Selection of optimum parameter and Electrode for machining of AL 2017 Alloy by machining in Electrical discharge Machining

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1 Selection of optimum parameter and Electrode for machining of AL 2017 Alloy by machining in Electrical discharge Machining 1 SENTHIL KANNAN.V, 2 Dr.K.LENIN 1 Assistant Professor, Department of Mechanical Engineering, J.J.College of Engineering and Technology, Tiruchirappalli,Tamilnadu, India 2 Associate Professor, Department of Mechanical Engineering, Jayaram.College of Engineering and Technology, Pagalavadi Kalipatty,Trichy,Tamilnadu, India Abstract- The work carried out for find the optimum process parameter and Electrode among the 3 electrode materials (copper, Brass, and Silver) by machining of AL 2017 alloy in Electrical Discharge machining by Taguchi based Grey Relational analysis. Al 2017 is the type of material which has highly corrosion resistant with high strength to low weight ratio mainly used in Aerospace industries. The Al 2017 is hard material which is not machined by traditional machine manufacturing. In this paper the experiments were conducted by Taguchi method with L27 orthogonal table to obtain optimum pulse-on time, pulse off time, current and electrode material by calculating material removal rate, electrode wear rate and surface roughness. The A N O V A analysis is utilized for finding the influence of each variable and optimal processing parameter and verified through experimentation to improve process. KEY WORDS- EDM, AL 2017 alloy, Taguchi method, Grey RelationalAnalysis,ANOVA I.INTRODUCTION Electrical discharge machining (EDM) is a non conventional machining method by which the material is melts and vaporizes from the work pieces by controlling electric discharge sparks between the work material and electrode. Since there is no physical contact between the work material and electrode the process was completely eliminate the mechanical stress, chatter and vibration. At the present time, Electrical discharge machine (EDM) is a widespread technique used in industry for high precision machining of all types of conductive materials such as: metals, metallic alloys, graphite, or even some ceramic materials, of whatsoever hardness [1].The Taguchi based Grey relational method can optimize the machining parameter of performance characteristics in EDM. Taguchi method is the powerful method of design of experiment [2]. Any material to be used as tool electrode is required to conductive electricity. In fact, there is a wide range of materials used to manufacture electrodes, for instance, copper,brass, tungsten carbides, electrolytic copper, copper-tungsten alloys, silver tungsten alloy, tellurium-copper alloys, coppergraphite alloys, graphite etc. Normally copper is used as a electrode due to the excellent surface finish and high MRR with low TWR achieved by the suitable parameters selected for machining. Like c o p p e r, copper-tungsten tool electrode gives good surface finish. It also provides a high material removal rate and low electrode wear depending on the EDM parameter Settings as compared to metallic electrodes [4]. Aluminium 2017 alloy is a heat treatable wrought alloy with intermediate strength. It is stronger but harder to machine. Workability is fair with ductility and formability better that of pure aluminium Arc and resistance weldability of this alloy are satisfactory while corrosion resistance is fair. In that of above properties Al 2017 alloy has widely used in the aircraft components, Pulleys and gauges and general structural components. Due to harder in nature the AL 2017 material is difficult to cut. Shaped holes in disc of an aircraft engine, made of Al 2017 alloy material are required to have good integrity and geometrical accuracy. This kind of shaped hole is produced by EDM currently. The Taguchi method was used to design the experiments with different parameters like pulse on, pulse off, current with 3 levels in EDM. The optimum parameter was found by calculating material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR).The optimum machining parameter value were determined by Grey relational analysis. II. EXPERIMENTAL ATERIAL, EQUIPMENT AND METHOD A.Experimental setup The primary equipment in this study was an Electric Discharge Machine ZNC (Z-axis ISSN: Page 29

2 Numerically Controlled) model S50; make: Sparkonix and a rotatin fixture and control device. Commercial kerosene was utilized as the dielectric medium between the work material and electrode in the tank. The chemical composition of the AL2017 alloy used is shown in Table 1. Material properties were as shown in Table 2. The experimental run was L27 orthogonal array as designed in the Taguchi method: parameters and 3 levels of parameter design were as shown in Table 3. In this work, each experimental run was conducted after the work material is secured in the table, the tool is fitted in the Z axis movement tool holder and the required parameters were set in the control panel. At each run the electrode is removed from the tool holder and cleaned and grind for obtaining smooth surface. Three cylindrical Copper, Brass and silver rods of 10mm diameter was selected as the electrodes for machining the work piece. For each run 2 mm depth of cut was given. B. Design of experiments using the Taguchi approach The Orthogonal Array forms the basis for the experimental analysis in the Taguchi method. The experiment was conducted based on the parameter factors and their corresponding levels. The experiment procedures are described as follows: Table 1 Chemical composition of AL2017 Element CU Mn Mg Fe Si Zn Cr Ti Wt% Table 2 Material properties of AL2017 Properties AL2017 Density (g/cm 3 ) Melting point(¾c ) Coefficient of expansion 13.3 (µm/m.¾c ) Table 3 Taguchi Experimental design using L27 orthogonal array. S.no Pulse on Pulse Off Current Electrode material µm µm A copper copper copper brass brass brass Silver Silver Silver Silver Silver Silver copper copper copper Brass Brass Brass Brass Brass Brass Silver Silver Silver copper copper copper C. Measurement Response The following response were selected for optimization of the parameter For calculating MRR and TWR after each run the weight of the work material and electrode was measured by precision weighing balance machine. MRR 3D profilometer was used to evaluate quantitatively the surface roughness of the m a c h i n e surface, presented by Ra. The surface roughness value was measured by the average of the 3 values measured from 3 places on the machined surface. C.Grey relational Analysis The Grey Relational Analysis is based on Grey Relation Grade (GRA), the run corresponding to the maximum value the GRA is the optimum parameter for machining of AL2017 alloy in EDM machine. The GRA is based on the Taguchi method analysis. Different calculation methods can b e applied as follows. Calculation of Ratio The smaller the-better, was applied for the objective optimal value is the small Such as in the surface roughness and electrode wear rate. n = -10*LOG10 1/n ( x i^2) i=1 ISSN: Page 30

3 The larger the better, was applied for the objective optimal value is the large for the response such as Material removal rate. N = -10*LOG10 1/n ( 1/ x ij^2) i,j=1 The calculated & ratios are given in Table 4 d.normalization of ratio The normalization of the parameter is need for converting all the values in to unit less equivalent values. This is carried out by, For lower the better, For Higher the better, Where, x ij is observed value for the i th for the j th response. Grey Relation Co-efficient GRC= Where, min and max are the global minimum and maximum values from the normalized values. is the weighting factor for each response. In this the equivalent weighting factor is given for the responses. e.grey relational grade (GRG) The performance of multi response is evaluated by GRG. It is calculated by, n Ƴ= 1/n GRC i=1 The calculated GRC and GRG values are given in Table 5 Table 4 calculated value and ratio for MRR, TWR and Surface roughness TWR Ra ratio MRR ratio TWR ratio Ra Table 5 GRC value for MRR, TWR and Surface roughness and GRG Sl.n o GRC GRG Ra MRR TWR ISSN: Page 31

4 III. ANALYSIS OF DATA AND RESULTS The experiments were conducted with L27 orthogonal Taguchi design, it is found from the table 5 the maximum GRG value occurred for the experimental run 20.Which shows that the optimum parameter for machining the AL2017 alloy with EDM. The obtained GRG is considered a single response for experiment the individual effect for each response with the parameter was obtained by main plots effects in ANOVA which is analyzed with Minitab 14. a.material removal rate(mrr) Fig1. Shows the main effect plot for the MRR, it is clearly shows that the MRR was increased with increase the pulse on time, the maximum MRR occur at the level of pulse on time. Consider with the pulse off time the MRR was decreased with increase in pulse off, this is due the effect of spark would be reduced on the work piece with increase in pulse off. The effect of current also shows that up to a particular level of increasing in current the MRR increases after that it decreases. According to the Electrode material is consider the Brass would be the optimum for MRR which is clearly shows on the plots. This is due to the high Thermal conductivity of the Brass is more than that of others. Fig.2 Parameter response plot for EWR b. Tool Wear rate (TWR) The Fig 2 shows the main effects of parameter for the TWR. Considering the pulse on the TWR was better in the 3 level which had the smaller ratio. With the increasing in pulse off would give higher wear on the electrode. The increasing in current also leads the high wear on the electrode material. From the plot it is clearly shown that the low electrode wear was in the Brass tool. C. Surface roughness Fig 3.Parameter response plot for Ra The individual parametric effect for the surface roughness was shown by the fig 3. In which the effect of increasing in pulse on results the better surface roughness due to the more number of spark pulses on the work material was remove the molten material from the work piece. While in the case of increasing the pulse off results the higher value surface roughness due to the lower spark pulses. The increase in current was considerably reduces the surface roughness value. This is due to solidification of removed material is avoided by high current. CONCLUSION Fig1. Parameter response plot for EWR This work evaluates the parameters that suitable for machining of Al2017 alloy by electrical discharge machining. Based on the above discussion, we conclude that a) The suggested optimal machining parameters for obtaining the maximum MRR with less TWR and Surface Roughness material removal rate ISSN: Page 32

5 are: pulse on 600μm, pulse off 20μm,current 12 A and Electrode material Brass. b) By considering the effect of parameter o n individual response, the recommended parameter for obtaining maximum MRR are pulse on 600μm,pulse off 20μm,current 8A c) The optimal parameters for getting lower TWR are: pulse on600μm, pulse off 20μm, current 8Aand tool material is Brass. d) The significant parameters for obtaining lower surface roughness are pulse on 200μm,pulse off 20μm,current 12A and electrode material is Brass. REFERENCES [1]. Anand Pandey, Shankar Singh, Current research trends in variants of Electrical Discharge Machining: A review, International Journal of Engineering Science and Technology, Vol. 2(6), 2010, [2]. Mr. V. D. Patel, Prof. C. P. Patel, Mr. U.J. Patel, Analysis of Different Tool Material On MRR and Surface Roughness of Mild Steel In EDM, International Journal of Engineering Research and Applications (IJERA),Vol. 1, Issue 3, pp [3].J. Valentincic, D. Brissaud, M. Junkar, EDM process adaptation system in tool making industry,journal of Materials Processing Technology, Journal of Materials Processing Technology,172 (2006) [4]. Qing GAO, Qin-he ZHANG, Shu-peng SU, Jianhua ZHANG, Parameter optimization model in electrical discharge machining process,j Zhejiang Univ Sci A (1): ISSN: Page 33