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In the electric discharge machining process, the material is separated from the surface of the work piece by an electric spark, and in the presence of the dielectric material and tool electrode, changes are made on the surface of the work piece. In this project, we investigated the effect of dielectric material and brass tool electrode diffusion on the surface of carbon steel workpiece by means of quantmetric analysis, X-ray diffraction and electrochemical impedance spectroscopy. The effect of the parameters such as electric current intensity (I), pulse on time (ton), pulse off time (toff) and machining time (τ) on the composition of work peace, microstructure and electrochemical corrosion of machined work peace, were investigated. The results of quantmetric analysis showed that with the increase of machining parameters, the amount of diffused copper (from brass tool electrode) and carbon (from pyrolysis of flashing dielectric material) on the surface of machined carbon steel was increased. X-ray diffraction results confirmed that the formation of different phases such as Fe1.92C0.08, Cu4, Cu2 and Fe2 on the surface of workpiece, which was accordance with the quantmetric results. Electrochemical studies showed that during the EDM process, the electrochemical corrosion resistance was increased. [ABSTRACT FROM AUTHOR]

The present work focuses on the impact of expanded graphite (EG) nano-powder along with spark-on-duration (T on) and spark-off-duration (T off), and current as factors on increasing material removal rate (MRR), reduction of surface roughness (SR), tool wear rate (TWR), dimensional deviation (DD), and surface defects for Ni 55.8 Ti. Taguchi's design having 4 factors at 3 levels was employed to perform the experimental trials. ANOVA has successfully validated the developed regression equations. EDM factors of PC, T off , current, and T off were found to be the largest contributing factors with the involvement of 76.91 %, 38.40 %, 34.36 %, and 44.54 % for MRR, TWR, SR, and DD respectively. TLBO algorithm was used in the present work to tackle the conflicting situation and to optimize the response variables. The simultaneous optimization conducted through the Teaching-learning-based optimization technique has yielded optimal parameters setting of T on at 7 µs, T off at 5 µs, PC at 1.5 g/L, and current at 10 A by giving optimal response values at MRR of 42.82 mm3/min, TWR of 0.4039 mm3/min, SR of 3.71 µm, and DD of 92.65 µm. Lastly, Scanning electron microscopy was utilized to check EG nano-powder significance on the machined parts' surface morphology. [ABSTRACT FROM AUTHOR]

Micro drilling using Electrical Discharge Machining (MD-EDM) has emerged as a prominent technique in precision manufacturing, offering the capability to create small and intricate holes. This study focuses on investigating the influence of input process parameters on nickel-based superalloy (Ni3 (Ti, Al)) material, while the electrode materials studied are copper, aluminum and tungsten, with varying electrode diameters of 0.4 mm, 0.5 mm and 0.6 mm. The research encompasses an L9-orthogonal array (L9-OA) in which the Machining Voltage, Peak Current (IP), Pulse off time (Poff) and Servo Standard Voltage (SVR) are varied. Three levels of Machining Voltage (4 volts, 6 volts and 8 volts) and IP are selected. Moreover, scanning electron microscope (SEM) imaging is employed to observe and analyze the morphological characteristics of the drilled holes. The experimental results reveal that the choice of electrode material and diameter significantly influences the SR of the drilled holes. Copper electrodes demonstrate the lowest SR, followed by tungsten and aluminum electrodes. Smaller electrode diameters result in improved surface finish due to reduced discharge energy concentration. Additionally, increasing the Machining Voltage leads to larger discharge energy and heat, resulting in increased SR. SEM imaging demonstrates distinct differences in hole morphology based on the electrode material and diameter, with copper electrodes producing cleaner and more precise holes with minimal recast layer formation compared to aluminum and tungsten electrodes. The influence of Machining Voltage is observed through the presence of craters and debris in higher voltage conditions. [ABSTRACT FROM AUTHOR]

High specific strength and good fatigue limit are the key properties to watch out during development of aerospace components. Aluminum composites are proven high specific strength materials especially graphene embedded composites worth a mention in this context. While, surface finish is an eminent parameter affecting fatigue strength of a component showcasing the importance of machining technique employed to transform a fabricated bulk into finished product. Current study, therefore emphasizes on electrical discharge machining (EDM) of aluminum composites embedded with graphene nanoplatelets (1.5 wt.%) fabricated through a hybrid approach of blending solid and liquid metallurgical routes. Further, mathematical and neurological forecast models are developed to individually predict the machining response variables namely surface roughness (SR), material removal rate (MRR) and tool wear rate (TWR). Among machining parameters current (I), pulse on-time (Ton), pulse off-time (Toff) and flushing pressure (P) considered; Ton is noted to greatly influence the surface quality of composite while TWR and MRR are affected by current during ANOVA analysis. On comparative understanding of forecast models, neurological models outperform quadratic non-linear mathematical models where accuracy of prediction achieved by developed artificial neural network (ANN) model is 96% for surface roughness. The error performance plots, error histograms and overall fit plots depict a marginal over-fit neurological model. However, significantly high coefficient of correlation (R) of 99% possessed by ANN model illustrates their potential in forecasting response parameters. [ABSTRACT FROM AUTHOR]

Polycrystalline diamond is widely used in many fields including ultra-precise machining, optical material and semiconductor electronic devices due to its excellent physical properties. However, traditional mechanical processing methods of polycrystalline diamond leads to severe tool wear, low efficiency and high costs. Although electrical discharge machining can overcome some limitations, it usually exhibits a low material removal rate and poor surface quality. This study aims to explore the effect of graphene powder and ultrasonic vibration on polycrystalline diamond by electrical discharge machining. It was found that the synergistic effect of graphene powder and ultrasonic vibration can achieve a good surface quality. The results show that ultrasonic-assisted powder mixed electrical discharge machining can obtain the smallest surface roughness (1.98 μm) at low parameter and the biggest material removal rate (0.773 mg/min) at high parameter compared with ultrasonic assisted electrical discharge machining and powder mixed electrical discharge machining. In addition, after ultrasonic-assisted powder mixed electrical discharge machining, the minimum value (0.807) of I D /I G is obtained. For polycrystalline diamond, this study also proposes a removal mechanism that diamond converts into graphite and is peeled under tensile stress. [Display omitted] • The U-PMEDM for PCD benefits from the synergistic of ultrasound and graphene. • Ultrasonic vibration acts on the chemical bonds in the graphene layers. • Graphitization of diamond occurs and graphene is peeled off under tensile stress. [ABSTRACT FROM AUTHOR]