PERFORMANCE OF DIFFERENT ELECTRODE MATERIALS IN ELECTRO DISCHARGE MACHINING DIN

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1 PERFORMANCE OF DIFFERENT ELECTRODE MATERIALS IN ELECTRO DISCHARGE MACHINING DIN LAKHWINDER SINGH 1, MANPREET SINGH 2, S.C.VERMA 3 1 Department of Mechanical Engg., Sant Longowal institute of Engineering and Technology, Sangrur (Punjab), India 2 Department of Mechanical Engg., Sant Longowal institute of Engineering and Technology, Sangrur (Punjab), India Abstract The aim of the study to investigate and optimization of electrical discharge machining for inspecting the machinability of DIN steel with cryogenically treated copper tool electrode (CT) and cryogenically untreated copper tool electrode (CUT) using Taguchi method. Din steel used for making forging dies of all types. This paper utilizes electrical discharge machining, which is thermal processing work pieces are not affected by mechanical properties of materials. This experiment utilizes the Taguchi method and L18 orthogonal table to obtain the electrode treatment (CT, CUT), current, pulse-on time, pulse-off time, voltage and flushing pressure in order to explore the material removal rate, tool wear rate and surface roughness. The influence of each variable and optimal processing parameter will be obtained through ANOVA analysis and verified through experimentation to improve process. Keywords: EDM, DIN steel, Performance electrode, Taguchi method. 1. INTRODUCTION Electrical discharge machining (EDM) is a machining method utilizing heat machining work pieces, controlling electric discharge spark to cause melting, erosion and vaporization to achieve the purpose of removing material. Because there is no direct contact between electrode and work piece, mechanical stress is not produced.work pieces can under EDM as long as they are electrically conductive and are relatively free of such limitations as material type of mechanical properties. As a result, EDM is often used to machine high strength or hard materials, materials which, if machined using traditional machining methods, would be uneconomical or suffer from imprecision [1]. The Taguchi method can optimize the machining parameter of performance characteristics in EDM as a Taguchi method is the powerful method of design of experiment [2]. According to the Droza [3] 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, brass, tungsten carbides, electrolytic copper, copper-tungsten alloys, silver tungsten alloy, tellurium-copper alloys, copper-graphite alloys, graphite etc. copper is the best choice because of its facility to be highly polished. The copper-tungsten tool electrode gives good surface finish. It provides a high material removal rate and low electrode wear depending on the EDM parameter Settings as compared to metallic electrodes [4]. Copper-tungsten has a much higher density than copper; this makes it the best material for large electrodes. Cryogenic treatment can be characterized by its application temperature, below 123 o K (-150 o C) or at about liquid nitrogen temperature (-196 o C). Cold treatment or sub-zero treatment includes below zero but higher temperatures than the cryogenic temperatures (down to about -80 o C). [5] The process has a wide range of applications from industrial tooling to improvement of musical signal transmission. Some of the benefits of cryogenic treatment include longer part life, less failure due to cracking, improved thermal properties, better electrical properties including less electrical resistance, reduced coefficient of friction, less creep and walk, improved flatness, and easier machining. The Taguchi method was used early on in the process of the experiment, inspecting the effects of different parameters and levels in EDM on the various characteristics of EDM, including material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). The optimum machining parameter values were determined in this manner. Volume 6, Issue 11, November 2017 Page 8

2 2. DETAILS OF EXPERIMENTAL 2.1 Experimental equipment The primary equipment in this study was an Electric Discharge Machine ZNC (Z-axis Numerically Controlled) model S50; make: Sparkonix and a fixture and control device. Commercial kerosene was circulated as the dielectric fluid in the tank. The chemical composition of the DIN steel used is shown in Table 1. Material properties were as shown in Table 2.Our experiment method used the L18 orthogonal array as designed in the Taguchi method: parameters and correlated levels design were as as shown in Table 3. In this study, an electrode was attached to the Z- axis of EDM and a work piece secured to the work plat form in the tank, after which EDM parameters were set to perform the experiment. Two type of cylindrical Copper electrodes, cryogenically treated (CT) and cryogenically untreated (CUT) copper rods of 12mm diameter was selected as the electrodes for machining the work piece. Machining time lasted for 20 min. After the completion of machining, the work piece and the electrodes were weighed, as material removal rate and electrode wear rate can be derived from comparing the weights of the work pieces and electrodes before and after machining. A surf coder was used to evaluate quantitatively the surface roughness of the EDMed surface, presented by Ra. For each work piece, the average of readings taken at three places of machining plane was chosen as the surface roughness value. 2.2 Design of experiments using the Taguchi approach Table 1: Chemical composition of DIN steel Element Mn Si C Ni Cr Mo V Wt% Table 2: Material properties of DIN steel Properties DIN steel Density (g/cm 3 ) 7.84 Thermal conductivity (W/m.K) 36.0 The Orthogonal Array forms the basis for the experimental analysis in the Taguchi method.first the experiment parameter factors and their corresponding levels are selected. Next, the experimental results are manipulated by the analysis of variance (ANOVA) method to determine the effect of each factor versus the objective function.the experiment procedures are described as follows: a) on the required quality objective the parameters factors are selected. b) The parameter factors levels are determined. c) Based on the variable factor layout of the orthogonal array.the practical experiment can then proceed. d) Experimental results are obtained and then the ANOVA for Signal-to-Noise (S/N ratio) and contribution are computed. e) Next the optimal parameters factor level combination is selected. f) Finally, the optimal parameter level combination is used to proceed with the confirmation experiment. For the Taguchi Method parameter design the basic method converts the objective parameter to the S/N ratio which is treated as the quality characteristics evaluation index. The least variation and the optimal design are obtained by means of the S/N ratio. The final step is to actually conduct the experiment to confirm whether or not the experiment is successful. The benefits of S/N ratio include increasing the factor weighting effect, decreasing mutual action, simultaneously processing the average and variation, and improving engineering quality.the higher the S/N ratio, the more stable the achievable quality Depending on the required objective characteristics, different calculation methods can be applied as follows. The smaller-the-better where the objective optimal value is the smaller the better Such as in the surface roughness and tool wear rate. = 10log Volume 6, Issue 11, November 2017 Page 9

3 The Larger-the-Better where the objective optimal value is the larger the better, Such as in the material removal rate. = 10log y i the ith result of the experiment,n the repeated number of the ith experiment. Table 3: Experimental layout using L18 orthogonal array Factors Symbol Levels Electrode Treatment ET CT -- CUT Current (A) I p Voltage (V) V g Pulse ON Time P on Pulse OFF Time P off Flushing Pressure (Kg/cm 2 ) F P ANALYSIS OF DATA AND RESULTS In investigating EDM properties over the course of the experiment, a Taguchi L18 orthogonal array was used to organize the set up of EDM parameters,which were ;electrode treatment, current, voltage, pulse-on time, pulse-offtime and flushing pressure. section A-C of the paper individually discuss the effects of each machining parameter on the machining characteristics of the material removal rate, tool wear rate and surface roughness.of the machining characteristics,material removal rate was the larger the best quality,while electrode wear rate and surface roughness were the smaller the best quality Major factors affecting material removal rate,tool wear rate and surface roughness was derived by performing ANOVA and contribution analysis. 3.1 Material removal rate This study selected L18 orthogonal array to conduct experimental runs. The factors and levels are shown in Table 4. All the factors have three levels except electrode treatment. The S/N ratios are calculated and the larger the best quality characteristics applied for the MRR. The ANOVA and contribution analysis proceed according to the S/N ratio of the MRR in Table 4 and results are shown in Table 5. Among the six control parameters, the most notable is the [B] current, contribution ratio 56.40%, will dramatically affect the MRR. The electrode treatment (contribution ratio 32.77%), voltage (contribution ratio 6.65%) and pulse-on-time (contribution ratio 3.06%) have physical and statistical influence the remaining minor parameters that is pulse-off-time and flushing pressure play slightly contribution to the evaluation of the MRR. Control parameters S/N ratio response plot by MRR effects is shown in Fig. 1. The optimal S/N ratio combination, the largest S/N ratio value for all control parameters, is denoted as A1/B3/C3/D3/E3/F2. Table 4: Experimental values and S/N ratios of MRR S No. ET I P V g P on P off F P Mean MRR S/N ratio 1 CT CT Volume 6, Issue 11, November 2017 Page 10

4 3 CT CT CT CT CT CT CT CUT CUT CUT CUT CUT CUT CUT CUT CUT Figure 1 Parameter response plot for MRR Table 5: Analysis of variance and contribution for MRR Factors DF SS MS F-ratio %Contribution Electrode Volume 6, Issue 11, November 2017 Page 11

5 Current (A) Voltage (V) Pulse on time Pulse off time Flushing pressure (Kg/cm 2 ) Residual error Total Tool wear rate The S/N ratios are calculated and the smaller is better quality characteristic is applied for the TWR. The ANOVA and contribution analysis proceed according to the S/N ratio of the TWR in Table 6. Among the six control parameters, the most notable is the current [B], the contribution ratio 42.19% will dramatically affect the TWR. In addition, [A] electrode treatment with contribution ratio 33.50% and voltage (contribution ratio 22.36%) will significantly affect the TWR.The pulse-on time (contribution ratio 1.60%), pulse-off-time (contribution ratio 0.31%) and flushing pressure (contribution ratio 32.77%) play slightly contribution to the evaluation of the TWR. Control parameter S/N ratio response plot by TWR effect is shown in Fig.2.The optimal S/N ratio combination, the largest S/N ratio value for all control parameters, is denoted as A1/B1/C1/D1/E1/F1. Table 6: Experimental values and S/N ratios of TWR S No. ET I P V g P on P off F P Mean TWR S/N ratio 1 CT CT CT CT CT CT CT CT CT CUT CUT Volume 6, Issue 11, November 2017 Page 12

6 12 CUT CUT CUT CUT CUT CUT CUT Table 7: Analysis of variance and contribution for TWR Factors DF SS MS F-ratio %Contribution Electrode Current (A) Voltage (V) Pulse on time Pulse off time Flushing pressure (Kg/cm 2 ) Residual error Total Fig.2 Parameter response plot for TWR 3.3 Surface roughness The S/N ratios are calculated and the smaller is best quality characteristic is applied for the SR. The ANOVA and contribution analysis proceed according to the S/N ratio of the SR in Table 8. Among the six control parameters, the most notable is the current [B] (contribution ratio 40.49%), voltage [C] the contribution ratio 34.23% will Volume 6, Issue 11, November 2017 Page 13

7 dramatically affect the SR. In addition, [A] the electrode treatment contribution ratio 22.71%, will significantly affect the SR.The flushing pressure (contribution ratio 1.05%), pulse-on time (contribution ratio 0.98%) and pulse-off-time (contribution ratio 0.54%) have physical and statistical influence. Control parameter S/N ratio response plot by SR effect is shown in Fig.3.The optimal S/N ratio combination; the largest S/N ratio value for all control parameters, is denoted as A1/B1/C1/D1/E2/F1. Table 8: Experimental values and S/N ratios of SR S No. ET I P V g P on P off F P Mean MRR S/N ratio 1 CT CT CT CT CT CT CT CT CT CUT CUT CUT CUT CUT CUT CUT CUT CUT Table 9: Analysis of variance and contribution for SR Factors DF SS MS F-ratio %Contributi on Electrode Current (A) Volume 6, Issue 11, November 2017 Page 14

8 Voltage (V) Pulse on time Pulse off time Flushing pressure (Kg/cm 2 ) Residual error Total Fig.2 Parameter response plot for SR 4. CONCLUSIONS This work evaluates the comparison study of cryogenically treated electrode and untreated electrode of machining DIN steel by electrical discharge machining. Based on the results presented here in, we conclude the following: 1. The suggested optimal machining parameters for material removal rate are: ET (CT), I p (21), V g (50). P on (150), P off (90) and F p (0.50) the parameter with the greatest effect on the material removal rate was current, cryogenic treated electrode, voltage and pulse-on-time showed the highest MRR. While pulse-off-time and flushing pressure have least effect on MRR. 2. The suggested optimal machining parameters for tool wear rate are: ET (CT), I P (12), V g (40), P on (90), P off (15) and F p (0.75). The significant parameters with the greatest effects on the tool wear rate were electrode treatment, current and voltage. 3. The suggested optimal parameters for surface roughness are: ET (CT),I P (12), V g (40), P on (90), P off (45) and F P (0.25). The significant parameters with the greatest effects on the surface roughness were electrode treatment, current and voltage. References [1] Norliana Mohd Abbas and Darius G. Solomon, A review on current research trends in electrical discharge machining (EDM), International Journal of Machine Tools & Manufacture 47 (2007) [2] K. H. Ho and S. T. Newman, State of the art electrical discharge Machining (EDM), International Journal of Machine Tools & Manufacture 43 (2003) [3] Ross P J. Taguchi techniques for quality engineering. McGraw-Hill,Newyork, [4] Droza T J. Tool and Manufacturing Engineering; Handbook; Machining. USA: Society of Manufacturing Engineering, Volume 6, Issue 11, November 2017 Page 15

9 [5] P. I. Patil and R. G. Tated, Comparision of effects of cryogenic treatment on different types of steels: A review. (2007), pp [6] M. M. Sundaram et. al. The Effects of Cold and Cryogenic Treatments on the Machinability of Beryllium-Copper Alloy in Electro Discharge Machining. [7] Ahsan Ali Khan and Mirghani I. Ahmed, Improving tool life using cryogenic cooling, journal of materials processing technology, (2008), vol. 196, pp [8] Rajmohan T et. al. Optimization of Machining Parameters in Electrical Discharge machining (EDM) of 304 stainless steel. Procsdia engineering 38 (2012) [9] S.H. Tomadi et. al. Analysis of the Influence of EDM Parameters on Surface Quality, Material Removal Rate and Electrode Wear of Tungsten Carbide, Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol II, IMECS 2009, March 18-20, 2009, Hong Kong. [10] F.L. Amorima and W.L. Weingaertner. The behavior of graphite and copper electrodes on the finish die-sinking electrical discharge machining (EDM) of AISI P20 tool steel. Journal of the Braz. Soc. of Mech. Sci. &Eng., 29:4 / 367, Volume 6, Issue 11, November 2017 Page 16