The paper presents a study of Rotary Electrical Discharge Machining (REDM) process. In REDM, simple shaped cylindrical electrodes are used to generate 3D complex shapes. A theoretical model of machining process is described, which takes into consideration the effect of tool electrode wear on machined surface profile. The software for computer simulation of REDM has been developed. In the paper, the effects of machining parameters on shape error of machined surface are discussed. Results of computer simulation have been confirmed in experimental practice. The simulation model for NC contouring REDM may also be applied to solving numerical control problems and optimization of tool electrode path.

Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.

This article deals with the effects of electrical discharge machining (EDM) on the chemical composition and microstructure of cast Alnico alloys, i.e., iron-based alloys composed of aluminum, nickel and cobalt. The experiments focused on determining the chemical composition of the surface layer before and after the EDM process. The microstructure of the material altered by the EDM was also examined. The study included measurement of the thickness of the white layer characteristic of EDM. It is evident that low values of the surface roughness parameters can be obtained by correctly selecting the EDM process parameters. The average surface roughness reported in the experiments was 1 μm. The surface roughness measurements were conducted with a Talysurf CCI lite non-contact profiler. The metrological results also indicate that lower surface roughness can be obtained at small discharge energies.

New materials require the use of advanced technology in manufacturing parts of complex shape. One of the modern non-conventional technology of manufacturing difficult to cut materials is the wire electrical discharge machining (WEDM). The article presents the results of theoretical and experimental research in the influence of the WEDM conditions and parameters on the shape deviation during a rough cut. A numerical model of the dielectric flow in the gap (ANSYS) was developed. The influence of the dielectric velocity field in the gap on the debris evacuation and stability of WEDM process was discussed. Furthermore, response surface methodology (RSM) was used to build empirical models for influence of the wire speed Vd, wire tension force Fn, the volume flow rate of the dielectric Qv on the flatness deviation after the WEDM.

2 wt.% TiB2 (mean particle size: 400 nm) reinforced Al 7075 metal matrix composites (MMCs) fabricated through mechanical stirring and ultrasonic agitation integrated squeeze casting process were subjected to electrical discharge machining (EDM) after determining the physical and mechanical properties. EDM was conducted with Cu electrode tools to investigate influence of machining factors, i.e. peak current (IP), pulse on time (TON) and gap voltage (VG) on the tool wear rate (TWR), material removal rate (MRR) and average surface roughness (ASR) of the machined surfaces. All the three responses increased on increasing IP and TON, but reduced on increasing VG. The machined surfaces were studied through scanning electron microscope (SEM). Significance of the EDM parameters on the individual responses were studied using analysis of variance (ANOVA) and regression models for the responses were developed using response surface method (RSM). The responses under consideration were optimized simultaneously using Taguchi embedded weighted principal component analysis (WPCA), which resulted the parametric combination of 4A (current), 100 μs (pulse duration) and 75V (voltage) was the optimal setting for the multi-criteria decision problem. Finally, the result of optimization was validated by conducting some confirmatory experiments.

Wire electrical discharge machining (WEDM) is a non-conventional material-removal process where a continuously travelling electrically conductive wire is used as an electrode to erode material from a workpiece. To explore its fullest machining potential, there is always a requirement to examine the effects of its varied input parameters on the responses and resolve the best parametric setting. This paper proposes parametric analysis of a WEDM process by applying non-parametric decision tree algorithm, based on a past experimental dataset. Two decision tree-based classification methods, i.e. classification and regression tree (CART) and Chi-squared automatic interaction detection (CHAID) are considered here as the data mining tools to examine the influences of six WEDM process parameters on four responses, and identify the most preferred parametric mix to help in achieving the desired response values. The developed decision trees recognize pulse-on time as the most indicative WEDM process parameter impacting almost all the responses. Furthermore, a comparative analysis on the classification performance of CART and CHAID algorithms demonstrates the superiority of CART with higher overall classification accuracy and lower prediction risk.

In this research work, the Ti-6Al-4V material was used for the investigation of machining parameters by means of hybrid micro electrical discharge machining to improve the machining process and reduce the negative effects of debris accumulation in the drilled hole. L9 orthogonal array was used in the Taguchi based grey relational analysis to optimize the parameters such as material removal rate and diametrical accuracy of the machining process for Ti-6Al-4V. This work encompasses the design, development, and calibration of the work piece vibration platform and experimental analysis of the process parameters by means of the hybrid micro electrical discharge machining process. The maximum material removal rate and minimum surface roughness was observed at the current value of 2.5 A, pulse on time is 2 µs and pulse off time is 14.5 µs. The maximum material removal rate was observed for the increase in pulse on time with 14.4  µs and 4 A current level. The diametrical accuracy of the microholes was increased while increasing the pulse off time and decreasing the pulse on time. The fluid flow simulation has been conducted to find out the pressure drop and to know the velocity of the flow inside the hole for the effective flushing of the debris during machining.