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MODELING AND OPTIMIZATION OF EDM PROCESS
PARAMETERS: A REVIEW
S. G. Badekar1, Dr. B. M. Dabade2 1
Department of Production Engineering, Government Polytechnic, Nanded, India, 2
Professor, Department of Production Engineering, SGGSIE&T, Nanded, India,
ABSTRACT
Electrical discharge machining (EDM) is one of the earliest non-traditional machining processes. EDM process is based on thermo electric energy between the work piece and an electrode. There are different input parameters which influence on the outputs such as MRR, TWR and SR. Optimization is one of the techniques used in manufacturing sectors to arrive for the best manufacturing conditions, which is an essential need for industries towards manufacturing of quality products at lower cost. Also due to difficulty of EDM, it is very complicated to determine optimal cutting parameters for improving cutting performance, which is an important action in machining. And modeling is necessary to make a precise relation between input and output parameters. This paper provide a review on the various research activities carried out in modeling and optimization of EDM process parameters by various techniques on different dielectric media, material of work piece and electrode material.
Keywords: Electrical Discharge Machining, Modeling, Optimization, Process Parameters
1. INTRODUCTION
Electric discharge machining (EDM) is an extensively used unconventional manufacturing process that uses thermal energy of the spark to machine electrically conductive as well as non-conductive parts regardless of the hardness of the work material. EDM can cut intricate contours or cavities in pre-hardened steel or metal alloy (Titanium, Hastelloy, Inconel, etc.) without the need for heat treatment to soften and re-harden the materials. During the EDM operation, tool does not make direct contact with the work piece eliminating mechanical stresses, chatter and vibration problems. EDM has thus, become an indispensable machining option in the meso and micro manufacturing of difficult to machine complex shaped dies and molds and the critical components of automobile, aerospace, medical and other industrial applications. The process has however, some limitations such
ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online)
Volume 6, Issue 2, February (2015), pp. 30-36
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as high specific energy consumption, longer lead times and lower productivity which limit its applications. Researchers worldwide are thus, focusing on process modeling and optimization of EDM to improve the productivity and finishing capability of the process [6]. In this paper research carried out by different researchers on various modeling and optimization techniques used in their research work by using various electrodes, workpiece materials is presented.
Nomenclature:
EDM Electrical Discharge Machining MRR Material Removal Rate
TWR Tool Wear Rate SR Surface Roughness
RSM Response Surface Methodology GRA Gray Regression Analysis ANN Artificial Neural Network FEM Finite Element Method GA Genetic Algorithm NN Neural Network
WEDM Wire Electrical Discharge Machining T-off Pulse off
Ip Pulse Current 2. LITERATURE REVIEW
Literature reports wide experimental and analytical studies on process modeling and optimization of EDM process to improve its accuracy and productivity.
Sushant Dhar et al. [1] evaluates the effect of current, pulse on time and air gap voltage on MRR, TWR and radial over cut on EDM of Al-4 Cu-6 Si alloy-10 wt% SiCp composite by developing a second order, non-linear mathematical model for establishing the relationship among machining parameters. Analysis of Variance (ANOVA) has been performed to verify the fit and adequacy of the developed mathematical models. They observed that current has major effect on MRR followed by pulse duration and voltage. TWR also increases with increase in current. An increase in current and pulse duration increases the radial over cut.
Ko-Ta Chig [2] proposed mathematical model for the modeling and analysis of the effects of machining parameters on the performance characteristics in the EDM process of Al2O3+Tic mixed ceramic which are developed using the response surface methodology to influence of machining parameters on the performance characteristics of the material removal rate, electrode wear and surface roughness. He found that the main two significant factors on the value of material removal rate are discharge current and duty factor. The discharge current and the pulse on time also have statistical significance on both the value of the electrode wear and surface roughness.
K. D. Chattopadhaya et al. [3] developed an empirical model for prediction of output parameters using linear regression analysis by applying logarithmic data transformation of non-linear equation. Three independent input parameters of the model viz. peak current, pulse on time and rotational speed of tool electrode are chosen as variables for evaluating the output parameters such as material removal rate, electrode wear ratio and surface roughness. Their result shows that peak current and pulse on time are the most significant parameters for MRR and EWR. But peak current and electrode rotation becomes the most significance parameter for SR. Their results further revealed that maximizing the MRR while minimizing the EWR and improving the surface roughness, cannot achieved simultaneously at a particular combination of control parameter setting.
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U. Esme et al. [4] uses two of the techniques, namely factorial design and neural network (NN) were used for modeling and predicting the surface roughness of AISI 4340 steel. Surface roughness was taken as a response variable measured after WEDM and pulse duration, open voltage, wire speed and dielectric flushing pressure were taken as input parameters. Relationships between surface roughness and WEDM cutting parameters have been investigated. They find that that, NN is a good alternative to empirical modeling based on full factorial design.
M. K. Pradhan and C. K.Biswas [5] uses response surface methodology to investigate the effect of four controllable input variables namely; discharge current, pulse duration, pulse off time and applied voltage on surface roughness of electric discharge machined surface. They found that discharge current, pulse duration and pulse off time and few of their interactions have significant effect on the surface roughness.
Vasmi Krishna Pasam et al. [6] studied WEDM of titanium alloy for surface finish for the various parameters using Taguchi parameter design. They develop mathematical model by means of linear regression analysis to establish relationship between control parameters and surface finish as a process response. They made an attempt to optimize the surface roughness prediction model using Genetic Algorithm.
S.N.Joshi and S.S.Pande [7] develop physics based process modeling using finite element method has been integrated with the soft computing techniques like a artificial neural networks and genetic algorithm to improve prediction accuracy of the model with less dependency on the experimental data. A two dimensional axi-symmetric numerical (FEM) model of single spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time and energy dependent spark radius, etc. to predict a comprehensive ANN based process model to establish relationship between input process conditions and the process response. They found the proposed integrated (FEM-ANN-GA) approach was efficient and robust as the suggested optimum process parameters were found to give the expected optimum performance of the EDM process.
Mohammadreza Shabgard et al. [8] carried out experimental studies to conduct a comprehensive investigation on the influence of Electrical Discharge Machining (EDM) input parameters on the characteristics of the EDM process. The studied process characteristics included machining features, embracing material removal rate, tool wear ratio, and arithmetical mean roughness, as well surface integrity characteristics comprised of the thickness of white layer and the depth of heat affected zone of AISI H13 tool steel as workpiece. The experiments performed under the designed full factorial procedure, and the considered EDM input parameters included pulse on-time and pulse current. They find that The increase in pulse on-on-time leads to an increase in the material removal rate, surface roughness, as well the white layer thickness and depth of heat affected zone. The increase in pulse current leads to a sharp increase in the material removal rate and surface roughness. The tool wear ratio decreases by the increase of pulse on-time, and increases by the increase in the pulse current. A slight decrease could be observed in the white layer thickness by an increase in the pulse current.
Md. Ashikur et al. [9] develops a single order mathematical model for correlation the various electrical discharge machining parameters peak current, pulse on time and pulse off time and performance characteristics surface roughness. They conduct experiments on Ti-6Al-4V with copper electrode retaining the negative polarity as per the design of experiments. Response surface methodology is utilized to develop the mathematical model as well as to optimize the EDM parameters. They found that increasing the pulse on time causes fine surface until a certain value and afterwards deteriorates in the surface finish.
Rametaz Ali Mahdavi Nejad [10] in their research optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining
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parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered.
S. Gopalakannan et al. [11] investigate the influence of process parameters and their interactions on MRR, EWR and SR of metal matrix composite of aluminum 7075 reinforced with 10 wt. % of B4C. They find that pulse current and pulse on time affect the MRR. The MRR first increases with an increase in pulse on time and then decreases with further increase in pulse on time. The pulse current and pulse on time have statistical significance on both EWR and SR. The higher pulse off time offers lower the EWR value. The value of SR increases with increase in pulse current and pulse on time.
Rajmohan T. et al. [12] investigate effect of electrical discharge machining parameters such as pulse on time, pulse off time, voltage and current on MRR in 304 stainless steel by using Taguchi method. They found that different combinations of EDM process parameters are required to achieve higher MRR. The current and pulse off time are most significant machining parameter for MRR. They also mention that based on minimum number of trails conducted to arrive at the optimum cutting parameters. Taguchi method seems to be an efficient methodology to find the optimum cutting parameters.
Pragya Shandilya et al [13] optimize the process parameters during machining of SiCp/6061 Al metal matrix composite by wire Electrical discharge machining using response surface methodology and mathematical model have been developed to investigate the kerf, microstructure and surface roughness. They found that input process parameters play a significant role in the minimization of kerf.
R. Rajesh and M. Dev Anand [14] studied the effect of current, voltage, oil pressure, spark gap, pulse on time and pulse off time on material removal rate and surface finish. Empirical models for MRR and Ra have been developed by them by conducting a design of experiments based on the Grey Relational Analysis. Genetic Algorithm based multi-objective optimization for maximization of MRR and minimization of Ra has been done by using the developed empirical models.
Manish Vishwakarma et al. [15] illustrate in their research the influence of input machining parameters on the material removal rate. They develop a mathematical model to formulate the input current, gap voltage, and pulse on time and flushing pressure to the MRR of EN-19 alloy steel. Analysis is carried out by them by using the response surface method and ANOVA analysis. They find that the most significant EDM process variable influencing all the used machinability parameters of EN-19 alloy steel is pulse current. The significance order of other parameters is gap voltage followed by pulse on time and gap voltage.
Sanjay Kumar Majhi et al. [16] had done a hybrid optimization approach for the determination of the optimal process parameters which maximize the material removal rate and minimize surface roughness & the tool wear rate. The input parameters of electrical discharge machining considered for this analysis are pulse current (Ip), pulse duration (T-on) & pulse off time (T-off). The influences of these parameters have been optimized by multi response analysis. The designed experimental results are used in the gray relational analysis & the weight of the quality characteristics are determined by the entropy measurement method. The effects of the parameters on the responses were evaluated by response surface methodology. Their result shows that the RSM, GRA and entropy analysis can be successfully implemented to find the best parametric combination. K. Kumar et al. [17] optimize the parameters of WEDM process by considering the effect of input parameters viz. time on, time off, wire speed and wire feed for material removal and surface roughness measurement by forming quadratic mathematical model and optimization is done through Taguchi method.
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U. K. Vates and N.K.Singh [18] investigate the surface roughness process parameter optimization during WEDM process for steel. Response Surface Methodology (RSM) is use to investigate the effect of five independent input parameter namely gap voltage (Vg), Pulse on time (Ton), pulse off time (T-off), wire feed (Wf) and flush rate (Fr) over CLA value of surface roughness (Ra).A fractional factorial Design of Experiment of two level were employed to conducted the experiment on EN-31 die steel with chromium coated copper alloy wire electrode. The responses were observed by mathematical modeling using RSM on experimental data. They find that Performance of WEDM largely depend not only upon the combination of material of workpiece and wire electrode but also the optimal combination of the independent control process parameter.
S. Assarzadeh et al. [19] model and optimize process parameters in EDM of tungsten carbide-cobalt composite using cylindrical copper tool electrodes in planning machining mode based on statistical techniques. They select four independent input parameters viz. current, pulse on time, duty cycle and gap voltage to assess the EDM process performance in terms of material removal rate, tool wear and surface roughness. Response surface methodology has been used to plan and analyze the experiments. They find that all the responses are affected by the rate and extent of discharge energy but in a controversial manner.
Shashikant et al. [20] investigate the effect of various process parameters on the surface roughness for EN19 material by using RSM. They observed that with these values improvement in surface roughness were obtained.
Raghuraman S. and Narinder Kumar [21] investigate the optimal set of process parameters such as current, pulse ON and OFF time in Electrical Discharge Machining (EDM) process to identify the variations in three performance characteristics such as rate of material removal, wear rate on tool, and surface roughness value on the work material for machining Mild Steel IS 2026 using copper electrode. Based on the experiments conducted on L9 orthogonal array, analysis has been carried out using Grey Relational Analysis, a Taguchi method. Response tables and graphs were used to find the optimal levels of parameters in EDM process. The confirmation experiments were carried out to validate the optimal results. Thus, the machining parameters for EDM were optimized for achieving the combined objectives of higher rate of material removal, lower wear rate on tool, and lower surface roughness value on the work material.
Shailendra Kumar Singh et al. [22] optimize the EDM parameters to get better surface finish on Titanium alloys. Their result has given optimal combination of input parameters which give the optimum surface finish on the EDM machined surface.
P. Balasubramanian and T. Senthilvelan [23] in their research work two different materials EN-8 and D-3 steel have been used as a work pieces. The important process parameters that have been selected are peak current, pulse on time, die electric pressure and tool diameter. The outputs responses are MRR, TWR and SR. The cast copper and sintered powder metallurgy copper has been used as tool electrode. They use response surface methodology to analyze the parameters and ANNOVA has been applied to identify the significant process parameters. They find that for EN-8 material mean value of MRR is high and low TWR value for cast electrode compared with sintered electrode, further more the SR value is marginally less for sintered electrode compared with cast electrode and for D-3 steel which has been machined by cast electrode, the mean value of MRR is high and TWR is low compared with sintered electrode, the mean value for SR is lower for sintered electrode than that of cast electrode.
M.Azadi Moghaddam and F.Kolahan [24] in their study the effect of input EDM parameters on the surface quality of AISI 2312 hot worked steel parts has been modeled and optimized. Their experimental result for the optimal setting shows that there is considerable improvement in the surface roughness; therefore, the proposed approach is quite capable in predicting and optimizing EDM process output.
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Vikas et al. [25] compare the MRR for EN-19 and EN-41 material in die sinking EDM machine. The various input factors like pulse on time, pulse off time, discharge current and voltage were considered as the input processing parameters, while the MRR is considered as the output. Optimization using Taguchi method was performed to predict the best combination of inputs towards maximum output. They found that the discharge current in case of the En-41 and EN-19 material had a larger impact as compared to other processing parameters on MRR.
3. CONCLUSION
The review is done based on previous and recent research on EDM. MRR, SR, TWR are the performance measures of EDM. The performance of these process parameters are affected by various process parameters during machining. The review paper evaluates various process modeling techniques used during machining and various optimization techniques applied for improvement of MRR, TWR and SR. This helps in identifying optimization parameters and modeling technique for EDM work.
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