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Artificial Intelligence (AI) methods, such as artificial neural networks (ANN) and machine learning, have found a common use in solving numerous challenges in engineering. The present research work consisted of the making of a hybrid polymer nanocomposite through reinforcing a polymer composite with natural fibers of kenaf (Hibiscus cannabinus) fiber (KF) and Nanographene. A chemical treatment to help in changing the surface property of the fibres facilitate its adhesion and interaction with the polymeric matrix was conducted using a potassium permanganate (KMnO4) solution in acetone (C3H6O). Originally, the ANN model was applied and trained to predict and optimize the tensile strength (TS) of the resultant KF/nanographene hybrid nanocomposite (KFN). The utilized model was the architecture of a single-layer perceptron with the configuration 3-5-1, and the hidden layer with five neurons. The Central Composite Design (CCD) was used to design the experiments in such a way that it gave a systematic approach towards knowing the impact of the specific variables on the tensile strength. Scanning electron microscopy (SEM) studies supported the great influence of the modification of KMnO4 on the fiber-matrix interface and hence on the mechanical properties of the composite. The outcomes of the mechanical testing carried out using analysis of variance (ANOVA) proved that the major factors had a significant impact on the tensile strength, whilst the model fitted excellently with a value of coefficient of determination R2 = 0.9805. By using the ANN-CCD model, the optimum tensile strength value of 46 MPa was predicted; this value was simply the nearest match to the experimental validation test method’s experimentally obtained value of 45.4 MPa, which translates to a near 98.45% accuracy in predicting the model’s result. This paper brings out the usefulness of using ANN with CCD to quickly get reliable estimations of mechanical properties and therefore save experimental design time and cost of production and resources of developing composite materials. The use of natural resources such as kenaf fiber combined with nanographene promotes a sustainable approach by enhancing material performance while supporting sustainable development goals in materials engineering.

In this study, we report an eco-friendly approach for synthesizing CuS nanorods using aqueous extract of Syzygium cumini seeds as a sustainable alternative. The formation of CuS nanorods were confirmed with UV-Vis, SEM and TEM investigations and were found to be around 6–10 nm in length with hexagonal phase. The adsorption of Cd 2+ ions onto CuS nanorods was optimized using Box-Behnken Design. The adsorption capacity of CuS nanorods towards Cd 2+ ions was calculated to be 266.5 mg g − 1 . Based on the error analysis, the Langmuir isotherm and pseudo second order kinetic models were best applicable for the obtained data. Activation energy of 24.73 kJ mol − 1 from Arrhenius equation suggests the physical adsorption of Cd 2+ ions by CuS nanorods. A maximum MO degradation was achieved within 120 min with first order kinetics explaining the degradation process. The CuS nanorods were explored for antimicrobial activity against E. coli and S. aureus and the zone of inhibitions were high due to the rod shaped CuS nanomaterial with ROS generation being mechanism of activity. These results highlight the ability of the CuS nanorods synthesised in this study in wastewater treatment with microbial activity making it a sustainable material for clean water management.

Particulate-reinforced aluminium matrix composites (PRAMCs) have gained significant attention for their high strength, good ductility, and excellent thermal conductivity, making them suitable for a wide range of modern engineering applications. In this study, micro-sized titanium carbide (TiC) particles were incorporated into an aluminium matrix through liquid-state stir casting, with TiC added at 0%, 3%, 6%, and 9% by weight. The investigation examined the combined influence of TiC content and sliding speed (0.75, 1.5, 2.25, and 3 m/s) on the wear behaviour of the composites when tested against an EN31 steel disc. All wear tests were performed under a constant load of 30 N over a sliding distance of 2000 m. The results show that increasing TiC content leads to a higher wear rate, whereas the coefficient of friction decreases correspondingly. Conversely, increasing sliding speed reduces the wear rate but results in a higher coefficient of friction. These findings demonstrate the coupled effects of TiC reinforcement and sliding velocity on the tribological performance of aluminium matrix composites and provide valuable insights for tailoring their behaviour in industrial applications.

While synthetic fiber composites offer some positive environmental attributes, researchers are trying to explore natural fiber composites (NFCs) due to the high cost and pollution associated with their production. As a result, it is essential to look at the tribological properties that natural composite materials exhibit. This research aims to provide a thorough examination of the current literature about the tribological characteristics of particle‐reinforced and fiber‐reinforced natural composites under lubrication, such as volume loss, friction, and wear. Additionally, the operational and material aspects influencing tribological behavior are also examined in this study. The results show that a wide range of material parameters, including particle size, volume fraction, fiber orientation, fiber length, surface treatment, and aspect ratio, as well as numerous operational factors, including normal load, sliding velocity, sliding distance, and temperature, significantly affect the tribological properties. The current review study, which focuses on the tribological characteristics of NFCs in lubricated environments, is assumed by the authors to have the ability to direct future research in the creation of innovative material designs for tribological applications.

The present work was done to optimize the process parameters of the oil extraction from the algae species spirogyra by using n-hexane as the solvent using the Soxhlet apparatus. The response surface methodology (RSM) and artificial neural network (ANN) were employed to optimize the particle size of the algae powder, dryness level of the algae powder, solid to solvent ratio, reaction time, and extraction temperature of the oil extraction process. Also, the physiochemical properties of the extracted oil were investigated. The comparative evaluation was done between the RSM and ANN models to select the more precise and accurate model. The coefficient of determination, R2 of 98.92%, and the mean absolute percentage deviation (MAPD) of 0.492% for ANN revealed that the current model created with a network topology of 3 : 11 : 1 with tansig (hyperbolic tangent sigmoid) transfer function in the input layer and purelin (pure linear) transfer function in the output layer trained with trainlm (Levenberg–Marquardt) algorithm found to provide the optimal solution with better accuracy in prediction of the output. The physicochemical properties investigated, such as heating value, flashpoint, density, viscosity, iodine number, acid value, saponification value, and cetane index, showed that the extracted oil from the algae spirogyra species can be used as an alternative fuel.

A study optimized drilling control variables for electric discharge machining (EDM) of aluminum (Al) 6061 matrix alloy, reinforced with 0.6 wt.% silicon carbide (SiC) and 0.2 wt.% boron carbide (B4C) hybrid nano metal matrix composite (MMC) using ultrasonic‐aided stir casting. Examination of physical, mechanical, and microstructural parameters revealed that adding nanoparticles increased the density of Al 6061 alloy to 2.698 g/cm3. The Vicker's microhardness was 63.795 HV, 18% higher than the Al 6061 matrix alloy. The metallurgical inspection validated the uniform distribution of SiC and B4C nanoparticles. Taguchi assessed the effects of pulse current, pulse on time, and gap voltage on surface roughness and overcut. Experimental studies of surface roughness show that pulse current (52.31%) is the drilling control variable with the highest impact. For overcut, pulse current is the primary drilling control variable at 66.02%. The hi‐resolution scanning electron microscope (HRSEM) showed that pulse current increased crater size. This study investigates the EDM machining of Al 6061 hybrid nanocomposites reinforced with SiC and B4C. Optimization of pulse current, pulse on time, and gap voltage significantly improved surface roughness and minimized overcut, confirming pulse current as the most influential drilling control variable.

Aluminum 6063 matrix composites are widely employed in wear‐resistant applications due to their high specific strength, lightweight nature, and excellent corrosion resistance. This study conducted a wear analysis on Al6063 composites reinforced with varying concentrations of titanium carbide (TiC), silicon nitride (Si3N4), and zinc oxide (ZnO) using a pin‐on‐disc apparatus. The investigation focused on four key input variables: applied load, sliding velocity, sliding distance, and a combined reinforcement composition (R) of TiC + ZnO + Si3N4. Wear performance was evaluated using two indicators—specific wear rate (SWR) and coefficient of friction (COF). The minimum SWR observed was 4.55 mm3/Nm under optimized conditions: 60 N load, 2 m/s sliding velocity, 1000 m sliding distance, and 4.5 wt% reinforcement. The lowest COF, 0.276, was achieved at a 60 N load, 4 m/s velocity, 2000 m distance, and 1.5 wt% reinforcement. The reduction in wear rate is attributed to the synergistic effect of the reinforcements, which enhance load‐bearing capacity and abrasion resistance due to their hardness and thermal stability. Increased reinforcement content led to notable reductions in both SWR and COF, whereas higher loads tended to increase both responses. An Adaptive Neuro‐Fuzzy Inference System (ANFIS) was employed to predict output responses based on the input parameters. This study investigates the wear behavior of Al6063 composites reinforced with TiC, Si3N4, and ZnO using a pin‐on‐disc setup. Key parameters include load, velocity, distance, and reinforcement. Specific wear rate and friction were minimized through optimized conditions. ANFIS modeling effectively predicted wear performance, highlighting the synergy of reinforcements in enhancing wear resistance.

Laser welding stands out as one of the most precise and efficient manufacturing techniques, with its ability to generate minimal heat‐affected zones and limit material distortion. This study introduces a cutting‐edge neural network–based predictive model designed to estimate tensile strength and welding deformation in laser welding operations. By incorporating three critical input parameters, laser incident angle, laser velocity, and laser power, the model harnesses the power of a neural network to refine process optimization and elevate the quality of welded joints. Among the tested models, the Bayesian regularization (BR) model demonstrated superior accuracy, achieving a remarkably low mean absolute error (MAE) of just 0.0001982. In contrast, the Levenberg–Marquardt (LM) model yielded an MAE of 89.29, while the scaled conjugate gradient (SCG) model recorded an MAE of 41.67. These findings underscore the effectiveness of the BR model in enhancing predictive accuracy for laser welding applications.

This research investigates the performance, combustion, and emission characteristics of a novel binary biodiesel blend synthesized from rice bran oil (RBO) and waste cooking oil (WCO), addressing the critical need for sustainable, nonedible second‐generation feedstocks. The primary objective was to evaluate the synergistic effects of combining these two distinct oils through transesterification and magnetic stirring to optimize fuel properties. The study uniquely identifies a 50:50 ratio of WCO to RBO as the optimum precursor for secondary blending with mineral diesel. Experimental results reveal that while biodiesel blends exhibit a slight reduction in Brake Thermal Efficiency (BTE) and an increase in Brake Specific Fuel Consumption (BSFC), specifically a 22.2% increase for the B70 blend, they provide superior safety profiles with flash and fire points significantly exceeding those of conventional diesel. The research demonstrates a substantial environmental benefit: B30 blend (30% biodiesel, 70% diesel) achieved a 23.5% reduction in hydrocarbon (HC) emissions and a 13.6% reduction in carbon monoxide (CO) compared to standard diesel. The uniqueness of this work lies in the strategic binary coupling of a high‐viscosity by‐product (RBO) with a post‐consumer waste (WCO) to achieve a balanced fuel profile that meets international standards without requiring engine modifications. This study evaluates the performance and emissions of CI engines using biodiesel blends derived from rice bran and waste cooking oil, revealing that a 30% biodiesel blend significantly reduces emissions, while a 70% blend affects specific engine parameters.

This study evaluates a single‐cylinder hydrogen port injection (HPFI) Diesel engine by systematically varying the hydrogen port‐injection timing and duration to identify settings that enhance overall operation. The objective is to enhance overall engine performance, strengthen combustion characteristics, and reduce harmful exhaust emissions while maintaining a constant operating speed of 1500 rpm under load conditions ranging from 0% to 100%. A structured test matrix was employed in which the hydrogen injection timing was varied from 6° BTDC to 12° ATDC, and the injection duration was adjusted between 9° and 54° crank‐angle degrees. This operating window enabled a systematic assessment of combustion behavior under dual‐fuel conditions. The most favorable response was obtained when hydrogen was introduced at TDC with a 54° CA injection duration. At this setting, the HPFI diesel engine achieved a brake thermal efficiency of 28.58% at 75% load, a notable improvement over the 18.86% observed with pure diesel. The overall fuel demand also decreased, with TEC dropping from 1.01 kJ/h under diesel operation to 0.66 kJ/h at full load. Emission measurements further highlighted the advantages of hydrogen enrichment, including reduced smoke opacity, improved combustion phasing, and a slight rise in peak cylinder pressure attributable to the faster premixed burn of hydrogen. At 75% load, NOx levels fell from 505 ppm with diesel to 433 ppm under hydrogen‐assisted operation, indicating reduced thermal NOx formation. These findings demonstrate that carefully tailoring hydrogen port‐injection timing and duration can simultaneously enhance performance, combustion efficiency, and emissions. Optimizing hydrogen port injection in diesel engines significantly improves efficiency and reduces emissions, demonstrating a sustainable dual‐fuel strategy for cleaner transportation solutions and lower environmental impact.