Electrical discharge machining (EDM) is one of the earliest non-traditional machining processes. EDM process is based on thermoelectric energy between the work piece and an electrode. A pulse discharge occurs in a small gap between the work piece and the electrode and removes the unwanted material from the parent metal through melting and vaporising. The electrode and the work piece must have electrical conductivity in order to generate the spark. There are various types of products which can be produced using EDM such as dies and moulds. Parts of aerospace, automotive industry and surgical components can be finished by EDM. This paper reviews the research trends in EDM on ultrasonic vibration, dry EDM machining, EDM with powder additives, EDM in water and modeling technique in predicting EDM performances.

A review on current research trends in electrical discharge machining (EDM)

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1275&context=mechengfacpub

Review of Electrochemical and Electrodischarge Machining

Highlights? Several evolutionary techniques are reviewed to optimize machining parameter. ? It was found that genetic algorithm was widely applied by researchers. ? The most employed machining operation was multipass-turning. ? The most considered machining performance was surface roughness. In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques.

Evolutionary techniques in optimizing machining parameters

This paper reports an intelligent approach for process modeling and optimization of electric discharge machining (EDM). Physics based process modeling using finite element method (FEM) has been integrated with the soft computing techniques like artificial neural networks (ANN) and genetic algorithm (GA) to improve prediction accuracy of the model with less dependency on the experimental data. A two-dimensional axi-symmetric numerical (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, material removal rate (MRR) and tool wear rate (TWR). The model is validated using the reported analytical and experimental results. A comprehensive ANN based process model is proposed to establish relation between input process conditions (current, discharge voltage, duty cycle and discharge duration) and the process responses (crater size, MRR and TWR) .The ANN model was trained, tested and tuned by using the data generated from the numerical (FEM) model. It was found to accurately predict EDM process responses for chosen process conditions. The developed ANN process model was used in conjunction with the evolutionary non-dominated sorting genetic algorithm II (NSGA-II) to select optimal process parameters for roughing and finishing operations of EDM. Experimental studies were carried out to verify the process performance for the optimum machining conditions suggested by our approach. The proposed integrated (FEM-ANN-GA) approach was found efficient and robust as the suggested optimum process parameters were found to give the expected optimum performance of the EDM process.

Intelligent process modeling and optimization of die-sinking electric discharge machining

Electrochemical discharge machining (ECDM) is a hybrid non-conventional machining process, used to machine electrically conductive and non-conductive materials. It is a preferred process to fabricate micro scale features like micro holes, micro channels, microgrooves and 3-dimensional intricate shapes on variety of materials. In order to improve the efficacy of ECDM process, certain technical augmentations are provided with basic configuration of ECDM. These augmentations result in developments of ECDM process variants. Further, research community has developed ECDM based triplex hybrid methods for further process enrichment. This review article presents a comprehensive review of these recent developments in ECDM process, its variants and their triplex hybrid methods. The future research possibilities are identified and presented as research potentials.

Developments in electrochemical discharge machining: A review on electrochemical discharge machining, process variants and their hybrid methods

Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper, artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.

Parameter optimization model in electrical discharge machining process

Dry electrical discharge machining (EDM) is an environmentally friendly modification of traditional EDM, but the material removal rate (MRR) of dry EDM is very low. A new machining method called powder mixed near dry EDM (PMND-EDM), which can improve MRR effectively, is proposed. In order to find the relationship between processing parameters and processing effect and improve processing efficiency, the MRR of PMND-EDM is studied systematically. The main processing parameters, such as peak current, pulse on time, pulse off time, powder concentration, flow rate, air pressure, and tool rotational speed, on MRR of PMND-EDM are studied in experiments. The experimental results showed that the MRR of PMND-EDM increased with the increase of peak current, pulse on time, flow rate, and air pressure, decreased with the increase of pulse off time and tool rotational speed, and increased firstly and then decreased with the increase of powder concentration. The significant test for process parameters on MRR of PMND-EDM is carried out through orthogonal design experiment. Analysis of variance for the result of orthogonal experiment indicates that peak current is significant at 99 % confidence level, pulse on time is significant at 95 % confidence level, and flow rate is significant at 90 % confidence level. Full factor experiments of peak current, pulse on time, and flow rate are conducted. The empirical formula of MRR is established based on experimental results. Its validity is demonstrated.

Research on material removal rate of powder mixed near dry electrical discharge machining

Understanding the effect of processing parameters on the tool electrode wear during micro-electrical discharge machining (micro-EDM) is helpful to predict and compensate the electrode wear, so as to improve the machining precision. In this paper, experiments are carried out and the influences of tool electrode diameter on the micro-EDM process are discussed based on the skin effect and area effect. It is demonstrated that the machining speed, tool wear, and taper rate are different with the increase of tool electrode diameter. Due to the skin effect and area effect, larger electrode diameter results in higher material removal rate along with higher tool wear rate. The electrode material removal increment is more than the workpiece material removal increment with the increase of tool electrode diameter, which leads to the increase of relative tool wear ratio. Discharge energy is concentrated on the tool surface which enhances the possibility of discharge on the side face and the corner of the tool electrod...

Effect of Electrode Size on the Performances of Micro-EDM

Machining efficiency of powder mixed near dry electrical discharge machining based on different material combinations of tool electrode and workpiece electrode

Qinhe Zhang

Papers

Parameter optimization model in electrical discharge machining process

Research on material removal rate of powder mixed near dry electrical discharge machining

Effect of Electrode Size on the Performances of Micro-EDM

Machining efficiency of powder mixed near dry electrical discharge machining based on different material combinations of tool electrode and workpiece electrode

Research on a single pulse discharge to discriminate EDM and EAM based on the plasma tunnel and crater geometry