) in peel up
delamination and (895 rpm, 0.05 mm/rev and TG3) in push out delamination as shown in
Figure 8.
Analysis for delamination, (a) peel up delamination (b) push out delamination
(see online version for colours)
MainEffects Plot for SN ratios
Data Means
N
f
-1.0
-1.2
-1.4
Mean of SNratios
Figure 8
-1.6
-1.8
895
1000
TG
1800
1
2
3
-1.0
-1.2
-1.4
-1.6
-1.8
Signal-to-noise: Smaller is better
(a)
0.05
0.08
0.10
236
Figure 8
M. Senthilkumar et al.
Analysis for delamination, (a) peel up delamination (b) push out delamination
(continued) (see online version for colours)
MainEffects Plot for SN ratios
Data Means
N
-1.00
f
-1.25
Mean of SNratios
-1.50
-1.75
-2.00
895
1000
1800
0.05
0.08
0.10
TG
-1.00
-1.25
-1.50
-1.75
-2.00
1
2
3
Signal-to-noise: Smaller is better
(b)
3.4 Effect of process parameter on exit burr height in titanium
The formation of burr at the exit is similar to formation of chips, burr formation initiates
when the material on the exit of the drill becomes too weak to support the thrust force
and the partially deformed chips starts bending in the direction of cutting velocity at the
end of the cut. Figure 9 shows the variation of burr height at various spindle speed, feed
rate and tool geometry in Ti6Al4V. From the experimental results it is inferred that feed
rate has major contribution to the burr height.
Figure 9
Variation of burr height in Ti6Al4V (see online version for colours)
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
237
The contribution of spindle speed is also significant and burr height values were found to
lower with increase in point angle and helix angle of the drill. Interaction between tool
geometry and speed has the maximum percentage of contribution which is clearly witness
based on ANOVA table for burr height in Ti6Al4V is shown in Table 8.
Table 8
ANOVA for exit burr height- Ti6Al4V
Source
N
F
TG
N*F
F*TG
N*TG
Error
Total
DF
2
2
2
4
4
4
8
26
SS
1.5946
3.4167
0.6152
6.6763
1.1613
0.1579
1.5564
14.7284
f
4.10
8.78
0.42
8.58
1.49
0.20
P
0.060
0.010
0.668
0.005
0.291
0.930
From Taguchi method of analysing factors based on S/N ratio the optimal condition to
minimise burr height was found to be combination of (1,000 rpm, 0.08 mm/rev and TG1)
in Ti alloy as shown in Figure 10.
Figure 10 Analysis for burr height (see online version for colours)
MainEffects Plot for SN ratios
Data Means
N
f
10
Mean of SNratios
8
6
4
2
895
1000
TG
1800
1
2
3
0.05
0.08
0.10
10
8
6
4
2
Signal-to-noise: Smaller is better
4
Multi-response optimisation
Technique for order preference by similarity to ideal solution (TOPSIS) method is very
popular and widely used as a multi-attribute decision making (MADM) methodology.
Deng’s similarity-based approach was used to find out the best alternative of the multi
criteria decision problem (Sonkar et al., 2014).
238
Table 9
M. Senthilkumar et al.
Formulation of decision matrix
Roundness (mm)
CFRP
Ti6Al4V
0.0975
0.0826
Thrust force (N)
Delamination factor
Burr height(mm)
CFRP
Ti6Al4V
Peel up
Pushdown
Ti6Al4V
0.0374
65
249
1.095
1.098
0.126
0.0399
120
467
1.024
1.142
0.168
0.0771
0.0397
69
354
1.102
1.112
0.471
0.0771
0.0387
89
429
1.165
1.113
0.144
0.0798
0.0209
160
666
1.052
1.183
0.118
0.1209
0.0387
92
360
1.105
1.124
0.079
0.1209
0.0389
95
356
1.178
1.129
1.607
0.0704
0.0483
176
668
1.123
1.192
1.877
0.1035
0.0571
93
555
1.112
1.132
1.089
0.2134
0.0393
81
249
1.183
1.138
0.350
0.2134
0.0366
171
593
1.134
1.212
0.054
0.1492
0.0338
69
555
1.125
1.151
0.880
0.1673
0.0649
102
356
1.192
1.267
1.100
0.1647
0.0391
186
707
1.162
1.229
1.850
0.2128
0.0242
93
481
1.16
1.166
2.130
0.3501
0.0297
90
522
1.203
1.281
1.880
0.5325
0.0391
154
525
1.181
1.241
1.856
0.4968
0.038
92
422
1.182
1.169
0.816
0.1571
0.0289
90
297
1.214
1.314
0.200
0.3493
0.0401
111
385
1.273
1.289
0.611
0.1883
0.036
85
256
1.2
1.172
0.598
0.263
0.0605
87
429
1.223
1.328
1.769
0.2136
0.1379
159
495
1.293
1.309
2.310
0.3963
0.0758
117
458
1.22
1.175
1.040
0.2387
0.0517
80
368
1.234
1.342
0.470
0.2687
0.0621
135
495
1.333
1.349
0.105
0.2876
0.0592
104
320
1.24
1.178
0.216
The first four steps in TOPSIS and Deng’s similarity-based approach are same. The
common four steps are as mentioned below. The multi-objective optimisation starts with
formulation of decision matrix (Table 9) consisting of experimentally determined values,
then normalisation of experimentally determined values (Table 10), so that all
performance measure comes into single dimensional scale between ‘0’ to ‘1’.
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
Table 10
239
Normalised decision matrix
Roundness (mm)
Thrust force (N)
Delamination factor
Burr height(mm)
CFRP
Ti6Al4V
CFRP
Ti6Al4V
Peel up
Pushdown
Ti6Al4V
0.0768
0.1399
0.1086
0.1036
0.1791
0.1750
0.0210
0.0651
0.1492
0.2005
0.1944
0.1675
0.1820
0.0280
0.0607
0.1485
0.1153
0.1473
0.1803
0.1772
0.0786
0.0607
0.1447
0.1487
0.1785
0.1906
0.1774
0.0240
0.0629
0.0782
0.2674
0.2772
0.1721
0.1886
0.0197
0.0952
0.1447
0.1537
0.1498
0.1808
0.1791
0.0132
0.0952
0.1455
0.1587
0.1482
0.1927
0.1799
0.2682
0.0554
0.1806
0.2941
0.2780
0.1837
0.1900
0.3132
0.0815
0.2135
0.1554
0.2310
0.1819
0.1804
0.1817
0.1681
0.1470
0.1354
0.1036
0.1935
0.1814
0.0584
0.1681
0.1369
0.2857
0.2468
0.1855
0.1932
0.0090
0.1175
0.1264
0.1153
0.2310
0.1840
0.1835
0.1469
0.1318
0.2427
0.1704
0.1482
0.1950
0.2019
0.1836
0.1297
0.1462
0.3108
0.2943
0.1901
0.1959
0.3087
0.1676
0.0905
0.1554
0.2002
0.1898
0.1858
0.3554
0.2757
0.1111
0.1504
0.2173
0.1968
0.2042
0.3137
0.4194
0.1462
0.2573
0.2185
0.1932
0.1978
0.3097
0.3913
0.1421
0.1537
0.1756
0.1934
0.1863
0.1362
0.1237
0.1081
0.1504
0.1236
0.1986
0.2094
0.0334
0.2751
0.1500
0.1855
0.1602
0.2083
0.2054
0.1020
0.1483
0.1346
0.1420
0.1065
0.1963
0.1868
0.0998
0.2071
0.2262
0.1454
0.1785
0.2001
0.2117
0.2952
0.1682
0.5157
0.2657
0.2060
0.2115
0.2086
0.3855
0.3121
0.2835
0.1955
0.1906
0.1996
0.1873
0.1736
0.1880
0.1933
0.1337
0.1532
0.2019
0.2139
0.0784
0.2116
0.2322
0.2256
0.2060
0.2181
0.2150
0.0175
Step 1
Establishment of decision matrix:
⎛ A1 x11 x12
⎜
⎜ A2 x21 x22
⎜ .
.
.
⎜
⎜ Ai xi1 xi 2
⎜
.
.
⎜ .
⎜A
⎝ m xm1 xm 2
x1n ⎞
⎟
x2 n ⎟
.
.⎟
⎟
xij
.⎟
⎟
.
.⎟
xmj xmn ⎟⎠
. x1 j
. x2 j
.
.
.
.
240
M. Senthilkumar et al.
Here Ai(i = 1, 2, …, m) represents the possible alternatives; xj(j = 1, 2, …, n)
represents the attributes relating to possible alternative performance,
i = 1, 2, …, n and xij is the performance of Ai with respect to Xj.
Step 2
Normalisation of matrix:
xij
rij =
∑
(2)
m
i =1
xij2
Here, rij represents the normalised performance of Ai with respect to Xj.
Step 3
Weighted decision matrix
V = ⎡⎣ vij ⎤⎦
V = w j rij
⎛ Y11 Y12
⎜
⎜ Y21 Y22
⎜
.
D=⎜ .
⎜ yi1 yi 2
⎜
.
⎜ .
⎜y
y
m2
⎝ m1
Here,
∑
n
j =1
(3)
. Y1 j Y1n ⎞
⎟
. Y2 j Y2 n ⎟
.
.
.⎟
⎟
. yij
.⎟
⎟
.
.
.⎟
⎟
. ymj ymn ⎠
wj = 1
The weights to each response are assigned based on its relative importance with
each other response. In this study, equal weights have been assigned to each
response. The weighted decision matrix is shown in Table 11.
Step 4
Determine the ideal (best) and negative ideal (worst) solutions:
a
The positive ideal solution (PIS):
A+ =
{( max y
ij
(
j ∈ J ) , min yij j ∈ J ' i = 1, 2, ..., m
)}
(4)
ii
= { y1+ , y2+ , ..., y +j , ..., yn+ }
b
The negative ideal solution (NIS):
A− =
{( min y
ij
(
j ∈ J ) , max yij j ∈ J ' i = 1, 2, ..., m
)}
(5)
ii
={
y1− ,
y2− , ...,
y −j , ...,
yn−
}
Here
J = {j = 1, 2, …, n | j}: associated with the beneficial attributes.
J’ = {j = 1, 2, …, n | j}: associated with non-beneficial attributes.
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
Table 11
241
Weighted normalised decision matrix
Roundness (mm)
Thrust force (N)
Delamination factor
Burr height(mm)
CFRP
Ti6Al4V
CFRP
Ti6Al4V
Peel up
Pushdown
Ti6Al4V
0.0109
0.0199
0.0154
0.0147
0.0254
0.0249
0.0030
0.0092
0.0212
0.0285
0.0276
0.0238
0.0258
0.0040
0.0086
0.0211
0.0164
0.0209
0.0256
0.0252
0.0112
0.0086
0.0206
0.0211
0.0254
0.0271
0.0252
0.0034
0.0089
0.0111
0.0380
0.0394
0.0244
0.0268
0.0028
0.0135
0.0206
0.0218
0.0213
0.0257
0.0254
0.0019
0.0135
0.0207
0.0225
0.0210
0.0274
0.0256
0.0381
0.0079
0.0256
0.0418
0.0395
0.0261
0.0270
0.0445
0.0116
0.0303
0.0221
0.0328
0.0258
0.0256
0.0258
0.0239
0.0209
0.0192
0.0147
0.0275
0.0258
0.0083
0.0239
0.0194
0.0406
0.0350
0.0263
0.0274
0.0013
0.0167
0.0179
0.0164
0.0328
0.0261
0.0261
0.0209
0.0187
0.0345
0.0242
0.0210
0.0277
0.0287
0.0261
0.0184
0.0208
0.0441
0.0418
0.0270
0.0278
0.0438
0.0238
0.0129
0.0221
0.0284
0.0269
0.0264
0.0505
0.0392
0.0158
0.0214
0.0309
0.0279
0.0290
0.0445
0.0596
0.0208
0.0365
0.0310
0.0274
0.0281
0.0440
0.0556
0.0202
0.0218
0.0249
0.0275
0.0265
0.0193
0.0176
0.0153
0.0214
0.0176
0.0282
0.0297
0.0047
0.0391
0.0213
0.0263
0.0228
0.0296
0.0292
0.0145
0.0211
0.0191
0.0202
0.0151
0.0279
0.0265
0.0142
0.0294
0.0321
0.0206
0.0254
0.0284
0.0301
0.0419
0.0239
0.0732
0.0377
0.0293
0.0300
0.0296
0.0547
0.0443
0.0403
0.0278
0.0271
0.0283
0.0266
0.0246
0.0267
0.0275
0.0190
0.0217
0.0287
0.0304
0.0111
0.0301
0.0330
0.0320
0.0293
0.0310
0.0305
0.0025
0.0322
0.0314
0.0247
0.0189
0.0288
0.0267
0.0051
Table 12
PIS and negative ideal solution
Thrust force (N)
CFRP
Ti6Al4V
Roundness
deviation(mm)
CFRP
Ti6Al4V
Delamination factor
Burr
height(mm)
Peel up
Pushdown
Ti6Al4V
A+
0.031080 0.029425
0.041940 0.051568
0.043613
0.043002
0.077097
A–
0.010861 0.010363
0.005545 0.007816
0.033503
0.035001
0.001802
In order to assess the separation distance, i.e., the deviation from ideal solution
is expressed in terms of positive and NIS and is shown in Table 12. It was
understood form the table that the deviation for burr height and roundness
deviation are significant based on the analysis.
242
M. Senthilkumar et al.
4.1 Evaluation using TOPSIS
Step 5
Step 6
Determine the distance measures. The separation of each alternative from the
ideal solution is given by n-dimensional Euclidean distance from the following
equations:
Si+ =
∑ (y
− y +j ) j = 1, 2, ..., m
(6)
Si− =
∑ (y
− y −j ) j = 1, 2, ..., m
(7)
n
ij
j =1
n
ij
j =1
2
2
Si+
distance between PIS and alternative
Si−
distance between NIS and alternative.
Calculate the overall performance coefficient closest to the ideal solution:
Ci+ =
Si−
i = 1, 2, ..., m : 0 ≤ Ci+ ≤ 1
Si+ + Si−
(8)
Ci+ Overall performance measure
Step 7
Rank the preference order. The alternative with the largest relative closeness is
the best choice.
The calculated values for step 5 to 7 are shown in Table 13.
4.2 Evaluation using Deng’s similarity-based approach
Step 5
Estimation of conflict between each alternative and the positive and the negative
ideal solution:
Ai , A+ = Ai A+ cos θ +
Ai , A+ =
∑y y
ij
⎛
Ai = ⎜
⎜
⎝
∑y
⎛
A+ = ⎜
⎜
⎝
∑y
cos θi+
=
m
2
ij
j =1
+
j
(10)
⎞
⎟
⎟
⎠
(11)
⎞
⎟
⎟
⎠
(12)
m
+2
ij
j =1
∑
⎛
⎜
⎝
(9)
∑
m
j =1
⎞⎛
yij2 ⎟⎜
j =1
⎠⎝
m
yij y +j
∑
⎞
yij+2 ⎟
j =1
⎠
m
(13)
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
cos θi−
Table 13
S+
=
∑
⎛
⎜
⎝
∑
m
j =1
⎞⎛
yij2 ⎟ ⎜
j =1
⎠⎝
m
yij y −j
∑
(14)
⎞
yij−2 ⎟
j =1
⎠
m
Computation of results in TOPSIS
S–
C+
S/N ratio
0.0096
0.0975
0.9106
–0.8130
0.0212
0.0913
0.8118
–1.8114
0.0155
0.0921
0.8558
–1.3526
0.0158
0.0941
0.8559
–1.3519
0.0335
0.0960
0.7413
–2.6000
0.0145
0.0931
0.8654
–1.2558
0.0398
0.0781
0.6625
–3.5761
0.0583
0.0713
0.5504
–5.1864
0.0369
0.0748
0.6698
–3.4813
0.0207
0.0870
0.8076
–1.8565
0.0372
0.0843
0.6939
–3.1741
0.0290
0.0833
0.7415
–2.5972
0.0377
0.0695
0.6482
–3.7664
0.0599
0.0677
0.5305
–5.5060
0.0540
0.0751
0.5815
–4.7088
0.0566
0.0669
0.5416
–5.3263
0.0730
0.0553
0.4312
–7.3059
0.0533
0.0700
0.5674
–4.9221
0.0145
0.0934
0.8655
–1.2544
0.0386
0.0736
0.6563
–3.6584
0.0211
0.0858
0.8024
–1.9119
0.0524
0.0599
0.5334
–5.4582
0.0880
0.0384
0.3036
–10.3545
0.0553
0.0523
0.4861
–6.2650
0.0289
0.0782
0.7301
–2.7319
0.0393
0.0743
0.6542
–3.6853
0.0339
0.0767
0.6934
–3.1809
Step 6
243
Assessment of the degree of similarity between each alternative and the positive
and the negative ideal solution.
Ci = cos θi−+ × Ai
(15)
244
M. Senthilkumar et al.
Ci =
Si−+ =
∑
⎛
⎜
⎝
∑
Ci
A−+
m
j =1
yij y −+
j
⎞⎛
y ⎟⎜
j =1 ij ⎠ ⎝
m
=
2
∑
m
cos θi−+ × Ai
A−+
−+ 2
j =1
y ij
⎛
×⎜
⎞ ⎜
⎟ ⎝
⎠
m
∑
j =1
⎞
yij2 ⎟
⎟
⎠
(16)
m
⎛
⎞
cos θi−+ × ⎜
yij2 ⎟
j
=
1
⎝
⎠
=
m
⎛
⎞
yij−+ 2 ⎟
⎜
j =1
⎝
⎠
∑
(17)
∑
In some cases TOPSIS was found inefficient, because comparing the distance
between two alternatives was not sufficient. Deng discovered that, the
comparison would be more effective, if magnitude, conflict between the
alternative and ideal solution are taken into consideration. The conflict between
each alternative, the positive (COSθ+) and the negative ideal (COSθ–) solution
(Step 5) and the degree of similarity between each alternative, the positive (C+)
and the negative (C–) ideal solution (Step 6) is shown in Table 14
Table 14
Conflict and degree of similarity between positive and negative ideal solution
COSθ+
COSθ–
C+
C–
0.9826
0.8160
0.0364
0.0302
0.9562
0.8164
0.0433
0.0370
0.9622
0.8577
0.0386
0.0344
0.9756
0.8061
0.0415
0.0343
0.9088
0.7192
0.0477
0.0378
0.9787
0.8276
0.0408
0.0345
0.8204
0.8997
0.0426
0.0467
0.7941
0.8702
0.0534
0.0586
0.8744
0.9191
0.0464
0.0488
0.9438
0.8864
0.0409
0.0384
0.9207
0.8148
0.0522
0.0462
0.9039
0.8836
0.0432
0.0422
0.8812
0.9510
0.0479
0.0517
0.8067
0.8851
0.0560
0.0614
0.7407
0.8713
0.0448
0.0527
0.7693
0.9087
0.0496
0.0586
0.7412
0.9258
0.0572
0.0715
0.7890
0.8973
0.0494
0.0562
0.9866
0.8113
0.0423
0.0348
0.8933
0.9080
0.0500
0.0508
0.9430
0.8960
0.0412
0.0392
0.8044
0.9584
0.0504
0.0601
0.7259
0.9623
0.0647
0.0857
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
Table 14
245
Conflict and degree of similarity between positive and negative ideal solution
(continued)
COSθ+
COSθ–
C+
C–
0.8243
0.9782
0.0547
0.0649
0.9351
0.9102
0.0472
0.0459
0.9325
0.8828
0.0554
0.0524
0.9071
0.9055
0.0479
0.0478
Step 7
Evaluation of overall performance index (OPI):
Pi =
Table 15
S+
Si+
, i = 1, 2, ......, n
Si+ + Si−
(18)
Computation of results using Deng’s similarity-based approach
S–
P
S/N ratio
1.0891
0.2921
0.7885
–2.0639
1.2969
0.3576
0.7838
–2.1154
1.1569
0.3331
0.7764
–2.1979
1.2435
0.3318
0.7894
–2.0545
1.4296
0.3654
0.7964
–1.9772
1.2219
0.3337
0.7855
–2.0973
1.2757
0.4519
0.7384
–2.6339
1.6001
0.5664
0.7386
–2.6321
1.3892
0.4716
0.7466
–2.5388
1.2259
0.3719
0.7673
–2.3011
1.5647
0.4473
0.7777
–2.1838
1.2942
0.4086
0.7600
–2.3834
1.4344
0.5000
0.7415
–2.5974
1.6773
0.5944
0.7384
–2.6347
1.3428
0.5102
0.7247
–2.7972
1.4846
0.5665
0.7238
–2.8073
1.7134
0.6913
0.7125
–2.9440
1.4800
0.5437
0.7314
–2.7175
1.2673
0.3366
0.7901
–2.0459
1.4972
0.4916
0.7528
–2.4661
1.2343
0.3788
0.7652
–2.3248
1.5104
0.5813
0.7221
–2.8280
1.9368
0.8292
0.7002
–3.0955
1.6380
0.6279
0.7229
–2.8185
1.4132
0.4443
0.7608
–2.3746
1.6582
0.5070
0.7658
–2.3173
1.4334
0.4621
0.7562
–2.4274
246
M. Senthilkumar et al.
Step 8
Determine the optimum process variable by using Taguchi method. The
optimum process parameter combination ensures highest OPI value. For
calculating S/N ratio (corresponding to the values of closeness coefficient);
higher-the-better (HB) criterion is to be considered. As larger the value of
closeness coefficient, better is the proximity to the ideal solution. The calculated
value of OPI and S/N ratio is shown in Table 15.
4.3 Determination of optimal parameter setting
Based on the OPI calculated using TOPSIS and Deng’s similarity-based approach, the
rank for each trial run is calculated.
Further the optimal parametric combination has been determined based on S/N ratio.
Since the objective is to improve quality by minimising the various performance
measures, optimal condition with highest S/N ratio (higher-is-better criterion) has to be
selected.
The optimal process setting for drilling of composite/metal stack (shown in
Figure 11) is found to be a combination as shown in Table 16 using TOPSIS and Deng’s
similarity-based method in combination with Taguchi’s philosophy.
Table 16
Optimal parameter setting
Spindle speed (RPM)
Feed rate (mm/rev)
Tool geometry
TOPSIS
895
0.05
1
Deng’s
895
0.05
1
It can be observed that the optimal parameter setting obtained using both TOPSIS and
Deng’s method are same, but the overall performance range of Deng’s similarity method
varied less as compared to TOPSIS.
Figure 11 Optimal process setting, (a) TOPSIS (b) Deng’s similarity-based method (see online
version for colours)
Main Effects Plot for SN ratios
Data Means
N
-2
F
Mean of SN ratios
-3
-4
-5
895
1000
TG
1800
1
2
3
-2
0.05
-3
-4
-5
Signal-to-noise: Larger is better
(a)
0.08
0.10
Multi-response optimisation in drilling of CFRP/Ti6Al4V stacks
247
Figure 11 Optimal process setting, (a) TOPSIS (b) Deng’s similarity-based method (continued)
(see online version for colours)
Main Effects Plot for SN ratios
Data Means
N
-2
F
Mean of SN ratios
-3
-4
-5
895
1000
1800
0.05
0.08
0.10
TG
-2
-3
-4
-5
1
2
3
Signal-to-noise: Larger is better
(b)
5
Conclusions
In this work, effort has been made for investigating the influence of process parameters
on performance measures to assess the optimal machining condition during drilling of
CFRP/Ti stacks. The following are the conclusions drawn based on the experiment and
analysis of data.
1
Thrust force increases linearly with increase in feed rate and tool geometry of drill.
While drilling CFRP/Ti stacks, the magnitude of thrust force during drilling Ti is
found to be three to four times higher than that during the drilling of CFRP.
2
Spindle speed and feed rate have significant contributions to roundness in CFRP.
3
Delamination (both peel up and push out) is found to be minimum at lower spindle
speed and feed rate and the influence of tool geometry is not found to be significant
on delamination.
4
Exit burr height in Ti decreases with increase in spindle speed. However, height of
the exit burr increases with increase in both spindle speed and feed rate (Kim
et al., 2013).
5
From the multi-objective optimisation technique, spindle speed of 895 rpm, feed rate
of 0.05 mm/rev and TG1 combination is identified to be the optimised parameters for
drilling CFRP/Ti stacks to achieve better hole quality. The results are in strong
correlation with the results obtained by Rahme et al. (2008).
248
M. Senthilkumar et al.
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