Electrical Discharge Machining (EDM) is a complex process due to the involvement of numerous machining indicators. The key input machining indicators affect machining efficiency, tool material, dielectric fluid, and electrical parameters. The conductive silicon carbide and aluminium powder, having a size of 50 microns is used for the improvement of the machining efficiency of EDM. The machining is carried out on AISI D3 material, which is difficult to machine by conventional techniques. The machining performance of EDM is identified by measuring the Surface Roughness (SR). Moreover, research explores the optimization of surface roughness prediction for silicon carbide and aluminium powder using Particle Swarm Optimization (PSO) combined with machine learning algorithms. The performance of the Random Forest (RF), Decision Tree (DT), and Gradient Boosting (GB) is evaluated, trained and tested on respective datasets, with hyperparameters optimized using PSO to minimize prediction errors. For the Aluminium Powder dataset, the Decision Tree model demonstrated superior performance with the lowest Mean Squared Error (MSE) of 0.0603, indicating its ability to accurately capture the dataset’s feature relationships. Conversely, the Gradient Boosting model excelled on the Silicon Carbide dataset, achieving the best MSE of 0.1021, attributed to its robust ensemble learning approach. The significant differences in model performance across the two datasets underscore the importance of dataset-specific optimization and model selection. The findings of the research work highlight the effectiveness of PSO in enhancing model accuracy and emphasize the necessity of modified strategies for different materials in predictive analytics. Moreover, it showed that aluminium powder results are better as compared to silicon carbide powders on Surface roughness analysis.

Surface integrity remains a crucial factor in ensuring the durability and functionality of machined parts, especially in large-scale production settings. This study investigates, through controlled experiments, the effects of three cooling and lubrication methods—dry cutting, flood cooling, and minimum quantity lubrication (MQL)—in combination with key machining variables on average surface roughness (Ra) during the turning of free-machining SAE 1215 MS steel. Tests were conducted on a Tsugami BO12 CNC lathe, employing a Taguchi L9 orthogonal array to systematically vary three levels of cutting speed (V c ), feed rate (f), and depth of cut (t). Statistical analysis used analysis of variance (ANOVA) and signal-to-noise (S/N) ratios to interpret the results. The ANOVA revealed feed rate as the primary factor influencing surface roughness, accounting for 50.4%–54.5% of the total variation, with cutting speed ranking second (20.5%–23.7%) and depth of cut ranking third (13.8%–17.6%). Cross-method comparisons showed that no single cooling technique was universally superior; MQL was most effective at a moderate speed of 94.20 m/min, but dry cutting delivered the best finish at a higher speed of 113.04 m/min—an unexpected benefit. The best surface finish achieved was 0.713 μm, recorded under dry conditions at 113.04 m/min, with a feed of 0.06 mm/rev and a depth of cut of 1.5 mm. This performance results from a significant interaction between V c and t, exclusive to dry machining, in which a greater depth of cut—usually seen as a disadvantage—becomes advantageous at high speeds, highlighting the complex role of process interactions in optimizing surface quality and underscoring the nuanced role of process interactions in optimizing surface quality.

Zinc oxide nanoparticles (ZnO NPs) have attracted considerable interest owing to their versatile optical, luminescent, magnetic, and electrical properties. A range of synthesis methods have been reported for ZnO NPs, including co-precipitation, hydrothermal, solid-state reaction, and sol-gel processing. In this study, pure ZnO, Ca 2+ doped ZnO, and Dy 3+ doped ZnO nanoparticles were synthesized via the sol-gel route. The resulting powders were characterized by powder X-ray diffraction (XRD), UV-Visible spectroscopy, and Fourier-transform infrared (FT-IR) spectroscopy to assess phase formation, optical behavior, and bonding environments. XRD and FT-IR analyses confirmed the formation of doped and undoped ZnO nanoparticles, with crystallite sizes estimated from the Debye-Scherrer formula in the range of approximately 31-40 nm. The direct optical band gap (E g ) and the Urbach energy (E U ) were extracted from UV-Vis transmittance data, with the undoped ZnO exhibiting an E g of 3.377 eV. The measured E U values span from 0.175 to 1.441 eV, reflecting dopant- and defect-related tail states that influence optical absorption. Across the series, the dopants modulate structural and optical properties, revealing clear dopant structure property relationships pertinent to ZnO based functional materials.

The presence and stability of banded carbides significantly influence the mechanical performance and service life of bearing steels, particularly in high-demand applications. In GCr15 bearing steel, the formation and distribution of chromium-rich [Formula: see text] carbides during heat treatment can lead to anisotropic properties and premature failure if not properly controlled. However, traditional methods for predicting carbide behavior lack precision and adaptability across varied heat treatment conditions. Addressing this limitation, this research aims to develop a machine learning (ML)-based predictive model to estimate the spatial distribution and interface stability of banded carbides in GCr15 bearing steel subjected to controlled heat treatments, thereby improving material design and performance forecasting. GCr15 bearing steel samples underwent austenitization and quenching treatments under varied thermal parameters. These features were used to train Dynamic Gannet Optimized Intelligent Natural Gradient Boosting (DGO-INGB) to predict carbide banding and interface instability. Post-processing evaluation through Rockwell hardness (RWH) tests validated the predicted mechanical behavior. The proposed ML model achieved a prediction of austenitization with quenching for MAE (0.061, 0.049), RMSE (0.095, 0.087), and [Formula: see text] (0.86, 0.89) for interface stability under unseen conditions. The predicted results closely correlated with experimental hardness values, confirming the model’s reliability. This integrated ML-based approach enables efficient prediction of microstructural features in bearing steel, offering a novel tool for microstructural control.

Novel Metal-Organic Frameworks (MOFs), specifically Ca-BTC-H 2 O (Ca(hbtc)⋅2H 2 O), featuring diverse sizes and shapes, have been synthesized via a surfactant-assisted method. In this synthesis, Ca(II) serves as the metal center, while trimesic acid acts as the organic ligand. Further research has delved into the correlation between the surfactant contents and the resulting particle morphology of the synthesized product. The samples underwent comprehensive characterization through a series of techniques, including but not limited to scanning electron microscopy (SEM), powder X-ray diffraction (XRD), and inductively coupled plasma mass spectrometry (ICP-MS). The results demonstrated that the size and shape of Ca-BTC-H2O are tunable by adjusting the contents of the surfactant used in the synthesis process. Rod- and sheet-like Ca-BTC-H2O are now readily accessible. Furthermore, the kinetic characteristics of removing heavy metal Pb(II) using the rod-shaped Ca-BTC-H 2 O were investigated. The findings revealed that the as-synthesized Ca-BTC-H2O effectively facilitates the removal of Pb(II) heavy metals from sewage, demonstrating its potential as a wastewater treatment agent.

In this study, using a mechanical alloying process, various amounts of carbon nanotubes (CNTs) were distributed in the alloy using Cu40-Ni60 particles. The effects of CNTs on nanocomposites were investigated using energy analysis, four-point probe surface magnetometer (VSM), vibrational sample magnetometer, scanning electron microscope (SEM), and X-ray diffraction (XRD) techniques to produce Cu-Ni/CNT nanocomposites. Microstructural study of nanocomposites using 5-hour XRD images showed that the Cu-Ni-Ni alloy was homogeneous. The effective role of nanotubes in the fineness of the structure was demonstrated for the composite sample containing 5 Wt.% of CNTs. The sample containing 5 Wt.% of CNTs contained the finest structure, indicating a homogeneous distribution of CNTs through the alloy matrix.

Bridge surfaces are loaded with heavy loads and harsh weather, causing wear and tear as well as a reduced lifespan. Conventional materials often do not effectively handle thermal regulation (temperature control) and bearing performance (load bearing) simultaneously, leading to constant repairs as well as danger. This research overcomes these shortcomings by employing a multi-functional composite surface that simultaneously optimizes heat control as well as structural integrity. The main objective is to create a bridge surface that is capable of keeping the structure cool under direct sunlight, with enough strength for heavy traffic. For this purpose, a step-by-step methodology was adopted: Selecting composite materials with heat-resistant ceramics and high-strength fibers to handle temperature fluctuations and mechanical loads. Designing multi-layer composite surfaces with thermal insulation and load-carrying capacities. Thermal and mechanical simulations are employed to analyze surface performance with different weather and load conditions. Cross-checking results with finite element analysis (FEA) for safety and durability. A Multi-Objective Owl Search Genetic Algorithm (MOOSGA) is utilized to determine the optimal balance between heat dissipation and bearing capacity by varying material ratios and surface thickness. MOOSGA is a Multi-Objective Genetic Algorithm (MOGA) and Owl Search Algorithm (OSA) hybrid optimization technique. The findings proved that the optimized composite surface lowered surface temperature but improved load-bearing capacity compared to ordinary concrete. The optimized composite MOOSGA has [Formula: see text]C, representing a reduction of 33.1%, exhibiting greater thermal resistance and heat dissipation capacity. In general, the multi-functional composite surface with collaborative optimization efficiently resolved thermal and mechanical problems, showing an appropriate method for durable, safe bridge engineering.

This study investigates the impact of various mechanical and acid-based pre-treatment techniques on the surface morphology and roughness of Titanium Grade V (Ti-6Al-4V) alloy in order to enhance its fibre adhesion capabilities in metal-fibre composites. Mechanical treatments like sandblasting and copper blasting, single and dual acid etching methods, as well as the combined effects, were systematically examined. For measuring surface roughness, the Mitutoyo portable surface roughness tester and for morphological changes observed by Scanning Electron Microscope (SEM) are used. Results showed that Sandblasting and copper blasting increased roughness from 0.27[Formula: see text] [Formula: see text]m (untreated) to 2.87[Formula: see text] [Formula: see text]m ((≈11 times) and 5.65[Formula: see text] [Formula: see text]m ((≈21 times), respectively, while further acid etching on sandblasted specimens enhanced roughness up to 3.69[Formula: see text] [Formula: see text]m ((≈14 times). Dual acid etching methods exhibited variable effects depending on the acid sequence and composition. The study concludes that copper blasting offers the highest enhancement in roughness, whereas the combination of sandblasting followed by dual acid etching yields a balanced surface morphology favorable for strong fiber bonding. These findings provide practical insights into tailoring titanium surface treatments for improved composite performance.

The usage of magnesium alloys is limited because of their deprived tribological performance. Hence, the extant effort is to develop the AZ91 matrix composites augmented with 12[Formula: see text]wt.% zirconium diboride (ZrB2) through the stir casting route. Metallurgical examinations depicted the presence and scattering of the ZrB2 in the matrix with no defects and pores. A pin-on-disc device was used in the dry sliding wear tests, which were conducted on hardened counter discs. The experiment was performed according to Taguchi’s L9 array by utilizing three factors, such as load [Formula: see text], sliding velocity [Formula: see text], and sliding distance [Formula: see text]. The gray relational analysis has been employed to predict the wear rate (Wr) and coefficient of friction (CF) for the synthesized composite. Analysis of variance (ANOVA) was used to find out the consequences of the involved variables on the outputs. The experimental finding shows that the lower Wr and CF were produced at 10[Formula: see text]N of L, 2[Formula: see text]m/s of V, and 1500[Formula: see text]m of D. ANOVA results indicate that L has the most consequence that affects the Wr and CF, followed by D and V, respectively.

The effects of heat-treated biosilica from red matta rice husk added to AA7075 metal matrix composites are the subject of this study. Matrix fabrication was carried out using the stir casting method. Specimens were subsequently characterized according to ASTM standards, covering mechanical, fatigue, dry and wet sliding behavior, and creep strain properties. Among the different designations produced, AA2 stands out as showing especially promising mechanical and fatigue properties. With an impact energy of 32.8[Formula: see text]J, a yield strength of 241[Formula: see text]MPa, a fatigue strength of 318[Formula: see text]MPa, and a tensile strength of 547[Formula: see text]MPa, AA2 is quite impressive. This classification emphasizes the beneficial effect of biosilica incorporation on the mechanical and fatigue properties of the composite. AA3, which contains 5[Formula: see text]wt.% biosilica, exhibits decreased creep strain and improved wear resistance in dry and wet sliding situations. The coefficient of friction (COF) is 0.57, and the specific wear rate is 0.018[Formula: see text]mm3/Nm for dry sliding wear, as shown in AA3. The specific wear rate in wet sliding wear is 0.007[Formula: see text]mm3/Nm, and there is a COF of 0.55. The biosilica, which is derived from red matta rice husk, acts as a reinforcing agent, improving the matrix’s microstructural stability and load transfer capabilities. At 2000, 4000, 6000, 8000, and 10,000[Formula: see text]s, respectively, AA3 shows reduced creep strain values of 0.0102, 0.02494, 0.0994, 0.221, and 0.379. Applications requiring exceptional fatigue performance, mechanical strength, and wear resistance should benefit from biosilica-reinforced AA7075 composites, according to this study. The results provide important information for developing new materials with better overall performance.