Investigation on Optimization of Machining Parameters in Wire EDM using Taguchi Technique
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This paper investigates the optimization of machining parameters for wire electrical discharge machining (WEDM) of cemented tungsten carbide using the Taguchi method, highlighting the influence of factors like peak current and pulse duration on metal removal rate and surface roughness. Results indicate that pulse on time is the key factor affecting both characteristics, leading to specific recommendations for optimal settings to enhance machining performance. Overall, the findings can assist in selecting appropriate parameters to achieve desired outcomes in industrial applications.
![Investigation on Optimization of Machining Parameters in Wire EDM using Taguchi Technique
(IJSRD/Vol. 1/Issue 9/2013/0020)
All rights reserved by www.ijsrd.com 1773
Work MaterialB.
Tungsten carbide (WC) is an inorganic chemical compound
containing equal parts of tungsten and carbon atoms.
Colloquially, tungsten carbide is often simply called carbide.
In its most basic form, it is a fine gray powder, but it can be
pressed and formed into shapes for use in industrial
machinery, tools, abrasives, as well as jewelry. Tungsten
carbide is approximately three times stiffer than steel, with a
Young's modulus of approximately 550 GPa, and is much
denser than steel or titanium. It is comparable with
corundum (α-Al2O3 or sapphire) in hardness and can only
be polished and finished with abrasives of superior hardness
such as silicon carbide, cubic boron nitride and diamond.
The density of the material is 15.8g/cm3
.
Machine DescriptionC.
The experiments were carried out on a Four-axes
ELECTRONICA MAXICUT WEDM machine. The
electrode material used was a 0.25 mm diameter brass wire.
A small gap of 0.025 mm to 0.05 mm is maintained in
between the wire and work-piece. The high energy density
erodes material from both the wire and work piece by local
melting and vaporizing. The di-electric fluid (de-ionized
water) is continuously flashed through the gap along the
wire, to the sparking area to remove the debris produced
during the erosion. A collection tank is located at the bottom
to collect the used wire erosions and then is discarded. The
wires once used cannot be reused again, due to the variation
in dimensional accuracy.
Process Parameters and DesignD.
Input process parameters such as Pulse-on time (A), Pulse-
off-time (B), and Wire Feed (C) used in this study are
shown in Table 1. Each factor is investigated at two levels to
determine the optimum settings for the WEDM process.
These parameters and their levels were chosen based on the
review of literature, experience, significance and their
relevance as per the few preliminary pilot investigations.
The smallest standard 3-level OA L9 (33
) is chosen for this
case.
Factor
notation
Control
Factors
Level
1
Level
2
Level
3
A Pulse on time (μs) 2 2 3
B Pulse off time (μs) 4 4 4
C Wire feed (m/min) 6 6 5
Table. 1: Factors and Levels
III. RESULTS AND DISCUSSIONS
Experiments were conducted as per the L9 (33
) orthogonal
array. After individual experiments for a set of values were
conducted on Tungsten Carbide for a size of 45×40×6 mm3
,
their surface roughness values were measured in surface
roughness tester to determine the surface finish.
In this study most important output performances in WEDM
such as Material Removal Rate (MRR) and Surface
Roughness (Ra) were considered for optimizing machining
parameters. The surface finish value (in μm) was obtained
by measuring the mean absolute deviation, Ra (surface
roughness) from the average surface level using a Computer
controlled surface roughness tester. The Material Removal
Rate (MRR) is calculated as
MRR = vf h δ b
Where,
vf → cutting speed of wire into the work piece in
mm/min,
h → work piece thickness or height in mm,
δ → density of the material in g/mm3
,
b → Kerf given by : b = dw + 2s
where,
dw → wire diameter in mm,
s → gap between work piece & tool.
Exp
No.
Pulse
On
time
(μs)
Pulse
Off
time
(μs)
Wire
Feed
(m/min)
MRR
(g/min)
Surface
Roughness,
Ra (Μm)
1 2 2 3 0.03513 3.2354
2 2 4 4 0.03384 3.4628
3 2 6 5 0.03287 2.6842
4 4 2 4 0.03835 3.8170
5 4 4 5 0.03739 3.2202
6 4 6 3 0.03642 3.6702
7 6 2 5 0.04190 4.1022
8 6 4 3 0.04093 3.8518
9 6 6 4 0.03964 3.5348
Table. 2: L9 (33
) Orthogonal Array with Responses
S/N ratio calculationA.
The characteristics that lower value represent better
machining performance such as surface roughness is called
“lower is better (LB)” in quality engineering. The equation
for calculating the S/N ratio is
[∑ ]
The characteristic that higher values represent better
machining performance such as material removal rate is
called “higher is better (HB)” in quality engineering. The
equation for calculating the S/N ratio is
[ ∑ ]
Exp.
No.
Performance Measures S/N Ratio
MRR
(g/min)
Surface
Roughness,
Ra (μm)
MRR
(db)
Surface
Roughness,
Ra
(db)
1 0.03513 3.2354 - 29.086 - 10.198
2 0.03384 3.4628 - 29.411 - 10.788
3 0.03287 2.6842 - 29.664 - 8.576
4 0.03835 3.8170 - 28.325 - 11.634
5 0.03739 3.2202 - 28.545 - 10.158
6 0.03642 3.6702 - 28.773 - 11.294
7 0.04190 4.1022 - 27.556 - 12.260
8 0.04093 3.8518 - 27.759 - 11.713
9 0.03964 3.5348 - 28.037 - 10.967](https://image.slidesharecdn.com/ijsrdv1i9020-140811083436-phpapp01/85/Investigation-on-Optimization-of-Machining-Parameters-in-Wire-EDM-using-Taguchi-Technique-2-320.jpg)
![Investigation on Optimization of Machining Parameters in Wire EDM using Taguchi Technique
(IJSRD/Vol. 1/Issue 9/2013/0020)
All rights reserved by www.ijsrd.com 1774
Table. 3: Experimental Results with S/N Ratio
Analysis of variance tableB.
ANOVA table is used to find the significant parameters
which influence the performance measures.
Parameters
Sum of
Squares
Variance Dof
F Test
Values
Contribution
%
Pulse on
time
4.9754 2.4877 2 2.9219 51.629
Pulse off
time
1.7743 0.8872 2 1.0420 18.412
Wire feed 1.1842 0.5921 2 0.6954 12.288
Error 1.7029 0.8514 2 17.671
Total 9.6368 8 100
Table. 4: ANOVA table for surface roughness
Parameters
Sum Of
Squares
Variance Dof
F Test
Values
Contribution
(%)
Pulse on
time
3.8572 1.9286 2
2571.46
7
90.927
Pulse off
time
0.3782 0.1891 2 252.133 8.915
Wire feed 0.0052 0.0026 2 3.467 0.123
Error 0.0015 0.00075 2 0.035
Total 4.2421 8
Table. 5: ANOVA table for Material Removal Rate
IV. CONCLUSION
This work is intended to study factors like pulse on time,
pulse off time and wire feed for maximizations of MRR and
minimization of surface roughness in WEDM process using
Taguchi Method. The analysis of the results leads to
conclude that pulse on time influences more on both MRR
and surface roughness. In order to get better performance
measures, factors at level A3 B1 C3 can be set for
maximization of MRR. Similarly, the factors at levels A1
B3 C3 is recommended for minimization of Ra. For
Tungsten Carbide, the experiments have been successfully
carried out and practical results for the WEDM process have
been obtained. The experimental results can be used in
industry in order to select the best suitable parameter
combination to get the required surface roughness values
and MRR for the products. Based on the minimum number
of trails conducted to arrive at the optimum cutting
parameters, Taguchi method seems to be an efficient
methodology to find the optimum cutting parameters.
REFERENCES
[1] Bert Lauwers, Weidong Liu, Wesley Eerasrts (2006)
‘Influence of the composition of wc-based cermets on
manufacturability by Wire-EDM’, journal of
manufacturing processes vol.8/No.2, pp. 1707-1720.
[2] Biing Hwa Yan, Hsien Chung Tsai, Fuang Yuan
Huang, Long Chorng Lee (2005) ’Examination Of Wire
Electrical Discharge Machining Of Al2O3p/6061Al
Composites’, International Journal of Machine Tools &
Manufacture vol. 45, pp. 251–259.
[3] Hewidy .M .S, El-Taweel .T .A, El-Safty .M .F (2005)
'Modeling the machining parameters of wire electric
discharge machining of Inconel 601 using rsm', Journal
of material processing technology Vol. 169, pp. 328-
336.
[4] Juhr .H, Schulze .H .P, Wollenburg .G, Kunanz .K
(2004) ‘Improved cemented carbide properties after
wire EDM by pulse shaping’, Journal of material
processing technology Vol. 149, pp. 178-183.
[5] Kanlayasiri .S, Boonmung .S (2007) ‘An investigation
on effects of wire EDM machining parameters on
surface roughness of newly developed DC53 die steel’,
Journal of materials processing technology Vol. 187,
pp. 26-29.
[6] Ko-Ta Chiang, Fu-Ping Chang, De-Chang Tsai(2006)
‘Modeling and analysis of rapidly resolidified layer of
SG cast iron in the EDM process through responsive
surface methodology’, Journal of Materials Processing
Technology Vol. 182, pp. 525-533.
[7] Liao .Y .S, Huang .J .T, Chen .Y .H (2003) 'A study to
achieve a fine surface finish in Wire EDM'- Journal of
material processing technology Vol. 149, pp. 165-171.
[8] Muthu Kumar V, Suresh Babu A, Venkatasamy R and
Raajenthiren M (2010), ‘Optimization of the WEDM
Parameters on Machining Incoloy800 Super alloy
with Multiple Quality Characteristics, International
Journal of Engineering Science and Technology Vol.
2(6), pp. 1538-1547.](https://image.slidesharecdn.com/ijsrdv1i9020-140811083436-phpapp01/85/Investigation-on-Optimization-of-Machining-Parameters-in-Wire-EDM-using-Taguchi-Technique-3-320.jpg)
Investigation on Optimization of Machining Parameters in Wire EDM using Taguchi Technique
- 1. IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 9, 2013 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 1772 Investigation on Optimization of Machining Parameters in Wire EDM using Taguchi Technique G. Lakshmikanth1 Nirmal Murali2 G. Arunkumar3 S. Santhanakrishnan4 1, 2, 3, 4 Department of Mechanical Engineering 1 SKP Engineering College, Tiruvannamalai 2 Travancore Engineering College, Kollam 3 Podhigai College of Engg & Tech, Tirupattur 4 Shri Venkateshwara University, Gajraula Abstract—In this paper, the parameters used in the cutting of Cemented Tungsten Carbide using wire electrical discharge machining (WEDM) with a Brass electrode was optimized using Taguchi method. Taguchi method is used to formulate the experimental layout and to analyze the effect of each parameter on the machining characteristics. Also it is used to predict the optimal choice for each parameter such as peak current, voltage, and pulse duration and interval time. It was found that these parameters have significant influence on machining characteristics such as Metal Removal Rate (MRR) and Surface Roughness (SR). The result of the work reveals that, the peak current significantly affects the Surface Roughness (SR) and the pulse duration mainly affects the Metal Removal Rate (MRR). Key words: Taguchi method, Cemented Tungsten Carbide, Metal Removal Rate, Surface Roughness, Wire EDM. I. INTRODUCTION In recent years, the technology of wire electrical discharge machining (WEDM) has been improved significantly to meet the requirements in various manufacturing fields, especially in the precision die industry. WEDM is a thermo electrical process in which material is eroded from the work piece by a series of discrete sparks between the work piece and the wire electrode (tool) separated by a thin film of dielectric fluid (deionized water) that is continuously fed to the machining zone to flush away the eroded particles. The movement of wire is controlled numerically to achieve the desired three-dimensional shape and accuracy of the work piece. The schematic diagram of WEDM is shown in Fig. 1 along with dielectric flow, power supply, working table and other control devices. Fig. 1: Schematic Diagram of WEDM Cemented Tungsten Carbide is a high quality material with high tensile strength, shock resistance, good ductility and resistance to wear. Tungsten Carbide is renowned for its wear resistance properties and also very high strength properties are required. It is very difficult to machine Tungsten Carbide by conventional machining processes and moreover, by conventionally used tool materials. Of late, modern machining techniques such as Wire Electrical Discharge Machining (WEDM) are increasingly being used for machining such hard materials. Hence, this study focused on machining of Tungsten Carbide using WEDM, in order to satisfy production and quality requirement. The selection of optimum machining parameters in WEDM is an important step. Improperly selected parameters may result in serious problems like short- circuiting of wire, wire breakage and work surface damage which is imposing certain limits on the production schedule and also reducing productivity. As Material Removal Rate (MRR) and Surface Roughness (Ra) are most important responses in WEDM; various investigations have been carried out by several researchers for improving the MRR and Surface Finish. However, the problem of selection of machining parameters is not fully depending on machine controls rather material dependent. For the optimal selection of process parameters, the Taguchi method has been extensively adopted in manufacturing to improve processes with single performance characteristic to achieve higher Material Removal Rate (MRR) and lower surface roughness (Ra). II. MATERIALS AND METHODS Taguchi MethodA. The Taguchi method is a traditional approach for robust experimental design that seeks to obtain a best combination set of factors/levels with the lowest societal cost solution to achieve customer requirements. In the Taguchi design method the design parameters (factors which can be controlled) and noise factors (factors which cannot be controlled, e.g. environmental), which influence product quality, are considered. Therefore, in the Taguchi design method, the objective is to select the levels of design parameters such that the performance of product or process is insensitive to noise factors. That is, parameter settings should be determined with the intention that the product response (quality characteristic) has minimum variation while its mean is close to the desired target. Nevertheless, until now the Taguchi method can only be used for a single response problem; it cannot be used to optimize a multiresponse problem. But, in practice more than one quality characteristic may have to be considered for optimizing the quality of product or process.
- 2. Investigation on Optimization of Machining Parameters in Wire EDM using Taguchi Technique (IJSRD/Vol. 1/Issue 9/2013/0020) All rights reserved by www.ijsrd.com 1773 Work MaterialB. Tungsten carbide (WC) is an inorganic chemical compound containing equal parts of tungsten and carbon atoms. Colloquially, tungsten carbide is often simply called carbide. In its most basic form, it is a fine gray powder, but it can be pressed and formed into shapes for use in industrial machinery, tools, abrasives, as well as jewelry. Tungsten carbide is approximately three times stiffer than steel, with a Young's modulus of approximately 550 GPa, and is much denser than steel or titanium. It is comparable with corundum (α-Al2O3 or sapphire) in hardness and can only be polished and finished with abrasives of superior hardness such as silicon carbide, cubic boron nitride and diamond. The density of the material is 15.8g/cm3 . Machine DescriptionC. The experiments were carried out on a Four-axes ELECTRONICA MAXICUT WEDM machine. The electrode material used was a 0.25 mm diameter brass wire. A small gap of 0.025 mm to 0.05 mm is maintained in between the wire and work-piece. The high energy density erodes material from both the wire and work piece by local melting and vaporizing. The di-electric fluid (de-ionized water) is continuously flashed through the gap along the wire, to the sparking area to remove the debris produced during the erosion. A collection tank is located at the bottom to collect the used wire erosions and then is discarded. The wires once used cannot be reused again, due to the variation in dimensional accuracy. Process Parameters and DesignD. Input process parameters such as Pulse-on time (A), Pulse- off-time (B), and Wire Feed (C) used in this study are shown in Table 1. Each factor is investigated at two levels to determine the optimum settings for the WEDM process. These parameters and their levels were chosen based on the review of literature, experience, significance and their relevance as per the few preliminary pilot investigations. The smallest standard 3-level OA L9 (33 ) is chosen for this case. Factor notation Control Factors Level 1 Level 2 Level 3 A Pulse on time (μs) 2 2 3 B Pulse off time (μs) 4 4 4 C Wire feed (m/min) 6 6 5 Table. 1: Factors and Levels III. RESULTS AND DISCUSSIONS Experiments were conducted as per the L9 (33 ) orthogonal array. After individual experiments for a set of values were conducted on Tungsten Carbide for a size of 45×40×6 mm3 , their surface roughness values were measured in surface roughness tester to determine the surface finish. In this study most important output performances in WEDM such as Material Removal Rate (MRR) and Surface Roughness (Ra) were considered for optimizing machining parameters. The surface finish value (in μm) was obtained by measuring the mean absolute deviation, Ra (surface roughness) from the average surface level using a Computer controlled surface roughness tester. The Material Removal Rate (MRR) is calculated as MRR = vf h δ b Where, vf → cutting speed of wire into the work piece in mm/min, h → work piece thickness or height in mm, δ → density of the material in g/mm3 , b → Kerf given by : b = dw + 2s where, dw → wire diameter in mm, s → gap between work piece & tool. Exp No. Pulse On time (μs) Pulse Off time (μs) Wire Feed (m/min) MRR (g/min) Surface Roughness, Ra (Μm) 1 2 2 3 0.03513 3.2354 2 2 4 4 0.03384 3.4628 3 2 6 5 0.03287 2.6842 4 4 2 4 0.03835 3.8170 5 4 4 5 0.03739 3.2202 6 4 6 3 0.03642 3.6702 7 6 2 5 0.04190 4.1022 8 6 4 3 0.04093 3.8518 9 6 6 4 0.03964 3.5348 Table. 2: L9 (33 ) Orthogonal Array with Responses S/N ratio calculationA. The characteristics that lower value represent better machining performance such as surface roughness is called “lower is better (LB)” in quality engineering. The equation for calculating the S/N ratio is [∑ ] The characteristic that higher values represent better machining performance such as material removal rate is called “higher is better (HB)” in quality engineering. The equation for calculating the S/N ratio is [ ∑ ] Exp. No. Performance Measures S/N Ratio MRR (g/min) Surface Roughness, Ra (μm) MRR (db) Surface Roughness, Ra (db) 1 0.03513 3.2354 - 29.086 - 10.198 2 0.03384 3.4628 - 29.411 - 10.788 3 0.03287 2.6842 - 29.664 - 8.576 4 0.03835 3.8170 - 28.325 - 11.634 5 0.03739 3.2202 - 28.545 - 10.158 6 0.03642 3.6702 - 28.773 - 11.294 7 0.04190 4.1022 - 27.556 - 12.260 8 0.04093 3.8518 - 27.759 - 11.713 9 0.03964 3.5348 - 28.037 - 10.967
- 3. Investigation on Optimization of Machining Parameters in Wire EDM using Taguchi Technique (IJSRD/Vol. 1/Issue 9/2013/0020) All rights reserved by www.ijsrd.com 1774 Table. 3: Experimental Results with S/N Ratio Analysis of variance tableB. ANOVA table is used to find the significant parameters which influence the performance measures. Parameters Sum of Squares Variance Dof F Test Values Contribution % Pulse on time 4.9754 2.4877 2 2.9219 51.629 Pulse off time 1.7743 0.8872 2 1.0420 18.412 Wire feed 1.1842 0.5921 2 0.6954 12.288 Error 1.7029 0.8514 2 17.671 Total 9.6368 8 100 Table. 4: ANOVA table for surface roughness Parameters Sum Of Squares Variance Dof F Test Values Contribution (%) Pulse on time 3.8572 1.9286 2 2571.46 7 90.927 Pulse off time 0.3782 0.1891 2 252.133 8.915 Wire feed 0.0052 0.0026 2 3.467 0.123 Error 0.0015 0.00075 2 0.035 Total 4.2421 8 Table. 5: ANOVA table for Material Removal Rate IV. CONCLUSION This work is intended to study factors like pulse on time, pulse off time and wire feed for maximizations of MRR and minimization of surface roughness in WEDM process using Taguchi Method. The analysis of the results leads to conclude that pulse on time influences more on both MRR and surface roughness. In order to get better performance measures, factors at level A3 B1 C3 can be set for maximization of MRR. Similarly, the factors at levels A1 B3 C3 is recommended for minimization of Ra. For Tungsten Carbide, the experiments have been successfully carried out and practical results for the WEDM process have been obtained. The experimental results can be used in industry in order to select the best suitable parameter combination to get the required surface roughness values and MRR for the products. Based on the minimum number of trails conducted to arrive at the optimum cutting parameters, Taguchi method seems to be an efficient methodology to find the optimum cutting parameters. REFERENCES [1] Bert Lauwers, Weidong Liu, Wesley Eerasrts (2006) ‘Influence of the composition of wc-based cermets on manufacturability by Wire-EDM’, journal of manufacturing processes vol.8/No.2, pp. 1707-1720. [2] Biing Hwa Yan, Hsien Chung Tsai, Fuang Yuan Huang, Long Chorng Lee (2005) ’Examination Of Wire Electrical Discharge Machining Of Al2O3p/6061Al Composites’, International Journal of Machine Tools & Manufacture vol. 45, pp. 251–259. [3] Hewidy .M .S, El-Taweel .T .A, El-Safty .M .F (2005) 'Modeling the machining parameters of wire electric discharge machining of Inconel 601 using rsm', Journal of material processing technology Vol. 169, pp. 328- 336. [4] Juhr .H, Schulze .H .P, Wollenburg .G, Kunanz .K (2004) ‘Improved cemented carbide properties after wire EDM by pulse shaping’, Journal of material processing technology Vol. 149, pp. 178-183. [5] Kanlayasiri .S, Boonmung .S (2007) ‘An investigation on effects of wire EDM machining parameters on surface roughness of newly developed DC53 die steel’, Journal of materials processing technology Vol. 187, pp. 26-29. [6] Ko-Ta Chiang, Fu-Ping Chang, De-Chang Tsai(2006) ‘Modeling and analysis of rapidly resolidified layer of SG cast iron in the EDM process through responsive surface methodology’, Journal of Materials Processing Technology Vol. 182, pp. 525-533. [7] Liao .Y .S, Huang .J .T, Chen .Y .H (2003) 'A study to achieve a fine surface finish in Wire EDM'- Journal of material processing technology Vol. 149, pp. 165-171. [8] Muthu Kumar V, Suresh Babu A, Venkatasamy R and Raajenthiren M (2010), ‘Optimization of the WEDM Parameters on Machining Incoloy800 Super alloy with Multiple Quality Characteristics, International Journal of Engineering Science and Technology Vol. 2(6), pp. 1538-1547.