EDM machinability and surface roughness analysis of TiB2 using copper electrodes

This paper presents the experimental investigation of the machining characteristics of AISI D2 tool steel on the electrical discharge machining (EDM) process by replacing the copper electrode tool with carbon nanotube (CNT) fused electrode, and checking for improvement in machinability factors like material removal rate (MRR), electrode wear rate (EWR) and surface finish (SR). The problems of high electrode wear in EDM machining, such as poor dimensional and shape accuracy of the machined part, need to be addressed. The work material is spark machined with pure copper and CNT-infused copper electrodes by varying parameters on an EDM. The results of the machining characteristics with and without the CNT are compared and analysed using Taguchi analysis. This study investigates the optimisation of EDM parameters for AISI D2 tool steel using TOPSIS method. Analysis of variance (ANOVA) and F-test are used to check the validity of regression model and determine the significant parameter affecting the surface roughness.

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.

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.

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.

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.

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.

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.

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.

Electric discharge machining (EDM) is one of the non-conventional machining processes that supports machining for high-strength and wear-resistant materials. It is a challenging task to select the process parameters in real-time to maximize the material removal rate since real-time process trials are expensive and the EDM process is stochastic. For the ease of finding process parameters, a modelling of the EDM process is proposed. Due to the non-linear relationship between the material removal rate (MRR) and discharge time, a model-free adaptive extremum-seeking controller (ESC) is proposed in the feedback path of the EDM process for finding an optimal value of the discharge time at which the maximum material removal rate can be achieved. The results of the model show a performance that is closer to the actual process by choosing steel workpieces and copper electrodes. The proposed model offers a lower error rate when compared with actual experimental process data. When compared to manual searching for an optimal point, extreme seeking online searching performed better as per the experimental results. It was observed that the experimental validation also proved that the ESC can produce a large MRR by tracking the extremum control. The present study has been limited to only the MRR, but it is also possible to implement such algorithms for more than one response parameter optimization in future studies. In such cases the performance measures of the process could be further enhanced, which could be used for a real-time complex die- and mold-making process using EDM.

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.

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

Validation of different tungsten carbide-cobalt grades as tool electrode material for sinking EDM

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.

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

Electrical discharge machining (EDM) is one of the most accepted machining processes in the precision manufacturing industry. In EDM process, finding an alternative tool material is the demand in modern manufacturing industry. Therefore, an attempt had been made to fabricate copper–titanium diboride powder metallurgy electrode to test in EDM on monel 400™ material. The experiments are planned using center composite second-order rotatable design and the model is developed by response surface methodology. The machining characteristics have analyzed using the developed model. In this study, four input parameters such as titanium diboride percentage, pulse current, pulse on time, and flushing pressure are selected to evaluate the material removal rate (MRR) and tool wear rate (TWR). The adequacy of the developed regression model has tested through analysis of variance test. The desirability-based multiobjective optimization is used to find the optimal process parameter which has given maximum MRR and minimum TWR. The optimum process parameters obtained were titanium diboride of 16%, pulse current of 6 A, flushing pressure of 1 Mpa, and pulse on time of 35 µs. The validity of the response surface model is further verified by conducting confirmation experiments.