EXPERIMENTAL INVESTIGATION OF MACHINING PARAMETERS IN ELECTRICAL DISCHARGE MACHINING USING EN36 MATERIAL

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1 EXPERIMENTAL INVESTIGATION OF MACHINING PARAMETERS IN ELECTRICAL DISCHARGE MACHINING USING EN36 MATERIAL M. Panneer Selvam 1, Ravikumar. R 2, Ranjith Kumar.P 3 and Deepak. U 3 1 Research Scholar, Karpagam University, Coimbatore, Tamilnadu, India 2 Department of Mechanical Engineering CMS Engineering College, Namakkal, Tamilnadu, India 3 Department of Mechanical Engineering, MAM School of Engineering, Trichy, Tamilnadu, India mpselvam7565@gmail.com ABSTRACT The quality of a product is the main factor for showing growth of a company. The quality of the product mainly depends upon the material and process parameters. Optimization technique plays a vital role to increase the quality of the product. Hence, many authors have presented their works on the optimization of process parameters for various machining processes. EDM process optimization with multiple performance characteristics based on orthogonal array with copper electrode. The process parameters such as gap, pulse on and pulse off were optimized with the considerations of multiple performance characteristics such as material removal rate, tool wear rate and surface roughness value on the work material Keywords: EDM, MRR, SR, EN36. INTRODUCTION Electrical Discharge Machining (EDM) is an important manufacturing process for machining hard metals and alloys. This process is widely used for producing dies, molds, and finishing parts for aerospace, automotive, and surgical components. The process is capable of getting required dimensional accuracy and surface finish by controlling the process parameters. EDM performance is generally evaluated on the basis of Material Removal Rate (MRR), Tool Wear Rate (TWR), Relative Wear Ratio (RWR), and Surface Roughness (SR). The important EDM parameters affecting to the performance measures of the process are discharge, pulse on, pulse off, arc gap, and duty cycle. In EDM, for optimum machining performance measures, it is an important task to select proper combination of machining parameters. Generally, the machining parameters are selected on the basis of operator s experience or data provided by the EDM manufactures. of the techniques used in manufacturing sectors to arrive for the best manufacturing Conditions. R.A. Mahdavinejad EDM process optimization via predicting a controller model Dr Navdeep Malhotra, Dr. Sona Rani and Khalid Sheikh Optimization of control parameters for surface roughness in side flushing form of die sink EDM EXPERIMENTAL METHODOLOGY LITERATURE REVIEW Rajneesh Kumar Singh and D.K. Singh Control parameters optimization of wire EDM by using genetic algorithm to improve and optimize the responses of a WIRE - EDM machining process. Raghuraman S, Thiruppathi K, Panneerselvam T, Santosh S Optimization of EDM parameters using Taguchi method and grey relational analysis. Optimization is one 1245

2 VOL. 11, NO. 2, JANUARY 2016 ISSN Figure-1. EDM experimental setup. Figure-2. EN

3 DESIGN OF EXPERIMENT S. No Table-1. Process parameters and their levels. Pulse off MINITAB-16 SOFTWARE By using Minitab-15 software have optimized the edm process parameters. DESIGN OF ORTHOGONAL ARRAY First Taugchi Orthogonal array is designed in minitab-15 to calculate S/N ratio and means which steps is given below. Create Taugchi Design is selected as shown in figure. Then a window of Taugchi design is opened. To start Minitab, click shortcut of Minitab on Desktop of computer. A window is opened in computer as shown in Figure. AN ORTHOGONAL ARRAY L9 FORMATION Table-2. An orthogonal array L 9 Formation. DESIGNATION Pulse off 1 A 1B 1C A 1B 2C A 1B 3C A 2B 1C A 2B 2C A 2B 3C A 3B 1C A 3B 2C A 3B 3C RESULT AND DISCUSSIONS After finding all the observation as given in Table-3 and 6, and S/N ratio and means are calculated and various graph for analysis is drawn by using Minitab-15. The S/N ratio for Ra, Machining and MRR is calculated on Minitab -15 Software using Taugchi Method. Finally press ok in window analyses Taugchi design. Graph and S/N ratio is generated as shown. 1247

4 Figure-3. S/N ration for corresponding factor. EXPERIMENTAL DATA DESIGNATION Table-3. Experimental data. Pulse off Mc MRR 1 A 1B 1C A 1B 2C A 1B 3C A 2B 1C A 2B 2C A 2B 3C A 3B 1C A 3B 2C A 3B 3C

5 SURFACE ROUGHNESSES (ANALYSIS OF RESULT) Table-4. Surface roughness and s/n ratios values for the experiments. DESIGNATION speed Feed DOC Ra in microns S/N Response valve (db) for Ra 1 A 1B 1C A 1B 2C A 1B 3C A 2B 1C A 2B 2C A 2B 3C A 3B 1C A 3B 2C A 3B 3C ROUGHNESS RESPONSE FOR EACH LEVEL OF THE PROCESS PARAMETER Table-5. Response table for signal to noise ratios. smaller is better Level speed feed DOC Delta Rank Table-6. Response for roughness. Level A B C Delta max-min Rank Process parameters Table-7. S/N Response values. Levels Roughness response value S/N response value (db) A B C

6 MACHINING TIME (ANALYSIS OF RESULT) Table-8. Machining and s/n ratios values for the experiments. DESIGNATION Pulse off Mc S/N Response valve (db) for mc 1 A 1B 1C A 1B 2C A 1B 3C A 2B 1C A 2B 2C A 2B 3C A 3B 1C A 3B 2C A 3B 3C MACHINING TIME FOR EACH LEVEL OF THE PROCESS PARAMETER Table-9. Response table for signal to noise ratios. smaller is better Level Pulse off Delta Rank CONCLUSIONS As a result, the performance characteristics such as SR and MRR can be improved through this approach. An experiment was conducted to confirm this approach. Based on the experimental results and confirmation test the conclusion can be drawn as follows. The results are very productive and therefore confirm the success of the experiment. In the future, the methodology presented in this paper could be applied to different machining conditions such as different work material, electrode etc. REFERENCES [1] Gokler, M. I., Ozanozgu, A. M Experimental investigation of effects of cutting parameters on surface roughness in the WEDM process, International Journal of Machine Tools and Manufacture. 40, [2] Puri, A.B. and Bhattacharyya, B Modeling and analysis of the wire-tool vibration in wire-cut EDM, Journal of Materials Processing Technology, 141, [3] Kuriakose. S, Shunmugam, M.S Characteristics of wire-electro discharge machined Ti6Al4V surface, Materials Letters. 58, [4] Mukherjee, I and Ray, P.K, A review of optimization techniques in metal cutting processes, Computers and Industrial Engineering, Vol. 50, 2006, pp [5] D.I. Lalwani, N.K. Mehta, P.K. Jain, Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel, Journal of materials processing technology. Vol. 206, 2008, pp [6] Neelesh K. Jain, V.K. Jain, Kalyanmoy Deb, Optimization of process parameters of mechanical type advanced machining processes using genetic 1250

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