1.
INTRODUCTION:
Fiber reinforced composite material
are alternative to steel and other materials in highly corrosive industrial
applications. In recent years, fiber reinforced composite material have been
extensively used in variety of engineering applications in different fields
such as Aerospace, Naval, and other industries. The machining of fiber
reinforced composites is different from conventional materials. The behavior of
composites is anisotropic.
The quality of machined products
depends upon the type of fibers, reinforced materials used, bonding strength
between fiber and matrix, type of weave, etc. Milling
of composite materials is a rather complex task owing to the heterogeneity of
the material and a number of other problems, such as surface roughness and
delamination factor, which appear during the machining process and are
associated with the characteristics of the material and the cutting parameters.
Surface roughness and delamination factor are parameters that have a great
influence on dimensional precision and performance of mechanical pieces.
For this reason,
research and development have been carried out through design experiments to
reach a specific surface roughness and a specific delamination factor. GFRP
are increasingly being used for varieties of engineering applications because
of their superior advantage over other engineering materials (BANNISTER, 2001).
The advantages include high strength to weight ratio, high fracture toughness
and excellent corrosion and thermal resistance. The tail arability of
composites for specific applications has been one of their greater advantages
and also one of the more perplexing challenges to adopting them as alternative
to conventional materials (KISHORE; TIWARI; SINGH, 2009). Even though Glass
fiber reinforce polymer (GFRP) pipe made by filament wind technique require
further machining to facilitate dimensional control for easy assembly and
control of surface quality for functional aspects (BHATNAGAR; RAMAKRISHNAN;
NAIK; KOMANDURAI, 1995).
The users of FRP are facing
difficulties when machining it, because basic knowledge and experience needed
for conventional materials cannot be applied for such new innovative materials,
whose ability to machine is different from that of conventional materials (MONTGOMERY,
1991). Thus it is desirable to investigate the behavior of FRPs during the
machining process. Everstine and Rogers, (1971), predicted a new approach of an
analytical theory of machining FRPs. In this study, they predicted a theory of
plane deformation of incompressible composites reinforced by strong parallel
fibers.
Bhatnagar et al (1995) studied how the fiber orientation influence
both the quality of the machined surfaces and tool wear rate. The machinability
of composites is influenced by the type of fiber inserted in the composites,
and especially by the mechanical properties. On the other hand, the selection
of parameters and tool are dependent on the type of fiber used in the
composites and which is very important in the machining process. Davim, Mata (2004)
revealed that the influence of cutting parameters on surface roughness in
turning glass-fiber reinforced plastics using statistical analysis. Ramulu, et al (1994), experimentally carried
out a study on machining of polymer composites and concluded that higher
spindle speeds give better surface finish.
Santhana, krishanan et al (1989) studied the surface
roughness on machining of GFRP composites, according to them, higher spindle
speed produce more surface damage on the machined edge. This is attributed to
higher cutting temperature, which results in local annealing of work material.
They also focused on the machinability of FRP composites using the USM
technique. According to Koing et al.
(1985), measurement of surface roughness in FRP is less dependable
compared to that in metals, because protruding fiber tips may lead to
additional errors it may cause the fibers to stick on the stylus.
Palanikumar et al. (2006) studied the effect of cutting parameters on
surface roughness on machining of GFRP composites by polycrystalline diamond
(PCD) tool by developing a second order model for predicting the surface
roughness average. Palanikumar et al
(2008) developed a procedure to optimize the factors chosen to attain
minimum surface roughness by incorporating response table and graph, normal
plot, interaction plots, and analysis of variance technique. The average
surface roughness of machined GFRP parts are important in manufacturing
engineering applications which have considerable effect on some properties such
as resistance to wear, reflection of
light, transmission of heat, coating and fatigue resistance. While machining,
quality can be achieved through proper machining conditions. In order to know
the quality of surface and dimensional accuracy in advance, it is necessary to
adopt theoretical models making it feasible to do predict in the function of
operation condition.
2.
EXPERIMENTAL SET UP:
2.1.
Schematic of
Machining:
The work material used for present
work is glass fiber reinforced polymeric composite material fabricated by hand
layup method of 33% fiber and 66% general purpose resin
with randomly oriented long fibers supplied by Saint Gobain Vetrotex India
Limited. The dimensions of the work piece are 300mmX50mmX25mm. In this study,
the experiments were carried out on a CNC vertical machining center (KENT
and ND Co. Ltd, Taiwan make) to perform
10mm slots on GFRP work piece by K10 carbide, four flute end milling cutter as
shown in Figure1.
Furthermore the spindle speed (rpm),
the feed rate (mm/min) and depth of cut (mm) are regulated in this experiment.
Each experiment was conducted three times and the maximum width of the
delamination damage (Wmax) around
the slot periphery using Travelling Microscope with an accuracy of 10µm is
measured at five places on each slot then average of them in mm after that
Delamination Damage Factor calculated. Similarly, the surface
roughness is measured at five places on each slot then average of them in
µm is considered by a surface analyser of Form Talysurf
50 (Taylor Hobson Co Ltd). Measured
observations are depicted in Table 3 and 4.
Figure 1:
Machining of GFRP by CNC vertical machining center with K10 carbide End Mill
2.2.
Delamination
and Surface Roughness and its Measurement
Failure
analysis of laminated composite structures has attracted a great deal of interest in recent years due to the increased
application of composite materials in
a wide range of high performance structures. Delamination, the separation of
two adjacent plies in composite laminates, represents one of the most critical
failure models in composite laminates. In fact, it is an essential issue in the evaluation of
composite laminates for durability and delamination damage tolerance. The value
of the delamination damage factor (Fd) can be obtained using the
following equation: F d = Wmax / W, Where Wmax is the
maximum width of the damage around the slot periphery and W is width of cut.
The surface roughness parameter used
to evaluate surface roughness in this study is the Roughness average (Ra). This
parameter is also known as the arithmetic mean roughness value, arithmetic
average or centerline average. Within the presented research framework, the
discussion of surface roughness is focused on the universally recognized Ra.
The average roughness is the area between the roughness profile and its Centre
line, or the integral of the absolute value of the roughness profile height
over the observed length.
Figure 2: Measurement of surface roughness using Form Talysurf 50 (Taylor Hobson Co Ltd)
Figure 3: Measurement of delamination damage using Tool makers Microscope
2.3.
Experimentation
as per Taguchi Design Method
A plan of experiments based on
Taguchi technique has been used to acquire the data. An orthogonal array and
Grey relational analysis has been employed to investigate the cutting
characteristics of GFRP material using K10 carbide tool. Finally, confirmation
test (ANOVA) have been carried out to compare the predicted values with the
experimental values confirm its effectiveness in the analysis of surface
roughness and delamination damage. Also, The orthogonal array forms the basis for the experimental analysis in the
Taguchi method. The selection of orthogonal array is concerned with the total
degree of freedom of process parameters.
Total
degree of freedom (DOF) associated with three parameters is equal to 6
(3X2=6).The degree of freedom for the orthogonal array should be greater than
or at least equal to that of the process parameters. There by, a L9 orthogonal
array having degree of freedom equal to (9-1= 8) 8 has been considered .But in
present case each experiment is conducted three times, therefore total degree
of freedom (9X3-1=26) 26 has been considered finally. The machining
parameters and their levels are given in table1. Plan of experiments based on
Taguchi orthogonal array shown in table 4.
Table 1: Parameters and their Levels.
|
Symbol |
Factors |
units |
Level 1 |
Level 2 |
Level 3 |
|
A |
cutting speed |
rpm |
1000 |
1250 |
1500 |
|
B |
feed rate |
mm/min |
200 |
300 |
400 |
|
C |
depth of cut |
mm |
0.5 |
1 |
1.5 |
Table 2: Summary
of observed experimental data (Surface Roughness):
|
Exp.No |
Surface
Roughness |
Surface Roughness average (Ra) µm |
||
|
Trial 1 |
Trial 2 |
Trial 3 |
||
|
1 |
2.600 |
2.640 |
2.650 |
2.630 |
|
2 |
5.260 |
5.220 |
5.600 |
5.360 |
|
3 |
3.800 |
3.730 |
3.900 |
3.810 |
|
4 |
5.168 |
5.310 |
4.996 |
5.158 |
|
5 |
2.106 |
3.021 |
1.071 |
2.066 |
|
6 |
6.200 |
5.920 |
6.444 |
6.188 |
|
7 |
3.260 |
3.860 |
2.678 |
3.266 |
|
8 |
7.920 |
8.260 |
9.830 |
8.670 |
|
9 |
3.850 |
3.786 |
3.920 |
3.852 |
Table 3: Summary of observed experimental data (Delamination
Damage Factor):
|
Exp.No |
Delamination
Damage Factor |
Average of Delamination Damage Factor(Fd)
|
||
|
Trial 1 |
Trial 2 |
Trial 3 |
||
|
1 |
1.25 |
1.18 |
1.23 |
1.22 |
|
2 |
1.76 |
1.69 |
2.10 |
1.85 |
|
3 |
1.36 |
1.18 |
1.18 |
1.24 |
|
4 |
1.42 |
1.61 |
1.65 |
1.56 |
|
5 |
1.37 |
1.53 |
1.51 |
1.47 |
|
6 |
1.71 |
1.64 |
1.69 |
1.68 |
|
7 |
1.48 |
1.53 |
1.52 |
1.51 |
|
8 |
1.22 |
1.15 |
1.17 |
1.18 |
|
9 |
1.08 |
1.10 |
1.18 |
1.12 |
Table 4: Responses observed in the Experimentation as per Taguchi Design
|
Exp.No |
Machining
parameters |
Responses |
|||
|
Cutting
Speed(A) rpm |
Feed rate(B) mm/min |
Depth of Cut(C) mm |
Delamination
damage Factor(Fd) |
Average Surface Roughness(Ra) µm |
|
|
1 |
1000 |
200 |
0.5 |
1.22 |
2.630 |
|
2 |
1000 |
300 |
1 |
1.85 |
5.306 |
|
3 |
1000 |
400 |
1.5 |
1.24 |
3.810 |
|
4 |
1250 |
200 |
1 |
1.56 |
5.178 |
|
5 |
1250 |
300 |
1.5 |
1.47 |
2.066 |
|
6 |
1250 |
400 |
0.5 |
1.68 |
6.192 |
|
7 |
1500 |
200 |
1.5 |
1.51 |
3.266 |
|
8 |
1500 |
300 |
1 |
1.18 |
8.670 |
|
9 |
1500 |
400 |
0.5 |
1.12 |
3.852 |
2.4.
Grey Relational
Analysis
In
grey relational analysis, black represents having no information and white
represents having all information. A grey system has a level of information
between black and white. This analysis can be used to represent the grade of
correlation between two sequences so that the distance of two factors can be
measured discretely. In the case where experiments are ambiguous or when the
experimental method cannot be carried out exactly, grey analysis helps to
compensate for the shortcoming in statistical regression .Grey relation
analysis is an effective means of analyzing the relationship between sequences
with less data and can analyze many factors that can overcome the disadvantages
of statistical method.
2.4.1.
Data
Pre-Processing
In
grey relational analysis, when the range of the sequence is large or the
standard value is enormous, the functions of factors are neglected. However, if
the factor goals and directions are different, the grey relational might
produce incorrect results. Therefore, one has to pre-process the data which are
related to a group of sequences, which is called ‘grey relational generation’
Data
pre-processing is a process of transferring the original sequence to a
comparable sequence. For this purpose the experimental results are normalized
in the range between zero and one. The normalization can be done form three
different approaches.
If
the target value of original sequence is infinite, then it has a characteristic
of “the larger-the –better”. The original sequence can be normalized as
follows.
(1)
If
the expectancy is the smaller-the better, then the original sequence should be
normalized as follows.
(2)
However,
if there is a definite target value to be achieved, the original sequence will
be normalized in the form.
(3)
Or
the original sequence can be simply normalized by the most basic methodology
i.e., let the values of original sequence be divided by the first value of
sequence
(4)
Where
is the value
after the grey relational generation (data pre-processing), max
is the largest value of
, min
is the smallest value of
and xn
is the desired value.
2.4.2.
Grey
relational coefficient and grey relational grade
Following
data pre-processing, a grey relational coefficient is calculated to express the
relationship between the ideal and actual normalized experimental results. They
grey relational coefficient can be expressed as follows:
(5)
Where
is the deviation sequence of the reference sequence
and the comparability sequence
, namely
= ||
-
||
Dmax =
Dmin =
V is distinguishing or identification coefficient V e to
[0,1]. V=0.5 is
generally used.
After
obtaining the grey relational coefficient, normally choose the average of the
grey relational coefficient as the grey relational grade. The grey relational
grade is obtained by
(6)
In
the grey relational analysis, the grey relational grade shows the relationship
among the sequences. If the two sequences are similar, then the value of grey
relational grade is equal to 1. The grey relational grade also shows the degree
of influence of the comparability sequence over the reference sequence. If a
particular comparability sequence is more important than the other sequences to
the reference, then the grey relational grade for that comparability and
reference sequence will be higher than other grey relational grades.
2.5.
4. Analysis and
discussion of Experimental results
In
the present study, surface roughness and delamination damage for different
parameters and experimental runs are listed in table 2.typically, lower values
of the surface roughness and delamination damage as the target values are
desirable. Therefore, the data sequences have the smaller-the-better
characteristic. The values of surface roughness and delamination damage are set
to be the reference sequence. More over the results of 9 experiments were the
comparability sequences xi*(k), i = 1 – 9, k= 1 – 3.
Table 3 Response sequences after data
pre processing
|
Comparability
sequence |
Surface Roughness (Ra) |
Delamination Damage Factor (Fd ) |
|
1 |
0.9146 |
0.8630 |
|
2 |
0.5012 |
0.0000 |
|
3 |
0.7359 |
0.8356 |
|
4 |
0.5317 |
0.3972 |
|
5 |
1.0000 |
0.5205 |
|
6 |
0.3758 |
0.2328 |
|
7 |
0.8182 |
0.4657 |
|
8 |
0.0000 |
0.9178 |
|
9 |
0.7295 |
1.0000 |
Table 3 Lists all of the sequences
following data preprocessing using equation(2) .Also, the deviation sequences ∆oi
, ∆max(k) and ∆min(k)
for i = 1 – 9, k= 1 – 3 can be calculated as follows.
∆o1(1)=│xo*(1)-x1*(1)│=│1.00-0.9146=0.0854│
∆o1(2)=│xo*(2)-x1*(2)│=│1.00-0.8630=0.1370│
∆max=∆05 (1) = ∆09 (2)
=1.0000
∆min=∆08 (1) = ∆02 (2)
=0.0000
The
distinguishing coefficient ζ can
be substituted for the grey relational coefficient in equation (5). If all the
process parameters have equal weight then ζ is 0.5.
Table 4: Grey
relational coefficient and grade
|
Runs |
Grey
Relational Coefficients |
Grey Relational
Grade( Γ) |
|
|
(Ra) |
(Fd ) |
||
|
1 |
0.8541 |
0.7849 |
0.8195 |
|
2 |
0.5006 |
0.3333 |
0.4169 |
|
3 |
0.6543 |
0.7525 |
0.7034 |
|
4 |
0.5163 |
0.4534 |
0.4848 |
|
5 |
1.0000 |
0.5104 |
0.7552 |
|
6 |
0.4447 |
0.3962 |
0.4204 |
|
7 |
0.7333 |
0.4834 |
0.6083 |
|
8 |
0.3333 |
0.8588 |
0.5960 |
|
9 |
0.6489 |
1.0000 |
0.8244 |
Table 4 lists the grey relational
coefficient and grade for each experiment of the L9 orthogonal array
by applying equations (5) and (6).
According
to the performed experiment design it is clearly observed from table 4 and fig
(3) that the end milling process parameter setting of experiment no.9 has the
highest grey relational grade. Thus the experiment 9 gives the best
multi-performance characteristics among the 9 experiments. The response table
of Taguchi method was employed here to calculate the average grey relational
grade for each factor level. The procedure was to group the grey relational
grades by factor level for each column in the orthogonal array and then to
average them. Grey relational grades for
factors A & B at level 1 can be calculated as follows.
Γ (A)
1= [0.8195+0.4169+0.7034] /3 = 0.6466
Γ (B)
1= [0.8915+0.4848+0.6083] /3= 0.6375
Using
the same method, calculations were performed for each factor level and response
table was generated, as shown in table5.
Table 5:
Average Grey Relational Grade for Factor and Levels of the Experiment
|
|
Spindle
speed(A) |
Feed rate (B) |
Depth of cut (C) |
|
|
|||
|
1 |
0.6466 |
0.6375 |
0.6881 |
|
2 |
0.5534 |
0.5893 |
0.4992 |
|
3 |
0.6762 |
0.6494 |
0.6879 |
Table 6:
ANOVA for Average Grey Relational Grade
|
Symbol |
Cutting Parameters |
DOF |
SS |
MS |
F |
|
|
A |
Spindle speed |
2 |
0.0372 |
0.0186 |
4.061* |
significant |
|
B |
Feed rate |
2 |
0.0061 |
0.0130 |
2.838 |
Insignificant |
|
C |
Depth of cut |
2 |
0.0717 |
0.0358 |
7.816* |
significant |
|
Error |
|
20 |
0.0916 |
0.00458 |
|
|
|
Total |
|
26 |
0.2066 |
|
|
|
*significant, F table at 95%confidence
level is F0.05, 2, 20 = 3.49, F exp ≥ F table
The analysis of variance (ANOVA) of
the experimental data was done to statistically analyze the multi response
characteristics of the parameters under the experimental investigation. From
Table 6, it is observe that depth of cut has statistical and physically more
significance (78.16%), spindle speed has
less significance (40.61%) and feed rate has moderately less significance
(28.38%) obtained in end milling of GFRP with standard K10 carbide 4 flute end
mill through grey-based taguchi method.
Table 7: Optimal values of
individual machining characteristics
|
Machining Characteristics |
Optimal combination of parameters |
Significant parameters (at 95% confidence level) |
Predicted optimum value |
Experimental value |
|
Surface Roughness (Ra) |
A3B3C3 |
A , C |
3.755µm |
3.824µm |
|
Delamination Damage Factor Fd ) |
A3B3C3 |
A , C |
1.251 mm/mm |
1.178mm/mm |
|
Average Grey Relational
Grade(GRG) |
A3B3C3 |
A, C |
0.8297 |
0.8244 |
3.
CONCLUSIONS
The machining characteristics of
Glass Fiber Reinforced Polymeric composites have been studied. The primary
machining characteristics such as surface roughness and delamination damage
factor were studied for End milling. The results obtained from the experiments
as follows.
§ From
average grey relational grade table, the combination of parameters having the
values of, 0.5mm, 1500 rpm and 400 mm/min are obtained for spindle speed, feed
rate and depth of cut respectively for optimizing surface roughness and
delamination damage.
§ From average grey relational grade table,
depth of cut, spindle speed and feed rate are the order of influence of
parameters (C1 A3 B3) on surface roughness and delamination damage during
machining of GFRP.
§ The
result of ANOVA for average grey relational grade, spindle speed and depth of
cut were most significant parameters for influencing surface roughness and
delamination damage
§ Finally, concluded that for end milling of GFRP composites to
minimize surface roughness and delamination damage the
parameters contributions in an order are 78.16% of depth of cut and 40.61 % of spindle speed for influencing the
observed responses.
REFERENCES
BANNISTER,
M. (2001). Challenges for composites into the next millennium - a reinforcement
perspective. Composites Part A: Applied Science and Manufacturing,
v.32, n. 7, p. 901-910.
KISHORE,
R. A.; TIWARI, R.; SINGH, I. (2009).
Investigation of drilling in [(0/90)/0] s glass fibre reinforced plastics using
taguchi method. Advances in Production Engineering and Manangement, v. 4, n.
1-2, p. 37-46.
BHATNAGAR,
N.; RAMAKRISHNAN, N.; NAIK, N.K.; KOMANDURAI, R., (1995) On the machining of
fiber Reinforced plastics (FRP) composite laminates, Int J.Machine Tool Manuf., v. 35, n. 5, p. 701-716.
MONTGOMERY,
DC. (1991) Design and analysis of
experiments. John Wiley and sons, NewYork
EVESTINE,
G. C.;ROGERS. T. G, (1971) A Theory of machining of fiber reinforced materials,
J. Comp. Mater, n. 5, p 94-105.
BHATNAGAR,
N.; RAMAKRISHNAN, N.; NAIK, N. K.; KOMANDURAI, R., (1995), “On the machining of
fiber Reinforced plastics (FRP) composite laminates”, Int J. Machine Tool Manuf., v. 35, n. 5, p. 701-716.
DAVIM,
J. P.; MATA, F. (2004) Influence of cutting parameters on surface roughness
using statistical analysis. Indus
Lubrication Tribol, v. 56, n. 5, p. 270-274.
RAMULU, M.; AROLA, D.; COLLIGAN, K. (1994) Preliminary
Investigation on the surface Integrity of fiber Reinforced Plastics, Engineering systems Design and Analysis,
ASME, v. 64, n. 2, p. 93-101.
SANTHANA
KRISHANAN, G.; KRISHNAMOORTHY, R.; MALHOTRA, S. K. (1989) Machinability
Characteristics of Fiber Reinforced Plastics Composites., JMWT., n. 17, p. 95-104.
KOING,
W.; WULF, CH.; GRAB, P.; WILLERSCHEID, H. (1985) Machining of Fiber Reinforced
Plastics, Annals of CIRP, n. 34, p.
537-548.
PALANIKUMAR,
K.; KARUNAMOOTHY, L.; KARTHIKEYAN, R. (2006) Assessment of Factors Influencing
Surface Roughness on the Machining of Glass Fiber- Reinforced Composites, J. of Materials and Design, v. 27, n. 10,
p. 862-871.
PALANIKUMAR,
K. (2008) Application of Taguchi and Response surface Methodology for surface
Roughness In Machining Glass Fiber Reinforced Plastics by PCD Tooling, IJMPT, v. 36, n. 1-2, p. 19-27.