A Review on Techniques for Modeling and Optimizing Process Parameters for Electro Discharge Machining

Transcription

1 IJIRST International Journal for Innovative Research in Science & Technology Volume 1 Issue 11 April 2015 ISSN (online): A Review on Techniques for Modeling and Optimizing Process Parameters for Electro Discharge Machining Nirav G. Patel ME Scholar SVBIT, Gandhinagar Naresh D. Chauhan SVBIT, Gujarat Technological University, India Parthiv T. Trivedi SVBIT, Gujarat Technological University, India Abstract Electro Discharge Machining Processes are used only when no other traditional machining process, like drilling, milling, grinding and other traditional machining operation can meet the necessary requirements efficiently and economically. Electro discharge machining is a non-traditional machining process, in general adopted to difficult to machine material, hard material components or machining geometrically complex, that are precise and difficult-to-machine such as ceramics, composites, heat treated tool steels, super alloys etc. Electro discharge machining is a thermo-electric non-traditional machining process in which material removal takes place through the process of controlled spark generation between a pair of electrodes which are submerged in a dielectric medium. The purpose of this paper is overview of EDM process, modelling of process parameters, and also discuss about controlling the electrical process parameter, and empirical relation between process parameters and optimization of process parameters in EDM process. Keywords: EDM Process, MRR, EWR, SR, Overcut, Process Parameter optimization I. INTRODUCTION Electrical Discharge machining (EDM),otherwise known as unconventional or thermo electric non-traditional machining which is oldest and most popular process, where tool and work piece do not come into contact with each other, but material removal takes place through the process of controlled spark generation between a pair of electrodes which are submerged in a dielectric medium [1]. Every spark locally erodes (melts and vaporizes) small amount of the material surface, the overall effect being a cavity as the complementary shape of tool electrode geometry over the work, so it is also known thermal erosion process [2]. The recently use of EDM process on latest manufacturing industries field, from the development of new materials that are high speed steel, hot die steel, cold work tool steel, stainless steel, plastic mould steel, EN series steel, die block steel, super alloy, composites, hastalloys, ceramics, waspalloy, metal matrix composites, carbides, which has geometrically complex-shaped products, widely used aerospace, medical, die and mould making industries, nuclear, aeronautics industries, sports, optical, dental, jewellry, automotive and research and development fields [3]. A. EDM Process: Fig. 1: Working principle of EDM All rights reserved by 217

2 The working principle of EDM process based on thermo-electrical material removal process, in which the tool electrode shape is, reproduced mirror wise effect into a work piece material, with the shape of the electrode defining the area in which the spark erosion will occur [3]. Material is removed from the work piece by a series of rapidly recurring current discharges between two electrodes, separated by a dielectric liquid and subject to an electric voltage. One of the electrodes is called the tool-electrode, or simply the "tool" or "electrode", while the other is called the work piece-electrode, or "work piece" [4].The dielectric fluid serves the aimed to focus the discharge energy into a channel of very small cross sectional areas. It also cools the two electrodes, and flushes away the products of machining from the gap. The electrical resistance of the dielectric influences the discharge energy and the time of spark initiation [5]. Short duration discharges are generated in a liquid dielectric gap, which separates tool and work piece. The material is removed with the erosive effect of the electrical discharges from tool and work piece [4]. In the EDM process eliminated the chance of mechanical stress, chatter and vibration problems because EDM has no contact and no cutting force processes, and therefore does not makes direct contact between electrode tool and work piece. II. LITERATURE REVIEW T. Muthuramalingam, B. Mohan et al. [6] Application of Taguchi-grey multi responses optimization on process parameters in electro erosion. They studied gap voltage, discharge current, duty factor during EDM of AISI 202 stainless steel. Taguchi L27 orthogonal array method can be adopted for design of experiment. Response parameters are material removal rate, electrode wear rate and surface roughness and using grey relational analysis technique for optimization. The Value of optimum electrical process parameters among the existing parameter combinations are gap voltage 70 V, discharge current 15A, and duty factor 0.6 with copper electrode has given the better multiple response characteristics. A. Torres, I. Puertas, C.J. Luis et al. [7] Modelling of surface finish, electrode wear and material removal rate in electrical discharge machining of hard-to-machine alloys. Attempted to study electrical discharge machine, pulse time, duty cycle, open circuit voltage during EDM of Inconel 718. Factorial Design jk (K=4 number of factors J=2 number of considered level) method using for design of experiment. Response parameters are material removal rate, tool electrode wear rate and surface roughness. Higher MRR achieved when current intensity is the maximum (8A) and so tool is the duty cycle (0.6). Lower electrode wear, where the value of value of the current intensity is less than the maximum (8A), and the pulse time is 150 μs. In the case of surface roughness parameters, the most influential factors turn out to be the current intensity and the pulse time. However, the open-circuit voltage and the duty cycle are the least influential factors. V. Muthukumar, N. Rajesh, R. Venkatasamy, A. Sureshbabu, N. Senthilkumar et al.[8] Mathematical Modeling for Radial Overcut on Electrical Discharge Machining of Incoloy 800 by Response Surface Methodology. Conducted a study on EDM of Incoloy 800, the effect of combination of current, pulse on time, pulse off time, voltage was determined by response surface methodology is applied for prediction of radial overcut and experiment were planned as per central composite design method. The significant coefficients were obtained by performing ANOVA at 5% level of significance. ANOVA result shows that current and voltage are highly significant parameters, while pulse on time and pulse off time are non-significant parameters by considering radial overcut response. The predicted values match the experimental results reasonably well with the coefficient of determination of for Radial Overcut. Mitali S. Mhatre, Sagar U. Sapkal, Raju S. Pawade et al. [9] Electro Discharge machining characteristic of Ti-6Al-4V alloy: A grey relational optimization. Examined electrical discharge machining of Ti-6Al-4V alloy. They studied the effect of duty cycle, pulse current, electrode type, gap voltage Taguchi L18 orthogonal array method can be adopted for design of experiment. Response parameters are material removal rate, electrode wear rate and surface roughness and using grey relational analysis technique for optimization. It is found that while all the factors have significant effect to varying degrees on the EDM performance, pulse current is the most significant factor affecting material removal rate, dimensional accuracy and surface integrity of drilled hole. Among the process parameters, it is the type of tool which has the most dominating effect followed by pulse on time. On increasing the pulse current and pulse voltage MRR increases to a certain degree and EWR decreases. Increasing the pulse duration decreases the EWR and the more pulse duration lower the EWR. Vinnet Srivastava, Pulak M. Pandey, et al [10] Statistical modeling and material removal mechanism electrical discharge machining process with cryogenically cooled electrode. Conducted a study on EDM of M2 grade high speed steel, the effect of combination of discharge current, pulse on time, duty cycle, gap voltage was determined by response surface methodology is applied for prediction of EWR, MRR and experiment were planned as per central composite design method. It can be concluded that the mechanism of sparking and material removal does not change, but due to cryogenic cooling of the electrode, the temperature of the electrode decreases, resulting in smaller formation of the crater. Milan Kumar Das, Kaushik Kumar, Tapan Kr. Barman, Prasanta Sahoo, et al. [11] Application of Artificial bee Colony Algorithm Optimization of MRR and Surface Roughness in EDM of EN31 tool steel. Attempted to study electric discharge machining of EN 31 tool steel, the effect of combination of pulse on time, pulse off time, discharge current, and voltage was determine by response surface methodology is appliedfor prediction of surface roughness and material removal rate and experiment were planned as per central composite design method. The artificial bee colony algorithm is successfully employed for finding out the optimal parametric combination of the four process parameters of EDM for optimum MRR and Ra. Also, ABC is applied for finding out the optimal process parameters of multi-responses (MRR and Ra). The optimum values optained All rights reserved by 218

3 from the analysis show good agreement with that of experimental values. It is seen that MRR and Ra are proportional to pulse on time and discharge current in the experimental regime. M Manohar, T Selvaraj, D Sivakumar, Shibhu Gopinath, Koshy M George et al [12] Experimental study to assess the effect of electrode bottom profile while machining Inconel 718 through EDM Process. Conducted a study on EDM of Incoloy 718, the effect of combination of Peak Current, Pulse on Time, Pulse off Time was determined by L18 Orthogonal Array is applied for prediction of Material Removal Rate, Surface Finish. It is experimentally demonstrated that the effect of erosion of the flat profile electrode could be overcome by replacing it with the convex profile electrode; also that the performance of concave profile electrodes simulates the machined surface generated by the eroded flat profile electrode, over a period of time. Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama et al. [13] Neural network modeling and analysis for surface characteristics in electrical discharge machining. The present work emphasizes the development of an artificial neural network model for predicting the surface roughness. Conducted a study on EDM process of Ti-5-2.5, the effect of combination of peak current, pulse on time, pulse off time, servo voltage was determined by response surface methodology is applied for prediction of surface roughness and experiment were planned as per central composite design method. The important findings from the work carried out in this research are summarized in this section. The error is within the agreeable limit and the developed neural network model is adequate for prediction the surface roughness, Ra. As the Ip increases initially the surface roughness increases up to 15 A of Ip afterwards a disparate characteristic of Ra is appeared. A decreasing trend of the Ra is appeared at high Ip (>23A). It is found that the Ra increase with Ton however, the decreasing tendency is apparent to too long Ton (about μs ). Jambeswar Sahu, Chinmaya P. Mohanty, S.S Mahapatra et al [14] A DEA approach for optimization of multiple response in Electrical Discharge Machining of AISI D2 steel. A response surface methodology (RSM) is adopted to establish effect of various process parameters such as discharge current (Ip), duty factor, pulse on time, flushing pressure on four important responses like MRR, TWR, SR and circularity of machined component. It is concluded that the best quality and productivity achieved at Ip=7 amp, Ton= 200μs, Duty Factor=90% and Fp=0.4 kg/cm2. With this best combination of factorial level, the experimental values of responses are obtained as MRR= mm3/min, TWR= mm3/min, Ra= μs and circularity= which are nearly equal to the predict result obtained from regression model. Thus, DEA method has the ability to hold the multiplicity of inputs and outputs and an easy optimization technique to find the best alternatives. M. K. Das, K Kumar, T. K Barman, P. Sahoo et al. [15] Optimization of surface roughness and MRR in EDM using WPCA. Conducted a study on EDM of EN31 Tool Steel, the effect of combination of Pulse on Time, Pulse off Time, Discharge Current, Voltage Different Level was determined by Taguchi method is applied for prediction of Material Removal Rate, Surface Roughness and experiment were planned as per L27 orthogonal array method. In this Study, the multiple responses (Surface roughness parameters and MRR) are efficiently optimized using the weighted principle component analysis (WPCA) along with Taguchi design in EDM of EN31 tool steel. ANOVA result shows that the discharge current is the most influencing parameter that significantly affects the roughness and MRR characteristics at a confidence level of 95%. S. Assarzadeh, M. Ghoreshi et al. [2] Statistical modeling and optimization of process parameter in electro-discharge machining of cobalt-bonded tungsten carbide composite (WC/6%Co). In this Study, attempts have been made to model and optimize process parameters in Electro-Discharge Machining (EDM) of tungsten carbide-cobalt composite using cylindrical copper tool electrodes in planning machining mode based on statistical techniques. Input parameters are, discharge current, duty cycle pulse-on time and gap voltage were selected to assess the EDM process performance in terms of material removal rate, tool wear rate, and average surface roughness. Response surface methodology, employing a rotatable central composite design scheme, has been used to plan and analyse the experiments. For each process response, a suitable second order regression equation was obtained applying analysis of variance (ANOVA) and student t-test procedure to check modelling goodness of fit and select proper forms of influentially significant process variables (main, two-way interaction, and pure quadratic terms) within 90% of confidence interval (p-value 0.1). The MRR increases steadily by increasing both discharge current and duty cycle. The most influential parameters on TWR are current, pulse on time, duty cycle, the interactions amongst duty cycle with current and pulse-on time as well as the pure quadratic effect of duty cycle. The Ra response is mostly affected by discharge current, pulse-on time, duty cycle, and the interaction effect between the first two. S. Gopalakannan, T Senthilvelan, S. Ranganathan et al. [16] Modeling and Optimization of EDM Process Parameters on Machining of Al 7075-B4C MMC Using RSM. In this study carried out by using central composite design of RSM. Analysis of variance was applied to investigate the influence of input parameters and their interactions viz., gap voltage, pulse current, pulse on time and pulse off time on material removal rate, electrode wear ratio and surface roughness. The optimum parameter of combination setting is voltage volt, pulse current Amps, Pulse on time 7.77 μs and pulse off time 5.00 μs for maximizing MRR, minimizing EWR and SR. Rajmohan T., Prabhu R., Subba Rao G., Palanikumar K. et al [17] Optimization of Machining Parameters in Electrical Discharge Machining (EDM) of 304 Stainless Steel. In this experimental work, the effect of EDM input parameters such as voltage, pulse on time, pulse off time, and current on responses parameter material removal rate, in 304 stainless steel was studies. The experiments are carried out as per design of experiments approach using L9 orthogonal array. On the basis of experimental results, calculated S/N ration, analysis of variance (ANOVA) and F test values, the current and pulse off time are the most significant machining parameter for MRR. All rights reserved by 219

4 R. Rajesh, M. Dev Anand. Et al. [1] The optimization of the Electro-Discharge Machining Process Using Response Surface Methodology and Genetic Algorithms. In this study, input parameter such as oil pressure, working voltage, working current, spark gap pulse on time and pulse off time on responses parameter MRR and SR has been studied. Empirical models for MRR and SR have been developed by conducting a designed experiment based on the Taguchi s L32 orthogonal Array. Genetic Algorithm based multi-objective optimization for maximization of MRR and minimization of SR has been done by using the developed empirical models. Finally the optimal conditions obtained by GA as. i.e., current at 3 A, voltage at 78 V, gap at 0.35, flow rate at 1, pulse ON as 1 and pulse OFF as 8 for maximizing MRR and minimize the surface roughness simultaneously among the experimental data. The most influencing factor obtained by the response table is the working current for the EDM process. T. M. Chenthil Jegan, M. Dev Anand, D. Ravindran. et al. [18] Determination of Electro Discharge Machining Parameters in AISI 202 Stainless Steel Using Grey Relational Analysis. This study describes the selection of input parameters pulse on time pulse off time and discharge current, in EDM for the machining process of the AISI 202 stainless steel material. The use of grey relational analysis for optimizing the machining parameters material removal rate and surface roughness. The results show that Discharge Current was the main parameter affecting the MRR. Hence by properly adjusting the control factors, work efficiency and product quality can be increased. Shankar Singh, S. Maheshwari, P.C. Pandey et al. [3], Some investigations into the electric discharge machining of hardened tool steel using different electrode materials. In this study shown the results of an experimental investigation the effects of machining input parameters such as pulsed current on responses material removal rate, diameteral overcut, electrode wear, and surface roughness in electric discharge machining of En-31 tool steel hardened and tempered to 55 HRc. The work material was EDM with copper, copper tungsten, brass and aluminium electrodes by varying the pulsed current at reverse polarity. For EN- 31work material, Copper and Aluminum electrodes offer higher MRR. Diameter Overcut produced on EN-31 is comparatively low when using copper and aluminum electrodes. Copper and Copper-tungsten electrodes offer comparatively low electrode wear for the tested work material. III. CONCLUDING REMARKS From above literature review it is indicated that Peak Current, Gap voltage, Discharge Current, Pulse on Time, Pulse off Time, Duty Factor, Open Circuit Voltage; Electrical Machine, Electrode type, Servo voltage, flushing pressure, voltage different level etc. are the important control parameters of Electro Discharge Machining process. The Factorial Design, Taguchi Method and Response Surface Methodology and j k factorial design can be applied as the DOE (Design of Experiment). The methods that can be applied for EDM process parameter optimization work are Grey Relation Analysis and ANOVA (Analysis of variance), Genetic Algorithm, Artificial Bee Colony Algorithm, Artificial Neural Network, DEA Methodology along with ARV approach, MINITAB, STATISTICA software and Lingo software is a useful aid for the above purpose. REFERENCES [1] R. Rajesh, M. Dev Anand, The optimization of the Electro-Discharge Machining Process Using Response Surface Methodology and Genetic Algorithms, 2012 Elsevier Ltd. [2] S. Assarzadeh, M. Ghoreshi, Statistical modeling and optimization of process parameter in electro-discharge machining of cobalt-bonded tungsten carbide composite (WC/6%Co), 2013 Elsevier Ltd. [3] Shankar Singh, S. Maheshwari, P.C. Pandey, Some investigations into the electric discharge machining of hardened tool steel using different electrode materials 2004 Elsevier Ltd. [4] Kuldeep Ojha, R. K. Garg, K. K. Singh MRR Improvement in Sinking Electrical Discharge Machining: A Review journal of Minerals & Materials Characterization & Engineering, Vol. 9, No.8, pp , 2010 [5] S. Chakraborty, V. Dey, S.K Ghosh A review on the use of dielectric fluid and their effects in electrical discharge machining characteristics Elsevier Ltd. [6] T. Muthuramalingam, B. Mohan Application of Taguchi-grey multi responses optimization on process parameters in electro erosion, 2014 Elsevier Ltd. [7] A. Torres, I. Puertas, C.J. Luis Modelling of surface finish, electrode wear and material removal rate in electrical discharge machining of hard-to-machine alloys, 2014 Elsevier Ltd. [8] V. Muthukumar, N. Rajesh, R. Venkatasamy, A. Sureshbabu, N. Senthilkumar Mathematical Modeling for Radial Overcut on Electrical Discharge Machining of Incoloy 800 by Response Surface Methodology, 2014 Elsevier Ltd. [9] Mitali S. Mhatre, Sagar U. Sapkal, Raju S. Pawade Electro Discharge machining characteristic of Ti-6Al-4V alloy: A grey relational optimization, 2014 Elsevier Ltd. [10] Vinnet Srivastava, Pulak M. Pandey, Statistical modeling and material removal mechanism electrical discharge machining process with cryogenically cooled electrode 2014 Elsevier Ltd. [11] Milan Kumar Das, Kaushik Kumar, Tapan Kr. Barman, Prasanta Sahoo, Application of Artificial bee Colony Algorithm Optimization of MRR and Surface Roughness in EDM of EN31 tool steel, 2014 Elsevier Ltd. [12] M Manohar, T Selvaraj, D Sivakumar, Shibhu Gopinath, Koshy M George, Experimental study to assess the effect of electrode bottom profile while machining Inconel 718 through EDM Process, 2014 Elsevier Ltd. [13] Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama, Neural network modeling and analysis for surface characteristics in electrical discharge machining 2014 Elsevier Ltd. All rights reserved by 220

5 [14] Jambeswar Sahu, Chinmaya P. Mohanty, S.S Mahapatra, A DEA approach for optimization of multiple response in Electrical Discharge Machining of AISI D2 steel, 2013 Elsevier Ltd. [15] M. K. Das, K Kumar, T. K Barman, P. Sahoo, Optimization of surface roughness and MRR in EDM using WPCA, 2013 Elsevier Ltd. [16] S. Gopalakannan, T Senthilvelan, S. Ranganathan, Modeling and Optimization of EDM Process Parameters on Machining of Al 7075-B4C MMC Using RSM, 2012 Elsevier Ltd. [17] Rajmohan T., Prabhu R., Subba Rao G., Palanikumar K., Optimization of Machining Parameters in Electrical Discharge Machining (EDM) of 304 Stainless Steel 2012 Elsevier Ltd. [18] T. M. Chenthil Jegan, M. Dev Anand, D. Ravindran, Determination of Electro Discharge Machining Parameters in AISI 202 Stainless Steel Using Grey Relational Analysis 2012 Elsevier Ltd. All rights reserved by 221