Optimization the machining parameters by using VIKOR and Entropy Weight method during EDM process of Al–18% SiCp Metal matrix composite

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.

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 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

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.

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.

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.

Hard materials cannot be machined effectively by the individual machining process. In order to machine workpiece made from hard and stiff materials effectively a concept of Hybrid machining process (HMP) is originated. The HMP is an integration of two or more machining process to get the advantage of each individual process. HMP is used to machine This study focuses on evolving a novel process using both oil and gas as dielectrics to analyse the effect on tool wear rate (TWR) and material removal rate (MRR). The flow of compressed gas through eccentric-hole rotating tool improved the debris removal from inter-electrode gap, hence it improve the flushing competence of the machining process. In this experimental investigation, the workpiece material is Al-20% SiC metal matrix composite (MMC) and the electrode material is copper. The experiments were conducted following the Taguchi method of design experiments. The effect of various machining parameters on MRR and TWR has been studied. The optimization of process parameter has also been done. The results of TWR and MRR are analysed using S/N ratio, ANOVA and main effect plots. The experimental results, revels that discharge current, gap voltage and pulse on time significantly affected MRR, and TWR. The experimental inference reveal that provision of compressed air through eccentric hole rotary tool has a positive effect on machinability of electrical discharge machining (EDM) process.

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.

Investigation on tool wear rate of brass tool during machining of Monel 400 alloy using electric discharge machine

Study of machining characteristics of Al-SiCp12% composite in nano powder mixed dielectric electrical discharge machining using RSM

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

Optimization of process parameters based on RSM and GRA method for machining of Inconel-600 by electric discharge machining

A model of tool wear in electrical discharge machining process based on electromagnetic theory

Metal matrix composites (MMC) are found to have many applications in the space and aviation industries. This paper presents the details of a comparative study made on the machinability of H11 die steel by electric discharge machining (EDM) process using copper and powder metallurgy (P/M) processed Cu-Cr MMC as the tool. Taguchi method was used for the experimental design and experiments were conducted using L9 orthogonal array for each tool. The present work investigates the influence of machining parameters such as peak current (Ip), pulse on time (Ton), duty cycle (DC) and gap voltage (Vg) on material removal rate (MRR), tool wear rate (TWR), diametral overcut (DOC) and surface roughness (SR). Cu-Cr composite tool demonstrates better performance than copper tool. Multi-objective optimization was performed using a combination of principal component analysis (PCA) with technique for order of preference by similarity to ideal solution (TOPSIS) to achieve the optimum parameter setting for copper and Cu-Cr tool. The optimal results obtained for copper tool was at Ip=3A, Ton=150µs, DC=80%, Vg=40V and for Cu-Cr tool was at Ip=3A, Ton=200µs, DC=90%, Vg=50V. Â