g–TiAl intermetallic has outstanding properties such as high resistance against fatigue, oxidation, corrosion, creep, dynamic vibration and high working temperature. These intermetallics are applied in aerospace and automotive industry, turbojet engines and blade manufacturing. In this paper, Powder mixed electrical discharge machining (PMEDM) of g–TiAl intermetallic by means of different kinds of Powders including Al, SiC, Gr, Cr and Fe is investigated to compare the output characteristics of the process such as surface roughness, tool wear rate, material removal rate and surface topography with each other. This is an experimental investigation, by means of die sinking EDM machine and a special tank for machining. The results indicate that, aluminum Powder as the most appropriate kind of Powder in the optimum particle concentration of 2 g/l, improves the surface roughness about 32% comparing with conventional EDM, decreases the tool wear rate about 19%, but decreases the material removal rate about 7.5% and also the Al Powder leads to improving the machined surface topography and decreasing the surface defects and micro cracks.

In this study, three series of electrical discharge machining experiments were carried out on γ-TiAl intermetallic. In the first series, by changing pulse current and pulse on time in EDM process and in the second series by changing the size and concentration of aluminum Powder particles mixed in dielectric fluid, output characteristics including material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) are evaluated. In the third series of machining experiments, after determination of the optimum size and concentration of aluminum particles, current and pulse on time, are changed at levels as exactly the same levels in Powder-free tests and MRR is evaluated. Finally, MRR of the EDM and optimum PMEDM processes, are compared. By increasing the aluminum particles concentration, TWR and SR will be decreased to an optimal level and then will be increased. For particle size of 2 μ m and 20 μ m, by increasing the aluminum Powder concentration, MRR increases at first and then decreases, while for particle size of 63μ m, by increasing Powder concentration, MRR decreases. The minimum and maximum increment of MRR in optimum PMEDM condition compared with EDM mode was 32% and 88%, respectively. With increase of electrical discharge energy, the addition of aluminum Powder has less effect on increase of sparking frequency and consequently the MRR.

The present paper deals with the design of experimental work matrices for two groups of experiments by using Response surface methodology (RSM). The first EDM group was dealt with the use of kerosene dielectric alone, while the second was treated by adding the graphite micro Powders mixing to dielectric fluid (PMEDM). The total heat flux generated and fatigue lives after EDM and PMEDM models were developed by FEM using ANSYS 15.0 software. The graphite electrodes gave a total heat flux higher than copper electrodes by (82.4%). The use of graphite Powder and both electrodes yielded more heat flux by (270.1%) and (102.9%) than the copper and graphite electrodes, respectively with use of kerosene dielectric alone. Using graphite electrodes and kerosene dielectric alone improved the WLT by (40.0%) when compared with the use of copper electrodes. Whereas, using copper electrodes and the graphite Powder improved the WLT by (66.7%) compared with the use of graphite electrodes under the same machining conditions. Copper electrodes with graphite Powder gave experimental fatigue safety factor higher by (30.38%) when compared with using graphite electrodes and higher by (15.73%) and (19.77%) when compared with using the copper and graphite electrodes and kerosene dielectric alone, respectively.

electrical discharge machining is a non-contact process based on thermoelectric energy between the electrode and workpiece. It is an efficient machining process to machine an advanced, difficult-to-machine material with high precision, complex shapes and high surface quality. Realizing the advantages and abilities of this machining method, electrical discharge machining research has caught the interest of many researchers. This paper reviews the electrical discharge machining process, including recent research fields and inventions that have been developed in order to improve workpiece surface quality and integrity, machining time, tool wear and material removal rate. electrical discharge machining apparatus and its servomechanism system, including mechanical structure developments using computer numerical control, a lead-screw mechanism and linear motor driven and piezoelectric actuators, are also reviewed. Furthermore, control strategies that are applied to electrical discharge machining systems, including servo-drive control and optimization techniques, such as fuzzy, genetic algorithms and artificial neural networks, are also discussed.

The products processed by electrical discharge machining (EDM) leave some undesirable e ects, the most prominent of which is Residual Stress (RS). These RS a ect fatigue behavior, dimensional stability, and stress corrosion. The latter is the cause of failure of processed products. It has been observed that products obtained by Powder Mixing Near Dry Electric discharge machining (PMND-EDM) have relatively low RS values. The purpose of this research is to minimize the RS caused in the machined workpiece (EN-31) by optimizing the process parameters that signi cantly a ect the machining characteristics. The selected parameters were tool diameter, mist ow rate, metal Powder concentration, and mist pressure, and the selected workpiece was EN-31 because it has ideal mechanical properties and is widely used in manufacturing. The minimum value of RS at optimized values of process parameters was found to be 106. 32 MPa.

Based on the review of the previous researches, more than 50% of the cost of ceramic parts is related to machining costs, about silicon nitride (Si3N4), which is one of the most important engineering ceramics, while up to 80% has been reported. This ceramic has a combination of desirable properties such as wear and corrosion resistance and high hardness. Due to the high tool wear in machining process, the use of traditional methods is not appropriate. The electrical discharging machining process is a method that can machined all conductive materials without regard to mechanical and metallurgical properties, but machining of non-conducting ceramics such as silicon nitride is not normally possible by electrical discharging machining process. Therefore, in this research, the assisted electrode method has been used to perform the electrical discharge machining of silicon nitride ceramics. After preparing ceramic samples, copper-carbon bilayer and single copper layer were coating on the Si3N4 and they were successfully machined. In order to further examine the machined surface, imaging was performed by SEM. The results show that an increase in material removal rate in two-layer coating compared to single-layer coating. Also, after finishing machining process, EDX analysis was performed from the surface of ceramic workpiece, which indicates the presence of carbon and copper elements in the machined surface. In addition, the copper tool wear ratio in two-layer coating is less than that of single-layer coating.

This paper deals with the application of discriminant analysis in an electrical discharge machining (EDM) process to determine the comparative contribution of each of its input parameters on the measured responses. It also identifies the most significant EDM process parameters influencing those responses. For this process, voltage, current, pulse-on time and pulse-off time are considered as the input parameters, whereas, material removal rate, electrode wear rate and surface roughness are the responses. Based on the past and simulated experimental data, both simultaneous and step-wise estimations are carried out for each of the three responses showing the relationships between the EDM process parameters and the considered responses. It is observed that in both these estimations, pulse-off time, current and pulse-on time respectively evolve out as the most significant parameters for material removal rate, electrode wear rate and surface roughness. Step-wise estimation identifies voltage as the least significant input parameter for all these responses. The developed discriminant functions, which can also help in predicting the responses, are finally cross-validated.