Investigation of mrr in wedm for wc co sintered composite

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Investigation of mrr in wedm for wc co sintered composite

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 349 INVESTIGATION OF MRR IN WEDM FOR WC-Co SINTERED COMPOSITE Y.S.Sable 1 , R.B.Patil 2 , Dr.M.S.Kadam 3 1 M.E (Manufacturing) IInd year student, Department of Mechanical Engineering, Jawaharlal Nehru Engineering College, Maharashtra-431001, India 2 Associate Professor, Department of Mechanical Engineering, Jawaharlal Nehru Engineering College,Maharashtra-431001 India 3 Professor & Head, Department of Mechanical Engineering, Jawaharlal Nehru Engineering College,Maharashtra-431001 India ABSTRACT Tungsten carbide/cobalt (WC/Co) is one of the important composite materials that are used in the manufacture of cutting tools, mining tools, metal-working tools, dies, and other special wear resisting applications. It has high hardness and excellent resistance to shock and wear, and it is not possible to machine easily using conventional techniques. Wire electrical discharge machining (WEDM) is a best alternative for machining of WC-Co composite into intricate and complex shapes. Efficient machining of WC-Co composite on WEDM is a challenging task since it involves large numbers of parameters. Therefore, in present work, experimental investigation has been carried out to determine the influence of important WEDM parameters on machining performance of WC-Co composite. Response surface methodology, which is a collection of mathematical and experimental techniques, was utilised to obtain the experimental data. Experiments were carried out based on face-centered central composite design involving five control factors such as pulse on-time, pulse off-time, peak current, servo voltage and wire tension. Material removal rate were considered as the measures of performance of the process. A mathematical equation is derived to predict performance. Surface, response contour plots were utilized to analyze performance. ANOVA was used to find out the most significant parameters which affect the response characteristics. Key Words: ANOVA, Material removal rate, Response surface methodology, WEDM, WC- Co. INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 3, May - June (2013), pp. 349-358 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 350 1. INTRODUCTION With the introduction of new hard materials to be machined, super-hard tool materials were developed. Among several super-hard tool materials, cemented carbides, especially WC-Co sintered composites were found to be potential materials to making cutting tools, metal-working tools, mining tools, dies and other special wear resisting applications. This is because of its greater hardness, strength and wears resistance over a wide range of temperatures. WC-Co composite is synthesized by the sintering of WC granules that are held together by Co binders. Machining of WC-Co composite is very difficult with conventional machining processes like turning, milling and grinding because of its high hardness and high melting temperature. Due to the low material removal rate and difficulty in machining of complex and intricate profiles in WC-Co composite, cost associated with conventional machining processes is very high [1]. Wire electro-discharge machining process was found to be an extremely powerful electro-thermal process for machining WC-Co composites with any intricate shape. This process erodes materials by minute electrical discharges between the wire-electrode (50-300 micron diameter) and the work-piece as shown in Fig. 1. Although WEDM was found to be potential for machining WC-Co composites, it has some major limitations particularly while machining this composite. A large difference in melting and evaporation temperatures of it constituents, makes the process unstable. The melting and evaporation temperatures of tungsten carbide are 28000 C and 60000 C, respectively while those for Co are 13200 C and 27000 C, respectively. Therefore, Co gets melted, evaporated and removed by the discharge energy even before the melting of WC. As a result, in absence of a binder (Co) the WC grains may be released without melting and may lead to unstable machining. Unstable machining causes short circuit, arcing etc. Hence, it is quite difficult to model such process by analytical approach based on the physics of this process [2]. There have not been significant research publications till today on processing of these hard WC-Co composite materials by WEDM. Thus, there is non availability of machining databases for this type of materials. In absence of a database, an appropriate model, capable of predicting machining behavior for a wide range of operating conditions, finds immense utility. Some attempts have been made to determine optimal machining conditions for WC- Co composite on EDM and WEDM. [3] Developed a mathematical model and investigates the effect of WEDM parameter such as Pulse on-time, Pulse off-time and ignition current on response characteristics such as material removal rate. The authors, in their previous work [4], have already done an extensive parametric study on this material by WEDM. This parametric study reveals that there is not available even a single input condition which can maximize cutting speed and minimize surface roughness or kerf width simultaneously. Therefore, it is a multi-objective optimization problem. In multi-objective optimization, user may require several solutions instead of a single one. These solutions are called Pareto- optimal solutions and they are equally important. Depending upon the requirement of the user, any one of them can be selected. Say for example, in rough cutting, a process engineer must select such an input condition from the Pareto-optimal solutions which can provide a larger cutting speed. This selected input condition may also produce higher kerf width (higher the kerf width, lower will be the dimensional accuracy). But the process engineer is not worried for that as his objective is to maximize cutting speed irrespective of any value of kerf width. Similarly in fine cutting, the selected input condition must be such that it will minimize kerf width irrespective of any value of cutting speed. As NSGA-II works with a
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 351 population of points, it may capture several solutions which would be required for the multi- objective optimization problem. NSGA-II requires a fitness function which is nothing but a relationship between input and output parameters. As RBFN is making that relationship, hence, it is coupled with NSGA-II, and thus making a Neuro-Genetic technique.[5] The researcher developed Neuro Genetic model that predicts results with six control factors pulse on-time, pulse off-time, peak current, capacitance, gap voltage, and wire feed rate for Cutting speed, surface roughness and kerf width. [6] The author noticed that grain size of WC and cobalt composition also shows noticeable influence on machining performance of WC-Co composite with WEDM. Varying the cobalt concentration and grain size of WC alters the thermal conductivity of the material, which affects the machining performance.[7] author studied the influence of EDM parameters on WC-30%Co and developed mathematical model with electrode rotation, pulse on time, current, and flushing pressure as a input parameter and Material removal rate and Surface roughness as output parameters.[1] Developed mathematical model and investigated the multimachining characteristics in WEDM of WC- 5.3%Co composite using a Response surface methodology. four input parameters –pulse-on time, pulse-off time, servo voltage and wire feed – were investigated for four output machining characteristics: CS, SR, and RoC. Because the WEDM involves multi-performance characteristics, the objective of the present study is to investigate the influence of process parameters and to develop the mathematical models for one performance characteristics namely Material removal rate in WEDM of WC-10%Co composite. Response surface methodology with face-centered central composite design has been utilised to conduct the experimentation which helps to investigate and to correlate the input parameters with output performance characteristics. Using these mathematical models, optimal combination of WEDM parameters can be selected for desired performance characteristics for WC-Co composite. Fig. 1 Mechanism of WEDM
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 352 2. EXPERIMENTAL WORK In the present research work, a 5-axis CNC WEDM (Make: Electronica Machine Tools Ltd., India, Model: SPRINT CUT) was used for the study. Brass wire electrode of 0.25 mm diameter employed as tool electrode, distilled water applied as di-electric fluid. This study took WC-10% Co sintered composite as the workpiece material. The composition and the physical properties of the work-piece are given in TABLE 1 TABLE 1 Composition and Physical properties of WC-10% Co Composition WC-90 wt%, Co 10 wt% Grain Size 0.7 microfine Hardness (Hv 30) >1550 Transverse Rupture Strength (N/mm2 ) >3600 In present investigation, five important WEDM parameters, namely pulse-on time, pulse-off time, servo voltage and wire tension have been considered with five levels each as shown in TABLE 2. The parameter range was selected on the basis of pilot experiments and literature survey and other parameters are kept constant at their default settings. The response variables selected for this study is Material removal rate (MRR) this can be calculated using the following expression: MRR = initial weight – final weight (1) Machining time TABLE 2 Process Parameters and their levels Parameters Levels -2 -1 0 +1 +2 Pulse on Time (TON) 106 112 118 124 130 Pulse off Time (TOFF) 30 34 38 42 46 Peak Current (IP) 190 200 210 220 230 Servo Voltage (SV) 4 6 8 10 12 Wire Tension (WT) 15 20 25 32 35 3. EXPERIMENTAL DESIGN USING RSM In RSM, it is possible to represent independent process parameters in quantitative form as y = ƒ (X1, X2, X3 . . . Xn) ± ε (2) where y is the response (yield), ƒ is the response function, ε is the experimental error, and X1, X2, X3, . . ., Xn are independent parameters. By plotting the expected response of Y, a
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 353 surface, known as the response surface, is obtained. The form of f is unknown and may be very complicated. Thus, RSM aims to approximate f by a suitable lower ordered polynomial in some region of the independent process variables. If the response can be well modelled by a linear function of the independent variables, the function (equation (2)) can be written as y = β0 + β1x1 + β2x2 + ….. + βkxk + Є (3) However, if a curvature appears in the system, then a higher order polynomial such as the quadratic model (equation (4)) may be used K k k y = β0 + ∑ βi Xi +∑ βii X2 ii + ∑ βij XiXj (4) i=1 i=1 i=1 The objective of using RSM is not only to investigate the response over the entire factor space, but also to locate the region of interest where the response reaches its optimum or near optimal value. By studying carefully the response surface model, the combination of factors that gives the best response, can then be established. The WEDM process was studied with a standard RSM design, CCD. The MINITAB 16 software was used for regression and graphical analysis of the data obtained. The optimum values of the selected variables were obtained by solving the regression equation and by analysing the response surface contour plot. 4. MATHEMATICAL MODELLING The 32 experiments according to CCD design were conducted and MRR values were obtained for each experimental run as listed in TABLE 3. For analysis of the data, to check the good fit of the model Analysis of Variance is very much required. Model adequacy checking includes testing for significance of the regression model, for significance on model coefficients, and for lack of fit. For this purpose, ANOVA is performed. The modelling was carried out in the following steps: (a) Identifying the important process control variables and finding their upper and lower limits; (b) Developing the design matrix; (c) Conducting the experiments as per the design matrix; (d) Recording the response parameters; (e) Developing regression model; (f) Checking the adequacy of models; (g) Testing the significance of coefficients and arriving at the final models; (h) Presenting the direct and interaction effects of process parameters on MRR and Ra in graphical form; (i) Analysis of results.
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 354 TABLE 3 Experimental results Trial No. TON (µs) TOFF (µs) IP (A) SV (V) WT (gram) MRR (gms/ min.) actual coded actual coded actual coded actual coded actual coded 1 112 -1 34 -1 200 -1 20 -1 10 1 0.0620 2 124 1 34 -1 200 -1 20 -1 6 -1 0.0833 3 112 -1 42 1 200 -1 20 -1 6 -1 0.0423 4 124 1 42 1 200 -1 20 -1 10 1 0.0690 5 112 -1 34 -1 220 1 20 -1 6 -1 0.0692 6 124 1 34 -1 220 1 20 -1 10 1 0.0958 7 112 -1 42 1 220 1 20 -1 10 1 0.0560 8 124 1 42 1 220 1 20 -1 6 -1 0.0770 9 112 -1 34 -1 200 -1 30 1 6 -1 0.0530 10 124 1 34 -1 200 -1 30 1 10 1 0.0790 11 112 -1 42 1 200 -1 30 1 10 1 0.0390 12 124 1 42 1 200 -1 30 1 6 -1 0.0600 13 112 -1 34 -1 220 1 30 1 10 1 0.0661 14 124 1 34 -1 220 1 30 1 6 -1 0.0860 15 112 -1 42 1 220 1 30 1 6 -1 0.0468 16 124 1 42 1 220 1 30 1 10 1 0.0730 17 106 -2 38 0 210 0 25 0 8 0 0.0421 18 130 2 38 0 210 0 25 0 8 0 0.0890 19 118 0 30 -2 210 0 25 0 8 0 0.0820 20 118 0 46 2 210 0 25 0 8 0 0.0499 21 118 0 38 0 190 -2 25 0 8 0 0.0562 22 118 0 38 0 230 2 25 0 8 0 0.0762 23 118 0 38 0 210 0 15 -2 8 0 0.0720 24 118 0 38 0 210 0 35 2 8 0 0.0590 25 118 0 38 0 210 0 25 0 4 -2 0.0635 26 118 0 38 0 210 0 25 0 12 2 0.0680 27 118 0 38 0 210 0 25 0 8 0 0.0664 28 118 0 38 0 210 0 25 0 8 0 0.0660 29 118 0 38 0 210 0 25 0 8 0 0.0661 30 118 0 38 0 210 0 25 0 8 0 0.0662 31 118 0 38 0 210 0 25 0 8 0 0.0658 32 118 0 38 0 210 0 25 0 8 0 0.0660 The ANOVA of the model for MRR is shown in TABLE 4. The lack-of-fit term is insignificant, which is desired. The results of the model for MRR are given in TABLE 4. The value of R2 = 99.94 %, R2 (adj) = 99.23 %, R2 (pred.) = 99.90 % which means that the regression model provides an excellent explanation of the relationship between the independent variables (factors) and the response (MRR). The associated P-value for the model is lower than 0.05 (i.e. α = 0.05, or 95 per cent confidence), indicating that the model is considered to be statistically significant [7].
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 355 TABLE 4 ANOVA of model Regression equation is in terms of uncoded parameters: Material Removal Rate (MRR) = - 0.184152 + 0.001962 * TON - 0.002036 * TOFF + 0.000510 * IP - 0.000648 * SV -0.000652 * WT Fig. 2 displays the normal probability plot of the residuals for MRR. Notice that the residuals are falling on a straight line, which means that the errors are normally distributed and the regression model agree fairly well with the observed values. 0.00100.00050.0000-0.0005-0.0010 99 95 90 80 70 60 50 40 30 20 10 5 1 Residual Percent N 32 AD 0.602 P-Value 0.108 Normal Probability Plot (response is MRR) Fig. 2 Normal probability plot 5. RESULT AND DISCUSSION From ANOVA TABLE 5 Pulse on time, Pulse off time, Peak current, Servo voltage, Wire tension are significant parameters for Material removal rate, because their P value<0.05. In TABLE 5 from the percentage of contribution column it is clear that contribution or effect of Pulse on time higher 56.96 % and below to that Pulse off time 27.29 % hence most significant. Peak current and Servo voltage having contribution 10.67%, 4.32%, respectively hence significant. Wire tension having percentage of contribution is 0.007% hence it is least significant. Source DF Seq SS MS F P Regression 5 0.005833 0.001167 8558.46 0.000 Significant Residual Error 26 0.000004 0.000000 Lack-of-Fit 21 0.000003 0.000000 3.81 0.071 Not Significant Pure Error 5 0.000000 0.000000 Total 31 0.005837
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May TABLE Source DF Seq SS TON 1 0.003325 0.003325 TOFF 1 0.001593 0.001593 IP 1 0.000623 0.000623 SV 1 0.000252 0.000252 WT 1 0.000041 0.000041 Total 31 0.005837 Fig. 3 Percentage 5.1 Response analysis From Figure 4 (a) the Material removal rate the increase of pulse on time and at the same time it decreases with the increase of pulse off time. In WEDM, Material removal rate flushing of the eroded material o increases the heat generation at the work surface which increases the This establishes the fact that Material removal rate during machining and is dependent not only on the energy contained in a pulse determining the crater size, but also on the applied energy rate or power. value decreases the discharge frequency and increases the overall machining time. Also long pulse-off time at high dielectric flow rate produces the cooling effect on work material and hence decreases the Material removal rate rate increases with increase in the peak current values. The higher is the peak current setting, the larger is the discharge energy. This leads to increase in peak current setting is too high, wire Figure 4 (c) that Material removal rate servo voltage closes the spark gap which results in rapid and large ionization of dielectric fluid which gives rise to more melting of work material and hence increases the removal rate. It is seen from Figure 4 wire tension. Increasing wire tension out of the spark gap and hence increases the 27.29% 10.67% International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 356 TABLE 5: Parameter ANOVA for MRR MS F P % p of Contribution 0.003325 24393.39 0.000 56.96% Significant 0.001593 11682.31 0.000 27.29% Significant 0.000623 4571.81 0.000 10.67% Significant 0.000252 1845.34 0.000 4.32% Significant 0.000041 299.45 0.000 0.007% Significant Percentage contribution of parameters Material removal rate is found to have an increasing trend with the increase of pulse on time and at the same time it decreases with the increase of pulse off Material removal rate depends on the melting of work surface and then flushing of the eroded material out of the spark zone. Increasing pulse duration (T increases the heat generation at the work surface which increases the Material removal rate Material removal rate is proportional to the energy consumed and is dependent not only on the energy contained in a pulse determining the crater size, but also on the applied energy rate or power. Increasing pulse-off time (T value decreases the discharge frequency and increases the overall machining time. Also long off time at high dielectric flow rate produces the cooling effect on work material and Material removal rate. It is seen from Figure 4 (b) that Material removal increases with increase in the peak current values. The higher is the peak current setting, the larger is the discharge energy. This leads to increase in Material removal rate peak current setting is too high, wire breakage may occur frequently. It is observed Material removal rate increases with decrease in servo voltage. Decreasing servo voltage closes the spark gap which results in rapid and large ionization of dielectric rise to more melting of work material and hence increases the Figure 4(d) that Material removal rate increases with tension leads to the easy and rapid escape of the eroded ma out of the spark gap and hence increases the Material removal rate. 56.96%27.29% 10.67% 4.32% %P OF CONTRIBUTION TON TOFF IP SV International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – June (2013) © IAEME Remark Most Significant Most Significant Significant Significant Least Significant is found to have an increasing trend with the increase of pulse on time and at the same time it decreases with the increase of pulse off depends on the melting of work surface and then ut of the spark zone. Increasing pulse duration (TON) Material removal rate. is proportional to the energy consumed and is dependent not only on the energy contained in a pulse determining off time (TOFF) value decreases the discharge frequency and increases the overall machining time. Also long off time at high dielectric flow rate produces the cooling effect on work material and Material removal increases with increase in the peak current values. The higher is the peak current setting, rate. While the breakage may occur frequently. It is observed from voltage. Decreasing servo voltage closes the spark gap which results in rapid and large ionization of dielectric rise to more melting of work material and hence increases the Material Material removal rate increases with increase in and rapid escape of the eroded material
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 357 Fig. 4 (a) Surface Plot of MRR vs Fig.4 (b) Surface Plot of MRR vs TON, TOFF TON, IP Fig. 4 (c) Surface Plot of MRR vs Fig.4 (d) Surface Plot of MRR vs TON, SV TON, WT 6. CONCLUSION In present work, experimental investigation has been reported on machining performance of WC-10%Co composite on WEDM. Response surface methodology (RSM), a statistical technique, has been utilised to investigate the influence of five important WEDM parameters – pulse on-time, pulse off-time, peak current, servo voltage and wire tension – on performance characteristics: Material removal rate. Face centered central composite design was employed to conduct the experiments and to develop a correlation between the WEDM parameters and each performance characteristics. Analysis of variance (ANOVA) on experimental data shows that model developed statically significant. ANOVA reveled that pulse on time, pulse off time are most significant parameters and greatly affected to MRR whereas, Peak current, servo voltage, wire tension are having less effect on MRR. Response surface curve shows that increasing pulse on time, peak current, and wire tension value increasing MRR, and decreasing pulse off time, servo voltage value decreases MRR.
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 358 7. ACKNOWLEDGEMENT The authors would like to thank Dr. S. D. Deshmukh, Principal, JNEC, Aurangabad for his valuable guidance along with Mr. G. S. Pujari, Vision Tech. Pvt. Ltd, for significant information and kind support for experimentation and making available their testing facility for Research Work. REFERENCES 1) K. Jangra , S. Grover “Modelling and experimental investigation of process parameters in WEDM of WC-5.3%Co using response surface methodology” Mechanical Science,3,63–72,2012. 2) P. Saha, P. Saha, S. K. Pal “Parametric Optimization in WEDM of WC-Co Composite by Neuro-Genetic Technique” Proceedings of the World Congress on Engineering 2011 Vol. III WCE 2011, London, U.K. 3) Muthuraman V, Ramakrishnan R, Karthikeyan L “Modeling and Analysis of MRR in WEDMed WC-CO Composite by Response Surface Methodology” Indian Journal of Science and Technology ,December 2012 ,ISSN:0974-6846. 4) Probir Saha, Abhijit Singha, Surjya K. Pal, Partha Saha “Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite” International Journal of Advance Manufacturing Technology (2008) 39:74–84. 5) D. Kanagarajan, R. Karthikeyan, K. Palanikumar, J. Paulo Davim, “Optimization of electrical discharge machining characteristics of WC/Co composites using non dominated sorting genetic algorithm (NSGA-II)” International Journal of Advance Manufacturing Technology (2008) 36:1124–1132. 6) Chang-Ho Kim , Jean Pierre Kruth (2001) “Influence of the Electrical Conductivity of Dielectric on WEDM of Sintered Carbide” KSME International Journal, 15, 1276– 1282, 2001. 7) D Kanagarajan, R Karthikeyan, K Palanikumar, and P Sivaraj “Influence of process parameters on electric discharge machining of WC/30%Co composites” Journal of Engineering Manufacture Proceeding 1 Mechanical Engineering (2008) Vol. 222 Part B-09544054JEM925. 8) Pallavi.H.Agarwal, Prof.Dr.P.M.George and Prof.Dr.L.M.Manocha, “Comparison of Neural Network Models on Material Removal Rate of C-Sic”, International Journal of Design and Manufacturing Technology (IJDMT), Volume 3, Issue 1, 2012, pp. 1 - 10, ISSN Print: 0976 – 6995, ISSN Online: 0976 – 7002. 9) Vishal Francis, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi and Santosh Kumar, “Application of Taguchi Method and ANOVA in Optimization of Cutting Parameters for Material Removal Rate and Surface Roughness in Turning Operation” International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 47 - 53, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 10) S V Subrahmanyam and M. M. M. Sarcar, “Parametric Optimization for Cutting Speed – A Statistical Regression Modeling for WEDM”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 1, 2013, pp. 142 - 150, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

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