Investigation on process response and parameters in wire electrical

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  • 1. INTERNATIONALMechanical Volume 4, Issue 1, January - February (2013) © IAEME– International Journal of JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 – 6359(Online) Engineering and Technology (IJMET), ISSN 0976 AND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online)Volume 4, Issue 1, January- February (2013), pp. 54-65 IJMET© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2012): 3.8071 (Calculated by GISI)www.jifactor.com ©IAEME INVESTIGATION ON PROCESS RESPONSE AND PARAMETERS IN WIRE ELECTRICAL DISCHARGE MACHINING OF INCONEL 625 Rodge M. K1, Sarpate S. S2, Sharma S. B3 1&2 Research scholars, 3Professor Production Engineering Dept., SGGSIE&T, Nanded, India. (mkrodge64@rediffmail.com) ABSTRACT Continuous research in the field of material science leads to production of very hard, tough, high temperature and corrosion resistant materials which are difficult-to machine with conventional methods. Advanced manufacturing processes play an important role in production of complicated profiles on such difficult-to-machine components. Inconel 625 is one of the recent materials developed to have high strength, toughness and corrosion resistant. The high degree of accuracy, fine surface quality and good productivity made wire electrical discharge machining (WEDM) a valuable tool in today’s manufacturing scenario. The right selection of the machining conditions is the most important aspect to take into consideration in the processes related to WEDM. As electrode wire is not reused, its wear is generally ignored. However, it is interesting to study wire wear as it may have an effect on kerf width and surface quality of the product. The present study is focused on investigation of the effect of process parameters on multiple performance measures such as cutting width, electrode wear and hardness during WEDM of Inconel 625. The control factors considered are: pulse-on time, pulse-off time, upper flush, lower flush, wire feed and wire tension. The relationships between control factors and responses are established by means of regression analysis. The study demonstrates that, there is a good agreement between experimental and predicted (theoretical) values of performance measures. Keywords: Orthogonal array (OR), Signal-to-noise (S/N) ratio, Taguchi’s design of experiment (DOE), Wire Electrical Discharge Machining (WEDM) 54
  • 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEME1. INTRODUCTION As newer and more exotic materials with requirement of complex shapes aredeveloped, conventional machining operations will continue to reach their limitations. Wireelectrical discharge machining (WEDM) is an extremely potential (thermoelectric) processhaving capacity to machine parts made up of conductive materials regardless of theirhardness, toughness and geometry. In WEDM, a series of discrete electrical sparks betweenthe work and tool electrodes immersed in a liquid dielectric medium melt and vaporizeminute amounts of the work material which is then ejected and flushed away by the dielectricfluid. Latest WEDMs are assisted by CNC table to produce any complex two and threedimensional profiles on work. Due to high process capability, this method is widely used inmanufacturing of car wheels, special gears, various press tools, dies and similar complex andintricate shapes. Hence, the increased use of the WEDM in manufacturing will continue togrow at an accelerated rate.Wire electrical discharge machining manufacturers and users emphasize on achievement ofbetter stability, higher machining productivity along with desired accuracy and surfacequality. However, due to involvement of large number of variables, even a highly skilledWEDM operator is rarely able to achieve the optimal performance. Proper selection ofprocess parameters for best process performance is a challenging job. An effective way toattempt this problem is to establish the relationship between performance measures of theprocess and its controllable input parameters. Optimization of process parameters can play aimportant role in this regard. In WEDM, the commonly affecting process parameters areignition pulse current, time between two pulses, pulse duration, servo voltage, wire speed,wire tension and dielectric fluid injection pressure. Any slight variation in one of theparameters can affect the production quality and economics of the process. The parametersettings given by manufacturers are only applicable for commonly used steel grades andalloys.The important performance measures in WEDM are metal removal rate (MRR), cutting width(kerf) and surface quality. In WEDM operations, MRR determines the economics ofmachining and rate of production where as kerf denotes degree of precision and dimensionalaccuracy. The internal corner radius to be produced is limited by the kerf. The gap betweenthe electrode wire and work usually ranges from 0.025 to 0.075 mm and it is constantlymaintained by a computer controlled positioning system. In setting the machining parameters,particularly in rough cutting operation, the goal is twofold: the maximization of MRR andminimization of kerf.Konda R. et al. [1999] classified the various potential factors affecting the WEDMperformance measures into five major categories: the different properties of work material,dielectric fluid, machine characteristics, adjustable machining parameters and componentgeometry. They have applied the design of experiments (DOE) technique to study andoptimize the possible effects of variables and validated the experimental results using noise-to-signal (S/N) ratio analysis. Different areas of WEDM research identified by Ho K. H. et al.[2004] are: such as process optimization, process monitoring and control. The settings for thevarious process parameters play a crucial role in producing an optimal machiningperformance. The application of adaptive control systems to the WEDM is vital for themonitoring and control of the process. The authors have investigated the advancedmonitoring and control systems including the fuzzy, the wire breakage and the self-tuningadaptive control systems used in WEDM process. Cabanes I. et al. [2008] analyzed new 55
  • 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEMEsymptoms that allow us to predict wire breakage. Symptoms may be increase in dischargeenergy, peak current, increase/decrease in ignition delay time. They have proposed a novelwire breakage monitoring and diagnostic system with virtual instrumentation system (VIS)that measures relevant magnitudes and diagnostic system (DS) that detect new quality cuttingregimes and predicts wire breakage. Almost in 80% of total wire breakage cases, theanticipation time longer than 50 ms has been detected. Efficiency of supervision system hasbeen quantified to 82%.Parashar Vishal et al. [2010] analyzed kerf width of wire cut electro discharge machining ofSS304L steel using DOE technique. They have used statistical methods and regressionanalysis for finding kerf width. Mixed OR of L32 is used for experimentation. ANOVA isused to find out the variables affecting kerf width more significantly. Results show that pulse-on time and dielectric flushing pressure are the most significant factor to the kerf width.Theoretical and experimental results appeared to be in good agreement. MohammadrezaShabgard et al. [2011] used 3D finite element for prediction of the white layer thickness, heataffected zone (HAZ) and surface roughness (SR) of electro discharge machined AISI H13tool steel. They carried out experimental investigations to validate the numerical results. Bothnumerical and experimental results show that increasing the pulse-on time leads to a higherwhite layer thickness, depth of HAZ and surface roughness and increase in the pulse currentslightly decrease the white layer thickness and depth of HAZ with increase in SR.Experimental and numerical results are closer to each other. Manoj Malik et al. [2012] havecarried out optimization of process parameters of WEDM using Zinc-coated brass wire forMRR, electrode wear rate (EWR) and SR. They observed that, for minimum EWR, pulse-ontime and pulse peak current should be high. For EWR, pulse peak current is the most criticalfactor and duty cycle time is the least significant parameter.From the literature it is found that, most of the authors have studied the effect processparameters on different response variables while WEDM of commonly used materials like,die steel, EN31, AISI H13 steel, etc. Studies on WEDM of Inconel 625 are scantly available.Inconel 625 is recently coming up as one of best candidate materials which is extensivelyused in various applications including: marine, aerospace, chemical processing, nuclearreactors and pollution control equipments. It is used in any environment that requiresresistance to heat and corrosion retaining mechanical properties. It is an alloy havingexcellent corrosion resistance in a wide range of corrosive media being especially resistant topitting and crevice. It is a favorable choice for sea water applications. Therefore, it isinteresting to study the effect of control parameters on work as well as electrode materials.The studies about effect of control factors on hardness of work material is important as it isone of the most critical parameter in relation to wear and tear of components made out ofWEDM.2. METHODOLOGY2.1 Taguchi Method Generally, the machine tool builder provides information in the form of tables to beused for setting machining parameters. The selection of process parameter relies heavily onthe experience of the operators. With a view to alleviate this difficulty, a simple but reliablemethod based on statistically designed experiments is suggested for investigating the effectsof various process parameters and to get optimal process settings. This new experimentalstrategy proposed by Genichi Taguchi is called Taguchi method. It is a powerful 56
  • 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEMEexperimental design tool which uses simple, effective and systematic approach for derivingthe optimal levels of machining parameters. This approach efficiently reduces the effect ofthe sources of variation. It requires minimum experimental cost due to reduction in number ofexperiments required to meet the specific requirements in terms of quality and reliability. Ituses specially constructed tables known as orthogonal arrays (OA). Each row represents a setof parameters for a particular experiment. This is a best way to study the effect of largenumber of variables on desired quality characteristics with small number of experiments.2.2 Design of Experiment To evaluate the effects of machining parameters on performance characteristics (kerf,wire wear and hardness of machined surface) a specially designed experimental procedure isrequired. In this study, the Taguchi method, a powerful tool for parameter design of theperformance characteristics is used to determine optimal machining parameters for minimumof kerf, minimum wire wear and higher hardness of the machined surface. Six control factorswith five levels each and three response variables are used to get qualitative results. Thecontrol factors represent stability in design of manufacturing process whereas the noisefactors denote all factors that cause variation. Table 1 shows six parameters with five levelseach. This may increase number of experiments to be carried out. However, it may help ingetting a good relationship between input and output parameters. Based on Taguchi methodwe use L25 orthogonal array (obtained using Minitab 16 software) as shown in Table 2. Theexperiments are performed as per orthogonal array which has 25 rows indicating 25 ofexperiments. The results are shown in Table 2. The kerf is measured with the help of amicroscope. The loss in weight of electrode wire per meter length is determined bysubtracting final weight from initial weight of the wire. The hardness of machined surface ismeasured with the help of hardness tester. Table 1: Parameters and their levels Factors level 1 level 2 level 3 level 4 level 5 Unit Pulse-on time (Ton) 3 4 5 6 7 µs Pulse-off time (Toff) 3 4 5 6 7 µs Wire Feed (WF) 6 7 8 9 10 mm/s Upper Flush (UF) 6 7 8 9 10 kg/cm2 Lower Flush (LF) 6 7 8 9 10 kg/cm2 Wire Tension (WT) 600 700 800 900 1000 gm2.3 Experimental set-up The inputs used in the present study are chosen through review of literature,experience and some preliminary investigations. Each time an experiment was performed, aparticular set of input parameters was chosen. The work piece is a block of Inconel 625 withlength 100 mm × width 30 mm × thickness 10 mm. The workpiece material composition istested at ICS, Pune. Its percentage composition is: C - 0.035, Mn - 0.130, Si - 0.247, S -0.010, P - 0.0064, Cr - 22.86, Ni – 58.1, Mo - 8.55, W - <0.0050, V - <0.0005, Al - 0.144,Co - 0.072, Cu - 0.022, Nb - 3.24, Ti - 2.98, Fe- 3.60. A brass wire of 0.25 mm diameter isused as an electrode for experimentation and it is discarded once used. The cuts of 10 mm 57
  • 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEMEdepth along the length of the work are taken. The experiments are performed on Maxicut–eWEDM (Figure 1). The machine allows operator to choose input parameters according togeometry and material of electrodes from a manual provided by the WEDM manufacturer. Figure 1: Maxicut-e WEDM2.4 Signal-To-Noise Ratio In signal-to-noise (S/N) ratio signal represents the desirable value (mean for outputparameters) and noise represents undesirable value (the square deviation of outputparameters). Thus, it is the ratio of mean to square deviation. It is designated by symbol ‘ƞ’with unit of dB. The characteristic for which the lower value represents better performance,the S/N ratio should be smaller the better (SB) and the characteristic for which the large valuerepresents better performance, the S/N ratio should be larger the better (LB). In this study theparameters kerf and wire wear should have lower values and hardness of the machinedsurface should have larger values.The loss function (L) for kerf width, wire wear and hardness is defined as: ଵ LSB = ௡ ∑௡ ܻ ଶ kerf ௜ୀଵ ଵ LSB = ௡ ∑௡ ܻ ଶ ww ௜ୀଵ ଵ LLB = ∑௡ 1/ܻ ଶhv ௡ ௜ୀଵwhere Ykerf, Yww and Yhv are the responses for kerf width, wire wear and hardnessrespectively and n denotes the number of experiments. The S/N ratios can be calculated as alogarithmic transformation of the loss function as shown below. S/N ratio for kerf width = -10 log10 (LSB) i) S/N ratio for wire wear = -10 log10 (LSB) ii) S/N ratio for hardness = -10 log10 (LLB) iii)The analysis is done using the popular software specifically used for design of experimentapplications known as MINITAB 16. The S/N ratio for kerf width, wire wear and hardness iscomputed using Eqs. i), ii) and iii) respectively for each treatment as shown in Table 2. Then,overall mean for S/N ratios of kerf width, wire wear and hardness are calculated as average ofall treatment responses for each level (Table 3, 4 and 5). The graphical representation of theeffect of the six control factors on kerf width, wire wear and hardness is shown in Figure 2, 3and 4 respectively. 58
  • 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEME Table 2: Orthogonal array, experimental results for kerf width (KW), wire wear (WW) and hardness (HV) of WEDMed surface along with S/N ratios S.N. Ton Toff UF LF WF WT KW S/N WW S/N HV S/N 1 3 3 6 6 6 600 0.33 9.6297 0.01 40.0000 310 49.8272 2 3 4 7 7 7 700 0.32 9.8970 0.01 40.0000 312 49.8831 3 3 5 8 8 8 800 0.33 9.6297 0.03 30.4576 286 49.1273 4 3 6 9 9 9 900 0.34 9.3704 0.01 40.0000 301 49.5713 5 3 7 10 10 10 1000 0.35 9.1186 0.03 30.4576 331 50.3966 6 4 3 7 8 9 1000 0 31 10.1728 0.01 40.0000 325 50.2377 7 4 4 8 9 10 600 0.30 10.4576 0.01 40.0000 325 50.2377 8 4 5 9 10 6 700 0.35 9.1186 0.03 30.4576 295 49.3964 9 4 6 10 6 7 800 0.30 10.4576 0.02 33.9794 276 48.8182 10 4 7 6 7 8 900 0.31 10.1728 0.02 33.9794 311 49.8552 11 5 3 8 10 7 900 0.30 10.4576 0.03 30.4576 325 50.2377 12 5 4 9 6 8 1000 0.29 10.752 0.02 33.9794 341 50.6551 13 5 5 10 7 9 600 0.28 11.0568 0.03 30.4576 296 49.4258 14 5 6 6 8 10 700 0.34 9.3704 0.04 27.9588 290 49.2480 15 5 7 7 9 6 800 0.30 10.4576 0.02 33.9794 325 50.2377 16 6 3 9 7 10 800 0.29 10.7520 0.03 30.4576 301 49.5713 17 6 4 10 8 6 900 0.28 11.0568 0.01 40.0000 309 49.7992 18 6 5 6 9 7 1000 0.28 11.0568 0.02 33.9794 299 49.5134 19 6 6 7 10 8 600 0.33 9.6297 0.03 30.4576 311 49.8552 20 6 7 8 6 9 700 0.33 9.6297 0.02 33.9794 309 49.7992 21 7 3 10 9 8 700 0.30 10.4576 0.05 26.0206 297 49.4551 22 7 4 6 10 9 800 0.29 0.4576 10.7520 0.01 40.0000 310 49.8272 23 7 5 7 6 10 900 0.29 10.7520 0.01 40.0000 298 49.4843 24 7 6 8 7 6 1000 0.32 9.8970 0.01 40.0000 295 49.3964 25 7 7 9 8 7 600 0.31 10.1728 0.02 26.0206 290 49.2480 Table 3: Response Table for S/N ratios (smaller the better) for kerf width Level Ton Toff UF LF WF WT 1 9.529 10.294 10.196 10.244 10.032 10.189 2 10.076 10.583 10.182 10.355 10.408 9.6950 3 10.419 10.323 10.014 10.081 10.128 10.410 4 10.425 9.7450 10.033 10.360 10.196 10.362 5 10.406 9.9100 10.429 9.8150 10.090 10.199 Delta 0.8960 0.8380 0.4150 0.5450 0.3760 0.7150 Rank 1 2 5 4 6 3 59
  • 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEME Table 4: Response Table for S/N ratios (smaller the better) for wire wear Level Ton Toff UF LF WF WT 1 36.18 33.39 35.18 36.39 36.89 34.98 2 35.68 38.80 36.89 34.98 34.48 31.68 3 31.37 33.07 34.98 34.48 30.98 33.77 4 33.77 34.48 33.77 34.80 36.89 36.89 5 36.00 33.28 32.18 32.37 33.77 35.68 Delta 4.82 5.73 4.70 4.02 5.91 5.20 Rank 4 2 5 6 1 3 Table 5: Response Table for S/N ratios (larger the better) for hardness Level Ton Toff UF LF WF WT 1 49.76 49.67 49.65 49.72 49.73 49.72 2 49.71 50.08 49.94 49.63 49.54 49.56 3 49.96 49.39 49.76 49.53 49.78 49.52 4 49.71 49.38 49.69 49.80 49.77 49.79 5 49.48 49.91 49.58 49.94 49.79 50.04 Delta 0.48 0.70 0.36 0.41 0.25 0.52 Rank 3 1 5 4 6 2 Figure 2: Graphs for kerf width Figure 3: Graphs for wire wear 60
  • 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEME Figure 4: Graphs for hardness of WEDMed surface Table 6: Experimental and Predicted values of kerf width (KW), wire wear (WW) and hardness (HV) S. N. EKW PKW EWW PWW EHV PHV 1 0.33 0.3138 0.01 0.0160 310 304.00 2 0.32 0.3166 0.01 0.0200 312 308.00 3 0.33 0.3258 0.03 0.0240 286 311.10 4 0.34 0.3300 0.01 0.0280 301 314.20 5 0.35 0.3378 0.03 0.0320 331 317.30 6 0.31 0.3000 0.01 0.0178 325 322.24 7 0.30 0.3200 0.01 0.0368 325 309.44 8 0.35 0.3200 0.03 0.0268 295 306.44 9 0.30 0.3100 0.02 0.0278 276 298.74 10 0.31 0.3200 0.02 0.0208 311 308.00 11 0.30 0.2800 0.03 0.0206 325 317.00 12 0.29 0.2900 0.02 0.0216 341 309.88 13 0.28 0.3100 0.03 0.0406 296 297.00 14 0.34 0.3200 0.04 0.0336 290 306.68 15 0.30 0.3200 0.02 0.0236 325 303.00 16 0.29 0.2880 0.03 0.0344 301 308.00 17 0.28 0.2900 0.01 0.0244 309 305.00 18 0.28 0.3000 0.02 0.0174 299 314.00 19 0.33 0.3210 0.03 0.0364 311 302.00 20 0.33 0.3100 0.02 0.0374 309 294.00 21 0.30 0.2800 0.05 0.0372 297 303.00 22 0.29 0.2900 0.01 0.0302 310 313.16 23 0.29 0.2800 0.01 0.0312 298 305.00 24 0.32 0.2900 0.01 0.0212 295 302.00 25 0.31 0.3100 0.02 0.0400 290 289.66 where E = Experimental, P = Predicted values from regression analysis 61
  • 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEMEThe regression analysis is used for modeling the responses in terms of process variables. Theregression equations are obtained by using Minitab software.Regression equation for kerf widthKW = 0.318 - 0.00760Ton + 0.00580Toff - 0.00100UF +0 .00320LF + 0.00040WF -0.000024WTRegression equation for wire wearWW = - 0.0098 + 0.00200Ton + 0.00140Toff + 0.00220UF + 0.00060LF + 0.00280WF -0.000030 WTRegression equation for hardness of machined surfaceHV = 286 -2.06Ton - 2.16Toff - 1.30UF + 2.16LF + 1.22WF + 0.0318WTFrom these equations, the predicted (theoretical) values of kerf width, wire wear andmachined surface hardness are determined manually for all set of parameters. Table 6 showsexperimental and predicted values for above said process responses.3. RESULTS AND DISCUSSION The purpose of the experimentation is to identify the factors which have strong effectson the machining performance. From mean of S/N ratios (Table 3) for kerf width, it is foundthat pulse-on time has highest rank ‘1’. Therefore, it has most significant effect on kerf width.As pulse-on time increases the kerf width increases significantly. The wire feed has leasteffect on kerf width. The order of other influencing parameters for kerf width is: pulse-offtime, wire tension, lower flush and upper flush. Also, from mean of S/N ratios (Table 4) forwire wear, it is observed that, the wire feed has highest rank ‘1’ and therefore, it affects wirewear significantly. The wire wear initially decreases significantly with increase in wire feed;however it increases with further rise in wire feed. This may be due the combined effect ofother factors. The lower flush has least effect on wire wear. The order of other influencingparameters for wire wear is: pulse-off time, wire tension, pulse-on time and upper flush.Table 5 shows that, for hardness of the machined surface, the pulse-off time has highest rank‘1’ and hence, it affects hardness of the machined surface most significantly. However, theeffect of increase in pulse-off time on hardness of the machined surface has no fixed nature.The hardness first increases and then decreases significantly. Again it increases. The wirefeed has least effect on hardness. The order of other influencing parameters of hardness is:wire tension, pulse-on time, lower flush, upper flush and upper flush.From Table 3, the optimal combination of process parameters for minimum kerf width isfound to be: A1B4C3D5E1F2. The symbols A, B, C, D, E and F represents processparameters: Ton, Toff, UF, LF, WF and WT respectively and numbers represents the levels.This means, to have minimum kerf width, Ton should be set on level 1, Toff on 4, UF on 3,LF on 5, WF on 1 and WT on 2. Similarly from Table 4, it is observed that, the optimalcombination of process parameters for minimum wire wear is: A3B3C5D5E3F2. This means,to have minimum wire wear, Ton should be set on level 3, Toff on 3, UF on 5, LF on 5, WFon 3 and WT on 2. It is to be noted that the optimal levels of factors differ widely for both theobjectives (for minimum kerf width and minimum wire wear). From Table 5, the optimalcombination of process parameters for hardness of wire electrical discharge machined surfaceis: A3B2C2D5E5F5. The hardness of the machined surface should be more for better workingperformance. This means to have high hardness Ton should be set on level 3, Toff on 2, UFon 2, LF on 5, WF on 5 and WT on 5.From Figure 5, it is observed that, the predicted values for kerf width determined from regressionanalysis are in agreement to experimental values. However, wire wear during the WEDM operations 62
  • 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEMEis sensitive to many operational parameters other than current characteristics of the system such aswire tension, wire feed, flushing conditions which must have attributed toward the non uniformity inexperimental wire wear reading with respect to predicted one (Figure 6). Lower wire wear generallyresults in higher kerf width and the present experimental study also depicts the similar results. Figure7, shows good agreement between experimental and predicted hardness values reasonably. Thehardness of the machined surface is first decreased and then improved when lower flush, wire feedand wire tension is increased. Whereas it first increases and then decreases when pulse-on, pulse-offand upper flush is increased. Thus, the recommended values are of the combined effect of the processparameters. Figure 5: Comparison of experimental and predicted values of kerf width EKW 0.37 PKW 0.35 0.33 Kerf width 0.31 0.29 0.27 0.25 1 3 5 7 9 11 13 15 17 19 21 23 25 Figure 6: Comparison of Experimental and predicted values of wire wear EWW 0.06 PWW 0.05 Wire wear 0.04 0.03 0.02 0.01 0 1 3 5 7 9 11 13 15 17 19 21 23 25 Figure 7: Comparison of Experimental and predicted values of hardness 360 EHV 340 PHV HARDNESS HV 320 300 280 260 240 1 3 5 7 9 11 13 15 17 19 21 23 25 63
  • 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEME4. CONCLUSIONS WEDM process parameter’s optimization is responsive not only to the processvariables but also the work materials. Therefore, for quality machining performance of amaterial, parameter optimization is essential to result cost effective usages of the material forthe given application. The present investigation revealed that pulse-on ranks high in terms ofmachining performance of Inconel 625 and it has a predominant effect on kerf width. Duringmachining of Inconel, as pulse-on increases, the kerf width increases which resultedrelatively lower wire wear. The wire wear initially decreases significantly with increase inwire feed; however it increases with further rise in wire feed. This may be due the combinedeffect of other factors. With regard to hardness of machined components, pulse-off causessignificant variation. Optimized process parameters could be used as guideline for WEDM ofInconel 625.5. REFERENCES1. Atul Kumar and D. K. Singh, ‘Strategic optimization and investigation effect of process parameters on performance of Wire Electrical Discharge Machine (WEDM)’, Int. J. of Engineering Science and Technology, Vol. 4, No. 6, 2012, 2766-2772..2. Atul Kumar and D. K. Singh, ‘Performance analysis of Wire Electrical Discharge Machining (WEDM)’, Int. J. of Engineering Science and Technology, Vol. 1, No. 4, 2012, 1-9.3. Cabanes I., Portillo E, Marcos M. and Sanchez J A, ‘On-line prevention of wire breakage in wire electro-discharge machining’, Robotics and Computer-Integrated Manufacturing, Volume 24, Issue 2, April 2008, Pages 287-2984. Choudhary Rajesh, H. Kumar and R. K. Garg, ‘Analysis and evaluation of heat affected zone in electrical discharge machining of EN-31 die steel’, Indian Journal of Engineering and Material Science, Vol. 17, 2010, 91-98.5. Debabrata Mandal, Surjya K. Pal and Partha Saha, ‘Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II’, Journal of Materials processing Technology 186 (2007) 154-162.6. Hsien-Ching Chen, Jen-Chang Lin, Yung-Kuang Yang, and Chih-Hung Tsai, ‘Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach’, Expert Systems with Applications 37 (2010) 7147-7153.7. Konda R., Rajurkar K. P., Bishu R. R. , Guha A. and Parson M., ‘Design of experiments to study and optimize process performance’, Int. J. Quality, Reliability and Management, 16 (1) (1999) 56–71.8. Mahapatra S. S. and Amar Patnaik, ‘Parametric optimization of WEDM using Taguchi technique’, Journal of Braz. Soc. of Mech. Sci. and Engg. (2006).9. Manoj Malik, Rakesh Kumar Yadav, Nitesh Kumar, Deepak Sharma and Manoj, ‘Optimization of process parameters of wire EDM using Zinc-coated brass wire’, International Journal of Advanced Technology & Engineering Research (IJATER), Vol. 2, No. 4, 2012, 127-130. 64
  • 12. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEME10.Manna A and Bhattacharyya, ‘Study for optimization of CNC-Wire cut EDM parameters during of Al/Sic-MMC using design of experiment’, 20th AIMTDR, BIT, Ranchi 13-14 December (2002) 294-299.11.Mohammadreza Shabgard, Samad Nadimi Bavil Oliaei, Mirsadegh Seyedzavvar and Ahmad Najadebrahimi, ‘Experimental investigation and 3D finite element prediction of the white layer thickness, heat affected zone, and surface roughness in EDM process’, Journal of Mechanical Science and Technology 25 (12) (2011) 3173-3183.12.Parashar Vishal, A. Rehaman, J. L. Bhagoria, Y.M. Puri, ‘Kerf width analysis for wire cut electro discharge machining of SS304L using design of experiments’, Indian Journal of Science and Technolgy, vol. 3 No. 4, 2010, 369-373.13.Pujari Srinivasa Rao, Koona Ramji and Beela Satyanarayana, ‘Prediction of material removal rate for Aluminum BIS-24345 alloy in Wire-Cut EDM’, Int. J. of Engineering Science and Technology, Vol. 2, No. 12, 2010, 7729-7739.14.Sarkar S., Mitra S. and Bhattacharyya B., ‘Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy’, Journal of Materials Processing Technology 159 (2005) 286-294.15.Scott D., Boyina S. and Rajurkar K. P., ‘Analysis and optimization of parameter combination in wire electrical discharge machining’, Int. J. Prod. Res. 29 (11) (1991) 2189–2207.16.Shajan Kuriakose and M. S. Shunmugam, ‘Characteristics of wire-electro discharge machined Ti6A14V surface’, Materials Letters, 58 (2004) 2231-2237.17.Sivakiran S., Bhaskar Reddy and Eswara Reddy, ‘Effect of process parameters on MRR in wire electrical discharge machining of En31 steel’, Int. J. of Engineering Research and applications (IJERA), Vol. 2, No. 6, 2012, 1221-1226.18.Tarng Y. S., Ma S. C. and Chung L. K., ‘Determination of optimal cutting parameters in wire electrical discharge machining’, Int. J. Mach. Tools & Manuf. 35 (12) (1995) 1693– 1701.19. Satyanarayana.B, Ranga Janardhana.G, Kalyan.R.R and Hanumantha Rao.D, “Prediction of Optimal Cutting Parameters For High Speed Dry Turning of Inconel 718 Using Gonns”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 3, 2012, pp. 294 - 305, Published by IAEME.20 U. D. Gulhane, A. B. Dixit, P. V. Bane and G. S. Salvi, “Optimization Of Process Parameters For 316L Stainless Steel Using Taguchi Method And Anova”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 67 - 72, Published by IAEME.21. U. D. Gulhane, S. B. Mishra and P. K. Mishra, “Enhancement of Surface Roughness of 316L Stainless Steel and Ti-6al-4v Using Low Plasticity Burnishing: Doe Approach”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 1, 2012, pp. 150 - 160, Published by IAEME. 65