Mechanics, Materials Science & Engineering, September 2017 ISSN 2412-5954
MMSE Journal. Open Access www.mmse.xyz
A Fuzzy Approach to Trim Down the Struggles in Machining of AMMC by
Optimizing the Tool Wear and Process Cost
1
G. Vijaya Kumar
1,a
, B. Haritha Bai
2,b
, P. Venkataramaiah
1,c
1 Department of Mechanical Engineering SV University, Tirupati, India
2 Department of Mechanical Engineering, JNTUA, Anantapur, India
a Vijayluther2003@yahoo.co.in
b Haritha324@gmail.com
c pvramaiah@gmail.com
DOI 10.2412/mmse.40.42.927 provided by Seo4U.link
Keywords: AMMC, machining, WEDM, tool wear, process cost, analysis, optimization, fuzzy-logic.
ABSTRACT. Aluminum Metal Matrix Composites (AMMCs) are the precise resources for aerospace, marine and
automobile industries, due to their elevated strength to mass ratio. In machining vicinity of these materials, industries are
facing lots of troubles, as the existence of abrasive particles such as silicon carbide, aluminium oxide etc., causes the brisk
tool wear and hence tool malfunction within a very near to the ground machining time. In other hand, machining the
difficult-to-machine electrically conductive components with the high degree of accessible accuracy and the fine surface
quality make WEDM priceless. Still, a threat occurred is the ceramic particles resists the current through the composites.
Hence this paper focused on trim down these struggles. For this selecting the matrix material among the three series of
aluminium materials available with the suppliers by means of the normalization criterion have been done. AMMC samples
are produced as per the taguchi experimental design in view of collective material and WEDM parameters and machined
to obtain the responses: Tool wear and process cost. These are analyzed and derived an optimal set of parameters with
the patronage of fuzzy approach.
Introduction. Aluminium Metal Matrix Composites are vastly developed advanced resources which
are fine alternatives to many conventional materials, mostly when high strength and low-weight parts
are needed. AMMCs have found many unbeaten engineering applications in recent years by means
of their incomparable properties such as high strength-to-weight ratio and high toughness etc. [1, 2].
Conventional machining of AMMCs causes serious tool wear due to the existence of abrasive
particles and hence tool malfunction [3]. As a result, researchers are attracted to machine MMCs
using various non-conventional machining methods such as abrasive jet machining, laser beam
machining and electrical discharge machining (EDM) [46]. WEDM is a better substitute As WEDM
process provides an effective solution for machining hard materials, it confirms easy control and can
machine obscure shapes [7, 8]. The discharge current has most significance on kerf width, among the
process parameters: discharge duration, pulse interval time, discharge current and the wire drum
speed [9].The pulse on time and peak current are the momentous parameters which affecting the,
surface roughness and cutting speed. The wire tension has minor effect on the cutting speed but it has
great effect on the surface roughness [10]. Factors like pulse on time, pulse off time, servo voltage,
rate of wire feed, tension of wire, servo feed, spark gap voltage and rate of dielectric fluid are playing
a momentous role in cutting operations for maximization of MRR, minimization of surface roughness
and minimization of spark gap in WEDM [11] Various optimization techniques have been used by
the researchers to find the best combination of process parameters [12]. Fuzzy logic is with an
immense potential to confine analysis, decision-making and other aspects [13]. A fuzzy logic’s rule
base contains three basic units: fuzzifier, inference engine and defuzzifier. The primary task of the
1
© 2017 The Authors. Published by Magnolithe GmbH. This is an open access article under the CC BY-NC-ND license
http://creativecommons.org/licenses/by-nc-nd/4.0/
Mechanics, Materials Science & Engineering, September 2017 ISSN 2412-5954
MMSE Journal. Open Access www.mmse.xyz
system is to create a relation between influential parameters and responses [14-16]. Many authors
have got assistance of fuzzy logic for optimizing machining parameters and succeed in their research
[17-20]. In the present paper an optimum combination of collective, material and machining
parameters has been derived using fuzzy logic to trim down the struggles in machining of AMMCs
by means of optimizing the Tool wear and process cost.
Design of experiments and production of AMMCS. As many numbers of parameters are
considered for this research, Taguchi experimental design has been incorporated to reduce the number
of experiments and cost. The parameters and their levels considered for this research (table 2) are
collected from the past research except the selection of base material is followed a normalization
technique.
A. Selection of Base Material. Selection of base material is one of the most important activities for
preparation of Aluminium Metal Matrix Composite materials and it was paying attention of many
researchers from past few decades. An inappropriate selection of materials may result in damage or
failure of a system and severely decreases the performances [21]. For selecting the base materials,
properties such as Tensile strength, hardness, melting point, density and cost of the material (table 1)
of various alloys of 5xxx, 6xxx, 7xxx series, which are available with the suppliers are considered.
For the present work, a general normalization procedure is followed to select the base material. The
properties whose higher values are desirable, such as strength, hardness and melting point are
normalized using equation1 and tabulated in the table1. In addition, properties whose smaller values
are always preferable, such as density and cost are normalized using equation2 and tabulated in the
table1.
Table 1. Properties and cost of various alloys and their normalized values.
Aluminium
Alloy
Properties of various alloys
and cost
Normalized values of alloys
properties and cost
Sum of
Normalized
values
H
MP
D
C
TS
H
MP
D
C
5XXX Series
Al5052
68
625
2.68
270
0.17
0.00
1.00
0.00
1.00
2.17
Al5083
85
615
2.66
450
1.00
1.00
0.60
1.00
0.00
3.60
Al5754
75
600
2.67
450
0.00
0.41
0.00
0.50
0.00
0.91
6XXX Series
Al6061
95
651
2.7
350
1.00
1.00
0.40
1.00
0.00
3.40
Al6063
73
654
2.7
250
0.00
0.00
1.00
1.00
1.00
3.00
Al6082
91
650
2.7
280
0.82
0.82
0.20
1.00
0.70
3.53
Al6351
95
649
2.71
300
0.63
1.00
0.00
0.00
0.50
2.13
7XXX Series
Al7050
147
629
2.83
550
0.00
0.00
0.00
0.00
0.33
0.33
Al7075
150
635
2.81
350
0.59
1.00
1.00
1.00
1.00
4.59
Al7475
150
635
2.81
650
1.00
1.00
1.00
1.00
0.00
4.00
NB* TS Tensile Strength, H Hardness, MP Melting Point, D Density, C Cost
From the table 1 it is observed that the sum of Normalized values of 5083 in 5XXX Series is larger,
Mechanics, Materials Science & Engineering, September 2017 ISSN 2412-5954
MMSE Journal. Open Access www.mmse.xyz
6082 in 6XXX Series is larger and 7075 in 7XXX is larger. Hence, these alloys are selected as Base
materials.
Table 2. Influential parameters and their levels.
Sl. No
Influential parameters
Level 1
Level 2
Level 3
Material Parameters
1
Base material (BM)
Al5083
Al6082
Al7075
2
Type of reinforcement material (RM)
SiC
Al
2
O
3
Flyash
3
Percentage of reinforcement particle
(PRFM)
2.5
5
10
WEDM Parameters
4
Pulse on time(T
on
)
108
110
112
5
Pulse off time (T
off
)
56
58
60
6
Water pressure(wp)
4
7
10
7
Wire feed (Wf)
1
2
3
8
Servo feed (SF)
1030
1050
1070
Experimental Design. For the present work, L
27
Taguchi experimental design (table 3) have been
obtained through mini-tab software by considering various influential parameters related to material
and WEDM (table 2).
Production of AMMC samples. For the present work nine AMMC samples are produced using stir
casting furnace as per Taguchi L27 experimental design (table 2). To produce AMMCs, First the stir
casting furnace with graphite crucible is switched on and allow it to raise the temperature up to 500
O
C
then the required amount of base material is poured into the crucible and the temperature is raised up
to 850
O
C and allow it to maintain the same up to complete melting of base material. At this stage, the
wetting agent Mg of 1% is added to the base material by reducing its temperature to 100
o
above the
melting point of the alloy. Then the reinforcement particles are added slowly to the molten base
material while the stirrer rotating. Before adding the reinforcement particles, they are heated to
oxidise their surfaces. After mixing, the temperature of the slurry is raised up to 850
O
C for getting
improved fluidity and stirring is continued up to 5 minuets. Then the mixed slurry was poured in
different preheated steel dies to produce the samples.
Experimental work and optimization of parameters. The experiments were conducted in ULTRA
CUT WEDM Machine (Supplied by Vellore Wire Cut. Pvt. Ltd, Vellore, Tamilnadu) as per the L
27
Taguchi experimental design and the experimental data is recorded in the Table 4. For these
experiments, brass wire is used as electrode and water as dielectric fluid. Experimental results are
optimized using fuzzy logic and analyzed as following
Normalization of Experimental Data.
Data normalization is required where the range and unit in one data sequence may differ from the
others. In data pre-processing, the original sequence is transformed to a comparable sequence.
Various methodologies are available for various quality characteristic of a data sequence.
For quality characteristic of the “larger – the - better”, the data can be normalized as
Mechanics, Materials Science & Engineering, September 2017 ISSN 2412-5954
MMSE Journal. Open Access www.mmse.xyz
(k) =




(1)
Table 3. Taguchi design of experiments.
Expt.
No
AMMC
Sample
No.
Material parameters
WEDM parameters
BM
RFM
PRFM
Ton
Toff
Wf
Wp
SF
1
1
Al5083
SiC
2.5
108
56
4
1
1030
2
Al5083
SiC
2.5
108
58
7
2
1050
3
Al5083
SiC
2.5
108
60
10
3
1070
4
2
Al5083
Al
2
O
3
5.0
110
56
4
1
1050
5
Al5083
Al
2
O
3
5.0
110
58
7
2
1070
6
Al5083
Al
2
O
3
5.0
110
60
10
3
1030
7
3
Al5083
Fly ash
10.0
112
56
4
1
1070
8
Al5083
Fly ash
10.0
112
58
7
2
1030
9
Al5083
Fly ash
10.0
112
60
10
3
1050
10
4
Al6082
SiC
5.0
112
56
7
3
1030
11
Al6082
SiC
5.0
112
58
10
1
1050
12
Al6082
SiC
5.0
112
60
4
2
1070
13
5
Al6082
Al
2
O
3
10.0
108
56
7
3
1050
14
Al6082
Al
2
O
3
10.0
108
58
10
1
1070
15
Al6082
Al
2
O
3
10.0
108
60
4
2
1030
16
6
Al6082
Fly ash
2.5
110
56
7
3
1070
17
Al6082
Fly ash
2.5
110
58
10
1
1030
18
Al6082
Fly ash
2.5
110
60
4
2
1050
19
7
Al7075
SiC
10.0
110
56
10
2
1030
20
Al7075
SiC
10.0
110
58
4
3
1050
21
Al7075
SiC
10.0
110
60
7
1
1070
22
8
Al7075
Al
2
O
3
2.5
112
56
10
2
1050
23
Al7075
Al
2
O
3
2.5
112
58
4
3
1070
24
Al7075
Al
2
O
3
2.5
112
60
7
1
1030
25
9
Al7075
Fly ash
5.0
108
56
10
2
1070
26
Al7075
Fly ash
5.0
108
58
4
3
1030
27
Al7075
Fly ash
5.0
108
60
7
1
1050
For quality characteristic of the “smaller – the - better” the data can be normalized as
Mechanics, Materials Science & Engineering, September 2017 ISSN 2412-5954
MMSE Journal. Open Access www.mmse.xyz
(k) =





(2)
Where i = 1…, m; k = 1…, n;
m is the number of experimental data items;
n the number of parameters;
(k) denotes the original sequence;
(k) the sequence after the data pre-processing;
max
(k) the largest value of
(k);
min
(k) the smallest value of
(k);
is the desired value.
For the experimental values of, tool wear and process cost, smaller-the-better is applicable. Hence,
its experimental values are normalized using Eq. 2 and tabulated the values in table in Table 4.
Resolving the Fuzzy Grade. A fuzzy logic unit contains a fuzzifier, defuzzifier, a fuzzy rule base,
membership functions and an inference engine. In the fuzzy logic analysis, the fuzzifier uses
membership functions to fuzzify the input values and then the inference engine performs a fuzzy
reasoning on fuzzy rules to breed a fuzzy value. Finally, the defuzzifier converts the fuzzy value into
a Fuzzy grade (table4). The structure built for this study is a Two input- one-output fuzzy logic unit
as shown in Fig. 1. The input variables of the fuzzy logic system in this study are the normalized
values of experimental data of Tool wear and process cost. They are converted into linguistic fuzzy
subsets using membership functions of a triangle form (fig2), and are evenly assigned into three fuzzy
subsets: low (L), medium (M), and High (H). Dissimilar with the input variables, the output variable
is assigned into relatively nine subsets i.e., very very low (VVL), very low (VL), Low(L) medium
low(ML),medium (M), medium high(MH) high(H), very high (VH), very very high(VVH) grade.
The fuzzy rule base consists of a group of If - then control rules to express the inference relationship
between input and output. For this work 9 fuzzy rules are defined and shown in Figure 3.
Fig. 1. Two input- one-output fuzzy logic unit.
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Fig. 2. Membership functions of a triangle form.
Table 4. Experimental results, normalized values of experimental data and fuzzy grade values.
Expt.
No
Experimental Results
Normalized values of
experimental results
Fuzzy
Grade
Tool wear
Process
Cost
Tool wear
Process
cost
1
0.018
633
0.3043
0.6652
0.3832
2
0.01
519
0.6521
0.7828
0.6692
3
0.014
533
0.4782
0.748
0.5272
4
0.018
477
0.3043
0.8271
0.4034
5
0.025
395
0
0.9119
0.223
6
0.018
569
0.3043
0.7316
0.3918
7
0.015
698
0.4347
0.5988
0.4462
8
0.011
705
0.6086
0.5915
0.6223
9
0.018
1277
0.3043
0
0.2833
10
0.013
567
0.5217
0.7336
0.5799
11
0.009
394
0.6956
0.9123
0.698
12
0.012
346
0.5652
0.962
0.6578
13
0.019
781
0.2608
0.5128
0.3238
14
0.013
822
0.5217
0.4698
0.5244
15
0.013
987
0.5217
0.2993
0.4815
16
0.015
408
0.4347
0.8975
0.4952
17
0.016
658
0.3913
0.6394
0.4215
18
0.009
510
0.6956
0.7937
0.6881
19
0.019
569
0.2608
0.732
0.3764
20
0.014
394
0.4782
0.9123
0.5565
21
0.016
414
0.3913
0.8194
0.4485
22
0.014
352
0.4782
0.9561
0.5653
23
0.002
309
1
1
0.9615
24
0.014
568
0.4782
0.7328
0.5244
25
0.013
470
0.5217
0.8338
0.5995
26
0.012
600
0.5652
0.9996
0.6616
27
0.015
561
0.4347
0.7394
0.4745
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MMSE Journal. Open Access www.mmse.xyz
Fig. 3. Nine fuzzy rules.
Obtaining the Optimal Combination of Influential Factors
After resolving the Fuzzy Grade, the consequence of each parameter is separated based on Fuzzy
Grade of various levels. The mean values of Fuzzy Grade for each level of the influential factors and
the effect of influential factors on multi responses in rank wise are summarized in Table 6. Mostly,
the parameter level with larger Fuzzy Grade is considered as optimized. From the table 5 and fig. 4,
the optimal combination of influential factors is Base material at level 3 i.e.. Al7075 reinforcement
material at level 1 i.e. SiC, percentage of reinforcement material at level 1 i.e.; 2.5 ton at level 3 i.e.;
112, T
off
at level 2 ie; 58, WP at level 1 i.e.; 3, WF at level 2 i.e.; 2, SF at level 3 i.e.; 1070.
(BM3RM1PRFM1TON3TOFF2WP1WF2SF3”) are the optimum influential parameters for
optimized tool wear and process cost.
Table 5. Fuzzy grade for each level of influential factors.
Level
BM
RM
PRFM
T
on
T
off
WP
WF
SF
1
0.438844
0.544078
0.581733
0.516100
0.463656
0.582200
0.480456
0.493622
2
0.541133
0.488789
0.521056
0.444933
0.593111
0.484533
0.542567
0.518011
3
0.574244
0.521356
0.451433
0.593189
0.497456
0.487489
0.531200
0.542589
Delta
0.135400
0.055289
0.130300
0.148256
0.129456
0.097667
0.062111
0.048967
Rank
2
7
3
1
4
5
6
8
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Fig. 4. Fuzzy Grade for each level of influential factors.
Confirmation experiment.
For the obtained optimal combination, confirmation test has been conducted and compared the results
(Table 6) with initial set of parameters. These results are satisfactory as the responses for optimal
combination shows better performance.
Table 6. Comparison of responses between AMMC with initial combination and optimal combination.
Influential parameters
combination
Combination of Controllable Parameters
Tool
Wear
Process
Cost
Initial Combination
BM 2RM2PRM2TON2TOFF2WF2WP2SF2
0.018
476
Optimal combination
BM3RM1PRFM1TON3TOFF2WP1WF2SF3
0.010
275
Gain
N/A
0.08
201
Summary. For this paper WEDM experiments are conducted by producing AMMC samples as per
L27 Taguchi experimental design which is considered the collective material and machining
parameters. The Fuzzy approach has been applied effectively for determining the set of optimum
influential parameters. After analyzing the data, it is concluded that Ton, RM and Toff are the most
significant parameters which influence the multi responses, PRM and BM are the medium influenced
parameters on multi responses and WP, WF SF are influenced lastly the multi responses. When
compared the conformational experimental results with initial set of parameters combination, the
better improvement is noted, and the improvement in tool wear is 0.08mm and in process cost is Rs
201. Hence, it is concluded that this approach provides a systematic and effective methodology for
optimizing the collective material and machining parameters which in turn reduces the manufacturing
cost and greatly enhances manufacturing efficiency.
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