The use of oil-contaminated sand in concrete is essential for sustainability and environmental purposes. The comparative characteristics of oilcontaminated sand are crucial to understand and optimize the use of this material, though several research gaps remain to be addressed. To date, few review papers have included recent comparative studies of the oilcontaminated sand and its effect on concrete. Therefore, this comparative review paper examines the impact of using oil-contaminated sand in concrete production. It focuses on how contamination affects sand, fresh and hardened concrete properties, structural performance, environmental and durability effects, microstructure and fiber impacts, sustainability, real-world and practical applications, challenges and solutions for existing challenges, and current research gaps and future research needs. The review synthesizes findings from experimental studies, theoretical models, and practical applications to provide a comprehensive understanding of the challenges and benefits associated with this practice. Main findings include a reduction in fresh and hardened properties up to a certain limit. Even though the usage of oil-contaminated sand in concrete offers numerous challenges, nevertheless, future research directions and field applications hold promise for overcoming these challenges. Addressing the research gaps, such as performance of hybrid materials, optimized treatment method, and standardization of material quality in the construction industry, can lead to the potential of oil-contaminated sand usage for sustainable infrastructure improvement.
Surface roughness (Ra) is one of the most important response indicators used to evaluate machining quality. Although various metaheuristic optimization algorithms have been applied in machining optimization, there remains limited comparative evidence regarding their relative performance under consistent modeling and computational conditions. Therefore, this study aims to evaluate and compare the performance of three widely applied metaheuristic optimization algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), in minimizing Ra during the milling of aluminum alloys. A predictive model for Ra is first constructed using the Group Method of Data Handling (GMDH). Among the eight functions tested in the GMDH network, the double-variable model provides the highest accuracy with an R² of 0.9990, RMSE of 0.0042, and MAPE of 0.2985 for training, and an R² of 0.9962, RMSE of 0.0083, and MAPE of 0.5724 for validation. This model is subsequently integrated into the optimization stage, where GA, PSO, and SA are implemented under consistent computational conditions to ensure a fair comparison. The optimal Ra values obtained by the algorithms are 0.6448 for GA, 0.6445 for PSO, and 0.6472 for SA. PSO achieves the best overall performance due to its rapid convergence, high stability, and small variance across repeated runs. GA produces competitive results with moderate variability, whereas SA converges more slowly and yields a wider spread of solutions. The findings confirm that PSO is the most effective algorithm among the three for surface roughness optimization in aluminum alloy milling and provide practical guidance for selecting machining parameters.
Proton Exchange Membrane Fuel Cell (PEMFC) is a promising clean energy technology that generates electricity from hydrogen through an electrochemical process, which produces only water and heat as byproducts. Efficient thermal management is crucial for PEMFC operation to ensure optimal performance, durability, and energy efficiency. The main challenge of current PEMFC technology is inadequate thermal management, which restricts performance, efficiency, and long-term durability. Conventional cooling methods with a simple channel design are not effective due to the lower thermal conductivity characteristics of base fluids, which restrict effective heat dissipation. Nanofluids with a superior heat transfer performance has been investigated in a honeycomb design of PEMFC cooling plate for its thermal performance improvement. In this study, a 0.5% volume concentration of Al_2 O_(3 ): SiO_(2 )nanofluids with mixture ratio of 10:90, 50:50 and 60:40 was investigated. The simulation was conducted at laminar flow and constant heat of 8000 W/m2 to mimic the operational condition of PEMFC. At 1.6 m/s, the result shows that the highest thermal performance was obtained by the Al₂O₃: SiO₂ (10:90) hybrid nanofluids, which reduced the maximum surface temperature by 6.47% and improved the temperature uniformity by 75.7% compared to water. A 68% rise in the Nusselt number and the highest heat transfer coefficient of 1483.84 W/m²·K, with 69.7% improvement, were also observed. However, although the pumping power increases, it remains manageable relative to the overall power output of a full-scale PEMFC system in practical applications.
The production of wastewater treatment plant (WWTP) sludge is constantly increasing due to global population growth, posing significant environmental challenges related to its sustainable management. This study aims to present an innovative approach for reusing dried waste sludge (DSS) as a cementitious additive in concrete production, contributing to reducing waste generated by WWTPs and mitigating negative environmental impacts. DSS was incorporated into concrete mixtures by replacing cement at levels of 0%, 5%, 10%, and 15% by weight. The mechanical properties of the resulting concrete, including compressive, tensile, and flexural strengths, were evaluated after 7, 14, and 28 days of curing. Additionally, microstructural and compositional analyses were performed using XRD, XRF, and SEM techniques. The results showed that the incorporation of DSS affects the mechanical properties of concrete, with a strength reduction of up to 40% observed when 5% of cement was replaced by DSS. These results indicate the potential of DSS as a sustainable alternative to conventional cement, with further studies needed to improve mechanical performance and balance sustainability with structural efficiency.
This study compared the performance of dry and wet machining on Inconel 718, then optimised the wet machining parameters by examining tool wear and surface roughness. Inconel 718 is renowned for its exceptional hardness and high heat resistance, which makes machining challenging. This research aims to analyse the machining performance in dry and wet cutting, then optimise the machining parameters to achieve lower tool wear and surface roughness. The experiment was conducted using coated carbide inserts while studying three independent variables: cutting speed (Vc) varying from 30 to 100 m/min, feed rate (fz) from 0.03 to 0.07 mm/rev, and depth of cut maintained constant at 1 mm. Experimental results demonstrate that wet cutting achieved a 57.14% superior surface finish compared to dry cutting. However, the minimum tool wear rate was achieved with both cutting methods at different cutting speeds (wet at 30 m/min and dry at 100 m/min). The optimisation results showed that wet cutting conditions utilise a low Vc (30 m/min) but differ in fz, where a low fz (0.03 mm/rev) is optimal for achieving the best surface quality. In comparison, a high fz (0.07 mm/rev) is suitable for maximising productivity with better tool life. Findings show that wet cutting conditions produce low surface roughness (Ra) through low Vc and fz, while high fz is suitable for long cutting tool life
The use of water-in-diesel emulsion fuel has been widely investigated as a strategy to mitigate harmful diesel exhaust emissions, particularly nitrogen oxides (NOx) and particulate-related smoke. However, practical implementation is often limited by emulsion instability, surfactant dependency, and storage-related challenges. This study aims to investigate the effects of surfactant-free, water-in-diesel emulsion fuel on harmful emissions from a common-rail diesel vehicle, using a rotary blade real-time emulsification system. Two fuels were employed: standard Euro 2 diesel and a 10% water-in-diesel emulsion prepared in real time by the rotary-blade system. Emission measurements were carried out at four idle engine speeds, namely 800, 1000, 1500, and 2000 revolutions per minute. Data was collected under steady-state conditions and compared against baseline diesel performance. The emissions measured during the tests included nitrogen oxides (NOx), carbon monoxide (CO), and smoke opacity. The results showed that the rotaryblade emulsification system consistently reduced nitrogen oxide emissions, with decreases ranging from 9.4% at low idle to nearly 40% at higher idle speeds. Smoke opacity was also significantly reduced at lower speeds, achieving reductions of more than 50%. However, at higher speeds, smoke benefits diminished and even reversed, showing an 8.9% increase at 2000 revolutions per minute. Carbon monoxide emissions, by contrast, increased across all operating conditions, with penalties as high as 50.6% at high idle. These results confirm that the rotary-blade system delivers the expected benefits of water-in-diesel combustion, such as improved atomization and lower flame temperatures, but also highlight the consistent drawback of incomplete carbon monoxide oxidation under cooler combustion conditions.
Electric vehicles (EVs) have emerged as a sustainable transportation alternative to reduce fossil fuel dependence and carbon emissions. However, EV performance is strongly influenced by battery efficiency and control system configuration, particularly controller selection. This study analyzes how controller variation and battery efficiency affect the performance of the Viar EV1 1000W electric motor. An experimental approach compared Bosch and QS Motor controllers on a 1000W BLDC motor, with torque, power, speed, voltage, and battery efficiency systematically measured using a dynamometer and digital multimeter. Results demonstrate that the QS Motor controller significantly outperforms Bosch across all parameters delivering higher torque, power, speed, voltage stability, and battery efficiency. The study concludes that QS Motor controller implementation enhances performance and efficiency in light EVs such as the Viar EV1 1000W. This research contributes practical insights for the automotive industry on selecting optimal controllers to improve energy efficiency and system reliability in electric vehicles.
The development of carbon fiber–reinforced composites (CFRCs) has significantly advanced material science by integrating high strength, low weight, and long-term durability into a single material system. In this study, electroless nickel (EN) plating was applied to carbon fiber rods to enhance their interfacial compatibility with the Al6061 alloy matrix fabricated through stir casting. Before plating, the fibers were cleaned, cut to size, sensitized, and activated before deposition under EN832 bath conditions. The treated fibers were then incorporated into molten Al6061 and solidified in preheated dies. Material characterization was carried out in accordance with ASTM standards, including density evaluation, Brinell and Vickers hardness testing, and pin-on-disc wear and friction analysis. Microstructural and compositional studies using SEM–EDX confirmed a uniform nickel coating with effective chemical distribution across the fiber surface. The Ni plating substantially improved wettability and interfacial adhesion, resulting in composites with higher hardness, improved strength, reduced density, lower coefficient of friction, and significantly enhanced wear resistance compared to unreinforced Al6061 alloy. An optimized nickel layer thickness provided strong bonding without compromising the integrity of the fibers. The objective of this study is to evaluate the influence of electroless nickel plating on interfacial bonding, mechanical behavior, and tribological performance of carbon fiber–reinforced Al6061 composites, and to establish its suitability for lightweight, highperformance engineering applications. The enhanced mechanical and tribological performance demonstrate that Ni-coated CF/Al6061 composites are promising candidates for aerospace, defense, automotive, and other advanced engineering industries.
By converting to an environmentally friendly energy system, water electrolysis technology based on renewable sources (like solar photovoltaic and wind power) has presented a sustainable route to carbon-neutral hydrogen generation. In this paper, we introduce a complete techno-economic characterization of renewable electric hydrogen production technologies within storage systems. Our review presents existing pricing profiles including electrolysis capital investment costs, renewable energy pricing systems, and operating parameters. We cover various storage forms (compressed gaseous hydrogen, cryogenic liquid hydrogen, geological formations) and three main electrolyzer technology stacks: alkaline electrolyzers (AEL), proton exchange membrane electrolyzers (PEM), and solid oxide electrolysis cells (SOEC). For these reasons, Levelized Cost of Hydrogen (LCOH) is considered the most significant economic indicator of these experiments. With data available that provides a glimpse of the potential cost reductions associated with scale-up of the manufacturing process, technological advancement, and decreasing costs associated with renewable energy, we sought to explore potential cost reductions regarding cost of this process. The total cost of producing low-carbon hydrogen is currently estimated at between $4 and $8 per kilogram, but it is projected to drop to $2 per kilogram by 2040, which is expected to be comparable to the cost of producing hydrogen from fossil fuels. To ensure the success of this technology, it is essential to develop integrated plans that combine, a supportive policy framework and the use of new methods to increase the efficiency of electrolysis, while maintaining reasonable cost-effectiveness, Total costs of producing low-carbon hydrogen.
This study was conducted to optimise the parameter sets for a melon seed shelling machine. The parameter sets are motor speed and moisture content with different levels were experimented and analysed to build mathematic models. The objective was to describe the relationship between the inputs parameter values and the outputs (shelling efficiency, breakage percentage and machine capacity). Single-objective and multi-objective models were constructed and studied to identify the optimal set, optimal trade-off set of parameters. Minitab software aid for analysis and decision-making. The optimisation revealed a significant trade-off between single objectives) (optimal settings for maximising shelling efficiency (96.7%), minimising seed breakage (1.41%), and maximising capacity (53.98 kg h-1) were mutually exclusive. Multi-objective analysis identified moisture content as the statistically dominant factor (p < 0.05), with motor speed being insignificant. The validated optimal parameter combination for balanced performance was moisture content of 26% and a motor speed of 2100 rpm, which simultaneously improved all three key performance metrics. The proposed solutions for handling single- and multi-objective optimisation through the framework are practical and can be extended to other post-harvest processing equipment.
Warranty management in the automotive aftermarket is increasingly challenged by large volumes of fragmented and heterogeneous data originating from IoT devices, repair logs, and service records. Traditional systems lack the scalability and analytical depth to extract meaningful insights, resulting in delayed claim resolution and higher operational costs. This study proposes a data driven approach to modernize warranty processes by integrating artificial intelligence with interactive visualization. The research utilizes 11,000 historical warranty claim records collected from OEM customers between 2019 and 2023, comprising data attributes such as part numbers, failure codes, service dates, and repair locations. A four-phase methodology based on the Product Design Specification framework was employed: Information Collection, Concept Generation, Product Configuration, and Parametric Analysis. The system architecture follows the Model View Controller design, with SQL and Python forming the backend for data processing and modelling, while Power BI serves as the visualization platform. Advanced analytics techniques including Weibull distribution modelling for failure prediction and Python based anomaly detection algorithms were implemented to identify high risk components and unusual claim behaviours. Integrated dashboards allowed for real time monitoring of key performance indicators such as Warranty Claim Rate, Average Claim Cost, and Claim Resolution Time. The system achieved a warranty cost reduction of RM 23.5K, reflecting a 75% improvement over the five-year period. This study contributes a novel, scalable solution that bridges traditional warranty analysis with AI enhanced predictive analytics. The platform provides manufacturers with improved visibility, accuracy, and strategic foresight. Limitations such as noisy data and model generalizability are acknowledged, with future work aimed at enhancing robustness through natural language processing and adaptive learning models.
The welding experiments were performed with a current range of 45 A to 90 A, a welding time of 3 s to 5 s, and an electrode-to-cup gap of 1.5 mm to 3.0 mm under pure argon shielding gas. Based on process parameters, the heat input domain was associated with nugget diameter, ambient ferrite morphology, tensile shear strength of weld joints, and the nature of fracture and the microhardness distribution in fusion zone (FZ), partially melted zone (PMZ), and heat-affected zone (HAZ). Examinations by optical and scanning electron microscopy showed that all weld zones comprised δ-ferrite in an austenitic matrix, whose morphology evolved from lath to vermicular scales with increasing heat input. Maximum tensile shear strength (4179 N) and weld toughness (5.529 J) were achieved for a balanced δ-ferrite content and an optimal average nugget size of 5.65 mm at 75 A, 3 s, and 1.5 mm, over high heat input of 90 A produced low mechanical features by over penetration and grain coarsening. The microhardness of the welded region increased to about 250 HV at a medium heat input. In contrast, a high heat input resulted in coarsening of δ-ferrite and a decrease in hardness. All fractures initiated in the PMZ, which was determined to be a critical region for joint failure. These results provide a process–structure– property framework to guide the optimization of tungsten inert gas (TIG) spot-welding parameters for thin-gauge stainless steels requiring high joint integrity.
Prototypes and components are increasingly printed using 3D printing. 3D printing issues depend on component shape and material. How 3D printing is done always affects structural component properties. Contemporary structural components, especially in small, unmanned planes, use 3D-printed curved panels with notches. This paper optimised 3D printing parameters to find the strongest notched curve panel using the Taguchi experimental design. PLA was used. The method involved 3D printing notched curve specimens with dimensions of 200 × 80 × 1.8 mm and a fixed notch diameter of 40 mm. Notched panel curve radius is 800 mm. Creality CR10S Pro-V2 3D printer printed the specimens. Three specimens were created for each configuration, utilising 3D printing parameters: printing direction, nozzle temperature, printing speed, layer thickness, and infill percentage, which were identified through literature reviews. The specimens were tested for strength using the universal testing equipment after printing. After collecting test data, the Taguchi design of experiment was used to determine the best process parameters. Specimen with configuration number five (5), printing direction 90, nozzle temperature 195 C, printing speed 60 mm/s, layer thickness 0.2 mm, and infill percentage 100%, produced the highest strength. Analysis of variance (ANOVA) and S/N ratio response validated this conclusion. The formula ‘larger-the-better’ showed that printing direction had the highest S/N Ratio (8.2659). Printing direction, nozzle temperature, and speed contributed 55%, 27%, and 8%, respectively, to the process optimisation, according to ANOVA. Taguchi design of experiment can optimise the 3D printed notched curve shell for strength.
A small-scale rotating detonation engine (RDE) is a detonation-based engine with potential propulsion and power generation applications, especially when weight and space are critical factors. In this study, experimental investigations on a small-scale RDE were fueled with a low methane-oxygen mixture flow rate to investigate the performance characteristics, including the detonation wave stability, detonation wave velocity, and thrust generated. The inner and outer annulus diameters of the small-scale RDE are 38 mm and 46 mm, respectively. The total mass flow rate, ṁtotal, of the methane-oxygen mixture employed in the present study was 0.0040 kg/s, and the equivalence ratios, φ, range from 0.8 to 1.2. The result shows two types of instability observed within the tested equivalence range: chaotic instability and waxing and waning instability. The least instability was observed at φ = 1.0, which can be considered the optimal mixture proportion for a stable, small-scale RDE operation. The average detonation wave velocity and thrust were found to increase as the equivalence ratio increased. Meanwhile, the average detonation pressure within the RDE annulus was observed to rise, reaching a maximum value, and decreasing as the equivalence ratio increases. Thus, achieving a well-balanced reactant mixture is important to ensure a stable detonation wave propagation and optimize the overall RDE performance.
Accurate prediction of buckling loads in composite structures is essential, as their anisotropic and inhomogeneous properties complicate structural analysis. However, traditional predictive models often face limitations such as the inclusion of statistically insignificant polynomial terms in Response Surface Methodology (RSM) and poor learning performance in Artificial Neural Networks (ANN) due to unprocessed and limited data sizes. This study aimed to develop and evaluate predictive models for the buckling load of hybrid graphite/glass epoxy composite laminates using different data sizes. Two datasets were employed, comprising 27 runs generated through a Full Factorial Design (FFD) under the Design of Experiment (DOE) approach and 100 customised experimental runs. Two modelling approaches, RSM and ANN, were employed to predict the buckling load obtained from finite element analysis (FEA). The overall range of computed buckling loads was wide, spanning from 3.627 kN to 1730.8 kN, confirming the strong sensitivity of the structure to the design variables. The highest buckling loads occurred at [45, 1, 3 mm] (angle, volume fraction, thickness), and for hybrid laminates at [45, 0.5, 3 mm]. The RSM predictions produced ratios close to one when compared with FEA results, while the ANN models showed both underprediction and overprediction tendencies. The t-test results indicated no statistically significant difference between the 27 and 100 experimental runs, suggesting that model accuracy was influenced more by modelling approach and data treatment than dataset size. This study may contribute to enhancing knowledge of the buckling behaviour and failure of hybrid graphite/glass composite structures, which will help engineers design safer structures by reducing the risk of buckling.
Wire arc additive manufacturing (WAAM) is a promising technique for fabricating aluminium alloy components due to their high strength-toweight ratio and good machinability. However, challenges such as porosity and the formation of coarse columnar grains occurring from rapid solidification hinder its broader application. This study investigates a hybrid WAAM-forging approach with the aim of reducing porosity and modifying crystallographic texture in ER5356 aluminium alloy. Comparative analyses were conducted on as-deposited and WAAM-forged samples using scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and density analysis for characterisation. Results indicate that WAAM-forging significantly refines grain structure, mitigates columnar grain alignment, and promotes more homogeneous texture distribution. Kernel average misorientation (KAM) analysis reveals a lower degree of local misorientation in forged samples, suggesting effective stress relaxation. The pole figure analysis confirms a suppression of strong texture components, particularly in the {100} and {111} orientations, potentially reducing anisotropy in mechanical properties. The hybrid WAAM-forging process demonstrates significant improvements in the structural integrity and performance of WAAM-fabricated aluminium components, particularly through a reduction of porosity from 4.05% to 2.13% and a refinement of grain size from 168 μm to 122 μm. These findings contribute to the advancement of hybrid additive manufacturing strategies for high-performance applications.
This study investigates the influence of fuel injection pressure and timing on the performance, combustion, and emission characteristics of a single-cylinder, 1500 rpm common rail direct injection (CRDI) diesel engine fueled with a diesel–biodiesel blend containing 20% palm kernel methyl ester and 80% diesel, enriched with hydrogen at a flow rate of 10 LPM (PKME20H10). The objective is to enhance engine efficiency and emission control through a green hydrogen–biodiesel strategy. Artificial Neural Network (ANN) models were developed to predict engine responses, and optimization was achieved using the hybrid NonDominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Experimental and computational analyses revealed that the optimal combination of an injection timing of 25 before top dead center (BTDC) and an injection pressure of 512 bar achieved a brake thermal efficiency (BTE) of 30.1%, a brake specific fuel consumption (BSFC) of 0.123 kg/kWh, and substantial reductions in emissions. Carbon monoxide emissions decreased by 10%, unburnt hydrocarbon emissions decreased by 45%, and smoke opacity decreased by 17%. The study demonstrates that hydrogen enrichment in biodiesel improves combustion quality and engine sustainability, offering a viable pathway for low-carbon automotive energy systems.
In engineering design, structural defects such as cracks can lead to catastrophic failures in machines and equipment. This study investigates the uniaxial stress state of an infinite sheet weakened by two elliptical holes with linear cracks. Due to the complexity of the geometric configuration and the absence of known conformal mapping functions for such regions, this problem has not been addressed in previous research. We solve a plane elasticity problem for a complex geometric configuration featuring two elliptical holes with linear cuts using the theory of complex variables and conformal mapping functions. The solution involves solving a system of linear algebraic equations derived from the theory of complex variables and Kolosov-Muskhelishvili potentials. By expanding the functions φ(z) and ψ(z) into series, we obtain an analytical solution and provide numerical examples to illustrate key theoretical aspects. The coefficients of the analytical functions are determined, and well-known elasticity theory formulas are applied to compute stress components at characteristic points. This research presents a novel approach to solving this specific problem, as conformal mapping functions for such complex configurations have not been previously established.
Vehicle modelling and verification are fundamental components of the modern automotive development process, enabling researchers to optimize controller designs and ensure safety before actual testing. By employing simulation techniques, researchers can accurately predict vehicle performance and behaviour under a wide range of driving conditions. This study focuses on the development and verification of a seven-degree-of-freedom (7-DOF) full vehicle dynamics model developed to simulate real vehicle responses. The developed model consists of a handling system model, tyre dynamics model, load distribution, and individual wheel dynamics, including functions for tyre slip ratio and slip angle. The model is driven by two inputs, namely steering wheel angle and throttle position. To verify the model, its responses are compared to those generated by the CarSim software under similar vehicle parameters and driving conditions involving a double lane change maneuver at a speed of 120 km/h. The results show that the model closely follows the dynamic behaviours obtained from CarSim. The percentage difference of Root Mean Square (RMS) between the developed model and the CarSim analysis is used to evaluate the accuracy of the model. Based on the RMS values at 120 km/h, the maximum error occurred in longitudinal acceleration with a percentage difference of 5.63%. Overall, the verification results indicate that the developed model effectively tracks CarSim outputs within an acceptable range of error, demonstrating its potential for representing vehicle system dynamics under various driving conditions.
Corrosion is a major factor limiting the service life of carbon steel, particularly in alkaline environments encountered in industries such as oil and gas. The objective of this study is to evaluate Arachis hypogaea (groundnut) shell extract as a sustainable corrosion inhibitor for AISI 1020 steel in alkaline media. The inhibitor was prepared via Soxhlet extraction and rotary evaporation and characterized using Fourier Transform Infrared (FTIR) spectroscopy. Corrosion performance was assessed using weight loss measurements, macrostructural and microstructural observations, and Rockwell hardness testing. Experiments were conducted at inhibitor concentrations of 10%, 20%, and 30% v/v, with immersion periods of 7, 14, and 21 days. Results revealed a clear correlation between inhibitor concentration, immersion time, and corrosion resistance. Increasing the inhibitor concentration markedly reduced the corrosion rate, achieving a maximum inhibition efficiency of 97.331% at 30% after 7 days. Prolonged immersion maintained superior protection compared to uninhibited control, indicating sustained inhibitor activity. Additionally, samples immersed with the presence of Arachis hypogea shell extract revealed improved hardness values, suggesting enhanced mechanical performance alongside corrosion resistance. The findings confirm the potential of Arachis hypogaea shell extract as an eco-friendly, cost-effective corrosion mitigation approach for carbon steel in alkaline environments, offering benefits in both protection and mechanical durability.
