Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode

EDM machinability and surface roughness analysis of TiB2 using copper electrodes

In this study, the electrical discharge machining (EDM) characteristics and its removal mechanism for one and two directional carbon fibre reinforced plastic (CFRP) composite was evaluated. Also, an investigation about the influence of carbon fibre direction to machining direction was discussed to attain high performance of EDM process. Then EDM characteristics with two different combinations of machining direction and carbon fibre direction were compared. EDM machining characteristics such as material removal rate, electrode wear rate, surface roughness and SEM images were determined. Experimental results clarified that the material removal mechanism in the EDM for one directional CFRP when the machining direction is perpendicular to the fibre direction is different from that in parallel machining. Also, the EDM machining for two directional CFRP is different from that for one direction. Therefore, efficient EDM for CFRP is possible by selecting appropriate machining direction to fibre direction and the type of CFRP according to its fibre direction.

Electrical discharge machining (EDM) is an unconventional machining process used for machining of hard-to-cut materials. Both EDM and micro-EDM processes are extensively used for producing dies and molds, complex cavities, and 3D structures. In recent years, researchers have intensively focused on improving the performance of both micro-EDM and EDM processes. This paper reviews the research work carried out by the researchers on vibration-assisted EDM, micro-EDM, and wire EDM. The consolidated review of this research work enables better understanding of the vibration-assisted EDM process. This study also discusses the influence of vibration parameters such as vibration frequency and amplitude on the material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR). The important issues and research gaps in the respective area of research are also presented in this paper.

In recent days, the additive manufacturing process plays a vital role in the production of tool electrodes, which are used in the electrical discharge machining (EDM) process. In this work, the copper (Cu) electrodes prepared using the direct metal laser sintering (DMLS) process are used for the EDM process. The performance of the DMLS Cu electrode is studied by machining the AA4032-TiC composite material using the EDM process. Then the performance of the DMLS Cu electrode is compared with the conventional Cu electrode. Three input parameters, such as peak current (A), pulse on time (s), and gap voltage (v), are selected for the EDM process. The performance measures, which are determined during the EDM process, are material removal rate (MRR), tool wear rate, surface roughness (SR), microstructural analysis of machined surface, and residual stress. At a higher pulse on time, more material was removed from the workpiece surface and thus MRR is enhanced. Likewise, at a higher peak current, the SR is amplified and thus wider craters are formed on the machined surface. The residual stress on the machined surface has influenced the formation of craters, microvoids, and globules. Lower SR and residual stress are attained by using DMLS Cu electrode, whereas MRR is higher when using conventional Cu electrode.

Experimental Study to Assess the Effect of Electrode Bottom Profiles while Machining Inconel 718 through EDM Process

This paper reports the development of an intelligent model for the electric discharge machining (EDM) process using finite-element method (FEM) and artificial neural network (ANN). A two-dimensional axisymmetric thermal (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 the shape of crater cavity, material removal rate, and tool wear rate. The model is validated using the reported analytical and experimental results. A neural-network-based process model is proposed to establish relation between input process conditions (discharge power, spark on time, and duty factor) and the process responses (crater geometry, material removal rate, and tool wear rate) for various work—tool work materials. The ANN model was trained, tested, and tuned using the data generated from the numerical (FEM) simulations. The ANN model was found to accurately predict EDM process responses for chosen process conditions. It can be used for the selection of optimum process conditions for EDM process.

Modelling Electrical Discharge Machining (EDM) processes based on physical laws has been the subject of much debate in the literature. Therefore empirical regression modelling is frequently used in practice. In this study, a polynomial regression model was developed to correlate the electrode wear with four EDM process parameters (current, pulse on-time, pulse off-time and capacitance) while machining the cobalt-bonded tungsten carbide ceramic. As many factor interactions were found significant, lambda plot was used to find an appropriate response transformation that can simplify the model and improve its interpretability. This was attained when adopting the inverse transformation as only three parameters (current, pulse on-time and pulse off-time) were significant with an adjusted-R2 of 0.939. Due to the possibility of rendering illogical predicted values when detransforming the results of the fitted model, an alternative Generalized Linear Model (GLM) with gamma distribution and a reciprocal link function was developed. Being associated with shorter mean response confidence intervals, the GLM was more reliable for estimating and predicting the response. This paper is the first to report the use of GLM in the context of modelling EDM processes.

A review on various methods to improve process capabilities of electrical discharge machining process

The emergence of white layer in each process of Electric Discharge Machining (EDM) becomes the focus of attention of the experts. It is harder than the base metal would be advantageous to withstand friction engine components, such as the plastic molding industry. From various studies it has been reported that the increase in the value of hardness of white layer either due to migration of the electrode material and dielectric fluid to the white layer. This paper discusses the influence of electrodes Cu, CuCr and graphite as well as jatrophacurcas dielectric fluid to the surface of the material SKD 61, microhardness of the white layer, Tool Wear Rate (TWR) of Material Removal Rate (MRR), Surface roughness (Ra) and the microstructure. Initial studies conducted are the transesterification and characterization of jatrophacurcas to determine the dielectric strength and the content of metal elements. Furthermore, the EDM testing is done to dies steel of SKD 61 using the electrodes of Cu, CuCr and Graphite. The use of jatrophacurcas yield the best MRR when using Graphite electrodes followed Cu last CuCr. While the smallest TWR produced by Cu electrode, CuCr and then the highest is Graphite. The relative wear which is the ratio TWR with MRR, for the three types of electrodes is always great at a small current then decreases and then tends to be stable with the increase of electric current. Jatrophacurcas for the three types of electrodes provide higher TWR than kerosene. This means that migration of materials is beneficial because larger material, but in terms of the geometric accuracy of the product can lead to decreasing of dimensional tolerances. Vickers hardness values of white layer is achieved by CuCr electrodes, Graphite lowest. Microstructural observation results of EDM white layer using graphite electrodes produce a more uniform thickness than the white layer using Cu and CuCr electrodes. The conclusion of this study that jatrophacurcas can be used as a dielectric fluid in EDM process, because electrode wear during EDM process is relatively high compared with kerosene that is mean more profitable in the surface treatment process of dies steel of SKD 61.

The main objective of this experimental work is to examine the geometric deviations and machinability of the electrical discharge machining (EDM) process on nickel-based Inconel-718 alloy using square profile copper tool electrodes. The significance of three major EDM process parameters, that is, peak current (IP), pulse on time (Ton) and pulse off time (Toff), have been analyzed to measure the material removal rate (MRR), electrode wear rate (EWR) and squareness deviation, respectively. The dominance of each parameter was measured using design of experiments and the Taguchi method. Non-dominating sorting genetic algorithm (NSGA-II) method was utilized for multiobjective optimization. Empirical modeling has been done using the artificial intelligence-based fuzzy logic system to measure the performance variables. The developed model using fuzzy logic system has an accuracy of 96.45%, that was calculated as the difference between predicted and experimental results; therefore, the developed models can be utilized for more reliable results. The optimized results obtained through this experimental work and empirical modeling will facilitate the aerospace, defense and advanced machining industries to improve the machinability and squareness deviation of nickel-based Inconel-718 alloy with higher MRR and dimensional stability. Finally, the geometric deviations obtained on the square slots of two plates by the previously described EDM process have been transferred to the assembled product in order to verify its assemblability.

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.

In this paper, an inverse heat conduction method is applied to estimate the amount of the energy (Fc) transferred to the workpiece during electric discharge machining (EDM) process. Embedded thermocouples which were connected to a four channel data logger were utilized to measure the temperature of a specific location on a rectangular workpiece during the EDM process. After temperature measurements were done, the 2-D heat conduction model of the workpiece and the Levenberg-Marquardt (LM) scheme were used to determine the energy transferred to the workpiece. This inverse procedure facilitates the determination of the heat energy at discharge-workpiece interface in EDM processes, which yet is a challenge for existing numerical models. The obtained results showed that the energy transferred to the workpiece varies with the discharge current and pulse duration from 5 % up to 45 %, which shows that the value of Fc is a function of discharge current and pulse duration and that the fixed value of energy assumed in majority of the previous researches is not in accordance with real EDM conditions. Furthermore, the effects of machining parameters such as discharge current and pulse duration on Fc were studied. It was evident that the Fc has a direct but non-linear relationship with both discharge current and pulse duration, while discharge current has a higher impact on Fc.

In this investigation, an ultrasonic vibration and an assisted magnetic force are integrated with the electrical discharge machining (EDM) to develop a hybrid process of EDM with ultrasonic vibration and assisted magnetic force (EDMUVAMF), and then the hybrid process is adopted to explore the effects of the main machining parameters on the material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR) and morphologies of the machined surface in machining SKD 61 mold steels. Moreover, the effects on expelling the debris from the machining gap are also studied via evaluating the discharge waveforms, and analyzing the morphology of the machined surface. The bottleneck correlated with large area using EDM process would be overcome when the optimal parameters of the hybrid process of EDMUVAMF is obtained. From the experimental results shown, the hybrid process of EDMUVAMF can improve the machining performance. The MRR was increased significantly and the SR was reduced to ameliorate the machining efficiency and machined surface quality. The hybrid process revealed the potential for the applications in large area via EDM technique.

In the electrical discharge machining (EDM) process, the aspects of the cutting performance such as material removal rate (MRR), surface roughness, crater geometry, and tool wear ratio are affected by the energy distribution. The difference in energy distribution during the EDM processing of Al6061, Inconel718, and SKD11 has been rarely studied. However, energy distribution is one of the most important parameters utilized in most existing models of the EDM process. In this paper, the energy distribution that occurs while processing these three materials is investigated by an experimental study under different EDM parameters, including current and pulse duration. Then, the relationship between the energy distribution and the specific discharge energy (SDE) is investigated. The results demonstrate that the discharge energy transferred to the above three kind of workpiece is small, and the fraction of energy distributed into the workpiece varies with discharge current and pulse duration form 7.998 to 12.005%, 2.879 to 3.485% and 2.976 to 3.716% for Al6061, Inconel718, and SKD11, respectively. These findings are consistent with previous studies. In addition, the optimization of the process parameters is investigated for these three materials, considering the MRR and the discharge energy efficiency simultaneously. This findings presented by this paper may be further used in existing thermal-physical models to improve the technological performance of such models.

Examining the electrical discharge machining (EDM) process is challenging in manufacturing technology due to the complexity of the physical events that take place in the gaps between electrodes. In this study, we examined the EDM process in detail and developed multiple machine learning (ML) models to describe the relationship between the EDM independent (process parameters) and dependent (responses) variables. The collected experimental data was used to train the machine learning models. According to the results, the GPR model outperformed other ML models across different materials, with average RMSE values of 0.9234 and 3.0216 for the material removal rate (MRR) and surface roughness (Sa), respectively, highlighting the effectiveness of ML tools at modeling complex machining processes, such as EDM. In addition, as a practical implication, this study opens the door to employing the developed ML models to predict the EDM process performance.