PARAMETRIC OPTIMIZATION OF NEAR DRY ELECTRICAL DISCHARGE MACHINING PROCESS FOR AISI SAE D-2 TOOL STEEL

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1 INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 6, Issue 1, January (2015), pp IAEME: IJARET.asp Journal Impact Factor (2015): (Calculated by GISI) IJARET I A E M E PARAMETRIC OPTIMIZATION OF NEAR DRY ELECTRICAL DISCHARGE MACHINING PROCESS FOR AISI SAE D-2 TOOL STEEL Mane S.G. 1, Hargude N.V. 2 1,2 Department of Mechanical Engineering, PVPIT Budhgaon, Sangli ,Maharashtra, India. ABSTRACT Present dissertation work has attempted to optimize the various significant process parameters for near dry EDM process by Taguchi method and design of experiments. The response variables are material removal rate (MRR), the surface roughness (SR) and tool wear rate (TWR). A low cost mist delivery system (MQL fluid dispenser) to supply the mist (liquid-gas mixture) at a controlled rate has been developed to conduct the experiments and has served it s purpose exceptionally well during the experimentation. The AISI SAE D-2 tool steel has been used as a work-piece material. The kerosene air mixture has been used as a dielectric medium. The various process parameters selected for the study were discharge current, gap voltage, pulse on time, duty factor, air pressure and electrode material. A standard L 18 orthogonal array was selected for design of experiments. The results obtained from the experimental runs were analyzed by using Minitab15 software. ANOVA for S/N ratios was done to find the most contributing process parameters affecting the MRR, TWR and SR. The best parametric settings for each of the maximum MRR, minimum TWR and minimum SR were determined with the help of ANOVA. The corresponding values of the response parameters were also calculated using mathematical formulae and confirmed by performing validation experimentation. From the present experimental study, it is observed that MRR, TWR and SR in near dry EDM process are mainly affected by the discharge current and electrode material. Copper-tungsten electrode material exhibited lower SR and low TWR than that of the copper electrode but higher MRR was obtained with copper electrode. Keywords: Near dry EDM, Design of experiments, Taguchi method, ANOVA, MRR, SR, TWR. 99

2 1. INTRODUCTION The metal working fluids (MWFs) are extensively used in conventional machining processes. The economical, ecological and health impacts of metal working fluids (MWFs) can be reduced by using minimum quantity lubrication referred to as near dry machining. In near dry machining (NDM), an air-oil mixture called an aerosol is fed onto the machining zone [10]. This concept of near dry machining can be well applied in EDM process, the process being referred to as near-dry EDM process. Advantages of near-dry EDM were identified as a stable machining process at low discharge energy input because the presence of liquid phase in the gas environment changes the electric field, making discharge easier to initiate and thus creating a larger gap distance. In addition, good machined surface integrity without debris reattachment that occurred in dry EDM was attained since the liquid in the dielectric fluid enhances debris flushing. Other potential advantages of neardry EDM are a broad selection of gases and liquids and flexibility to adjust the concentration of the liquid in gas. The dielectric properties can thus be tailored in near-dry EDM to meet various machining needs, such as high MRR or fine surface finish. Also Near dry EDM shows advantages over the dry EDM in higher material removal rate (MRR), sharp cutting edge, less debris deposition and better surface finish. Compared to wet EDM, near dry EDM has higher material removal rate at low discharge energy and generates a smaller gap distance [2]. A comparative study of wet, dry and near dry EDM has been tabulated in Table 1. But the technical barrier in near-dry EDM lies in the selection of proper dielectric medium and process parameters. From the review of literature it is seen that experimental investigations have been carried out in order to study the effect of various input parameters like discharge current, gap voltage, pulse on time, gas pressure, fluid flow rate, electrode orientation and spindle speed on material removal rate, surface roughness and tool wear rate and to improve the performance of near dry EDM process [1-6, 16]. Table1. Comparison of wet, dry and near dry EDM processes Sr. No Aspect Wet EDM Dry EDM Near Dry EDM 1 Dielectric medium Liquids Gases Liquid-gas mixture 2 Dielectric used Hydrocarbon based oils, Kerosene, EDM oil,deionized water Air, Oxygen gas, Argon gas, Nitrogen gas, Helium gas etc Water-air, Wateroxygen, Keroseneair, Kerosenenitrogen mixtures etc. 3 Dielectric consumption Heavy ---- Very less 4 Pollution problem Major problem Odor of burning Very less 5 Fire Hazard Highly flammable oilsmore fire hazard No fire hazard No fire hazard 6 Energy input High Low Low 7 Process stability Good Poor (arching problem) Good 8 Debris reattachment No Major problem No 9 Discharge Initiation Difficult Easier 10 Dielectric properties Can t be tailored Can be tailored Can be tailored 11 Gap Distance more Less 12 Surface finish Good poor Good 13 Surface Integrity Good poor Good 14 MRR Lower Higher Higher 100

3 International Journal of Advanced anced Research in Engineering and Technology (IJARET), ISSN (Print), ISSN (Online), Volume 6, Issue 1, January (2015), pp IAEME However, irrespective of its inherent advantages over wet and dry EDM processes, not much attention has been given towards the parametric optimization of the near-dry near dry EDM process. It is necessary to optimize the input parameters for maximum material removal removal rate (MRR) and minimize the surface roughness (SR) to make the near dry EDM process cost effective and economically viable one. In the present study, the best parametric settings for each of the maximum MRR, minimum TWR and minimum SR have been determined determined with the help of ANOVA and S/N ratios. 2. EXPERIMENTAL SET UP The experimentation was carried out on the Electronica make smart ZNC sinker EDM. A mist delivery system (MQL fluid dispenser) developed was used to supply the mist (kerosene-air (kerosene mixture) at a controlled rate to the gap between work-piece work & electrode.. A perfect mist with a smaller quantity of liquid was formed and a very sharp and fine spray of the mist was achieved and it became possible to machine the components using very small fluid flow rate of 4 ml/min. Hence the idea of minimum quantity lubrication (MQL) could be implemented,, consuming very small amount of dielectric fluid (kerosene) and giving justice to the name of the process and making the process near-dry in a true sense. The experimental exper setup shown in Figure 1 shows the mist delivery system developed for experimentation. The responses selected for experimentation were material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Response characteristics are given in i the Table 2. Rotating tool Arrangement Spray gun Electrode Sparking Work piece Fig.1. Experimental Setup for NearNear Dry EDM and sparking achieved Table 2. Response Characteristics Response Response name Unit type the Material Removal Rate Larger gm/min (MRR) better the Tool Wear Rate (TWR) Smaller gm/min better the Surface Roughness Smaller Ra value in (SR) better microns 2.1. Selection of the process parameters and their levels The process parameters and their levels given in Table able 3 were selected based on extensive literature survey and considering the range limitation of EDM machine [12, 14, 15, and 17]. 17] Three levels for each of the parameters B, C, D, E and F are selected because because the non-linear non behavior of process parameters can only be studied if more than two levels of a parameter are used [18]. Also 101

4 some of the constant parameters and their values or conditions selected for the experimentation are tabulated in Table 4[12, 14, 15, and 17]. Table 3. Process parameters under study and their levels Factors Levels Level 1 Level 2 Level 3 Electrode Copper- Copper Air pressure kg/cm Discharge Amps Gap voltage volts Pulse on time µs Duty factor % Table 4.Constant parameters and their values / conditions for experimentation Parameters Value/Condition Work-piece material AISI SAE D2 tool steel Work-piece size 50 mm * 50 mm * 6 mm Tool electrode diameter 15 mm Tool electrode Rotating Dielectric medium Kerosene-Air mixture Fluid flow rate 4 ml/min Polarity Straight (Electrode ve, work-piece Machining time 20 min. 2.2 Selection of the orthogonal array In the present experiment, the L 18 orthogonal array meets the requirements of experiment as it is a smallest mixed 2-level and 3-level array [18]. The experimentation was carried out as per the L 18 orthogonal array given in Table 5. Expt. No. Electrode material Table 5 Design of Experiments L 18 ( ) array Air pressure kg/cm 2 Discharge current Amps 102 Gap voltage volts Pulse on time Duty Factor 01 Cu W Cu W Cu W Cu W Cu W Cu W Cu W Cu W Cu W Cu Cu Cu Cu Cu Cu Cu Cu Cu

5 2.3 Experimental procedure Copper-Tungsten and copper as the tool electrode materials and kerosene-air mixture as the dielectric medium were used for conducting the experiments [12]. A constant fluid flow rate of 4 ml/min for kerosene was maintained throughout the experimentation. The straight polarity (Electrode ve, work-piece +ve) was maintained during the experimentation [2]. A rotating tool arrangement was used to keep the electrode rotating at a constant speed during machining [12]. The various process parameters and their levels shown in table 5 were set while conducting each of the experimental run. Total eighteen no. of experimental runs each of 20 min duration were carried out as per the design matrix. 3. MEASUREMENT OF RESPONSE PARAMETERS 3.1 Measurement of MRR MRR of each sample is calculated from weight difference of work piece before and after the performance trial, which is given by: = ( ) / (Equation...1) Where Wi = Initial weight of work piece material (gm) Wf = Final weight of work piece material (gm) t = Time period of trail in minutes The weights of the work-pieces before and after machining for calculation of MRR were measured using a weighing machine of Contech model CA Measurement of TWR TWR of each sample is calculated from weight difference of tool electrode before and after the performance trial, which is given by: = ( ) / (Equation...2) Where Ti = Initial weight of tool electrode (gm) T f = Final weight of tool electrode (gm) t = Time period of trail in minutes The weights of the electrodes before and after machining for calculation of TWR were measured using a weighing machine of Contech model CA Measurement of SR Surface roughness was measured using the Surf Test model SJ210 of Mitutoyo, Japan. Surface roughness of each sample was measured at three different locations of machined area and a mean is taken. 3.4 Experimental Results and S/N ratios The experimental results for material removal rate, tool wear rate and surface roughness by varying the selected control parameters as per L 18 orthogonal array are shown in Table 6. The S/N ratios worked out by using MINITAB 15 software are also tabulated in Table

6 Expt. No. MRR gm/min Table 6. Results for MRR, TWR and SR TWR gm/min SR Ra SN Ratio of MRR SN Ratio of TWR SN Ratio of SR RESULTS AND DISCUSSION All observations are transformed into S/N ratio and results for S/N ratios of have been analyzed by ANOVA method to find the significance of various control parameters and their best level. The analysis and graphical presentations have been made using MINITAB 15 software. The most significant parameters affecting the selected response variable and their best level value are determined. The optimal design for each of the response parameter has been decided and confirmed by conducting a confirmation test. 4.1 Analysis of Variance (ANOVA) for S/N ratios of MRR The S/N ratio consolidates several repetitions into one value and is an indication of the amount of variation present. The S/N ratios have been calculated to identify the major contributing factors that cause variation in the MRR. MRR is Larger is better type response which is given by: (S/N) LB = - 10 log (MSD) LB (Equation.3) Where ( ) = (Equation..4) (MSD) LB = Mean Square Deviation for Larger-the-better response. where, y is value of response variable and n is number of observations in the experiments. Table 7 shows the ANOVA results for S/N ratio of MRR at 99 % confidence interval. Discharge current was observed to be the most significant factor affecting the MRR, followed by electrode material and gap voltage according to F test. All the remaining parameters namely, pulse on time, duty factor and air pressure are insignificant to affect the MRR. 104

7 International Journal of Advancedd Research in Engineering and Technology (IJARET), ISSN 0976 The percentage contribution of each of the control parameter can following formula: % contribution of control factor= [SS (Respective Factor)/SS (Total)] *100 be calculated by the For example, for discharge current, (Equation.5) % contribution = [375.61/ ]* 100 = % Table 7.Analysis of Variance for S/N ratios of MRR Source DF Seq SS Adj SS Adj MS F P Contribution % Remark Electrode S Air pressure NS Discharge S Gap voltage S Pulse on Time NS Duty Factor NS Residual Error Total S = R-Sq = 96.8% R-Sq(adj) = 90.8% S: Significant factor; NS: Non- significant factor Percentage contribution of control parameters for MRR Air pressure 6.95 Error 3.24 Discharge current Electrode material Pulse on Time 9.11 Duty Factor Gap voltage Discharge current Electrode material 20.8 Gap voltage Duty Factor Pulse on Time Air pressure Errorr Fig. 2.Percentage contribution of control parameters for MRR The percentage contribution of each of the control parameters under study for MRR is shown by a pie chart in Figure 2. It can be seen that discharge current contributes significantly (30.46 %), followed by electrode material (20.80 %) and gap voltage (18.66 %). S/N ratio values of MRR are used to calculate mean of S/N ratios at three levels of all factors and are given in Table 8. It gives us rank of all factors in this study considering the mean of S/N ratios for MRRR at different levels in terms their relative significance. Current has the highest rank signifying highest contribution to 105

8 MRR, followed gap voltage and electrode material. Air pressure has the lowest rank. Duty factor and pulse on time were observed to be insignificant in affecting MRR. Table 8.Response Table for Signal to Noise Ratios of MRR Level Electrode Discharge Pulse on Air Pr. Gap voltage material current time Duty factor Delta Rank Main Effects Plot for SN ratios Data Means -21 Electrode mtl. air pr. dis. cu Mean of SN ratios cuw cu gap vol pulse on time duty factor Signal-to-noise: Larger is better Fig. 3.Main effects plot for S/N ratios of MRR Main effects plot for S/N ratios of MRR is shown in the Figure 3. The graph shows that with increase in discharge current, S/N ratio increases. The S/N ratio increases with an increase in pulse on time and air pressure as well. As can be observed from the graph, S/N ratio decreases slightly with an increase in gap voltage from 40 V to 60 V. However, a steep increase in S/N ratio can be observed from a gap voltage of 60V to 80V. Further it can be observed that S/N ratio reduces with an increase in duty factor. Lastly, it can be observed that out of the two electrode materials, Copper electrode has larger S/N ratio compared to Copper-Tungsten electrode. For optimizing a product or process design, S/N ratio is used because additivity of factor effects is good when an appropriate S/N ratio is used. Otherwise, large interactions among the control factors may occur resulting in high cost of experimentation and potentially unreliable results. In optimization, we use S/N ratio as the objective function to be maximized[18]. To conclude the discussion, for maximum MRR, the level value with higher S/N ratio of each of the control parameter under study should be selected at this stage. Thus, with high discharge current of 16A, high pulse on time of 200 µs, high gap voltage of 80 V, low duty factor of 7, higher air pressure of 6 kg/cm 2 and copper electrode should be selected. Thus, it can be concluded that the optimum combination for MRR is A2 B3 C3 D3 E3 F1. 106

9 After evaluating the optimal parameter settings, the next step of the Taguchi approach is to predict and verify the enhancement of quality characteristics using the optimal parametric combination, which is not available in L 18 array under study. Hence theoretical optimum value of MRR has to be calculated. The estimated S/N ratio using the optimal level of the design parameters can be calculated. The optimal value of S/N ratio is given by the formula n opt =n m + a i=1 (n i - n m ) (Equation..6) where n m is the total mean S/N ratio, n i is the mean S/N ratio at optimum level and a is the number of main design parameters that effect quality characteristic. Based on the above equation the estimated multi-response signal to noise ratio can be obtained. n opt = ( ) + ( ) + ( ) + ( ) + ( ) + ( ) n opt = Optimal value of S/N ratio = The corresponding value of MRR is given by the formula = ƞ Thus, y 2 = y opt = gm/min (Equation..7) A confirmation experiment is performed by setting the control parameters as per the optimum levels achieved. The experimental result obtained for the MRR is gm /min. Thus, the experimental value agrees reasonably well with prediction. The maximum deviation of predicted result from experimental result is about 2.14 %. Hence, the experimental result confirms the optimization of MRR using Taguchi method and the resulting model seems to be capable of predicting MRR. 4.2 Analysis of Variance (ANOVA) for S/N ratios of TWR The S/N ratios have been calculated to identify the major contributing factors that cause variation in the TWR. TWR is Smaller is better type response which is given by: (S/N) SB = - 10 log (MSD) SB (Equation...8) Where ( ) = ( ) (Equation....9) (MSD) SB = Mean Square Deviation for smaller-the-better response. where, y is value of response variable and n is number of observations in the experiments. Table 9 shows the ANOVA results for S/N ratio of TWR at 94 % confidence interval. Electrode material was observed to be the most significant factor affecting the TWR, followed by discharge current and gap voltage according to F test. All the remaining parameters namely, pulse on time, duty factor and air pressure are insignificant to affect the TWR. The percentage contribution of each of the control parameters under study for TWR is shown by a pie chart in Figure 4. It can be seen that electrode material contributes significantly (47.7%), followed by discharge current (17.68 %) and gap voltage (13.07%). S/N ratio values of TWR are used to calculate mean of S/N ratios at three levels of all factors and are given in Table 10. It gives us rank of all factors in this study considering the mean of S/N ratios for TWR at different levels in terms their relative significance. 107

10 International Journal of Advancedd Research in Engineering and Technology (IJARET), ISSN 0976 Table 9. Analysis of Variance for S/N ratios of TWR Source DF Seq SS Adj SS Adj Contribution F P MS % Electrode Air pr Discharge Gap voltage Pulse on Duty Factor Residual Error Total S = R-Sq = 91.8% R-Sq(adj) = 76.8% S: Significant factor; NS: Non- significant factor Remark S NS S S NS NS Percentage contribution of control parameters for TWR Pulse on Time 6.76 Error 8.19 Gap voltage Discharge current Air Duty pressure Factor Electrode material 47.7 Electrode material Discharge current Gap voltage Error Pulse on Time Air pressure Duty Factor Fig. 4. Percentage contribution of control parameters for TWR Table 10. Response Table for S/Noise Ratios of TWR Level Electrode Air Discharge Gap Pulse on material Pr. current voltage time Delta Rank Duty factor Electrode material has the highest rank signifying highest contributionn to TWR, followed by discharge current and gap voltage. Duty factor has the lowest rank. Pulse on time and air pressure were observed to be insignificant in affecting TWR. Main effects plot for S/N ratios of TWR is shown in the Figure 5. The graph shows that with increase in discharge current from 8A to 12A, S/N ratio decreases. However as the discharge current increases from 12A to 16A, S/N ratio go on increasing. The S/N ratio decreases with an increase in pulse on time from 100 πs to 150 πs. Further as the air pressure is increased S/N ratio decreases initially from 4 kg/cm2 to 5 kg/cm2 air pressure and shows an increasing trend as the air pressure 108

11 increases to 6 kg/cm2. It can be seen that, as the gap voltage increases from 40 V to 60 V, S/N ratio increases and shows a decreasing trend further as the gap voltage increases to 80 V. Also as the duty factor increases from 7 to 9, S/N ratio increases with a slight decrease thereafter as the duty factor increases to 11. It can be also observed that, Copper-Tungsten electrode has larger S/N ratio compared to Copper electrode. Main Effects Plot for SN ratios Data Means 66.0 Electrode matl Air Pr Disc. Currrent Mean of SN ratios CuW Cu Gap Voltage Pulse on Time Duty Factor Signal-to-noise: Smaller is better Fig. 5 Main effects plot for S/N Ratios of TWR To conclude the discussion, for minimum TWR, the level value with higher S/N ratio of each of the control parameter under study should be selected at this stage. Thus, a low discharge current of 8A, low pulse on time of 100 µs, moderate gap voltage of 60 V, moderate duty factor of 9, low air pressure of 4 kg/cm2 and copper-tungsten electrode material should be selected. Thus, it can be concluded that the optimum combination for TWR is A1 B1 C1 D2 E1 F2. This optimal parametric combination is not available in L 18 array under study. Hence theoretical optimum value of TWR has to be calculated. By using the equation 6 from section 4.1, the estimated multi-response signal to noise ratio can be obtained. n opt = ( ) + ( ) + ( ) + ( ) + ( ) + ( ) n opt = Optimal value of S/N ratio = The corresponding value of TWR is given by the formula ƞ =10 (Equation 10) Thus, y 2 = * y opt = gm/min A confirmation experiment is performed by setting the control parameters as per the optimum levels achieved. The experimental result obtained for the TWR is gm /min. Thus, the experimental value agrees reasonably well with prediction. The maximum deviation of predicted result from experimental result is about 4.51 %. Hence, the experimental result confirms the optimization of TWR using Taguchi method and the resulting model seems to be capable of predicting TWR. 109

12 International Journal of Advancedd Research in Engineering and Technology (IJARET), ISSN Analysis of Variance (ANOVA) for S/N ratios of SR The S/N ratios have been calculated to identify the major contributing factors that cause variation in the SR. SR is Smaller is better type response which is given by equations 8 and 9 in section 4.2. Table 11 shows the ANOVA results for S/N ratio of SR at 93 % confidence interval. Electrode material was observed to be the most significant factor affecting the SR, followed by discharge current and air pressure according to F test. All the remaining parameters i.e. pulse on time, duty factor and gap voltage are non-significant to affect the SR. Table 11 Analysis of Variance for S/N ratios of SR Source DF Seq SS Adj SS Adj MS F P Contribution % Remark Electrode S Air pr S Discharge S Gap voltage NS Pulse on Time NS Duty Factor NS Residual Error Total S = R-Sq = 90.3% R-Sq(adj) = 72.4% S: Significant factor; NS: Non- significant factor Percentage contribution of control parameters for SR Residual Error 9.75 Gap voltage 9.93 Air pr Duty Factor 2.92 Pulse on Time 1.67 Electrode material Discharge current Electrode material Discharge current Air pr. Gap voltage Residual Error Duty Factor Pulse on Time Fig. 6 Percentage contribution of control parameters for SR The percentage contribution of each of the control parameters under study for SR is shown by a pie chart in Figure 6. It can be seen that electrode material contributes significantly (31.08 %), followed by discharge current (29.91 %) and air pressure (14.74 %). S/N ratio values of SR are used to calculate mean of S/N ratios at three levels of all factors and are given in Table 12. It gives us rank of all factors in this study considering the mean of S/N ratios for SR at different levels in terms their relative significance. Discharge current has the highest rank signifying highest contribution to SR, followed by Electrode material and air pressure. Pulse on time has the lowest rank. Gap voltage and duty factor were observed to be insignificant in affecting SR. One thing to be noted here is that there is very minute difference as far as the contribution of 110

13 electrode material and discharge current is concerned. But the analysis has ranked discharge current as no1 and electrode material as no.2. Main effects plot for S/N ratios of SR is shown in the Figure 7. The graph shows that with increase in discharge current from 8A to 12A, S/N ratio increases. However as the discharge current increases from 12A to 16A, S/N ratio go on decreasing. The S/N ratio decreases with an increase in pulse on time. Further as the air pressure is increased S/N ratio increases initially from 4 kg/cm 2 to 5 kg/cm 2 air pressure and shows a decreasing trend as the air pressure increases to 6 kg/cm 2. It can be seen that, as the gap voltage increases, S/N ratio shows a decreasing trend. Also as the duty factor increases, S/N ratio increases. It can be also observed that, Copper-Tungsten electrode has larger S/N ratio compared to Copper electrode. Table 12. Response Table for Signal to Noise Ratios of SR Level Electrode Discharge Air Pr. material current Gap voltage Pulse on time Duty factor Delta Rank Main Effects Plot for SN ratios Data Means Electrode mtl. Air pr. Dis. current Mean of SN ratios CuW Cu gap voltage Pulse on time Duty factor Signal-to-noise: Smaller is better Fig. 7 Main effects plot for S/N ratios of SR To conclude the discussion, for minimum SR, the level value with higher S/N ratio of each of the control parameter under study should be selected at this stage. Thus, a moderate discharge current of 12A, low pulse on time of 100 µs, low gap voltage of 40 V, higher duty factor of 11, moderate air pressure of 5 kg/cm 2 and copper-tungsten electrode material should be selected. Thus, it can be concluded that the optimum combination for SR is A1 B2 C2 D1 E1 F3. This optimal parametric combination is not available in L 18 array under study. Hence theoretical optimum value of SR has to be calculated. By using the equation 6 from section 4.1, the estimated multi-response signal to noise ratio can be obtained. 111

14 n opt = ( ) + ( ) + ( ) + ( ) + ( ) + ( ) n opt = Optimal value of S/N ratio = The corresponding value of SR is given by the equation 10 (see sect.4.2). Thus, y 2 = y opt = µm A confirmation experiment is performed by setting the control parameters as per the optimum levels achieved. The experimental result obtained for the SR is µm. Thus, the experimental value agrees reasonably well with prediction. The maximum deviation of predicted result from experimental result is about 3.53 %. Hence, the experimental result confirms the optimization of SR using Taguchi method and the resulting model seems to be capable of predicting SR. 5. CONCLUSIONS 1. The MRR, TWR and SR in near dry EDM process are mainly affected by the discharge current and electrode material. 2. Copper-tungsten electrode material exhibited lower SR and low TWR than that of the copper electrode but higher MRR was obtained with copper electrode. 3. Increase in the discharge current leads to an increase in the MRR but deteriorating the surface finish (higher SR values).however, an increase in discharge current initially increases the TWR but at higher discharge currents TWR was found to be decreasing. 4. The process parameters pulse on time and duty factor were found to be insignificant to affect the selected responses under study viz. MRR, TWR, and. SR. Air pressure was found to be significant to affect only SR. 5. Higher material removal rate (MRR) can be achieved with high discharge current and high gap voltage with copper electrode. 6. Low tool wear rate (TWR) can be achieved with lower discharge current and moderate gap voltage with copper-tungsten electrode. 7. Low surface roughness (SR) values (Better surface finish) can be achieved with moderate discharge current and moderate air pressure with copper-tungsten electrode. 6. FUTURE SCOPE 1. The AISI SAE D-2 tool steel has been used as a work-piece material and copper and coppertungsten are used as tool electrode materials in the present work. Copper infiltrated graphite is also a good candidate for tool electrode material. Various combinations of electrode materials and liquid gas mixtures as dielectric mediums can be tried to check the feasibility of near dry EDM process for various work piece materials. 2. Various concentrations of liquid in gas can be tried. The combination of additional liquids such as hydrocarbon oil, and gases such as nitrogen, oxygen, and helium can be tried in near dry EDM with a goal to tailor unique properties of the EDM dielectric fluid to achieve machining efficiency and quality improvements, such as high MRR and fine surface roughness in near dry EDM. 3. The machined surface and subsurface properties, such as microstructure, micro hardness, residual stress, and material composition, can be investigated to characterize the near-dry EDM process. 4. The topographical analysis of the machined surfaces by near dry using SEM images can be done to study the presence of micro-cracks, blowholes and dimples and the surface integrity. 112

15 5. Multi objective optimization can be done by using techniques like gray relational analysis by using the same experimental results of the present work. 6. Apart from experimental work, ample scope exists for theoretical modeling and process simulation in near dry EDM. Current literature is insufficient in this regard. 7. Practical application of the near dry EDM process can bring a lot of advantages for machine makers and machine end users. Important factor is the simplicity of the machine construction, not requiring sophisticated and specious dielectric circulation and cooling system. The design, manufacturing and material costs can be reduced. 7. REFERENCES 1. C.C. Kao,Jia Tao, Albert J. Shih, Near Dry Electrical Discharge Machining, International Journal of Machine Tools & Manufacture 47 (2007), Jia Tao, Albert J. Shih,Jun Ni, Experimental study of the Dry & Near-Dry Electrical Discharge Milling Processes, Journal of Manufacturing Science & Engineering (Feb2008), Vol.130 / Y Jia, B.S. Kim, D.J. Hu & J Ni, Parametric study on near-dry wire electrodischarge machining of polycrystalline diamond-coated tungsten carbide material, Proceedings of the Institution of Mechanical Engineers, Part B : Journal of Engineering Manufacture (2010) Vol. 224, M. Fujiki, Gap-Yong Kim, Jun Ni, Albert J. Shih, Gap control for near-dry EDM milling with lead angle, International Journal of Machine Tools & Manufacture 51 (2011), M. Fujiki, Jun Ni, Albert J. Shih, Investigation of the effect of electrode orientation & fluid flow rate in near-dry EDM milling, International Journal of Machine Tools & Manufacture 49 (2009), Jia Tao, Albert J. Shih, Jun Ni, Near-Dry EDM Milling of Mirror-Like Surface Finish, International Journal of Electrical Machining 13 ( January 2008) P. Govindan, Suhas S. Joshi, Experimental characterization of material removal in dry electrical discharge drilling, International Journal of Machine Tools & Manufacture 50 (2010), Fabio N. Leao, Ian R. Pashby, A review on the use of environmentally-frendly dielectric fluids in electrical discharge machining, Journal of Materials Processing Technology 149 (2004), Sourabh K. Saha, Experimental Investigation of the Dry Electrical Machining process, A thesis submitted in partial fulfillment of the requirements for the degree of Master of Technology to the Department of Mechanical Engineering, Indian Institute of Technology Kanpur, (Apr. 2008) 10. Viktor P. Astakhov, General Motors Business Unit of PSMI, USA, Ecological Machining : Near Dry Machining. 11. Jeffy Joseph, Department of Mechanical Engineering, College of Engineering, Thiruvananthapuram, The University of Kerala,(Nov.2009). 12. Jia Tao, Investigation of Dry & Near Dry Electrical Discharge Milling Process, A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Mechanical Engineering) in The University of Michigan, (2008). 13. Sourabh K. Saha, S.K.Choudhury, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Multy-objective optimization of the dry electric discharge machining process, (Jan. 2009). 113

16 14. S.H.Tomadi, M.A.Hassan, Z. Hamedon, Analysis of the Influence of EDM Parameters on Surface Quality, Material Removal Rate and Electrode Wear of Tungsten Carbide, Proceedings of the International Multi Conference of Engineers and Computer Scientists Vol. II (2009). 15. Singh S., Maheshwari S., Pandey P.C, Some investigations into the electric discharge machining of hardened tool steel using different electrode materials, Journal of Materials Processing Technology, (2004), Vol.149, pp P. R. Cheke, D. S. Khedekar R. S. Pawar, M. S. Kadam, Comparative performance of wet and near dry EDM process for machining of oil hardened non sinking steel material, International Journal of Mechanical Engineering and Technology (IJMET), ISSN (Print), Volume 3, Issue 2, (May-August 2012),pp Shankar Singh, S. Maheshwari, P.C. Pandey, Some investigations into the electric discharge machining of hardened tool steel using different electrode materials, Journal of Materials Processing Technology 149 (2004), pp M. S. Phadke, Quality Engineering Using Robust Design, AT and T Laboratories, Prentice Hall, Englewood Cliffs, New Jersey Mane S.G., Hargude N.V., PVPIT Budhgaon, Sangli, An Overview of Experimental Investigation of Near Dry Electrical Discharge Machining Process International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 3, Issue 2, 2012, pp , ISSN Print: , ISSN Online: Z Dr. Maan Aabid Tawfiq and Saad Hameed Najem, Optimization of Electrode Wear Rate on Electrical Discharge Machining Aisi 304 Ss with Multi Hole Electrode International Journal of Mechanical Engineering & Technology (IJMET), Volume 5, Issue 7, 2014, pp , ISSN Print: , ISSN Online: