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Frontiers of Mechanical Engineering

Front Mech Eng    2013, Vol. 8 Issue (2) : 201-214     https://doi.org/10.1007/s11465-013-0256-8
RESEARCH ARTICLE
Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept
Rupesh CHALISGAONKAR1(), Jatinder KUMAR2
1. Krishna Institute of Engineering and Technology, Ghaziabad-Meerut Highway (NH-58), Ghaziabad, Uttar-Pradesh-201206, India; 2. Department of Mechanical Engineering, National Institute of Technology, Kurukshetra, Haryana-136119, India
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Abstract

This paper describes the development of multi response optimization technique using utility method to predict and select the optimal setting of machining parameters in wire electro-discharge machining (WEDM) process. The experimental studies in WEDM process were conducted under varying experimental conditions of process parameters, such as pulse on time(Ton), pulse off time(Toff), peak current (IP), wire feed (WF), wire tension (WT) and servo voltage (SV) using pure titanium as work material. Experiments were planned using Taguchi’s L27 orthogonal array. Multi response optimization was performed for both cutting speed (CS) and surface roughness (SR) using utility concept to find out the optimal process parameter setting. The level of significance of the machining parameters for their effect on the CS and SR was determined by using analysis of variance (ANOVA). Finally, confirmation experiment was performed to validate the effectiveness of the proposed optimal condition.

Keywords wire electro-discharge machining (WEDM)      Taguchi method      analysis of variance (ANOVA)      utility concept      cutting speed (CS)      surface roughness (SR)     
Corresponding Author(s): CHALISGAONKAR Rupesh,Email:rupesh_chalisgaonkar2000@yahoo.com   
Issue Date: 05 June 2013
 Cite this article:   
Jatinder KUMAR,Rupesh CHALISGAONKAR. Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept[J]. Front Mech Eng, 2013, 8(2): 201-214.
 URL:  
http://journal.hep.com.cn/fme/EN/10.1007/s11465-013-0256-8
http://journal.hep.com.cn/fme/EN/Y2013/V8/I2/201
Fig.1  Schematic diagram of WEDM
Fig.2  Ishikawa cause and effect diagram
Fig.3  Modified linear graph for L27 array
Fig.4  Electronica Sprintcut Model (Electra- Elplus 40A DLX) CNC WEDM
Fig.5  Workpiece geometry
Fig.6  Mitutoyo surface roughness tester
Process parametersParameter designationLevel 1Level 2Level 3
Pulse on time/μsA108112116
Pulse off time/μsB203040
Peak current/AC80140200
Wire feed/(m?min-1)D6810
Wire tension/gmE7 (1020)9 (1260)11 (1500)
Servo voltage/VF305070
Tab.1  Process parameters with their values at three levels
NCFeOTi
0.0010.060.100.00299.82
Tab.2  Chemical composition of pure Ti
RunA1B2A × B3A × B4C5A × C6A × C7B × C8D9E10B × C11F12-13
123456789101112131415161718192021222324252627111111111222222222333333333111222333111222333111222333111222333222333111333111222111222333333111222222333111123123123123123123123123123123123123231231231312312312123123123312312312231231231123231312123231312123231312123231312231312123312123231123231312312123231231312123123312231123312231123312231123312231231123312312231123123312231312231123231123312
Tab.3  L27 orthogonal array with parameters and interactions assigned to columns
S.No.CS1CS2CS3MeanSR1SR2SR3Mean
12.212.012.122.114891.9302.2002.1232.084
22.131.942.032.034232.0932.2172.1142.141
31.461.241.461.386361.9832.2111.9972.063
40.810.750.770.775201.9832.0372.0232.014
52.151.881.931.986382.0832.1732.1522.136
61.681.421.731.609152.0202.0391.9902.016
70.870.760.800.811551.9902.0572.0162.021
80.740.650.690.694541.8932.0001.9921.961
91.681.451.771.632952.0172.1172.0192.050
102.552.262.302.370872.2232.4432.3342.333
112.091.881.911.960122.1932.2802.2682.247
122.252.602.542.462682.3332.4572.3582.382
132.332.482.402.405002.1772.6402.5522.456
142.472.222.292.326822.2872.2672.2972.283
151.751.622.121.828772.2102.3872.1922.263
160.920.960.990.958312.2402.2202.3372.265
172.442.602.582.539772.3672.4872.3962.416
181.932.062.412.133982.3902.4872.3572.411
192.102.272.222.197172.3202.2102.2552.261
202.842.942.882.886422.8732.9922.7822.882
213.764.083.713.848992.8903.0102.4232.774
222.452.552.492.495862.5202.4202.5102.483
232.192.272.252.236302.5972.3772.4782.483
242.873.253.283.133252.5332.5432.5402.538
252.452.662.582.563312.5202.3502.4502.44
262.582.512.542.542282.5072.2432.3472.365
271.881.682.021.858712.4532.4102.4402.434
Tab.4  Experimental data of cutting speed and surface roughness
Fig.7  Mean effect plot for means of cutting speed
Fig.8  Mean effect plot for means of surface roughness
Fig.9  Main effect plot for S/N ratio of cutting speed considering “larger is better”
Fig.10  Main effect plot for S/N ratio of surface roughness considering “smaller is better”
Fig.11  Main effect plot for means of multiple response data considering “larger is better”
Fig.12  Main effect plot for S/N ratios of multiple response data considering “larger is better”
S. No.R=1R=2R=3S/N ratioMean
17.1419745.7273446.18355315.94926.35096
26.3227665.5666576.10887715.52425.99943
36.9002254.4292265.76152914.68095.69699
46.8045333.8557483.98559312.95814.88196
55.385285.6640075.81878914.98545.62269
64.1302745.50376.23325514.07565.28908
76.7973433.8029354.11572113.01544.90533
86.6030713.6479313.83868912.57164.69656
94.3289975.2229616.16347514.11385.23848
103.0411655.0966695.54950812.24054.56245
115.2903325.2350765.32325514.45685.28289
125.8211315.4094065.71581715.02645.64878
135.9224234.6457114.86313714.08465.14376
145.6739965.717545.68079615.10335.69078
156.0702944.4395145.89813714.49285.46931
166.1014113.728743.35027612.03244.39348
174.7149325.3011075.61370914.26745.20992
182.959844.6968975.5832511.94834.41333
195.7565526.0025855.76490215.32545.84135
203.2402143.9703984.56627611.61903.92563
213.4066594.7673356.45645212.89164.87682
225.4126285.4944135.10676614.53445.33794
235.8723355.3525264.95822114.57675.39436
244.9833375.6815355.71591614.69145.46026
254.7380915.8659515.41482414.44865.33962
265.4860536.1311615.75754315.22905.79159
275.2697214.4483284.81626213.64334.84477
Tab.5  Multiple responses data
Fig.13  Pie chart of factor % contribution for cutting speed (ANOVA raw data)
FactorDOFSeq SSAdj SSAdj MSF ratioP valuePercent of contribution/%
Ton219.217919.21799.6090416.300.000*45.71
Toff25.10985.10982.5549110.690.000*12.15
IP21.89491.89490.947541.050.000*4.51
WF20.97940.97940.489721.220.000*2.33
WT20.47060.47060.235310.190.000*1.12
SV211.457511.45755.7287248.190.000*27.25
Ton × Toff40.45930.45930.11484.970.002*1.09
Ton × IP40.70080.70080.17527.590.000*1.67
Toff× IP40.46470.46470.11625.030.002*1.11
Error561.29261.29260.02310.002*3.07
Total8042.0474
Tab.6  Analysis of variance for CS (mm/min) raw data, using adjusted SS for tests.
Fig.14  Pie chart of factor % contribution for surface roughness (ANOVA raw data)
SourceDOFSeq SSAdj SSAdj MSF ratioP valuePercent of contribution/%
Ton22.956482.956481.47824131.310.000*62.76
Toff20.109740.109740.054874.870.011*2.31
IP20.071460.071460.035733.170.049*1.50
WF20.030410.030410.015201.350.2670.63
WT20.098160.098160.049084.360.017*2.08
SV20.327410.327410.1637014.540.000*6.94
Ton × Toff40.165600.165600.041403.680.010*3.50
Ton × IP40.150550.150550.037643.340.016*3.18
Toff× IP40.170480.170480.042623.790.009*3.60
Error560.630440.630440.112613.37
Total804.71074
Tab.7  Analysis of variance for SR (μm) raw data, using adjusted SS for tests.
MethodResponse (units)Optimal conditionPredicted valueExperimental valueCICE
Multi response optimizationCS/(mm?min-1)Ton1 Toff 2 IP2WF3 WT2 SV11.821.891.60<μCS<2.036
SR/μm2.0652.1121.584<μSR<2.537
Tab.8  Confirmatory experimental results (Multi response optimization)
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