Optimization of Electrical Discharge Machining ofComposite 90WC-10Co Base on Taguchi Approach 427from the work piece at a controlled rate. Thus, the material is removed by a succession of electricaldischarges that, occur between the electrode and the work piece. During the EDM process, the workpiece and the electrode are submerged in dielectric fluid oil, which is an insulator that helps to controlthe arc discharge. The dielectric oil, which provides a means of flushing, is pumped through the arcgap between electrode and the work piece. This process removes suspended particles of the work pieceand the electrode form the work area. The schematic diagram of EDM is shown Fig. 1, along with theprocedure for dielectric flushing. EDM is one of the non-traditional machining techniques widely usedto manufacture harder materials for the automotive, aerospace, and surgical, moulds and dies (Ponappaet al., 2010). Therefore, the EDM technique is an essential approach for mould and die makingindustries to fabricate their products with superior performance and accuracy (Lin et al., 2009). Thismachining process produces tools with complex shapes and is extensively used in industrial settings.EDM can operate as a surface finish in the last stage of tool production (Singh et al., 2004). Tungstencarbide (WC-Co) is an important tool and die material mainly because of its high hardness, strengthand wear resistance (Mahdavinejad and Mahdavinejad, 2005). Due to its high melting point of 2870 oC,WC-Co cannot be processed easily by conventional machining techniques. The principle of the EDMprocess is based on erosion of materials by electrical sparking, and particles that are removed could besolid, liquid, or gas (Mukherjee and Ray, 2006). Currently, an insulating material can be machinedwith EDM using assisting electrode (Fukuzawa et al., 2004). Muttamara et al. (2003) studied theprobability of precision micro-machining of insulating Si3N4 ceramics by the EDM process. Copper-tungsten electrodes are important in machining small holes in the EDM process.Therefore the EDM process will open up an opportunity for the machining of tungsten carbide.Tungsten carbide is a type of cemented carbide, in which particles of carbide such as WC-Co andtitanium carbide (TiC) are bonded the process of powder metallurgy. In tungsten carbide, small cobaltparticle, approximately 1-10 µm, are used as binders (Puertas et al., 2004). Microcracks are observedon the surface of tungsten carbide work piece when they are machined with EDM. Because of theirlower melting point, cobalt particles can melt and separate away from tungsten carbide and result inmicrocracks. When the work piece is used as a mould or tool, an important consideration is the productlifespan. Singh et al. (2004) studied the effects of material removal rate (MRR), electrode wear ratio(EWR), surface roughness (SR), and diametral overcut of grade EN-31 cutting tool steel, when used asan electrode material. The experimental results showed that an increasing current could increase MRR,SR, and diametral overcut. The best electrode is copper due to its maximum MRR, minimum EWR,SR, and over-cut. Lee and Li (2001) researched the effects of electrode material in machining tungstencarbide by comparing copper, graphite, and copper tungsten electrode. The results showed that coppertungsten had the highest MRR and the lowest EWR. In an EDM operation, optimizing sparking performance requires the use of correct parameters.However, choosing the correct parameters often calls experience, an instruction manual or a largenumber of experiments that can consume both material and time. The Taguchi method solves thisproblem by using specially designed orthogonal arrays. The process parameters can be studied with aminimum number of experiments (Wang et al., 2000). Recently, the Taguchi method was widelyemployed in several industrial field and research applications. Mahapatra and Patnaik (2006) used thismethod to optimize the process parameters of wire electrical discharge machining (WEDM). Marafonaand Araujo (2009) used this method to study the influence of work piece hardness on EDMperformance. Their results show that the work piece hardness and its interaction influence the MRRand the SR of the work piece. Prihandana et al. (2009) studied the effect of micro-powder suspensionand ultrasonic vibration of dielectric fluid in micro-EDM process, while. Sundaram et al. (2008)studied the process parameters of ultrasonic assisted micro-EDM using the Taguchi approach as well.Tzeng and Chen (2007) reported the application of fuzzy logic analysis coupled with Taguchi methodto optimize the precision and accuracy of the high-speed EDM process. Gaitonde et al. (2008)presented the application of the Taguchi optimization method for simultaneously minimizing burrheight and burr thickness with respect to the influence of cutting drill and geometry. Kao et al. (2009)
428 Pichai Janmanee and Apiwat Muttamaraoptimized the EDM parameters with multiple quality characteristics on machining Ti-6Al-4V based onthe Taguchi method. Lin et al. (2009) showed grey relational analysis is more straight forward than thefuzzy Taguchi method for optimizing the EDM process with multiple process responses. The objective of this research was to use the Taguchi method to study the performance of theEDM process on machining tungsten carbide. The most important performance measures in EDM werematerial removal rate (MRR), electrode wear ratio (EWR) and microcrack density (Cr.S.Dn) on thework piece surface. Figure 1: The schematic diagram of EDM2. Experimental Methods2.1. Experimental MaterialsTungsten carbide was selected as the work piece for this research. The sample had 10% cobalt with90% tungsten carbide and was bought from United Tungsten Co., Ltd. Tungsten carbide is a class ofhard material composite. It is widely used as a tool material in a variety of applications where thedemands on hardness and toughness are high. The essential properties of the work piece material areshown in Table 1. The work piece had a diameter of 25 mm and thickness of 20 mm. The graphiteelectrode (EDM-3) purchased from Poco Graphite (Thailand) Co., Ltd. was made from powdersproduced by the semi-sintering process. The electrode was 3 mm in diameter and 50 mm in length, andit was held on the spindle chuck of the EDM machine. Table 2 shows the essential properties of theelectrodes. The dielectric oil used in this investigation was Shell EDM Fluid 2A from Shell Co., Ltd.(Thailand).Table 1: Essential properties of tungsten carbide Essential properties Description Melting point (oC) 2,870 Density (g/cm3) 15.7 Thermal expansion (oC) 5x10-6 Hardness (HRA) 87.4 Elastic modulus (Gpa) 648 Electrical resistivity ( cm) ・ 17×10-6 Thermal conductivity (W/mK) 63Table 2: Essential properties of graphite electrodes (EDM-3) Essential properties Description Melting point (oC) 3,350 Density (g/cm3) 1.81 Average particle size (µm) <5 Electrical resistivity ( cm) ・ 1.491×101
Optimization of Electrical Discharge Machining ofComposite 90WC-10Co Base on Taguchi Approach 429Table 2: Essential properties of graphite electrodes (EDM-3) - continued Flexural strength (kg/cm2) 950 Compressive strength (kg/cm2) 1,5002.2. Experimental ProceduresThe experiments were performed on a numerical control model EDM-FORM-2-LC manufactured byCharmilles Technologies Corporation. A negative polarity electrode with depth of cut of 3 mm wasused. The machining parameters such as MRR (mm3/min), EWR (mm3/min), and Cr.S.Dn were variedto determine the most important parameters that could affect performance characteristic. The MRR ofthe work piece was measured by dividing the weight of the work piece before and after machining bythe machining time. The EWR in this study was defined by the ratio of the electrode weight to thework piece weight and expressed as a percentage. Similar procedures for measuring the weight of thework piece have been used to determine the weight of the electrode before and after machining(Tomadi et al., 2009). Microcrack density on finished surfaces of work piece in the EDM process is animportant measurement of defects in the material (Lee and Li, 2003). The microcrack density on the work piece surface can be measured by (O’Brien et al., 2003):(1) number of microcrack per area, or numerical crack density per area, Cr.Dn (no. of crack/mm2): (2)total length of microcrack per area, or surface crack density, Cr.S.Dn (µm/mm2); and (3) mean cracklength, Cr.Le (µm). In this research, measuring technique 2 was selected because the work piececontained cracks of various widths. The unit of measurement was µm/0.05 mm2. The values of visuallymeasured microcrack width multiplied by the weight factor are shown in Table 3. For this experiment,the EDM process parameters studied were as follows polarity, on time, off time, open-circuit voltage,discharge current and electrode material. The detailed experimental conditions used in thisinvestigation are shown in Table 4. Finally, the optimal EDM parameters of material removal rate,electrode wear ratio, and microcrack density were determined by the Taguchi method.Table 3: Weight factor of width of microcracks (Cr.S.Dn) measurement Width (µm) Weight factor(x) Less than 3.23 1 3.23-6.45 2 6.45-9.68 3 9.68-12.90 4 12.90-16.13 5Table 4: Experimental conditions Working conditions Descriptions Work piece 90WC-10Co Electrode EDM-3 Polarity Nagative (-) On-time 25 µs Off-time 2,510,1600 µs Open circuit voltage 90,150,250 V Discharge current 1.5,38,75 A Dielectric fluid Oil (Shell fluid 2A)2.3. Procedure for the Taguchi ApproachThe Taguchi method is statistical method developed by Genichi Taguchi to improve the quality ofmanufactured goods. More recently has been applied to the field of (Rosa et al., 2009) engineering,biotechnology, marketing and advertising (Sreenivas et al., 2004). The method consists of a plan toacquire data from experiments in a controlled way, and to obtain information about the behaviour of a
430 Pichai Janmanee and Apiwat Muttamaragiven process (Ponappa et al., 2010). There are three characteristics of the Taguchi methodology:smaller-the-better, larger-the-better, and nominal-the-best. In general the Taguchi method provides asignificant reduction in the size of experiments with considerable savings in time and cost, therebyacclerating the experimental process (Sundaram et al., 2008; Lajis et al., 2009). Fig. 2 shows theTaguchi method applied to the experimental procedures step. In this research, the Taguchi method wasused to determine optimal machining to parameters maximize MRR and minimize EWR, as well asCr.S.Dn in the EDM process. The method uses orthogonal arrays (OA) and calculates signal-to-noise(S/N) ratios. In the L9 (33) orthogonal array design, three columns and nine rows set up three individuallevels. The first column was assigned to the discharge current (A), the second column to off-time (B),and the third column to open-circuit voltage (C). In addition to the S/N ratio, a statistical analysis ofvariance (ANOVA) was also employed to indicate the impact of process parameters. To calculate theS/N ratio, the HB value for “the higher the better” and LB value for “the lower the better” were firstdetermined by equations (1), (2), and (3): 1 n 1 HB = ∑ 2 n i =1 y MRR (1) 1 n 1 LB = ∑ 2 n i =1 y EWR (2) n 1 1 LB = ∑ y2 n i =1 Cr .S . Dn (3)where y MRR , y EWR and yCr .S . Dn are material removal rate, electrode wear ratio and surface crackdensity, respectively. n is the number of experiments in the trial, beginning with the ith experiment.The S/N ratio can then be calculated as a logarithmic transformation of the loss function, as shown inequations (4), (5), and (6): S N ratio for MRR = −10 log ( HB ) (4) 10 S N ratio for EWR = −10 log10 ( LB ) (5) S N ratio for Cr.S.Dn = −10 log 10 ( LB ) (6) Figure 2: Taguchi method of procedure step Table 5 shows the experimental values of the Taguchi approach on EDM machining controlparameters and the levels of machining parameters according to the S/N ratio.
Optimization of Electrical Discharge Machining ofComposite 90WC-10Co Base on Taguchi Approach 431Table 5: Machining parameter of tungsten carbide Levels Symbol Control parameters Observed values I II III A Discharge current (A) 1.5 38 75 MRR (mm3/min) B Off time (µs) 2 510 1600 EWR (%) C Open circuit voltage (V) 90 150 250 Cr.S.Dn (µm/mm2)3. Results and DiscussionsThe experimental results of each set of input parameters in the L9 orthogonal array are given in Table6. The table also contains a detailed list of MRR, EWR and Cr.S.Dn correlated with each experimentalmeasurement of the EDM process on the composite WC-Co. Data analysis was done using theMINITAB software, version 14.Table 6: Experimental results of L9 orthogonal array Parameters Response Order A B C MRR EWR Cr.S.Dn 1 1 1 1 0.163 455.217 346.13 2 1 2 2 0.084 365.667 183.87 3 1 3 3 0.125 275.550 460.97 4 2 1 2 0.540 355.556 954.84 5 2 2 1 0.281 287.143 1119.36 6 2 3 3 0.238 237.143 885.48 7 3 1 3 2.731 276.460 1459.68 8 3 2 1 1.730 87.360 1024.84 9 3 3 2 1.472 37.234 1056.453.1. Analysis of MRRFor the S/N ratio of MRR with larger-the-better algorithm, the results showed that discharge current(A) had an effect on MRR. The experimental data analysed by ANOVA showed that discharge currenthad an effect on MRR as well, at the 95% confidence level. Tables 7, 8 and Fig. 3 show a list of thecorresponding ANOVA results, where the contribution of each parameter is calculated. For the relationbetween discharge current and MRR of work piece were found that an increased current have influenceto increasing MRR. That means, though a higher current causes more removal work piece material.The optimal parameters for maximum MRR, as predicted by the MRR results were as follow:discharge current of 75 A, on-time of 2 µs, and open-circuit voltage of 250 V. These values werechosen because mean of the predicted values were similar to the experimental values of 2.531 and2.731, as shown in Table 6.3.2. Analysis of EWRTables 9 and 10 show the orthogonal array based on experimental results of electrode EWR and theircorresponding S/N ratio. The analysis of EWR with smaller-the-better algorithm revealed thatdischarge current (A) and off-time (B) had an influence on EWR. Fig. 4 shows the main effect of EWRof each factor for various level condition. According to Fig. 4, the EWR decreases with the two majorparameters, A and B. Moreover, to observed that mean the machining voltage (negative polarity),maximum discharge current, and off-time may imply a smaller EWR (Lajis et al., 2009). Therefore, theANOVA results indicated that discharge current (A) significantly affected EWR and also off-time, atthe 95% confidence level. P-value of off-time (B) was 0.082 close to therefore the off-timefactor was shown to be a risk factor to EWR as well. Since P-values of factors A and B were less than0.05, they had a statistically significant effect on MRR at the 95% confidence level.
432 Pichai Janmanee and Apiwat MuttamaraTable 7: S/N ratio of MRR MRR Factors I II III Delta A -18.444 -9.615 5.615 24.059 B -4.127 -9.260 -9.057 5.132 C -7.821 -7.836 -6.787 1.049Table 8: ANOVA of MRR Source Df SS MS F P A 2 6.1281 3.0640 28.21 0.034 B 2 0.4908 0.2454 2.26 0.307 C 2 0.2330 0.1165 1.07 0.483 Error 2 0.2172 0.1086 Total 8 7.0691 Figure 3: Main effect plot of MRR Main Effects Plot for MRR Fitted Means A B 2.0 1.5 1.0 0.5 0.0 Mean 1 2 3 1 2 3 C 2.0 1.5 1.0 0.5 0.0 1 2 3Table 9: S/N ratio of EWR EWR Factors I II III Delta A -51.08 -49.23 -39.69 11.38 B -51.01 -46.42 -42.57 8.43 C -46.50 -44.57 -48.93 4.37Table 10: ANOVA of EWR Source Df SS MS F P A 2 84411 42206 19.22 0.049 B 2 49483 24741 11.27 0.082 C 2 1166 583 0.27 0.790 Error 2 4392 2196 Total 8 139452
Optimization of Electrical Discharge Machining ofComposite 90WC-10Co Base on Taguchi Approach 433 Figure 4: Main effect plot of EWR Main Effects Plot for EWR Fitted Means A B 350 300 250 200 150 Mean 1 2 3 1 2 3 C 350 300 250 200 150 1 2 33.3. Analysis of Cr.S.DnFig. 5 show the main effects of Cr.S.Dn of each factor for various level condition. According to thisfigure the Cr.S.Dn increases with high value of discharge current, off-time and open-circuit voltage.However, the results from the experimental study indicate that when the higher value of processparameters, had a significant influence on Cr.S.Dn. Because of more electrical energy and thermal intothe machining zone. The analysis of S/N ratio of Cr.S.Dn with smaller-the-better algorithm andANOVA revealed that discharge current (A) and open-circuit voltage (C) had a significant influence onCr.S.Dn. Since P-values of factor A, B and C were less than 0.05, these factors had a statisticallysignificant effect on Cr.S.Dn as well, at the 95% confidence level. This is shown in Tables 11, and 12.Table 11: S/N ratio of Cr.S.Dn Cr.S.Dn Factors I II III Delta A -49.78 -59.84 -61.33 11.54 B -57.89 -55.49 -57.56 2.40 C -56.65 -55.12 -59.18 4.06Table 12: ANOVA of Cr.S.Dn Source Df SS MS F P A 2 1190691 595345 740.61 0.001 B 2 35634 17817 22.16 0.043 C 2 147944 73972 92.02 0.011 Error 2 1608 804 Total 8 1375877Table 13: Results of the confirmation experiments Optimal parameters Optimal parameters Optimal parameters ofDetails of MRR of EWR Cr.S.Dn Prediction Experimental Prediction Experimental Prediction ExperimentalLevel A3 B1 C3 A3 B1 C3 A3 B3 C2 A3 B3 C2 A1 B2 C2 A1 B2 C2Mean 2.53156 2.731 41.5171 37.234 173.262 183.87
434 Pichai Janmanee and Apiwat Muttamara Figure 5: Main effect plot of Cr.S.Dn Main Effects Plot for CrSDN Fitted Means A B 1200 1000 800 600 400 Mean 1 2 3 1 2 3 C 1200 1000 800 600 400 1 2 34. Confirmation ExperimentsTo verify the improvement of the observed the optimal combination of the machining parameters wereused to perform confirmation experiments (Mahaparata et al., 2006). The estimated S/N ratios werecalculated by equation (7), n0 η = η m + ∑ (η i − η m ) ˆ i =1 (7) ˆwhere η is the estimated S/N ratios for optimal combinations of machining parameters, η m is the totalmean S/N ratio, η 0 is the number of significant parameters, and η i is the mean S/N ratios at theoptimal level (Lin et al., 2009). The results of the confirmation experiments are shown in Table 13.The experiment performed at the A3 B1 C3 level of parameters showed that the maximum MRRincreased from 2.531 mm3/min to 2.731 mm3/min. The experiment performed at the A3 B3 C2 level ofparameters showed that the minimum EWR decreased from 41.517 % to 37.234 %. The experimentperformed at A1 B2 C2 level of parameters showed the minimum Cr.S.Dn increased from 173.262µm/mm2 to 183.870 µm/mm2. In addition, the SEM micrograph in Fig. 7 shows the Cr.S.Dn of theEDM surface with the orthogonal array parameter A1B2 C2 (a) as the best parameters with microcrackdensity per area of 183.870 µm/mm2. The array parameter A3B1C3 (b) was poor with a microcrackdensity per area of 1459.68 µm/mm2.Figure 6: SEM micrographs of Cr.S.Dn on surface EDM a) the best parameters condition : A1 B2 C2, b) the poor parameters condition : A3 B1 C3 a) A1 B2 C2 b) A3 B1 C3
Optimization of Electrical Discharge Machining ofComposite 90WC-10Co Base on Taguchi Approach 4355. ConclusionsThis study investigated the optimization of EDM machining parameters on the MRR, EWR andCr.S.Dn in tungsten carbide (90WC-10Co) work pieces. A 3 mm diameter, EDM-3 grade graphiteelectrode with dielectric oil Shell EDM Fluid 2A was used for machining. Experimental results showedthat: • The maximum MRR was obtained at discharge current of 75 A, an off-time of 2 µs, and an open-circuit voltage of 250 V. • The minimum EWR, was obtained at a discharge current of 75 A, an off-time of 1600 µs, and open-circuit voltage of 150 V. • The minimum Cr.S.Dn, was obtained at a discharge current of 75 A, an off-time of 510 µs, and open-circuit voltage of 150 V. • The Taguchi method was used to significantly reduce the size of experiments. Confirmation experiments verified the optimal EDM machining parameters obtained from the experimental results.AcknowledgementThe authors are grateful to the Thailand Research Fund, Office of the Higher Education Commissionand the National Research Council of Thailand for their funding support. The authors would like tothank the National Metal and Materials Technology Centre (MTEC) for its kind support in supplyingmaterials and equipments for analysis.References Beri, N., Maheshwari, S., Sharma, C., Kumar, A., 2008. Performance Evaluation of Powder Metallurgy Electrode in Electrical Discharge Machining of AISI D2 Steel Using Taguchi Method. International Journal of Aerospace and Mechanical Engineering 2 (3), pp.167-171. Fukuzawa, Y., Mohri, N., Tani, T., Muttamara, A., 2004. Electrical Discharge Machining Properties of Noble Crystals. Journal of Materials Processing Technology 149 (1-3), pp. 393- 397. Gaitonde, V.N., Karnik, S.R., Achyutha, B.T., Siddeswarappa, B., 2008. Taguchi Optimization in Drilling of AISI 316L Stainless Steel to Minimize Burr Size Using Multi-Performance Objective Based on Membership Function. Journal of Materials Processing Technology 202 (1-3), pp. 374-379. Kao, J.Y., Tsao, C.C., Wang, S.S., Hsu, C.Y., 2009. Optimization of the EDM Parameters on Machining Ti–6Al–4V With Multiple Quality Characteristics. The International Journal of Advanced Manufacturing Technology 47, pp. 395-402. Lajis, M.A., Radzi, H.C.D.M., Amin, A.K.M.N., 2009. The Implementation of Taguchi Method Process of Tungsten Carbide. European Journal of Scienctific Research. 26 (4), pp. 609-617. Lee, S.H., Li, X.P., 2001. Study of the Effect of Machining Parameters on the Machining Characteristics in Electrical Discharge Machining of Tungsten Carbide. Journal of Materials Processing Technology 115(3), pp. 344-358. Lee, S.H., Li, X.P., 2003. Study of the Surface Integrity of the Machined Workpiece in the EDM of Tungsten Carbide. Journal of Materials Processing Technology 139 (1-3), pp. 315- 321. Lin, Y.C., Chen, F.C., Wang, D.A., Lee, H.S., 2009. Optimization of Machining Parameters in Magnetic Force Assisted EDM Based on Taguchi Method. Journal of Materials Processing Technology 209 (7), pp. 3374-3383.
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