Metaheuristic Approach of Multi-Objective Optimization during 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.

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 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.

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

The aim of this study is to develop an integrated study of surface roughness (Ra) in the die-sinking electrical discharge machining (EDM) process of Ti-6AL-4V titanium alloy with positive polarity of copper-tungsten (Cu-W) electrode. Regression analysis and glowworm swarm optimization (GSO) algorithm were considered for modelling and optimization process. Pulse on time (A), pulse off time (B), peak current (C) and servo voltage (D) were selected as the machining parameters with various levels. The experiments have been conducted based on the two levels of full factorial design with an added center point design of experiments (DOE). Moreover, mathematical models with linear and 2 factor interaction (2FI) effects of the parameters chosen were developed. The validity test of the fit and the adequacy of the developed mathematical models have been carried out by using analysis of variance (ANOVA) and F-test. The statistical analysis showed that the 2FI model outperformed with the most minimal value of Ra compared to the linear model and experimental result.

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.

The wire electrical discharge machining (WEDM) allowed success in the manufacture of the hard, fragile, and materials difficult to cut, especially for electroconductive ceramic materials. In this study, the mathematical models of material removal rate (MRR) and surface roughness (SR) used for the machinability evaluation in the WEDM process of aluminum oxide-based ceramic material (Al2O3 + TiC) have been carried out. The experimental plan adopts the face centered central composite design (CCD). The mathematical models using the response surface methodology (RSM) are developed so as to investigate the influences of four machining parameters, including the peak current, pulse on time, duty factor, and wire speed, on the performance characteristics of MRR and SR. It has been proved that the proposed mathematical models in this study would fit and predict values of the performance characteristics, which would be close to the readings recorded in experiment with a 95% confidence level. The significant parameters that critically affect the performance characteristics are examined.

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 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.

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.

It is essential to establish multi-criteria decision-making for computing the optimal process factors required to enhance the performance of the electrical discharge machining (EDM) process. For this purpose, criteria decision-making based on the Taguchi–grey analysis was employed in this study. Several responses such as material removal rates, average surface roughness, microhardness, and average white layer thickness were chosen to evaluate machinability. From the existing factor combinations, the optimum electrical factors that resulted in improved surface performance measures were identified as a peak current of 5 A, gap voltage of 50 V, pulse on time of 18 µs, and pulse off time of 37 µs, with a standard deviation within 4.1%. The maximum high- and low-grade value shows that the peak current affects performance measures, as it is essential in determining the spark energy in the EDM process. Moreover, significantly improved surface performance measures were achieved using the optimal process parameter combinations for the EDM process.

In the present research, two neuro-fuzzy models and a neural network model are presented for predictions of material removal rate (MRR), tool wear rate (TWR), and radial overcut (G) in die sinking electrical discharge machining (EDM) process for American Iron and Steel Institute D2 tool steel with copper electrode. The discharge current (I p), pulse duration (T on), duty cycle (τ), and voltage (V) are considered as inputs to the network. A full-factorial design was used to conduct the experiments with various levels of I p, T on, τ, and V. The analysis of variance results reveal that I p is the most influencing factor for MRR and G, having the highest degree of contributions of 87.61% and 81.90%, respectively. In case of TWR, T on has the highest degree of contribution of 46.05% and is the most significant factor. The half of the experimental data set was used to train the networks and was tested for convergence with a different set of data to obtain appropriate number of neurons, epoch, and the fuzzy rule base. The mean square error convergence criteria, both in training and testing, came out very well. The developed models are found to approximate the responses quite accurately. Moreover, the predicted results based on above models have been confirmed with unseen validation set of experiments and are found to be in good agreement with the experimental results. The comparison results reveal that the artificial neural network and the neuro-fuzzy models are comparable in terms of accuracy and speed, and further, the proposed models can be employed successfully in prediction of MRR, TWR, and G of the stochastic and complex EDM 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.

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.