This research focuses on a comprehensive exploration of the experimental and mechanical aspects of the electrical discharge machining (EDM) process, specifically targeting the machining characteristics of AA2014/Si₃N₄/Mg/cenosphere hybrid composites. The aim is to optimize the process parameters for enhanced machining performance through a combination of testing, optimization, and modelling methodologies. The study examines the effects of key EDM variables—peak current, pulse on time, and pulse off time—on critical output responses: surface roughness (Ra), electrode wear rate (EWR), and material removal rate (MRR). Leveraging an L9 Taguchi orthogonal array experimental design, the impact of controllable factors on these responses is analysed. An integrated approach utilizing MATLAB’s logic toolbox and Mamdani’s technique is employed to model the EDM process, and a multiple-response performance index is calculated using fuzzy logic theory, enabling multiobjective optimizations. Furthermore, a mechanical behaviour evaluation of AA2014/Si₃N₄/Mg/cenosphere hybrid composites is performed through mechanical testing, with a comparison between experimental machining results and predicted values. Scanning electron microscopy (SEM) images reveal the presence of filler reinforcements within the base alloy, displaying an improved microstructure and uniform reinforcement dispersion. An X-ray diffraction (XRD) analysis confirms the major elemental constituents—aluminium, silicon, and magnesium—in the hybrid composites. A microstructural analysis of the hybrid metal matrix composites (MMCs) prepared for EDM showcases closely packed reinforcement structures, circular ash-coloured spots indicating silicon and nitrates, and a fine dispersion of cenosphere reinforcement particles. The study’s outcomes demonstrate a promising application potential for these hybrid composites in various fields.

This research investigation has been carried out in Computer Numerical Control (CNC) turning of 40–50 Hardness Rockwell C (HRC) hardened high chromium high carbon steel (HCHCR-D3) specimen for the findings of surface roughness (Ra) and the tool wear. The HCHCR-D3 steel, which has excellent abrasion and wear resistance, is machined with the physical vapor deposition (PVD) coated carbide (CNMG) turning insert nomenclature based on shape, clearance angle, tolerance and type of tool inserts. The coatings preferred are Titanium Nitrate (TiN), Aluminium Chromium Nitrate (AlCrN) and Latuma for the coating thickness of 3–4μm. The varying input parameters of speed and depth of cut under constant feed rate are used as machining parameters for this CNC turning operation. The machined surface characterization and tool wear have been investigated analytically in this manuscript along with the predicted results of effective stresses and temperatures under dynamic cutting conditions in Deform 3D can be related. The outcomes indicate that the depth of cut and the hardening effect (HRC) are the major influencing parameter on surface roughness. Less tool wear and machining time are obtained by the usage of coated CNMG tool insert for high-speed cutting conditions which results in minimization of wear interruption and growth in surface improvements.

During the stir casting process, different percent weights of hexagonal boron nitride (HBN) and cubic boron nitride (CBN) were mixed with aluminum alloy 6061. The test specimens are then machined from the cast aluminum metal matrix composites. The tests are carried out utilizing an ASTM G99‐compliant pin‐on‐plate tribometer on a pivoting EN32 circle. Minitab 16 is used to plan the dry sliding wear trials, which are set up in an orthogonal array. The input parameters are percent HBN addition and CBN addition, sliding speed, and load, and the wear rate was considered to be the output parameter. The actual density of the cast specimens was found to be greater than 90% of their theoretical density. The accumulation of HBN and CBN greatly enhances the wear resistance of aluminum metal matrix composites, according to research. The technique of regression analysis is utilized to establish genuine links between the wear rate and input parameters. The morphology of the worn out surfaces was examined using a scanning electron microscope (SEM). After numerous iterations, the simulation source of DEFORM 3D forecasts the stress and velocity component of the frictional surface contact area.

Electrical discharge machining is a thermo-physical-based material removal technique. 25 combinations of process variables were formulated with the aid of Taguchi technique for EDM of adsorbed Si₃N₄–TiN. Machining variables like pulse current, pulse-on time, pulse-off time, dielectric pressure, and spark gap voltage varied, and impact of each variables on the performance metrics (MRR, EWR, SR, ROC, θ, CIR, and CYL) was assessed. MCDM strategies like grey relational analysis and TOPSIS are utilized to find out the ideal arrangement of machining parameters to achieve most acute productivity of the multitude of reactions. Likewise, metaheuristic algorithm in particular GRA combined with teaching-learning-based optimization algorithm is utilized for getting global optimized input factors. Important factors like pulse current, pulse-on time, and spark gap voltage characteristically affect the outputs. It is recognized that the pulse-on time and the pulse current are the most significant input factors than others. The ideal machining parameters in view of GRA and TOPSIS techniques for acquiring better output factors are I, 12 amps; PON, 7 μsec; POFF, 4 μsec; DP, 12 kg/cm²; and SV, 36 volts.