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Parametric Optimization of KERF Width and Surface Roughness in Wire Electrical Discharge Machining (WEDM) of Hybrid Aluminium (Al6061/SIC/GRAPHITE) Composite using TAGUCHI-Based Gray Relational Analysis A.Muniappana*, C.Thiagarajanb and S.Somasundramc a
Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha University, Chennai, India b Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha University, Chennai, India c Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chennai, India * Corresponding author. Tel.: +91-9841647429; E-mail address:
[email protected]
Abstract-- In this present investigation, the effect and optimization of machining parameters on the kerf width and surface roughness in wire electrical discharge machining (WEDM) operations of Al 60661 hybrid composite was studied. The hybrid metal matrix composite was fabricated by stir casting process using particulates SiC and graphite each in Al6061 alloy. The experiments were designed with L27 orthogonal array. The experimental studies were conducted under varying pulse on time, pulse off time, peak current, gap set voltage, wire feed rate and wire tension. The effect of the machining parameters on the kerf width and surface roughness (SR) is determined by using analysis of variance (ANOVA). A multi-response optimization, Taguchi-based grey relational analysis was used to find the optimal process parameter setting for the best quality machined characteristics. Confirmation test was conducted at selected optimal parameter levels, which shows improvement in grey relational grade, thus confirming the strength of grey relational analysis.
Index Term--
WEDM, ANNOVA, surface roughness, kerf width, hybrid composite, Taguchi, stir casting
INTRODUCTION Tosun et al. studied that metal matrix composite are
finding increased applications in aerospace and automobile industries due to their enhanced properties such as high strength to weight ratio, high stiffness and good wear resistance [1]. Hung et al. reviewed that hybrid composite materials are fabricated with more than one reinforcement materials to achieve better mechanical properties [2]. Aluminium composites containing solid lubricants such as graphite, MOS2 showed better friction and wear behaviour. Machining of these HMMCs is difficult by conventional machines due to abrasive nature of reinforcement. Lauws et al. reviewed that among nonconventional machining methods, wire electrical discharge machining is most versatile and useful technological process to machine these materials [3]. WEDM process has capability to machine intricate shapes and profiles irrespective of
hardness of materials. The most important response in WEDM is material removal rate (MRR), surface roughness (SR) and kerf width. The high value of MRR will result in reduced production cost, whereas low value of SR will improve the product quality. Good quality surfaces improve fatigue strength, corrosion and wear resistance of the work-piece. Kerf, that is, cutting width, determines the dimensional accuracy of the finished parts. LITERATURE REVIEW Patel et al. investigated effect of discharge current, pulse on time, duty cycle and gap voltage on metal removal rate and lower surface roughness of Al2O3-SiC-TiC ceramic composite during EDM [4]. Nilesh Ganpatro et al. investigated the electric discharge machining characteristics of silicon carbide particulate reinforced aluminium matrix composites. They found that decreased MRR due to an increased percentage of ceramic composite [5]. Velmurugan et al. investigated the experimental investigations on machining characteristics of Al 6061 hybrid metal matrix composites processed by electrical discharge machining [6]. They found that surface roughness of composite increases with increase in current, pulse on time, voltage and flushing pressure. Mahapatra et al. studied on parametric optimization of WEDM process on D2 tool steel using L27 orthogonal array (OA) Taguchi method [7]. The results of analysis of variance (ANOVA) showed that discharge current, pulse duration and discharge flow rate are the significant factors for maximizing the MRR and surface finish (SF). Genetic algorithm (GA) was used to obtain the optimum machining parameters for multiobjective outputs using several combinations of the weight. The study concluded that optimal machining performance with maximization of MRR and SF occurs under equal importance of weighting factors. Datta et al. studied the process behaviour of WEDM with process parameters on MRR, SF and kerf width for D2 tool steel using Taguchi L27 OA. GRA was also adopted to
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International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:17 No:01 convert the multi-objective criteria into an equivalent singleobjective function [8]. Optimal setting was verified and it showed good agreement with practical values through confirmatory tests. Shyamlal et al. investigated multi – response optimization of wire electrical discharge machining process Shah et al. investigated the effect of different WEDM process parameters on the machining responses, such as the MRR, kerf and surface roughness while machining tungsten carbide samples using the Taguchi method [10]. Based on the study, the authors concluded that the material thickness has little effect on MRR and kerf, but was a significant factor for SR. Results of ANOVA showed that discharge duration and wire tension were the most significant factors for the kerf. The percentage contribution by discharge duration and wire tension was 39.64 and 26.47 %, respectively. Kerf increased with increase in discharge duration, but decreased with wire tension. Open voltage and pulse interval time affected kerf to a lesser degree. Gupta et al. evaluated the effect of WEDM process parameters on kerf width using the response surface methodology (RSM) while machining high-strength low alloy steel (HSLA) [11]. It was revealed from the results that kerf width decreased with increase in process parameters such as discharge duration, pulse interval time, spark gap voltage and discharge peak current. The analysis of results indicated that spark gap voltage, discharge duration, discharge peak current and pulse interval time had a significant effect on the kerf width. Yan et al. examined the effect of WEDM machining parameter discharge duration on kerf (width of slit) while machining 10 and 20 vol % Al2O3/6061 Al composite [12]. The experimental results revealed that the width of slit increased with increasing the discharge duration. The wire electrode wear rate (EWR) increases with increase in the percentage of reinforcing Al2O3 particles (0, 10, and 20 vol % of Al2O3/6061 Al). Also, it increased gradually as the discharge duration increased. The effects of process parameters including wire tension, flushing rate and wire speed of WEDM on wire breakage were investigated. A very low wire tension, a high flushing rate and a high wire speed are required to prevent wire breakage. Amitesh et al. studied the investigation of surface integrity, material removal rate and wire wear ratio for WEDM of nimonic 80A using GRA and Taguchi methods [13]. The authors found pulse on time and pulse off time to be the most significant factors for MRR. The two factor interactions (Ton x Toff and Ton x IP) have been found to contribute most to the variation in WWR. MATERIALS AND METHODS PREPARATION OF HYBRID COMPOCITE In this study, the hybrid MMC has been fabricated by stir casting process. The hybrid composite consists of 10 wt% SIC and 5 wt% Graphite particulates in metal matrix Al6061 alloy. The Al alloy of 6xxx series is having great potential to
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parameters for Al7075/Al2O3/SiC hybrid composite using Taguchi-based grey relational analysis [9]. The optimum parameters for multiple parameters optimization setting were found to be the pulse on time of 4 ms, pulse off time of 6ms,pulse current of 2 A and the wire drum speed of 4 m/min for optimum value of surface roughness and kerf width. be utilized in aerospace and automotive industries because of its high strength-to-weight ratio and good resistance to corrosion. The weight % composition of Al6061 alloy is shown in Table I. Reinforcements SiC and graphite in particulate form are used to fabricate the hybrid composite. These reinforcements have 10– 13 micron size particles of SiC& graphite. Table I Composition of Al6061 alloy Mg
Si
Fe
Cu
Cr
Al
1.1
0.64
0.48
0.33
0.04
Remaining
Machining parameters and response six input process parameters in WEDM, namely, pulse on time, pulse off time, pulse current, gap set voltage, the wire drum speed and wire tension were chosen to study their effects on surface roughness and kerf width while machining the hybrid composite. The ranges of these process parameters were selected on the basis of the pilot experiments. The levels of various parameters and their designations are presented in Table II Table II Process parameters and their levels Symbol
Process parameter
Level 1
Level 2
Level 3
A
Pulse on time
108
117
126
B
Pulse off time
40
50
60
C
Pulse current
90
160
230
D
Gap set Voltage
10
30
50
E
Wire drum speed
3
4
5
F
Wire tension
4
8
12
EXPERIMENTAL DESIGN USING TAGUCHI METHOD Taguchi technique is an efficient tool for the design of a high-quality manufacturing system. It is a method based on OA experiments, which provide much reduced variance for the experiment with optimum setting of process control parameters. The six control parameters, that is, pulse on time (A), pulse off time (B), peak current (C),Gap set voltage (D), wire drum speed (E) and wire tension(F) at three levels were selected in this study. The experiments were done according to Table III. This table only represents particular level of the various factors of the process at which the experiments would be conducted. Column 1 presents serial order of experiments. Columns A–F indicates various levels of the parameters according to OA. The Minitab 15 software was used to analyse the results.
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International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:17 No:01 EXPERIMENTAL SET-UP FOR WEDM PROCESS Experiments were conducted on Electronica Sprint cut (Electra-Elplus 40A Dlx) CNC wire electrical discharge machine to study the surface roughness and cutting speed affected by the machining parameters at different levels. The sparks are generated between the work piece and the wire electrode. The dielectric fluid is continuously fed into the machining zone with required pressure. The material is getting
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removed by a series of discrete sparks taking place at the area to be machined through electro-thermal mechanism. Experimental set up of the wire electrical discharge machine is shown in Fig. 1. During machining process small gap maintained between the work and wire material. The machined particles were flushed away by the continuous flow of the dielectric fluid.
Table III L27 Orthogonal array Experimental run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
A 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
B 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
Control factors and levels C D 1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 2 3 2 3 2 3 3 1 3 1 3 1 1 2 1 2 1 2 3 2 3 2 3 2 1 3 1 3 1 3 2 1 2 1 2 1
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E 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
F 1 2 3 1 2 3 1 2 3 2 3 1 2 3 1 2 3 1 3 1 2 3 1 2 3 1 2
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Table IV L27 S/n ratios of kerf width and SR Experiment number
SR(µm)
SNRSR
Kerf( µm)
SNRCS
1
2.605
-8.316154553
302
-49.6001
2
2.82
-9.004982166
300
-49.5424
3
3.185
-10.06218873
292
-49.3077
4
2.555
-8.147818089
304
-49.6575
5
2.625
-8.382586155
300
-49.5424
6
2.985
-9.498886709
301
-49.5713
7
2.52
-8.028010816
302
-49.6001
8
2.965
-9.440493954
303
-49.6289
9
3.115
-9.86916102
297
-49.4551
10
3.625
-11.18616022
303
-49.6289
11
3.48
-10.83158488
309
-49.7992
12
3.475
-10.81909618
314
-49.9386
13
3.57
-11.05336432
317
-50.0212
14
3.59
-11.10188897
313
-49.9109
15
3.975
-11.98674266
322
-50.1571
16
2.7
-8.627275283
317
-50.0212
17
3.125
-9.897000434
313
-49.9109
18
2.57
-8.198662467
327
-50.2910
19
3.515
-10.91850659
302
-49.6001
20
3.725
-11.42252554
317
-50.0212
21
3.81
-11.61849951
302
-49.6001
22
3.67
-11.29332129
310
-49.8272
23
3.32
-10.42276167
324
-50.2109
24
3.47
-10.8065895
322
-50.1571
25
4.125
-12.30847906
316
-49.9937
26
4.256
-12.58003239
325
-50.2377
27
4.11
-12.27683644
318
-50.0485
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Table V Calculated Grey relational coefficients and overall Grey relational grade Experiment number
SR(µm)
Norm
GRGSR
Kerf(µm)
Norm
GRGCS
GRG
1
2.61
0.048963134
0.3445812
302
0.28571429
0.411764706
0.378172944
2
2.82
0.17281106
0.3767361
300
0.22857143
0.393258427
0.384997269
3
3.19
0.383064516
0.4476534
292
0
0.333333333
0.390493381
4
2.56
0.02016129
0.3378747
304
0.34285714
0.432098765
0.384986712
5
2.63
0.060483871
0.3473389
300
0.22857143
0.393258427
0.370298681
6
2.99
0.267857143
0.4057971
301
0.25714286
0.402298851
0.404047976
7
2.52
0
0.3333333
302
0.28571429
0.411764706
0.37254902
8
2.97
0.256336406
0.402038
303
0.31428571
0.421686747
0.411862364
9
3.12
0.342741935
0.4320557
297
0.14285714
0.368421053
0.400238401
10
3.63
0.636520737
0.5790527
303
0.31428571
0.421686747
0.500369724
11
3.48
0.552995392
0.5279805
309
0.48571429
0.492957746
0.510469141
12
3.48
0.550115207
0.5263796
314
0.62857143
0.573770492
0.550075058
13
3.57
0.60483871
0.5585586
317
0.71428571
0.636363636
0.597461097
14
3.59
0.616359447
0.5658409
313
0.6
0.555555556
0.560698247
15
3.98
0.838133641
0.7554395
322
0.85714286
0.777777778
0.766608645
16
2.70
0.103686636
0.3580858
317
0.71428571
0.636363636
0.497224722
17
3.13
0.348502304
0.4342171
313
0.6
0.555555556
0.494886332
18
2.57
0.028801843
0.339859
327
1
1
0.669929522
19
3.52
0.573156682
0.5394655
302
0.28571429
0.411764706
0.475615106
20
3.73
0.694124424
0.6204432
317
0.71428571
0.636363636
0.628403405
21
3.81
0.743087558
0.6605784
302
0.28571429
0.411764706
0.536171546
22
3.67
0.662442396
0.5969739
310
0.51428571
0.507246377
0.552110121
23
3.32
0.460829493
0.481153
324
0.91428571
0.853658537
0.667405765
24
3.47
0.547235023
0.5247884
322
0.85714286
0.777777778
0.651283085
25
4.13
0.924539171
0.8688689
316
0.68571429
0.614035088
0.741451978
26
4.26
1
1
325
0.94285714
0.897435897
0.948717949
27
4.11
0.915898618
0.8560158
318
0.74285714
0.660377358
0.758196569
Average value of overall GRG = 0.5252 GRA: grey relational analysis; SR: surface roughness; CS: cutting speed GRG: grey relational grade; GRGSR: grey relational coefficient for surface roughness; GRGCS: grey relational coefficient for cutting speed
Fig. 1. WEDM experimental setup (Electronica Sprint cut
The wire is held by a pin guide at the upper and lower parts of the work piece. Since the wire is subjected to complex oscillations due to electrical discharge between wire and the work piece. So it is essential to hold the wire in its designed position against the work piece. The wire is not reused once used for a cut at set level of process parameters. A fresh length of wire is used for each cut in next experiment. The work specimen size used in this study is 95 x 80 x 8 cm rectangular plate. 10 x 5 x 8 mm size rectangular work was cut from the specimen. Zinc coated brass electrode wire of 0.25 mm diameter was used in this study. Deionised water was used as dielectric fluid at room temperature. After machining, the specimens were cleaned with acetone. SR (Ra) was measured in micrometer using Mitutoyo Surf test SV-2100. On each
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International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:17 No:01 machined surface, SR was measured at three places spread over the entire machined area. S/N RATIO CALCULATION Taguchi method parameter design, the basic method converts the objective parameter to the signal to noise (S/N) ratio which is treated as the quality characteristic evaluation index. The S/N ratio is used to measure the sensitivity of the quality characteristic being investigated in a controlled manner. In Taguchi, the term signal represents the desirable effect (mean) for the output characteristic. And the term „noise‟ represents the undesirable effect (Signal disturbance) for the output characteristic which influences the outcome due to external factors namely noise factors. The least variation and the optimal design are obtained by means of S/n ratio. In Taguchi method a loss function value is defined to calculate the deviation between experimental values and the designed values. Loss function value is further converted into S/N ratio. Smaller-the-better (SB) is selected for SR and kerf width. The S/N ratio for SR and kerf width are given by the expression Smaller the better
( )
∑
(1)
Where yi is the ith result of experiment and n is the repeated number of the ith experiment. S/N ratio values for SR & kerf width are calculated according to equation (1) and shown in Table IV GRA THEORY GRA is an improved method for identifying key system factors and is useful for variable independence analysis. Grey system with incomplete information gives a variety of feasible solutions. Grey analysis provides techniques for determining a good solution, that is, an appropriate solution for real-world problem. NORMALIZING THE DATA In GRA, the dimensions of factors are usually different and their magnitude difference is large. Therefore, experimental data are first normalised ranging from 0 to 1. The process is known as grey relational generation (GRG). The GRG also expresses the deviation between the experimental value and the ideal value. There are three criteria for optimization in GRA namely larger-the better „Smaller- the better‟ and „nominal-the best‟. In the present analysis, the lower value of SR and kerf width represents better machining performance, and therefore the „lower-the better‟ is selected. The normalized values of SR and kerf width are computed according to equation (3)& (4), and are shown in table V.
100 (3)
Where ij= Xoj – Xij and Xoj are the reference data or best data; maxis the maximum value of ij , min is the minimum value of ij and is the distinguishing or identification Coefficient, and its value lies between 0 and 1. Usually, the distinguishing co efficient is assumed at 0.5 to fit the practical requirements. The calculated GRC for SR and kerf width are shown in table V. The highest normalised value is 1 and appearing at experiment number 18 while minimum value is 0 at experiment number 3 for kerf width. The highest normalised value is 1 and appearing at experiment number 26 while minimum value is 0 at experiment number 7 for surface roughness. After calculating the GRC, the GRG is calculated from the equation (4) for the quality characteristics and is directly integrated to determine single over all GRG by utilizing a weighting method. The GRG (γi) for the ith experiment can be calculated as follows ∑ (4) Where m is the number of quality characteristics and Wj is the weighting factor jth response. The value of j for calculating the GRG is taken to be 0.5. The results of GRG are calculated with equal weighting ratio for the quality characteristics SR and cutting speed. This approach converts a multiple response process optimization problem into a single response optimization situation with the objective function as overall GRG. The optimal parameter setting for optimizing the overall GRG can be performed by Taguchi method. The results of GRG are shown in Table V. EFFECT OF PROCESS PARAMETERS ON SURFACE ROUGHNESS Surface roughness (SR) increases with the increase of pulse on time and decreases with increase in pulse off time from Fig.2. As the pulse off time decreases, the number of discharges increases which causes poor surface finish. When peak current increases from 90A to 160A, surface roughness also increases because of high discharge energy discharge per spark, which makes the surface rough. But further increase of peak current reduces roughness value. When peak current exceeds a certain value, the average discharge gap gets widened resulting into better surface finish. The effects of wire feed and wire tension are not very much significant for surface roughness. When gap set voltage increases from 10A to 30A, surface roughness decreases but further increase of gap set voltage reduces surface roughness.
Lower the better (2)
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time, pulse of time and wire drum speed have impact on kerf width. Pulse current, gap set voltage and wire tension have little impact on kerf width.
Main Effects Plot (data means) for Means A
0.7
B
C
0.6 0.5
Mean of Means
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Main Effects Plot (data means) for Means
0.4 1
2
3
1
D
0.7
2
3
1
2
E
A
0.7
3
F
B
C
0.6
0.6
Mean of Means
0.5
0.5 0.4 1
2
3
1
2
3
1
2
3
0.4 1
2
3
1
D
0.7
2
3
1
E
2
3
F
0.6
Fig. 2. Effect of process parameters on surface roughness
0.5
0.4 1
EFFECT OF PROCESS PARAMETERS ON KERF WIDTH Kerf width increases with the increase of pulse on time, pulse off time and wire drum speed and decreases with increase in pulse current, gap set voltage and wire tension. It is also seen that pulse on time has maximum influence for increase in kerf width From Fig.3. When peak current increases from 90A to 160A, kerf width decreases. Pulse on
2
3
1
2
3
1
2
3
Fig. 3. Effect of process parameters on kerf width
Table VI Analysis of variance for GRG Source
DF
Sequential SS
Adjusted SS
Adjusted MS
F
P
Pulse on time
2
0.349685
0.349685
0.174843
93.31
0
Pulse off time
2
0.050371
0.050371
0.025185
13.44
0.001
Peak Current
2
0.01558
0.015258
0.007629
4.07
0.04
Gap set voltage
2
0.073614
0.073614
0.036807
19.64
0
Wire feed
2
0.023846
0.023846
0.011923
6.36
0.011
Wire tension
2
0.04319
0.043192
0.021596
11.53
0.001
Residual error
14
0.026233
0.026233
0.001874
Total
26
0.5822
DF :degree of freedom; SS: sum of square; MS: mean square
ANOVA FOR RESPONSE ANOVA can be used to find the relative effect of the different factors involved in WEDM. This analysis was done using Minitab 14 software. The results of ANOVA are shown in Table VI based on the 95% confidence level (α=0.05). Table VI shows the parameters such as sum of squares (SS), mean square (MS), degree of freedom (DOF), Fisher‟s ratio (F), probability value. All six parameters such as pulse on time, pulse off time, peak current, gap set voltage, wire drum speed & wire tension have significant effect(p
