INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
Parameter Optimization of Wire EDM in a Range of Thickness for EN8 Die Steel S.Vignesh1
M.Sivakumar2
R.Shanmugaprakash3
Department of Mechanical Engineering, Bannari Amman Institute of Technology, 1 Anna University vignezmech@gmail.com
Department of Mechanical Engineering, Bannari Amman Institute of Technology, 2 Anna University muthusivaa@gmail.com
Department of Mechanical Engineering, Bannari Amman Institute of Technology, 3 Anna University summugaa1691@gmail.com
Abstract— Wire Electrical Discharge Machining (WEDM) is also known as an electro thermal production process in then, nor electricity) allows the wire to cut through the metal by the use of heat from electrical sparks. The present work process approach to parameter optimization in wire electric discharge machining of EN-8 die steel (of thickness 50 and 75mm) using Taguchi method which optimizes four process parameters (factors) such as pulse ON time, pulse OFF time, current, voltage and two important response such as material removal rate (MRR) and in surface roughness (Ra –average roughness) of machined workpiece is considered in this study. Taguchi is an efficient search technique, is used to obtain the optimal setting of the desired responses and to avoid the conflicting nature of response. Taguchi designs focus on reducing variability, as well as setting the mean to target. Index Terms— EN8 die steel, Thickness, Material removal rate, Surface roughness, Taguchi technique. ————————————————————
1 INTRODUCTION Wire EDM is a non-traditional thermo-electric process which erodes material from the workpiece by a series of discrete sparks by continuously moving wire electrode. The horizontal movement of the worktable controlled by computer numeric controlsystem, which determines the path of the cut. During the machining operations, dielectric medium is to stabilize the spark erosion path by increasing the conductivity of the medium and to flush-out the eroded metal from the cutting zone. There is virtually no cutting force to the machining parts during the process, because the wire electrode and workpiece never make contact. WEDM is a special form of thermal machining process used for precision machining of conductive materials with varying hardness and complex shapes.
2
LITERATURE REVIEW
Chattopadhyay et al. have used Taguchi’s design of experiment (DOE) approach to conduct experiment on rotary EDM using EN8 steel and copper as work piece-tool combinations and developed empirical relations between performance characteristics (MRR and EWR) and process parameters such as peak current, pulse-on-time and rotational speed of the tool electrode.It is found
that peak current and rotational speed of the tool significantly on both the responses[1].
electrode
influence
Selvakumar et al have used Taguchi experimental design method for the selection of the most optimal machining parameter combinations for wire electrical discharge machining (WEDM) of 5083 aluminium alloy. A series of experiments were performed by considering pulse-on time, pulse-off time, peak current and wire tension as input parameters. The surface roughness and cutting speed
were considered responses. The cutting speed has been found to have an increasing trend with the increase of pulse-on time and peak current. The surface finish decreases, namely Ra increases, with the increase in pulse-on time and peak current [2]. Mevada et al investigated the wire wear rate at different machining parameter Such as peak current, pulse on time, pulse off time, based on a full factorial design of experiment method.Different setting of peak current, pulse on time and pulse off time with three levels. Molybdenum wire of 0.18 mm diameter with and medium strength EN-8 steel material was used as a tool and work piece material.From the experimental work, results and study shows that peak current, pulse on time, pulse off time are the main parameter for the wire wear. With the increase in peak current, wire wear increases, but it is slowly. With the increase in pulse on time, wire wear increases rapidly. Pulse off time is not much effects on the wire wear rate [3]. Singh et al in their paper have discussed about the effect of various process parameters of WEDM like pulse on time (TON), pulse off time (TOFF), gap voltage (SV), peak current (IP), wire feed (WF) and wire tension (WT) to reveal their impact on MRR of hot die steel (H-11). The optimal set of process parameters is identified to maximize the MRR, using one variable at a timeapproach [4]. Kanlayasiri et al have studied the effect of machining parameters on surface roughness of wire EDMed DC53 die steel. The investigated machining parameters were pulse-on time, pulse-off time, pulse-peak current, and wire tension. Results of ANOVA shows that pulse-on time and pulse-peak current are significant variables to surface roughness of wire-EDMed DC53 die steel [5]. From the literature several researchers have been applied Taguchi method to optimize the performance parameters in wire edm process [6]. From the literature survey, the gap identified was different materials were machined using wire electrical discharge machining
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 process. But only limited studies were found in EN-8 die steel material as workpiece. Also the research work carried so far were of 10mm and 20 mm thickness. So machining of EN-8 die steel material of thickness 50mm and 75mm were selected as a workpiece material in this study. In the present work, medium carbon steel EN8 is considered for measuring the output parameters like material removal rate, surface roughness using Taguchi method.
3 EXPERIMENTAL DESIGN The experiments were performed on a WEDM of CNC-E3 (MCJDK7220CH) is as shown in fig1. This machine consists of X-Y coordinate worktable, U-V auxiliary table, wire running system, wire frame, microcomputer based control cabinet and liquid dielectric supply system.
Aluminium
0.021
3.2 Formation of Orthogonal Array The experiments are planned based on the orthogonal array to conduct small, highly fractional factorial experiments up to fullfactorial experiments. The use of orthogonal arrays is just one methodology to design an experiment, but probably the most flexible in accommodating a variety of situations and yet easily for non-statistically oriented people to execute on particular basis. The design of experiments (or) experimental design was based on L18 (33 *12).The control factors considered for the study are pulse-ON time (Ton), pulse-OFF time (Toff), Peak Current (IP) and, voltage(V). The various level of control factors in this level of experimental study are as given in table II. TABLE II LEVELS OF VARIOUS CONTROL FACTORS
Symbol
Machining parameter
Unit
Level 1
Level 2
Level 3
A
Pulse ON Time
µs
15
25
35
B
Pulse OFF Time
µs
4
5
6
C
Peak Current
Amps
2
3
4
D
Voltage
V
75*
100*
-
*Machine input parameters are 0 for 75V and 1 for 100V Fig. 1: Wire EDM Machine
3.1 Material Selection The workpiece material selected in this study is hardened EN-8 die steel of 50mm and 75mm thickness [7]. The chemical composition of EN-8 die steel material is as shown in table I. Demineralized water is chosen as a dielectric fluid in this study [8]. The tool material chosen for cutting the selected material is Molybdenum wire of 0.18mm diameter
The DOE L18 orthogonal array formed is as shown below in table III. This array formed is used for machining both 50mm and 75mm of EN8 material.The experiments are conducted according to L18 orthogonal array (33 *12) design obtained from minitab16 software. The L18 orthogonal array contains 18 experimental runs at various combinations of four input variables. Three factors of three levels and two factors of two levels were chosen for performing the experiments. TABLE III L18 ORTHOGONAL ARRAY
TABLE I CHEMICAL COMPOSITION OF EN8 DIE STEEL ELEMENT Iron Carbon Silicon Manganese Phosphrous Sulphur
COMPOSITION IN % 98.23 0.425 0.258 0.952 0.952 0.016
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S.NO
A Ton(µS)
B Toff(µS)
C IP(Amps)
D* V(Volts)
1
15
4
2
0
2
15
5
3
0
3
15
6
4
0
4
25
4
2
0
INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 5
25
5
3
0
6
25
6
4
0
7
35
4
3
0
8
35
5
4
0
9
35
6
2
0
10
15
4
4
1
11
15
5
2
1
12
15
6
3
1
13
25
4
3
1
14
25
5
4
1
15
25
6
2
1
16
35
4
4
1
17
35
5
2
1
18
35
6
3
1
using surface roughness tester, SURFTEST SJ-210 series. The surface roughness parameter chosen in this experimental study is Roughness Average(Ra ).
4 RESULTS AND DISCUSSION The results obtained are analyzed using signal-to-noise (S/N) Ratios, Response table and Response Graphs with the help of Minitab16 software. Minitab is a computer program designed to perform basic and advanced statistical functions. It is a popular statistical analysis package for scientific applications, in particular for design and analysis of experiments.The S/N ratio measures how the response varies relative to the nominal or target value under different noise conditions. The mathematical model suggested here is the regression equation is an algebraic representation of the regression line and is used to describe the relationship between the responses and predictor variables. The regression equation takes the form of, Response = Constant + Coefficient (predictor) + ... + Coefficient (predictor) (Or) đ?‘Œ = đ?‘?0 + đ?‘?1 đ?‘‹1 + đ?‘?2 đ?‘‹2 +. . . +đ?‘?đ?‘˜ đ?‘‹đ?‘˜
*Machine input parameters are 0 for 75V and 1 for 100V
3.4 Determination of MRR The experiments were performed based on the run order as per mentioned in above OA. The time taken for removing (5*5*50)mm and for (5*5*75)mm square pieces of different parameter combinations as per L18 DOE formed is taken and then material removal rate is calculated as per the following formula.
The following table IV shows the experimental results and then it is analyzed to obtain the optimum process parameters for the material removal rate (MRR) and average roughness(đ?‘…đ?‘Ž ) of EN8 50mm and EN8 75mm thickness workpiece. The signal-to-noise (S/N) ratio is calculated for each factor level combination. The formula for the larger-is-better S/N ratio using base 10 log is, đ?‘†/đ?‘ = −10 ∗ log
(1/đ?‘Œ 2 ) /đ?‘›
Where Y = responses for the given factor level combination and n = number of responses in the factor level combinations. The signal-to-noise (S/N) ratio is calculated for each factor level combinations. The formula for the smaller-is-better S/N ratio using base 10 log is:
MRR=Vc*b*h (mm3 /min) Where, Vc =Cutting length/time taken (mm/min) h =Height of the workpiece (mm)
đ?‘†/đ?‘ = −10 ∗ log
(1/đ?‘Œ 2 ) /đ?‘›
b =Breadth of the workpiece (mm) Where Y = responses for the given factor level combinations and n = number of responses in the factor level combinations. The objective of the present study is to maximize the Material Removal Rate (MRR) and minimize surface roughness (đ?‘…đ?‘Ž ) [9][10][11].The combined objective function considered in this study is as given below.
b =2Wg+d (mm) Where, Wg = Spark gap (mm), d=Diameter of the wire (mm)
3.5 Determination of Surface Roughness The surface roughness of the machined workpieces is measured
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
TABLE IV RESULTS OF OUTPUT PARAMETERS
Results for EN8 50mm thickness workpiece S.No
Results for EN8 75mm thickness workpiece
Mean MRR
Mean Ra
S/N Ratio of MRR
S/N Ratio of Ra
Value of Z
Mean MRR
Mean Ra
S/N Ratio of MRR
S/N Ratio of Ra
Value of Z
1
5.63236
4.02500
15.01381
-12.09532
0.19460
5.65747
2.87500
15.05244
-9.17276
0.119927
2
6.22700
4.37200
15.88558
-12.81360
0.20865
6.97232
3.22200
16.86755
-10.16251
0.118601
3
7.50085
4.79700
17.50221
-13.61939
0.21222
7.66373
3.64700
17.68880
-11.23872
0.139952
4
6.40839
4.02800
16.13498
-12.10179
0.17545
6.46298
2.87800
16.20866
-9.18182
0.100062
5
9.16667
4.64600
19.24423
-13.34158
0.15800
9.41244
3.49600
19.47404
-10.87143
0.082507
6
8.04976
5.08475
18.11566
-14.12539
0.22248
8.91892
3.93475
19.00624
-11.89834
0.134732
7
10.00000
5.03575
20.00000
-14.04128
0.16964
10.88032
3.88575
20.73283
-11.78950
0.081242
8
10.47619
5.60875
20.40407
-14.97732
0.20549
10.86957
4.45875
20.72424
-12.98426
0.133602
9
5.32301
4.62900
14.52315
-13.30974
0.25267
5.58754
3.47900
14.94441
-10.82909
0.176584
10
17.84266
3.81550
25.02919
-11.63103
-0.12810
17.07191
4.11250
24.64564
-12.28212
-0.05293
11
7.33333
3.21800
17.30603
-10.15172
0.08483
8.03702
3.51500
18.10190
-10.91851
0.118620
12
9.85222
3.62525
19.87068
-11.18676
0.05579
9.98185
3.92225
19.98422
-11.87071
0.107022
13
16.50413
4.12250
24.35185
-12.30321
-0.06906
16.23217
4.41950
24.20753
-12.90746
-0.00403
14
18.24212
4.50850
25.22151
-13.08064
-0.08034
17.07191
4.80550
24.64564
-13.63477
0.010066
15
7.84034
3.71325
17.88670
-11.39508
0.11342
7.98451
4.01025
18.04497
-12.06343
0.164955
16
19.89150
4.71575
25.97335
-13.47102
-0.10430
19.41176
5.01275
25.76130
-14.00152
-0.02959
17
9.85222
3.53925
19.87068
-10.97822
0.04863
9.05350
3.83625
19.13633
-11.67814
0.122413
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 18
14.35095
4.47300
23.13761
-13.01198
0.01397
12.53323
4.77000
21.96126
-13.57037
0.120306
Fig. 4. Response Graphs for Means of Ra
(Minimization) Z= (0.5)*(Ra/Max. Ra value) - (0.5)*(MRR/Max. MRR Value) The Fig2 and Fig3 shows the response graph and signal to noise graphs of material removal rate (MRR).It shows that MRR increases with the increase in pulse on time(A), peak current(C) and voltage(D) .Fig 4 and fig5 shows that Ra decreases with the increase in pulse on time(A), pulse off time(B) and peak current(C).
The Fig2, Fig3 Fig4 and, Fig5 shows the response graph for EN8 50mm thickness workpiece. The Fig6 and Fig7 shows the response graph and signal to noise graphs of material removal rate (MRR).
Main Effects Plot for SN ratios Data Means
Main Effects Plot for Means Data Means A
14
B
B
20 Mean of SN ratios
12 10 Mean of Means
A
22
8 15
25
35
4
5
C
14
6
D
18 16 15
25 C
35
2
3
4
22
4
5 D
6
20
12 10
18
8 2
3
4
75
16
100
75
100
Signal-to-noise: Larger is better Fig. 2. Response Graphs for Means of MRR Fig. 3. Response Graphs for S/N ratios of MRR
Main Effects Plot for Means Data Means A
4.8
Main Effects Plot for SN ratios
B
Data Means
4.6
B
-12.0
4.2
-12.5
4.0 15
25
35
4
5
C
4.8
Mean of SN ratios
Mean of Means
A
-11.5
4.4
6
D
4.6 4.4
-13.0 -13.5 15
25
35
4
5
C
-11.5
6
D
-12.0 -12.5
4.2
-13.0
4.0
127 2
3
4
75
100
-13.5 2
3
Signal-to-noise: Smaller is better
4
75
100
INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 dependent variable, using several independent variables [12]. Fig. 5. Response Graphs for S/N ratios of Ra
It shows that MRR increases with the increase in pulse on time(A), peak current(C) and voltage(D) Fig8 and fig9 shows that Ra decreases with the increase in pulse on time(A), pulse off time(B) peak current(C) and voltage(D).
Main Effects Plot for SN ratios Data Means A
22.5
Main Effects Plot for Means
21.0
Data Means B
Mean of SN ratios
A
14 12
Mean of Means
10 8 15
25
35
4
5
C
14
B
6
D
19.5 18.0 15
25
35
4
5
C
22.5
6
D
21.0 19.5 18.0
12 10
2
3
4
75
100
Signal-to-noise: Larger is better
8 2
3
4
75
100
Fig. 7. Response Graphs for S/N ratios of MRR Fig. 6. Response Graphs for Means of MRR
Main Effects Plot for Means
Main Effects Plot for SN ratios
Data Means
Data Means
B
4.2
-10.5
4.0
-11.0
3.8
-11.5 Mean of SN ratios
Mean of Means
A
3.6 3.4 15
25 C
35
4
5 D
6
4.2 4.0
A
B
-12.0 -12.5 15
25
35
4
5
C
-10.5
6
D
-11.0 -11.5
3.8
-12.0
3.6
-12.5
3.4 2
3
4
75
100
2
3
4
75
Signal-to-noise: Smaller is better
Fig. 8. Response Graphs for Means of Ra Fig. 9. Response Graphs for S/N ratios of Ra
The multiple regression analysis is carried out to explain a model that explains as much as much as possible, the variability in a
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 The regression equation for EN8 50mm thickness workpiece for, MRR = - 13.4 + 0.129 A - 1.95 B + 3.30 C + 0.235 D
S = 1.28221 R-Sq = 94.3% R-Sq(adj) = 92.5% The regression equation for, Ra = 4.41 + 0.0346 A + 0.0483 B+0.448 C - 0.0289 D
TABLE V COMPARISON OF EXPERIMENTAL & OPTIMIZED RESULTS
S = 0.158807 R-Sq = 95.0% R-Sq(adj) = 93.4% The regression equation for EN8 75mm thickness workpiece for, MRR = - 9.59 + 0.108 A - 1.92 B + 3.19 C + 0.200 D S = 1.02195 R-Sq = 95.6% R-Sq(adj) = 94.2% The regression equation for, Ra = - 1.08 +0.0346 A +0.0483 B +0.448 C +0.0290 D S = 0.158807 R-Sq = 95.0% R-Sq(adj) = 93.5%
5 SELECTION OF OPTIMAL PARAMETER COMBINATION By substituting the mean of MRR and Ra values of EN8 50mm thickness workpiece in the combined minimization objective equation,
En8-50mm
Experimental Results
Taguchi Results
Deviation
MRR
17.84266
17.435
0.40766
Ra
3.81550
4.0242
0.2087
En8-75mm
Experimental Results
Taguchi Results
Deviation
MRR
17.07191
17.11
0.03809
Ra
4.11250
4.3242
0.2117
The results obtained from the experiment and Taguchi technique is depicted in the table V. The above table also illustrates the deviation of experimental and Taguchi results of MRR and Ra of EN8-50mm and EN8-75mm thickness workpiece respectively.
(MINIMIZATON) Z=(0.5)*(Ra/6)-(0.5)*(MRR/ 20)
6 CONCLUSION
The optimal value is obtained and the minimum value of Z=-0.128108, and the corresponding combinations for this minimum value is (15-4-4-1).
From the combined minimization objective equation results it was found that MRR maximization and minimization of surface roughness for both EN8-50mm and EN8-75mm thickness workpiece is obtained in the following parameter combinations (15-4-4-1), that is pulse on time = 15µs, pulse off time = 4µs, current = 4amps, and voltage = 1(100volts).
The corresponding values of MRR and R a for EN8 50mm thickness from the above equation are, 3
MRR=17.435 mm /min
REFERENCES [1]
Chattopadhyay KD, Verma S, Satsangi PS, Sharma PC. ―Development of empirical model for different process parameters during rotary electrical discharge machining of copper – steel (EN-8) system‖, Journal of Materials Processing Technology, Vol. 209, pp. 1454-1465, 2009
[2]
Selvakumar G, Sornalatha G, Sarkar S, and Mitra S, ―Experimental investigation and multi-objective optimization of wire electrical discharge machining ( WEDM ) of 5083 aluminum alloy‖, ELSEVIER, vol. 24, pp. 373–379, 2014.
[3]
Mevada J R, Shah C D, and Khatri B C, ―A Wear Investigation of Repeatedly Used Wire in Wire Cut Electrical Discharge Machine‖, International Journal of Emerging Technology and Advanced Engineering, vol. 2, pp. 1–4, 2012.
[4]
Singh H and Garg R, ―Effects of process parameters on material removal rate in WEDM‖, Journal of Achievements in Materials and Manufacturing Engineering, vol. 32, pp. 70–74, 2009.
Ra =4.0242 µm By substituting the mean of MRR and R a values of EN8 75mm thickness in the combined minimization objective equation, (MINIMIZATON)Z=(0.5)*(Ra/5.5)-(0.5)*(MRR/ 20) The optimal value is obtained and the minimum value of Z=-0.052934, and the corresponding combinations for this minimum value is (15-4-4-1). The corresponding values of MRR and Rafor EN8 75mm thicknessfrom the above equation are, MRR=17.11 mm3 /min Ra =4.3242 µm
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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 Method‖, International Journal of Modern Engineering Research, vol. 3, pp. 2281–2286, 2013. [5]
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Author Profile: S.Vigneshis currently pursuing master’s degree program in Engineering Design at Bannari Amman Institute of Technology, India. PH-09659124659. E-mail: vignezmech@gmail.com M.Sivakumar is currently working as associate professor in department of Mechanical Engineering at Bannari Amman Institute of Technology, India E-mail: muthusivaa@gmail.com R.Shanmugaprakashis currently pursuing master’s degree program in Cad Cam at Bannari Amman Institute of Technology, India. E-mail: sunmugaa1691@gmail.com of scientific and industrial research, vol. 68, pp. 32–35, 2009. [13] Lokeswara Rao T and Selvaraj N, ―Optimization of WEDM Process Parameters on Titanium Alloy Using Taguchi
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