Soft robotics has emerged as a promising and rapidly evolving field, replacing traditional rigid systems with highly compliant elastomer actuators that offer adaptable interactions with their surroundings. Driven by cost-effective fabrication techniques like molding, 3D-printing, and laser cutting, these systems frequently utilize pneumatic actuators to mimic biological muscle behavior. However, optimizing these designs remains difficult due to the highly nonlinear mechanical behavior of rubber-like materials and the complex fluid-structure interactions that occur during pneumatic actuation. To address these challenges, the primary aim of this research is to analyze the mechanical behavior of laser-cut, pneumatically actuated multi-material soft robotic structures. To achieve this, finite element (FE) simulations, validated against experimental measurements, were used to evaluate how different geometries and material compositions respond to actuation. By systematically examining these mechanical responses, this research aims to contribute to a broader understanding of how soft robots can be deployed more effectively and widely in the future. The main results showed that the triangular actuator shape is optimal for low-pressure, high-precision applications, whereas the rectangular and elliptical actuators can generate high grasping forces. The results also highlight the importance of the internal design of the actuator, showing that increased chamber density enhances bending in certain shapes while causing localized distortions in others.
Ranking manufacturing processes has significant technical and economic importance in production operations. However, this task is intricate due to the inherent diversity of technical and economic parameters in different manufacturing processes. This complexity makes the ranking of manufacturing processes a Multi-Criteria Decision-Making (MCDM) problem. This study evaluated the application of the recently proposed method, named Deviation-Based Pairwise Assessment Ratio Technique (DEPART), to rank production processes across three distinct scenarios, including ranking nine alternatives in metal grinding using slotted grinding wheels, ranking nine metal turning processes, and ranking six extraction processes in the chemical field. In each example, the process ranking results obtained via the DEPART method were compared to those generated by six other MCDM methods, including Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Multiobjective Optimization On the basis of Ratio Analysis (MOORA), COmplex PRroportional Assessment (COPRAS), Root Assessment Method (RAM), and probability method. The findings indicated that the DEPART method is suitable for ranking production alternatives. However, upon reviewing all utilized MCDM techniques, the probability method emerged as the most appropriate method for ranking production alternatives within the analyzed problems.
This study examines the addition of silica sand nanoparticles at 12 wt.%, 22 wt.%, 32 wt.%, 42 wt.%, 52 wt.%, and 62 wt.% into epoxy resin matrices (DGEBA, Medapoxy HR EA, and Medapoxy HR EB) to enhance their physical, mechanical, and microstructural properties. In research, Silica was successfully incorporated up to 42 wt.%, exceeding the typical 25 wt.% inorganic threshold. This incorporation significantly boosts the compressive, flexural, and tensile strengths of the nanocomposites to 113.685 MPa, 49.723 MPa, and 33.452 MPa, respectively, showing increases of 57.49%, 40.33%, and 37.31% over pure epoxy. The optimal silica content for maximizing mechanical properties lies between 32 wt.% and 42 wt.%. Scanning electron microscopy (SEM) reveals that pure epoxy has a smooth and brittle fracture surface, whereas epoxy/SiO₂ composites exhibit cohesive deformations and fractures due to the silica. However, silica content above 42 wt.% leads to nanoparticle agglomeration, which reduces performance. The study concludes that silica nanoparticles improve the mechanical properties of epoxy resin, making the nanocomposites more economical, durable, and suitable for various industrial and domestic applications. These findings support the development of high-performance materials from abundant natural byproducts.
Spent fluid catalytic cracking (FCC) catalysts represent an environmental challenge. Due to their high Al₂O₃ and SiO₂ content, they have significant potential for reuse as raw materials and additives in ceramics. In this work, the sintering behaviour, phase evolution and densification of kaolin- based ceramics were studied. Compositions containing 30, 50, and 70 mass% spent FCC catalyst (designated as C30, C50, and C70) were developed. The pressed blends were fired at temperatures between 1250 and 1500°C. X‑ray diffraction results show that the C50 samples present the highest mullite content, while cristobalite dominates in the C70 composition. C30 composition achieved the best densification. At 1500°C, they reach an apparent density of 2.38 g/cm3, an apparent porosity of up to 5.4% and water absorption of 2.3%. The decrease in surface area of the C30 sample—from 58.9 m²/g in the raw state to 1.3 m²/g after firing at 1250°C—confirms effective sintering. These results show that the spent FCC catalyst is an effective additive in ceramic production.
This study focuses on short-term (10-minute) forecasting of Global Horizontal Irradiance (GHI) using artificial neural networks (ANNs) enhanced by three metaheuristic optimization algorithms: the FireFly Algorithm (FFA), Particle Swarm Optimization (PSO), and the White Whale Optimization Algorithm (WWOA). The models were trained using meteorological and astronomical data collected from a monitoring station in Khenchela, Algeria. To identify the solar radiation component most strongly correlated with GHI, one additional radiometric input, selected from the available components, was introduced in separate experiments. Model performance was assessed using standard statistical metrics: relative Root Mean Squared Error (rRMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Results indicate that meteorological and astronomical variables alone are insufficient for highly accurate GHI prediction. However, when Global Tilted Irradiance (GTI) was incorporated as an additional input, all three hybrid models exhibited significantly improved accuracy. The ANN-FFA model achieved the best performance, with rRMSE=3.73%, MAPE=6.07%, and R²=0.9962. These findings demonstrate that GTI is the solar radiation component most closely related to GHI under the studied conditions. Furthermore, the study confirms the effectiveness of FFA, PSO, and WWOA in optimizing ANN hyperparameters for solar irradiance forecasting, with FFA yielding the most robust results.
This study investigates the addition of Ni into SAC305 solder alloy with varying nickel content (0–4 wt.% Ni) using experimental techniques and computational thermodynamics. The experimental part employed various techniques (scanning electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, and differential scanning calorimetry). The microstructure of the investigated alloys consisted of primary β-Sn and an eutectic mixture containing β-Sn and two intermetallic compound particles (Ag3Sn and Cu6Sn5). Small Ni additions (up to 0.2 wt.%) refined the microstructure and significantly reduced the undercooling from 30°C (SAC305) to about 12°C. However, Ni additions exceeding 0.4 wt.% led to microstructural coarsening and formation of Ni3Sn4 phase. For the computational part, Thermo-Calc software was used to investigate the conditions of Cu6Sn5 and Ni3Sn4 phase formation. The experimental results were consistent with computations from Thermo-Calc. The results suggest that minor Ni additions (up to 0.2 wt.%) offer possibilities to refine the microstructure and reduce undercooling, potentially improving the properties of solders.
In the field of modern optimization, heuristic algorithms are widely used in various optimization tasks due to their excellent performance on complex problems. This paper proposes a new heuristic optimization algorithm, the Advanced Ceramic Process Heuristic Optimization Algorithm (ACP-MO). Inspired by the ceramic machining process, the algorithm uses forming operations and reverse design repair strategies to simulate the dynamic process in ceramic machining. By optimizing 10 typical test functions, the experimental results show that ACP-MO outperforms multiple common algorithms in terms of optimization accuracy. ACP-MO refers to the three- stage optimization process of the advanced ceramic manufacturing process, which includes the forming stage, sintering stage and repair stage. These three stages correspond to exploration, quality assessment and local refinement, respectively. A new integration of temperature control convergence, Gaussian perturbation and inverse design heuristic correction mechanism is introduced, which provides a new perspective for the design and development of meta-heuristic algorithms.
This study investigates resistance spot welding of dissimilar materials, namely 37.2 carbon steel, 304 stainless steel, and commercial aluminium. The effect of welding parameters on nugget growth, tensile shear strength, and failure modes in various material combinations was investigated using a combined experimental and finite element modeling (FEM) approach. Experimental studies included a welding current range (5-15 kA) and time range (10-30 cycles), complemented by tensile testing and hardness measurements. It was observed that Carbon Steel-Stainless Steel (CS-SS) joints achieved the highest strength (9.5 kN at 9 kA), while aluminium-containing joints exhibited lower strengths but required higher optimal currents. Hardness profiles showed extensive variations across weld zones, particularly for aluminium-steel joints. Failure mode analysis showed a prevalence of pullout failures for CS-SS joints, in contrast to more interfacial failures in aluminium-steel combinations. A finite element model was developed and validated against experimental data, showing excellent predictive capability for nugget size and joint strength (R²>0.96). This study contributes to the development of dissimilar material welding by providing new insights into parameter optimization, failure mechanisms, and industrial application, particularly for automotive and aerospace industries.
Various types of tribotechnical polymeric materials are widely used for metal-polymer (MP) gears to ensure their reliable operation in dry sliding friction conditions. These include polyamides, polyacetals, polyetheretherketones, polyethylene terephthalate, and other polymers. The wear resistance of polyamides (PA6, PA66, PA6+30GF, PA6+30CF, PA6+MoS2, PA6+oil), polyacetals (POM-H, POM+35PTFE, POM+20PTFE, POM+60Bronze, POM+20PTFE+30Bronze; POM+10PTFE+20Bronze, POM+15PTFE+15GF, POM+10PTFE+10GF, POM+10PTFE+20Bronze+10GF, POM+10PTFE+15Bronze+5GF), polyetheretherketones (PEEK, PEEK+30GF), and polyethylene terephthalates (PET, PET+PTFE) on steel has been studied in this work. In order to determine their wear resistance and sliding friction coefficient, the authors’ method of model triboexperimental studies of materials under sliding friction according to the pin-on-disk scheme was used. As a result, their wear resistance indicators were established and used to determine the wear resistance characteristics by the least squares method. They are necessary for the mathematical modeling of the wear kinetics of MP gears using the corresponding author's method. In order to compare the wear resistance of the studied polymeric materials and their compositions over an extended range of specific friction forces, graphical indicators of wear resistance (wear resistance diagrams) were constructed. On the basis of these diagrams, quantitative and qualitative regularities of the tribological behavior of the studied polymers in a tribopair with steel 45 were established. The change in the friction coefficient with increasing contact pressure was also investigated. The qualitative and quantitative parameters of this effect were determined.
Gas turbine energy technologies are one of the most important components of the modern and advanced energy industry. An important task is to ensure the uninterrupted operation of the equipment in a given period; therefore, monitoring and diagnostics of the technical condition of the equipment continue to play an important role in ensuring the quality of the gas turbine engine. The article examines the work on equipment diagnostics using machine learning. It discusses various solutions for combining machine- learning methods and dealing with unbalanced data to solve the problem of predicting the failure of gas turbine equipment on a dataset that has the above disadvantages. There is a review of the solutions and methods under consideration to deal with the problems of the dataset. At the end, the authors provide a comparative table of the results of the application of the considered solutions based on the quality metrics of the Recall, Precision, F1-score classification, and PR-AUC and ROC-AUC curves.
This paper investigates the impact of the production methods on the tensile and thermal characteristics of polyvinyl chloride composite films. Untreated and vinyltrimethoxysilane-treated eggshells at various concentrations were utilized as fillers. The films are obtained via solution casting and co-precipitation techniques, and their tensile strength, elongation at break, and Young’s modulus were compared. The results indicate that the co-precipitation method ensures better dispersion of the filler in the polyvinyl chloride matrix, and films have almost twice as high values of tensile strength (up to 7.46 MPa) and Young’s modulus (up to 59.27 MPa) compared to those produced via solution casting (4.11 MPa and 34.5 MPa, respectively). The treatment of eggshells with vinyltrimethoxysilane enhances interfacial adhesion and further improves these characteristics. It was found for film 1:2 – polyvinyl chloride (PVC): vinyltrimethoxysilane-treated eggshell (VTMS-ES), an increase of about 30°C in the initial temperature of thermal degradation and a 12% reduction in mass loss compared to the same composition with untreated eggshells.
Nepal has been rapidly adopting electric vehicles (EVs) to achieve its long- term strategy for net-zero emissions; however, the transportation sector still heavily relies on imported fossil fuels. This study uses a sample survey to develop the baseline of energy consumption in the transportation sector and accordingly forecasts energy and emissions in the transportation sector using a macroeconomic model. In this study, the Low Emission Analysis Platform (LEAP) modelling tool is used to forecast energy demand and greenhouse gas emissions (GHGs) in the transportation sector of Nepal. Based on historical energy use trends in the sector and the Government of Nepal‘s policies, three scenarios were developed: Business as Usual (BAU), Sustainable Development (SD), and Net-Zero Emission (NZE). In the base year, i.e. 2022, the energy consumption in the transportation sector amounts to 64.92 PJ. The BAU scenario, based on the historical energy consumption pattern, projects an increase in overall energy demand to 92.41 PJ by 2030 and 180.46 PJ by 2045, with a compound annual growth rate (CAGR) of 4.55%. The electricity consumption in the transportation fuel is expected to rise from 0.07 PJ in 2022 to 0.64 PJ by 2045, while GHGs are projected to increase to 11.39 mMTCO2eq by 2045. Similarly, in the SD scenario, based on the targets of the second nationally determined contribution (NDC), energy demand is projected to reach 68.35 PJ by 2030 and 106.94 PJ by 2045, with electricity penetration increasing at a CAGR of 26.68% and GHGs amounting to 6.55 mMTCO2eq by 2045. The NZE scenario anticipates energy demand peaking at 70.27 PJ by 2030 before declining to 36.94 PJ by 2045, with electricity demand growing at a CAGR of 31.12%. GHGs are projected to reach 4.41 mMTCO2eq by 2030 and achieve NZE by 2045.
With increasing freshwater scarcity worldwide, due to rapid population growth, it is difficult to maintain freshwater supplies; in places like Rajasthan and Gujarat in India, the desalination and purification techniques are proving to be the primary means of meeting the increasing demand for freshwater. However, the growing cost of fossil fuels is making typical distilling practices less competitive compared to other types. Therefore, this study shows that solar stills with TiO2 nanocoating are a cost-effective and sustainable technique for the production of freshwater for regions with water scarcity due to their improved efficiency without the use of conventional energy sources. The experiments were conducted in May and March and included two types of solar stills with a 1 m2 area and water depths of 10 mm, 20 mm, and 30 mm. Each configuration was subjected to three surface coatings of black paint, 2%, and 4% TiO2 nanopaint coatings. The results showed that nanocoating increased thermal efficiency and water yield in single and double-slope solar stills. During March, the single- slope solar still with 4% TiO2 nanocoating attained the maximum yield of 1811 ml/m2 at a water depth of 10 mm, an improvement compared to 1377 ml/m2 with simple paint. The yield for 20 mm and 30 mm water depths also increased significantly with the use of nanocoating. In March, the 4% nanocoated double slope still reached 2488 ml/m 2 at 10 mm of depth, greatly surpassing the 1970 ml/m2 of the simple painted version. Due to elevated ambient temperatures and solar radiation, all stills performed better in comparison to March in the month of May. Double- slope solar stills proved to be more efficient than single-slope stills due to greater condensation surface area and better heat retention. Lower water depths led to higher output, as heating the water required significantly less thermal energy.
The use of ultrasonic welding for joining thermoplastic materials has also attracted much interest as a precise and efficient method. This study examines the application of ultrasonic welding in the production of casings for power circuits in battery chargers, utilising Acrylonitrile Butadiene Styrene (ABS) material. Systematic control experiments have been carried out with energy, weld time, and hold time variations to investigate their influence on weld properties. The weld test pieces were evaluated using both destructive and non-destructive testing methods to determine their quality and assess the effects of pressure and thermal gradients on the microstructure of the weld. The analysis techniques employed are X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Thermogravimetric Analysis (TGA), and Atomic Force Microscopy (AFM). Based on a detailed analysis of the experimental results, the present study offers valuable information concerning the effect of process parameters on the morphology of ABS material battery charger casings. This study showed microstructural and thermal modifications in ABS. Non-optimal parameters may result in localized chain reorientation and nanoscale porosity at the weld interface. The welding procedure preserved the polymer chemistry, but rapid heating and cooling caused localized property alterations, owing to welding-induced phase separation. The conclusions drawn from this research can guide future investigations and advance industrial practices aimed at optimizing the ultrasonic welding process for ABS materials in the context of specific device production.
This paper presents the results of modeling and analyzing the influence of new structural materials on the strength and reliability characteristics of automotive chassis hubs using SolidWorks. The possibility of replacing traditional metal materials used in the production of automotive hubs with polymer composite material (PCM) designed with predetermined properties is discussed. During the study, the manufacture of automobile hubs using 3D printing technology was optimized with modern software. The simulation results of three different materials, namely PCM Acrylonitrile Butadiene Styrene / Glass Fiber (ABS/GF), titanium alloy Ti-8Mn and steel C45, using finite element analysis (FEA), showed that the newly developed ABS/GF PCM material based on glass fiber reinforced thermoplastic polymer is more suitable for manufacturing automotive axles than two existing metals, titanium alloy Ti-8Mn and steel C45, thanks to its high strength and being 72.78% lighter than steel C45 and 55.11% lighter than titanium alloy Ti-8Mn. The design of the automobile hubs was optimized using 3D modeling and FEA. Tests demonstrated the high strength and durability of the new material, making it promising for all-weather operation under real road and traffic conditions.
The development of new materials with improved features requires the use of nanocomposite materials and polymer blends. Their special combination provides enhanced performance in a range of environmental, biomedical, and industrial applications. Using the traditional casting procedure, the polyvinyl alcohol (PVA) / poly acrylic acid (PAA) polymer blend doped with silicon carbide (SiC) / multi-walled carbon nanotubes (MWCNTs) nanocomposites was successfully created. Nanocomposites (NPs) were evenly distributed over the polymer mix matrix, and the polymer blend was well dispersed in the solution, according to the optical microscopy image. The films’ surface morphology of the polymer blend exhibits a homogeneous grain distribution, according to FE-SEM examination. The generated materials do not include any new functional groups, according to the FTIR analysis, indicating that just a physical interaction has taken place. It was observed from the study of optical properties that the increase in SiC/MWCNTs nanoparticles led to enhancement of all optical features, such as absorbance, refractive index, optical conductivity, real and imaginary parts of the dielectric constant, while transmittance and energy gaps were decreased. The energy gap decreased from 4.8 eV to 3.82 eV for the allowed transition, and from 4 eV to 3.02 eV for the forbidden transition. These results reveal that PVA/PAA doped SiC/MWCNTs films can be utilized in a variety of advanced applications.
Micro wind turbines (MWTs) are becoming a promising source of electricity generation for decentralised electricity generation, especially in rural areas. The efficiency of MWTs depends on some design and operational factors, including the number of blades, blade radius and wind speed. This paper seeks to establish the effects of these parameters on the performance of the turbine and determine the best configuration that will yield the highest power and efficiency. The experimental design was done systematically using the Taguchi method with an L16 orthogonal array to reduce the number of experiments required for the analysis. Two dependent variables, namely power output and coefficient of performance (Cp), were recorded for each configuration tested. The results of the experiments were analyzed using Analysis of Variance (ANOVA) to test the significance of each input factor and the Weighted Sum Model (WSM) for multi-objective optimization. As for the WSM method, unequal weights were assigned to power (0.35) and Cp (0.65), with efficiency taking precedence over other factors. The optimization studies revealed that the highest performing turbine was the three-bladed turbine with a radius of 0.26 m and a wind speed of 12 m/s. Confirmation experiments under these conditions also showed the same results with little variability, thus confirming the experimental results. The present work offers a systematic, quantitative approach to improve MWT performance, useful for the design and implementation of small-scale wind energy systems in distributed energy applications.
This study introduces the Barrel Theory-based Optimizer (BTO), a novel metaheuristic algorithm inspired by the wooden barrel theory, where the weakest component constrains overall performance. In BTO, each solution is viewed as a barrel and each variable as a plank. Low-fitness solutions, which can be seen as the limiting planks, are updated frequently via a population-level adjustment strategy called Barrel Adjustment. As a result, the overall search capability improves. Besides, BTO uses an adaptive elite selection mechanism that gradually adjusts the number of elite solutions. It enables a smooth transition from exploration to exploitation. The elite set further guides directional updates with a gradually decreasing disturbance factor. The performance of BTO was tested on three groups of problems, including CEC2022 benchmark functions, classical benchmarks, and eight well-known engineering design problems from mechanical and structural engineering. Experimental results show that it achieves higher solution quality, faster convergence, and more stable performance than well-known algorithms, including Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). These findings establish BTO as a reliable and effective algorithm for complex and real-world engineering optimization tasks.
Applying data science methodology, the study examined the separation of gas and crude oil in the state of the QE2 compressor in Monagas, Venezuela. It began with a descriptive statistical study that found and eliminated 2.22% of anomalous data, revealing a trimodal behavior for crude oil and a bimodal for gas. With skewness and a coefficient of determination (R2) where 0.7645 for the gas-crude ratio, both variables had a coefficient of variation greater than 20%. The K-means algorithm was used, which found four well-formed clusters. However, the Kruskal-Wallis method could not find statistically significant differences between them, suggesting that the variability is due to different operating rules, crude types or process errors, rather than clearly differentiated groups. Finally, a Random Forest algorithm was developed with one hundred trees. The most significant achieved an accuracy of 0.9929. Despite an initial Gini value of 0.725 (moderate impurity), it was segmented into two branches. The branch with a raw value ≤1.15 Thousands of Barrels of Crude Oil per Day (MBNPD) showed superior performance, with a Gini value of 0.01, indicating near-perfect purity. This shows that this branch classifies with high accuracy.
The present study investigated the multi-response optimization of turning using nanofluids as coolants to determine the best parametric combination for surface roughness, flank wear, and material removal rate (MRR) by employing the Taguchi method and Grey relational analysis. Eighteen experimental runs were carried out using an orthogonal array of the Taguchi method within the defined experimental domain to derive and optimize the goal functions. The selected objective functions related to the turning process parameters included the volume fraction of nanoparticles (0.04%, 0.08%), cutting speed (110, 170, and 230 m/min), feed rate (0.125, 0.15, and 0.175 mm/rev), type of nanoparticles (MoS2, multi-walled carbon nanotubes (MWCNT), and SiO2), and depth of cut (0.3, 0.6, and 0.9 mm). The multi-response optimization problem was addressed using the Taguchi approach in conjunction with Grey relational analysis. The significance of the factors affecting the overall quality characteristics in the Minimum Quantity Lubrication (MQL) turning of AISI 4340 with nanofluid was quantitatively evaluated through Signal-to-Noise ratio (S/N) analysis and Analysis of Variance (ANOVA) to determine the contribution of each parameter to performance outcomes. The cutting speed was identified as the most significant parameter. Verification experiments were conducted to validate the optimal results. These findings demonstrated the effectiveness of the Taguchi technique and Grey relational analysis in continuously improving product quality in the manufacturing sector.
