The powder-mixed electric-discharge machining (PMEDM) process has been the focus of researchers for quite some time. This method overcomes the constraints of conventional machining, viz., low material removal rate (MRR) and high surface roughness (SR) in hard-cut materials, tool failure, and a high tool wear ratio (TWR). However, to determine the optimal machining parameter levels for improving MRR, surface finish must be measured during actual experimentation using various parameter levels across different materials. It is a very costly and time-consuming process for industries. However, in the age of Industry 4.0 and artificial intelligence machine learning (AI-ML), it provides an efficient solution to real manufacturing problems when big data is available. In this study, experimentation was conducted on AISI D2 steel using the PMEDM process for SR analysis with different parameters, viz. current, voltage, cycle time (T_On), powder concentration (PC), and duty factor (DF). Moreover, machine learning models were used to predict SR values for selected parameter levels in the PMEDM process. In this research, Gaussian process regression (GPR) with a squared exponential kernel, support vector machines, and ensemble regression models were used for computational analysis. The results of this work showed that Gaussian regression, support vector machine, and ensemble regression achieved 95%, 92%, and 83% accuracy, respectively. The GPR model achieved the best predictive performance among these three models.

Titanium alloy Ti-6Al-4V possesses excellent mechanical and corrosion-resistant properties; therefore, it is widely employed in aerospace, automotive, and biomedical fields. However, its poor machinability restricts traditional processing methods. To overcome this limitation, the current work presents a symmetry analysis approach to evaluate the effects of tool coating on the micro-electric discharge machining (micro-EDM) characteristics of Ti-6Al-4V. Tungsten carbide (WC) microelectrodes were fabricated in three forms: uncoated, copper-coated, and carbon-coated. The chemical vapor deposition (CVD) method was used to coat the carbon layer, and the integrity of the coating was confirmed by Energy-Dispersive X-ray Spectroscopy/Analysis (EDS/EDX). The effect of input variables—namely, voltage, capacitance, and spindle rotational speed—on two responses was studied—the machining depth (Z-axis displacement) and tool wear rate (TWR)—using a Taguchi L9 orthogonal array. Analysis conducted using Minitab statistical software 17 revealed that both voltage and capacitance contributed to the response parameters as optimized variables. The comparative study showed that the copper- and carbon-coated WC microtool could obtain a better Z coordinate and lower tool wear ratio compared with those of the uncoated tool. The findings confirm that applying thin conductive coatings to WC tools can significantly improve the stability, precision, and overall symmetry of the micro-EDM process when machining difficult-to-cut titanium alloys.

In micro-electrical discharge milling (μED-milling), the rotation and forward feed motion of the tool is the key element responsible for evacuation of the debris particles. The inability to eliminate debris from the interelectrode gap (IEG) causes secondary discharge, short-circuit, and hampers removal rate. This problem is predominant in electrical discharge machining (EDM) where the tool is stationary. Various methods have been devised in the literature to boost debris removal from the IEG. In this paper, the use of a slotted tool with different cross-sections is proposed. The dielectric flow-field, debris trajectory, and cooling rate are calculated using computational fluid dynamics (CFD). Effect of different parameters such as tool rotation, gap size, jet velocity, the shape of slots on the dielectric flow is determined. The slots on the tool create turbulence which enhances the debris removal from the IEG. It also provides the space to accumulate the debris particles, thereby increasing the removal rate. The variation in the dielectric flow-field greatly affects the debris flushing from the IEG. The trajectory of the debris particles depends on their initial position in the IEG and they cool rapidly, which is validated using simulation and mathematical approach.

This study examines the capabilities and optimisation of electrochemical jet machining (ECJM), a component of the electrochemical machining (ECM) production chain. A localised electrolyte jet helps remove material from selective areas; it is a suitable process for contoured parts and hard-to-machine material without inflicting thermal or mechanical stresses. In this regard, the study incorporates details of an experimental layout and variation in parameters in terms of voltage, electrolyte concentration, and jet velocity. The most striking findings indicate that the material removal rate and surface quality are susceptible to parameters such as applied voltage and stand-off distance, and electrolyte concentration and jet velocity (via electrolyte supply rate) fixed. Higher voltages and fixed electrolyte concentrations give higher removal rates, though this might impair the surface finish, thereby requiring a trade-off at best. These results provide insights into optimising process parameters for enhanced precision and efficiency in ECJM. Future research could focus on advanced electrolytes and improving scalability for industrial applications.