Performance Evaluation of Electrical Discharge Machining (EDM) Process Using Cryogenically Cooled Electrode

A novel aluminium metal matrix composite reinforced with SiC particles were prepared by liquid metallurgy route. Recent developments in composites are not only focused on the improvement of mechanical properties, but also on machinability for difficult-to-machine shapes. Electrical discharge machining (EDM) was employed to machine MMC with copper electrode. using EDM. Experiments were conducted using pulse current, gap voltage, pulse on time and pulse off time as typical process parameters. The experiment plan adopts face centered central composite design of response surface methodology. Analysis of variance was applied to investigate the influence of process parameters and their interactions viz., pulse current, gap voltage, pulse on time and pulse off time on material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). The objective was to identify the significant process parameters that affect the output characteristics. Further a mathematical model has been formulated by applying response surface method in order to estimate the machining characteristics such as MRR, EWR and SR.

The present study focuses on studying the effect of liquid cum gas as dielectric during Electric Discharge Machining (EDM) of carbon chromium die steel. In this work, a hybrid process of EDM employing both liquid (commercial kerosene oil) and gas (compressed helium gas) as dielectrics in die-sinking EDM has been explored. Experimentation has been done to the study effect of process factors like discharge current, pulse on time, duty cycle, tool rotation and discharge gas pressure on material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). Further, a comparative study of conventional EDM with liquid dielectric and hybrid EDM with liquid cum gaseous dielectric has been performed. It has been found that high MRR, low EWR and low SR are obtained when liquid cum gaseous dielectric was used as compared to conventional EDM with liquid dielectric. Analysis of surface morphology reveals that the formation of recast layer and surface cracks is less on specimen machined with liquid cum gaseous dielectric with respect to specimen machined with conventional liquid dielectric. The results show that the use of compressed helium gas has a positive impact on the machining performance. Superior surface finish and higher MRR reveal the possible implementation of the process in modern machining.

Electrical discharge machining (EDM) process is a non-conventional machining process used for the material which are difficult to machine. In this research work, an attempt has been made to determine the influence of Boron Carbide (B4C) particles on the machinablity of the Al (6351) alloy reinforced with 5 wt. % Silicon Carbide (SiC) Metal Matrix Composite (MMC) through EDM. Influence of machining parameters such as pulse current (I), pulse on time (Ton), duty factor (τ), and gap voltage (V) on affecting the output performance characteristics namely Electrode Wear Ratio (EWR), Surface Roughness (SR) and Power Consumption (PC) which are studied. The result shows that the addition of B4C particles significantly affects the machinablity of the composite, with a contribution of 1.6% on EWR, 3.5% on SR and 19.8% on PC. The crater, recast layer formation, and Heat Affected Zone (HAZ) in the machined surface of the composite are also reported in detail.

Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM) is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical discharge machining ZnO/Al2O3 ceramic.

Electrical discharge machining (EDM) is a well-established machining option for manufacturing geometrically complex or hard material parts that are extremely difficult-to-machine by conventional machining processes. In this research work, investigations were carried out on the machining of AISI D2 tool steel under conventional EDM and cryogenically cooled EDM process (CCEDM). The input process parameter such as current (Ip), pulse on time (Ton) and gap voltage (V) were investigated and the output responses like electrode wear ratio, metal removal rate and surface roughness were explored. Experiments were conducted using L18 orthogonal array in EDM machine. Experimental results showed reductions in the electrode wear of 16% over CCEDM process. A grey relational analysis was used to solve the EDM process with multi performance characteristics to optimize EDM parameters.

Comparisons of single pulse discharge crater geometries in EDM and EAM

Electrical Discharge Machining (EDM) is commonly used as non-traditional manufacturing methods for geometrically complex shaped or hardened materials that are extremely difficult-to-machine with traditional machining processes. In this experimental study, investigations were carried out on the machining of Conventional Heat Treated three different deep cryogenically treated and tempering processes (DCT&T) of AISI 4140 steels in EDM process. The process parameter such as discharge current (9, 16, 23, and 30 A), pulse on time (120, 180, 240, 300 µs) and pulse off time (12, 18, 24, and 30 µs) were investigated and the output responses like surface roughness, electrode wear ratio, material removal rate, and machining time were explored. Experiments were conducted using L16 orthogonal array in EDM machining process. The data obtained from experiments were analyzed. As a result, determination of discharge current (30 Amps), pulse off time (12 to 18 µs), pulse on time (180 to 240 µs) and deep cryogenically treatment with 24 h and 48 h holding time as the optimum machining processing parameters for tempered steels to be subjected to DCT&T process. As a result of multi-criteria optimization, it is suitable for best surface quality and fast material removal rate.

Wire EDM process parameter optimization for D2 steel

The high carbon high chromium die steel is most widely used in various industrial applications. However, effective machining of high carbon-chromium die steel is extremely difficult due to its high hardness and stiffness. In the present work, comparative analysis of Gas Assisted Hybrid EDM (GAHEDM), Rotary EDM (REDM), and Conventional EDM processes have been done with respect to process responses like electrode wear ratio (EWR), material removal rate (MRR), and surface roughness (SR). Experimentation has been done to the study influence of process factors like discharge current, pulse on time, duty cycle, tool rotation and discharge gas pressure on MRR, EWR and SR. Further, from a comparative study, it has been observed that high MRR, low EWR and low SR are obtained in GAHEDM process in comparison of REDM and EDM processes. Analysis of surface morphology reveals that the formation of recast layer and cracks on the surface are fewer in number on work piece machined by GAHEDM process with respect to specimen machined with REDM and EDM processes. The results show that the use of compressed helium gas in GHEDM process has a positive impact on the machining performance. This process may be used for industrial machining operation.

As one of the non-traditional manufacturing processes, micro electrical discharge machining (EDM) has been widely applied for manufacturing precise and complex microstructures. However, a number of issues remain to be studied before micro EDM can become a reliable processing method. Efforts to get higher machining quality and to improve efficiency have been carried out. The electrode wear compensation method is one of the key factors in micro EDM milling. This paper proposes a new electrode wear compensation method, the combination of the linear compensation method (LCM) and the uniform wear method (UWM), called the CLU method. This approach combines LCM, UWM and the theoretical model. Experimental results indicate that machining performance such as material removal rate, electrode wear ratio and surface roughness using the proposed method has been improved compared to that by the uniform wear method.

This paper presents a unique and novel empirical study of electrical discharge machining (EDM) supported by artificial intelligence and statistical analysis. Electrical discharge machining (EDM) is a general non-traditional machining process for machining geometrically complicated parts or hard materials that are very difficult to machine by traditional machining operations. EDM creates the material removal process by using electric spark erosion. This paper experimentally investigates the process parameters of EDM on high-speed steel AISI M2 as a workpiece material with copper and brass as the electrodes. The effect of various process parameters on machining performance is investigated in this study using AI and statistical analysis where current, pulse on time and pulse off time are used for the experimental work and their effect on surface roughness (Ra) are studied. The results of the present work show that the optimum Ra levels in copper and brass electrodes are 2.16 µm and 3.43 µm, respectively, at current of 10 A, pulse on time of 100 µs, and pulse off time of 25 µs. The high levels of Ra in copper and brass electrodes are found to be 6.37 µm and 7.93 µm, respectively, at current of 42 A, pulse on time of 200 µs, and pulse off time of 4 µs. Deep learning neural networks and statistical analysis are used to evaluate the results. It has been found that there is a significant correlation between the process current and average surface roughness, and the pulsation time was not found significant. The use of deep learning neural networks has shown that AI could predict the average Ra values with an average error of about 0.39% for copper and of 0.26% for brass indicating the benefits of using AI in predicting the performance of manufacturing processes and the potential use of AI in future process modelling and applications. The drive to increase productivity and enhance quality is attracting manufacturers into adopting Industry 4.0 and artificial intelligence in their facilities to increase flexibility, reduce waste, and enhance efficiency. EDM is considered to be one of the most complex operations in manufacturing due to its high variability. Therefore, this paper suggests the use of deep learning neural networks to model the process and to predict the surface roughness outcome with limited input data. Statistical analysis was also used to test the statistical significance of each process parameter on the outcome.

The study presents experimental investigation to assess influence of cryogenic treatment of electrodes on machinability in powder-mixed electrical discharge machining (PMEDM). Response surface methodology is used to extract maximum process related information with less number of experimental runs. The machining characteristics of the process are evaluated in terms of material removal rate (MRR), electrode wear ratio (EWR) and surface roughness which are function of machining parameters viz. open circuit voltage, discharge current, pulse-on-time, duty factor, concentration of fine graphite powder and cryogenically treated work tool pair. The study reveals that discharge current, pulse-on-time, concentration of powder and work-tool pair exhibit significant effect on all the performance measures. It is also observed that treatment to both the electrodes results in more beneficial in comparison to treatment of either the tool or work piece. Finally, a novel multi-objective particle swarm optimisation algorithm (MOPSO) has been proposed in order to achieve optimal Pareto front.

In the last few decades, non-traditional machining has increased the efficiency of the machining process. One of the most prevalent non-traditional methods of milling is through the use of an EDM process. Traditional machining methods can't easily machine materials with a complicated profile or hardness that EDM can handle. The industrial sector makes extensive use of EDM. Cryogenically treated copper electrodes and regular copper electrodes were used to conduct an experiment on an EN24 material using varied input parameters, such as electrode rotation, gap voltage, and discharge current. Electric discharge machining was used in a study. The Taguchi method is used to design experiments. Three input parameters were employed in an orthogonal L16 array to find an optimal value for each of the three components. OC (overcut), TWR (Tool wear rate), and Ra (surface roughness) are the three output variables. The S/N ratio can be used to determine the best values and relative relevance of each of these factors using a statistical analysis of variance (ANOVA) table. Additionally, the overcut, tool wear rate, and surface roughness of traditional and non-traditional EDM are examined. For the deep cryogenic and non-traditional machining method, conventional and non-traditional electric discharge machining methods were found to have optimal or dominant factors for the TWR and Ra.

Electrical discharge machining (EDM) is a non-traditional machining process which is widely used in machining of difficult-to-machine materials. EDM process can produce complex and intrinsic shaped component made of difficult-to-machine materials, largely applied in aerospace, biomedical, die and mold making industries. To meet the required applications, the EDMed components need to possess high accuracy and excellent surface finish. In this work, EDM process is performed using Nitinol as work piece material and AlSiMg prepared by selective laser sintering (SLS) as tool electrode along with conventional copper and graphite electrodes. The SLS is a rapid prototyping (RP) method to produce complex metallic parts by additive manufacturing (AM) process. Experiments have been carried out varying different process parameters like open circuit voltage (V), discharge current (Ip), duty cycle (τ), pulse-on-time (Ton) and tool material. The surface roughness parameter like average roughness (Ra), maximum height of the profile (Rt) and average height of the profile (Rz) are measured using surface roughness measuring instrument (Talysurf). To reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L27 orthogonal array has been chosen. The surface properties of the EDM specimen are optimized by desirability function approach and the best parametric setting is reported for the EDM process. Type of tool happens to be the most significant parameter followed by interaction of tool type and duty cycle, duty cycle, discharge current and voltage. Better surface finish of EDMed specimen can be obtained with low value of voltage (V), discharge current (Ip), duty cycle (τ) and pulse on time (Ton) along with the use of AlSiMg RP electrode.

The characteristic feature of edge disintegration easily appears in the electric discharge machining (EDM) drilling processing of cobalt-bonded tungsten carbide (WC-Co). Such tendency reduces the strength against fatigue and results in a poor assembly tolerance. The objective of this paper was to present the mathematical models for modeling and analysis of the effects of process parameters, including the discharge current, pulse time on, duty factor, and capacitance value, on the disintegration factor at the entrance edge of drilled hole in the EDM drilling process of cobalt-bonded tungsten carbide. An experimental plan of a central composite design based on the response surface methodology (RSM) was employed to carry out the experimental study. The quadratic model of RSM associated with the sequential approximation optimization method was used to find the optimum settings of processing parameters. With the experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical model of disintegration factor. The results show that the interaction effect of discharge current with capacitance value has the greatest influence on the disintegration factor, followed by the capacitance value and the quadratic term of duty factor. The optimal settings of processing parameters obtained in this study represent the reduction of the 5.53% disintegration factor, which were compared with the results of initial processing parameters for drilling the cobalt-bonded tungsten carbide in the EDM process.