Electrical Discharge Machining Process Research Articles

PERFORMANCE EVALUATION AND ECONOMIC ASSESSMENT IN ELECTRICAL DISCHARGE MACHINING OF SS316 USING GRAPHITE POWDER DISPERSED IN BIODEGRADABLE PALM OIL DIELECTRIC

In today’s rapidly evolving manufacturing industry, researchers and engineers are constantly seeking innovative methods to improve machining processes. One such area of interest is the use of electrical discharge machining (EDM) for difficult-to-machine metals like SS316. This research compares the performance and economic aspects of traditional EDM and powder-mixed EDM when machining SS316 using copper electrode. Graphite powder dispersed in biodegradable palm oil is investigated as a dielectric media with the aim of enhancing EDM performance. According to the findings, the analysis of the cutting performance of different machinability aspects revealed that using graphite powder mixed with palm oil as a dielectric in EDM outperformed conventional EDM relating to material removal, surface finish, surface hardness, electrode wear rate and hole overcut. The flushing condition and unique properties of graphite powder and palm oil, such as cooling, and thermal conductivity, contributed to the improved performance observed in EDM processes. These findings highlight the potential of graphite powder mixed with palm oil as a promising alternative dielectric in EDM, offering enhanced machining capabilities and improved surface quality. The total machining expenditure per part under PMEDM (INR 505.50) is lowered by 9.2% than expenditure under conventional EDM (INR 525). The surface microhardness of the machined component attained using graphite powder-mixed EDM is enhanced in comparison to that achieved using standard EDM due to the creation of a carbon-rich re-solidified layer.

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

Comprehensive Analysis of Heavy Metal Aerosol Emissions and Health Risk from the Electrical Discharge Machining Process: A Control and Mitigation Approach for Green Manufacturing

Comprehensive Analysis of Heavy Metal Aerosol Emissions and Health Risk from the Electrical Discharge Machining Process: A Control and Mitigation Approach for Green Manufacturing

Towards artificial intelligence empowered performance enhancement of EDM process using nano-graphene mixed bio-dielectric supporting the carbon neutrality and sustainable development

The growing population with every passing day sets an alarming situation with respect to the conservation climate protocols. The increasing needs of society also demand a significant enhancement in the manufacturing capacity to augment the situation. However, it's a stringent requirement of the hour to propose sustainable and clean manufacturing processes to realize the goal of carbon neutrality to support a healthy life on the earth. Specifically, the processes that are energy intensive like electric discharge machining (EDM) are of serious concern regarding sustainability viewpoint. The role of the said process cannot be essentially eliminated as advent of new materials of superior characteristics demand the application of EDM for accurate cutting of intricate profiles. Nevertheless, the commonly used oil-based dielectric (kerosene) in EDM releases aerosol, deposit particles, oxides of carbon (CO2 & CO), thus contributing to the environmental contamination. It is pertinent to mention that industries are compelled to tune their processes to achieve the goals of Net-Zero. Therefore, this study thoroughly investigates the potential of nano-graphene mixed rice bran oil to make the EDM process cleaner and sustainable which has never been investigated so far. Moreover, the process has been successfully modeled using artificial neural network (ANN) and optimized by non-dominated sorting genetic algorithm-II (NSGA-II) which is another novel aspect of this study as it eradicates the need of extensive experimentation. Experimentation has been performed via Taguchi's experimental strategy followed by a detailed explanation of the findings based on process physics. In comparison to the traditional dielectric an improvement of 98.8% in material removal rate (MRR) and 93.9% reduction in specific energy consumption (SEC) are realized if the said novel combination is applied without compromising the quality. CO2 emissions determined for both rice bran oil and kerosene oil have revealed that rice bran oil provides 99.96% lesser CO2 emission in comparison to its counterpart.

A study on die-sinking electrical discharge machining behaviour of friction stir processed AA5052/WS2 surface composite

A study on die-sinking electrical discharge machining behaviour of friction stir processed AA5052/WS₂ surface composite

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Optimization of Process Parameters of Wire Electric Discharge Machining Process for Machining AISI A2 Tool Steel

In this investigation, AISI A2 tool steel is considered as the workpiece material, which is typically used to manufacture blanking tools, punches die etc., due to its good toughness and wear resistance. In this work, the effect of controlling parameters of the Wire Electric Discharge Machining (WEDM) process is investigated. Molybdenum tool electrodes of 0.18mm diameter and de-ionized water dielectric medium are utilized. Peak current, on-time, off-time and voltage are considered as the controlling parameters. Surface roughness average and material erosion rate are considered as the response parameters. The type of design of experiments considered for this work is Taguchi’s L27 orthogonal array. Analysis of variance indicates the percentage contribution of each machining parameter on response parameters. The optimum combination of machining parameters yields a minimum surface roughness of 2.87 μm and the highest material removal rate obtained in this work is 774 mm3/hr.

Comprehensive stratified K-fold machine learning for surface roughness prediction with 3D printed electrodes: Analyzing dielectric variability and heat treatment effects

Electrical discharge machining (EDM) is popular for its accuracy in cutting difficult materials. Material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR) are deemed as the success indicators in EDM. The positive attributes of sustainable manufacturing make it a suitable choice for diverse applications. EDM electrodes used in this work are prepared by 3D printing and further subjected to deep cryogenic treatment (DCT) to investigate the influence of heat treatment on EDM performance. In the second instance, the experiments are conducted with different dielectric fluids viz. conventional dielectric (CD) and lemon peel biodiesel dielectric (LPD) to analyze the impact of dielectric change. When the dielectric is changed to lemon peel biodiesel, the roughness of the electro-discharged machined surface is reduced to a maximum of about 33%. A further improvement of about 11% is possible in the surface finish of machined surfaces when the EDM electrodes are subjected to DCT under an identical operating condition. LPD as dielectric also helps in attaining the primary objective of green manufacturing by reducing specific evaporative emissions. During the EDM process, LPD adoption causes a reduction of 96% in nitrogen oxide (NO) emissions and 86% in hydrocarbon (HC) emissions. A modified adaptive neuro fuzzy inference system (ANFIS) with a stratified k-fold machine learning approach with a prediction accuracy of about 95% is also developed in this work to envisage the surface roughness of electro-discharge machined surfaces.

Electrical discharge machining process investigation and optimization of parameters for machining of aluminum silicon dioxide (Al7075–SiO2) composite

AbstractThis research paper explores the impact of electrical discharge machining process parameters, including current, pulse‐on time (Ton), and pulse‐off time (Toff), on the material removal rate (MRR) and surface roughness (SR) during the fabrication of aluminum silicon dioxide (Al7075–SiO2) composites. The Al7075–SiO2 composites were produced through stir casting, incorporating 10% by weight of SiO2 reinforcements with an average particle size of 54 µm. To systematically investigate the process parameters' effects, Taguchi's L9 orthogonal array was implemented in the design of experiments. Subsequently, the experimental data underwent analysis of variance (ANOVA) for thorough analysis. The response table for MRR and SR demonstrated that the current significantly influenced both responses (MRR and SR). The ANOVA results provided valuable insights into the percentage contribution of each parameter to the responses. Through the analysis, the optimal control variable settings for achieving the highest MRR were identified as a current of 3 A, a pulse‐on time of 1500 µs, and a pulse‐off time of 1 µs. Conversely, to attain the lowest SR, the ideal control variable settings were determined to be a current of 1 A, a pulse‐on time of 500 µs, and a pulse‐off time of 1 µs. Results also suggest that the error was within admissible range of 5% and the percentage contribution of the current (70.19%) had the largest effect on MRR.

Machining and wear rates in EDM of D2 steel: A comparative study of electrode designs and materials

Electrical discharge machining (EDM), especially the die-sinking variant, is a well-known slow machining process among non-conventional manufacturing processes. Different approaches are being researched to improve EDM performance characteristics especially increasing its material removal rate (MRR) and reducing the tool wear rate (TWR). This study aims to improve the EDM process performance through modifications in tool design and employing different tool materials. The performance of conventional EDM tool is compared with different tool designs while machining D2 steel. Moreover, tool designs are employed on different tool materials including copper (Cu), copper-tungsten alloy (CuW), and graphite (Gr) to investigate the combined effect of tool designs and materials on important performance indicators of EDM die-sinking. The machining time (MT), material removal rate (MRR), lateral tool wear (TWR_L), and diametric tool wear (TWR_D) are selected as the response measures. The modified tool designs reduce the machining time by 45%. Likewise, Cu and CuW remarkably improve the material removal rate by 80%. Similarly, the lateral and diametric tool wear of modified designs are less than the wear corresponding to conventional design. Microscopic analysis revealed two wear zones which are named as aggressive sparking zone and mild sparking zone. All materials (Cu, CuW, and Gr) with the modified tool designs show significantly less sparking zone compared with the conventional design. Moreover, the most appropriate combination of tool design and material is proposed to achieve better EDM performance.

Analysis of wire-cut electric discharge machining behaviour of AA6061 / C / ZrO2 hybrid nano composites using Taguchi’s method

This study focuses on the optimization of wire-cut electric discharge machining (WEDM) process for hybrid metal matrix nanocomposites. AA6061 alloy reinforced with graphene (C) and zirconia (ZrO2) was fabricated through the stir casting method with ultrasonic assistance for three different weight proportions: 92% AA6061 / 3% C / 5% ZrO2, 87% AA6061 / 3% C / 10% ZrO2, and 82% AA6061 / 3% C / 15% ZrO2. Microstructural examination confirmed the uniform distribution of reinforcements in the matrix alloy. The fabricated composite was machined by WEDM using an L27 orthogonal array designed by Taguchi’s method. Four electrical control factors of pulse on-time, pulse off-time, wire feedrate and peak current were considered, along with one reinforcement factor and three output responses: kerf width, surface finish and material removal rate to analyse the machining behaviour. Analysis of Variance was performed to determine the significant parameters. The results revealed that the most significant factor is pulse on-time among the five different factors. Optimized parameters were identified and examined through confirmation experiments resulting in improved machining characteristics.

Optimization of Electrical Discharge Machining Process by Metaheuristic Algorithms

Because of its versatility and ability to work with difficult materials, Electrical Discharge Machining (EDM) has become an essential tool in many different industries. It can produce precise shapes and intricate details. EDM has transformed fabrication processes in a variety of industries, including aerospace and electronics, medical implants and surgical instruments, and the shaping of small components. Its capacity to machine undercuts and deep cavities with little material removal makes it ideal for producing complex geometries that would be challenging or impossible to accomplish with conventional machining techniques. Several attempts have been carried out to solve the optimization problem involved in the EDM process. This paper emphasizes optimizing the EDM process using three metaheuristic algorithms: Glowworm Swarm Optimization (GSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The study's outcome showed that the GWO algorithm outperformed the GSO and WOA algorithms in solving the EDM optimization problem and achieved the minimum surface roughness value of 1.7593µm.

Thermal model of crater formation process in electrical discharge machining

The formation mechanism of the discharge crater in electrical discharge machining process is not fully understood, which affects the application of electrical discharge machining technology. In this paper, a thermal-fluid coupling model was proposed by COMSOL Multiphysics software, and the influence of discharge parameters, including peak current, pulse on-time, and the material of tool electrode and workpiece, on crater formation process was analyzed. The temperature distribution, the flow field velocity, heating region, and morphological changes of the discharge crater were analyzed to illustrate the formation mechanism of the discharge crater. By comparing the experimental results with the numerical simulation results, it was found that under the same discharge energy, the peak current has a greater influence on the discharge crater size than the pulse on-time. Moreover, through analysis of the influence of tool electrode materials and workpiece materials, it was found that the crater size is influenced by the boiling point of tool electrode material and the melting point of workpiece material. The experimental and numerical simulation results showed that the simulation results are consistent with the experimental results, which verifies the applicability of the thermal-fluid coupling model in studying the crater formation mechanism in electrical discharge machining.

Bi-objective optimization of an EDM process with Cu-MWCNT composite tool using single-valued neutrosophic grey relational analysis

Purpose In many of today’s manufacturing industries, such as automobile, aerospace, defence, die and mould making, medical and electrical discharge machining (EDM) has emerged as an effective material removal process. In this process, a series of discontinuous electric discharges is used for removing material from the workpiece in the form of craters generating a replica of the tool into the workpiece in a dielectric environment. Appropriate selection of the tool electrode material and combination of input parameters is an important requirement for performance enhancement of an EDM process. This paper aims to optimize an EDM process using single-valued neutrosophic grey relational analysis using Cu-multi-walled carbon nanotube (Cu-MWCNT) composite tool electrode. Design/methodology/approach This paper proposes the application of grey relational analysis (GRA) in a single-valued neutrosophic fuzzy environment to identify the optimal parametric intermix of an EDM process while considering Cu-MWCNT composite as the tool electrode material. Based on Taguchi’s L9 orthogonal array, nine experiments are conducted at varying combinations of four EDM parameters, i.e. pulse-on time, duty factor, discharge current and gap voltage, with subsequent measurement of two responses, i.e. material removal rate (MRR) and tool wear rate (TWR). The electrodeposition process is used to fabricate the Cu-MWCNT composite tool. Findings It is noticed that both the responses would be simultaneously optimized at higher levels of pulse-on time (38 µs) and duty factor (8), moderate level of discharge current (5 A) and lower level of gap voltage (30 V). During bi-objective optimization (maximization of MRR and minimization of TWR) of the said EDM process, the achieved values of MRR and TWR are 243.74 mm3/min and 0.001034 g/min, respectively. Originality/value Keeping in mind the type of response under consideration, their measured values for each of the EDM experiments are expressed in terms of linguistic variables which are subsequently converted into single-valued neutrosophic numbers. Integration of GRA with single-valued neutrosophic sets would help in optimizing the said EDM process with the Cu-MWCNT composite tool while simultaneously considering truth-membership, indeterminacy membership and falsity-membership degrees in a human-centric uncertain decision-making environment.

Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel electrodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms—artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors—to evaluate their ability to predict wear patterns accurately. Each algorithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.

Influence of machining process of MoS2/B4C/Az31 Mg alloy composite and its tribological characteristics

The automotive, biomedical, and aerospace industries are among those with a rising need for lightweight materials with enhanced mechanical and tribological qualities. Composites based on magnesium alloys have attracted interest because of their excellent strength-to-weight ratio and promise to improve component performance. Magnesium (Mg) alloy-based composites find applications in sports and leisure equipment, aerospace, biomedical implants, and more. The research outlined here serves a critical need in the field of materials science and engineering, particularly regarding the development of advanced magnesium (Mg) alloy-based composites. In this study, we have created a new aluminum composite using the AZ31 alloy mixed with 5% boron carbide (B4C) and 5% molybdenum disulfide (MoS2) as reinforcement through a powder metallurgical technique. The magnesium alloy contains 3% aluminum and 1% zinc. Our research aims to understand the mechanical and tribological behaviors and the impact of Electrical Discharge Machining (EDM) process parameters on AZ31 magnesium alloy. We need to modify these properties for various applications. Many industrial researchers have studied the machinability of magnesium alloys using EDM. We conducted wear tests on AZ31 alloy reinforced with both B4C and MoS2 in altered quantities using a pin-on-disc setup. The outcome displays that the wear resistance of these composites is considerably better matched to other magnesium matrix composites (MMCs). We also measured various densities of the hybrid composite, including apparent density, green density, and sintered density, which were found to be 0.839, 1.495, and 1.504 g/cm3, respectively—better than other composites. In addition, the hybrid composite exhibited a substantial increase in micro hardness, reaching 22.012 HV, indicating improved wear resistance of the material. Comparatively, low density, minimum wear profile, and maximum hardness were recorded for the sample of AZ31 + 5%MoS2 + 5%B4C. The influence of EDM parameters was discussed.

Design, fabrication, and characterization of a high-sensitivity integrated quartz vibrating beam accelerometer.

This paper describes the design, fabrication, and characterization of a quartz vibrating beam accelerometer consisting of a metal spring-mass and quartz double-ended tuning forks (DETFs). In this approach, the inertial force of the proof mass pulls or compresses the DETFs, affecting their resonance frequency and, thus, enabling the quasi-digital measurement of acceleration. An isolation structure was specifically designed to prevent the external interference stress from transforming into the DETFs and to decrease the DETFs' thermal stress as the ambient temperature changes. A stress-free and high-precision wire-cut electrical discharge machining process was introduced to solve the fabrication problem of flexible hinges, and a femtosecond laser was used to release the proof mass, comprehensively considering the compatibility of the fabrication process and structural design. The oscillation excitation and detection of the DETFs were analyzed, and the DETFs were fabricated using a micro-electromechanical systems process. Sensor dimensions were optimized to improve sensor sensitivity. An accelerometer prototype was fabricated, and its performance was characterized. The tested scale factor was 157.28 Hz/g, and its stability was 16.54 ppm. The bias stability and 1g stability at 1h were 18 and 7.84 µg, respectively. The experimental results validated the feasibility of the sensor design.

Circular usage of waste cooking oil towards green electrical discharge machining process with lower carbon emissions

A global manufacturing community is dedicatedly striving to implement the concept of NetZero in precision cutting of difficult-to-machine materials, specifically, Inconel 617 (IN617) with due consideration to environmental protocols. The fast strain hardening issue of the said alloy during conventional processing rationalizes the application of electric discharge machining (EDM). However, EDM has been criticized for its high energy consumption and limited cutting efficiency. Moreover, conventional dielectric (kerosene) employed in EDM has drastic environmental and operator health concerns. To address the abovementioned issues, waste cooking oil (WCO) has been employed in this study which enhances the reusability of resources and minimizes the cost of the dielectric. Making the process sustainable is imperative along with continuously escalating scarcity of engineering resources. Therefore, the potential of shallow and deep cryogenically treated electrodes (SCT and DCT) has been comprehensively examined against nanofilled WCO to achieve the aforementioned objective. Three different concentrations of powder (Cp) and surfactant (Cs) to uplift the machining responses are investigated through a detailed parametric experimental design. Core machining factors such as material removal rate (MRR), surface roughness (SR), and specific energy consumption (SEC) are examined through optical and electron microscopy studies and 3D surface profilometry. Hereafter, machining factors are modelled using the artificial neural network (ANN) technique. An exceptional improvement of 80%, 25.3%, and 75.16% has been achieved in MRR, SR, and SEC respectively using nanopowder-mixed WCO against SCT brass compared to the responses’ values obtained against conventionally used kerosene. Furthermore, compared to kerosene, the maximum CO2 reduction of 79.97 ± 11.2% is achieved with WCO.

Experimental study on electrode wear during the EDM of microgrooves with laminated electrodes consisting of various material foils

Electrode wear during electrical discharge machining (EDM) is inevitable, and tool electrodes of different materials exhibit different wear rates. Unlike a single-material tool electrode, for a laminated electrode consisting of various material foils (LE-VMF), the components suffer from significantly different amounts of wear and also influence each other in EDM, however, the form and cause of the wear are not clear. Thus, the LE-VMFs including symmetric and asymmetric LE-VMFs were prepared and used in EDM in this paper. The wear of foils in LE-VMFs and their mutual influence during the EDM process were investigated through experiments. The wear forms of a single foil electrode and a foil of the same material in the LE-VMF were also compared. The experimental results show that copper foil with a lower wear rate had a protective effect on an adjacent brass foil with a larger wear rate in the LE-VMF, while the protective effect of the brass foil in the symmetric LE-VMF was larger than that in the asymmetric LE-VMF. The closer the distance from the copper foil, the stronger was the protection effect and the smaller was the wear. In addition, the working surface of the brass foil in the LE-VMF did not show any concavity-like wear in the middle region of its thickness after EDM, as was the case for the single brass foil electrode, even if the thickness of the brass foil was greater than 150 μm.

Investigation on Influence of Die Electric Medium in Electrical Discharge Machining of Monel 400 Alloy

Electrical Discharge Machining (EDM) is a non-conventional thermal energy based erosive process, which primarily applied for machining hard materials. Material Removal Rate (MRR) and surface roughness are the response parameters used to characterize the dielectric nature of the machined surface in EDM process. Addition of ingredients in the dielectric fluid improves the properties of fluid for better machining of the samples. The dielectric fluid medium plays a key role in controlling the electrical discharge and heat absorption, thereby removes the debris and cools the work piece during the machining process. In the current study, comprehensive work is done by investigating the effect of different dielectric fluid medium on machining parameters of EDM process with the addition of different powders in the dielectric fluid, which results in high precise and better topography in the machined part surface. Addition of powders such as Titanium (Ti), Silicon (Si), Graphite (Gr), Copper (Cu) and Aluminium Oxide (Al2O3) in dielectric fluid increases the convection property in the work piece tool interaction with increase in the micro-hardness of material. This work analyses the performance study of Electrical Discharge Machining (EDM) of Monel 400 alloys, which can be improved by adding metallic powder into the dielectric medium. Material Removal Rate (MRR) is measured in the samples machined out of EDM process. In addition, Taguchi L27 Orthogonal Array is formulated for conducting machining in a sequential order to understand the implications of machined process parameters on the material removal rate over different dielectric mediums. It is found that the Aluminium oxide, graphite powder mix with EDM oil gives better material removal rate and less machining time. Furthermore, the introduction of Cu powders in the dielectric fluid provides better machinability response parameters. But it is preferable to parts with high slenderness ratio especially holes.