Numerous researchers have employed various shape descriptors in the dimensional synthesis of mechanisms. This study introduces a methodology for the path synthesis of spherical four-bar linkages utilizing Elliptical Fourier Descriptors (EFDs). EFDs, a Fourier-based shape representation method, allow the independent acquisition of Fourier coefficients through the Fourier expansion of individual components of the 3D coupler curve, rather than relying on a function as in traditional Fourier analysis. This approach eliminates the need to project the curve onto any plane for Fourier analysis, while preserving invariance characteristics under similarity transformations. Additionally, a method for establishing the shape signature for the open curve is developed. By integrating this process with traditional EFD and optimization algorithm, the proposed method becomes essentially applicable for synthesizing spatial four-bar linkages for both open and closed curves in a single-step design process. Finally, the effectiveness of the proposed method is demonstrated through several examples of spherical four-bar linkages.
The phenomenon of liquid sloshing affects the performance of UAVs used in agricultural spraying. The multi-directional sloshing forces that occur inside the tank can cause the drone to deviate from the specified route and collide with an obstacle. In this study, multiple blade-shaped baffles were used to suppress liquid sloshing in a sprayer drone tank. Numerical simulations using standard k-epsilon, standard k-omega, SST k-omega turbulence models were performed to evaluate effectiveness of this sloshing suppressor and compare it with a conventional suppressor. In these CFD simulations, the changes in total pressure, fluid velocity at a certain point in the tanks, sloshing force, and water elevation were examined for 50% and 65% filling ratios. It was demonstrated through numerical analyses that both suppressors were very effective in sloshing damping.
In this study, a binocular stereo vision measurement system with novel edge-enhancement feature algorithm is proposed for the electrode circular hole array position in electric vehicle battery modules. A simulated workpiece of the electrode circular hole array in an electric vehicle battery module has been created for testing. The proposed system combines image processing techniques with a novel edge-enhancement feature algorithm to identify the features of circular hole arrays in the workpiece and performs three-dimensional point coordinates reconstruction of the feature centers. The proposed system is capable of measuring at a distance of 215 mm from the workpiece and has a measurement range of 450 mm x 200 mm, size of electric vehicle battery modules, which can be increased by using stitching process. The experimental results show that 3D points with a measuring accuracy of 0.1 mm and measuring repeatability of 0.03 mm can be achieved.
In response to the growing demand for sustainable and lightweight bicycle structures, this study investigates the application of natural fiber-reinforced composites in frame design. Sisal and jute fibers were selected as reinforcement materials, and their mechanical performance was evaluated through finite element simulations based on the ISO 4210-6 impact test standard. A composite stacking block consisting of four fiber orientations (0 degrees, 30 degrees, 60 degrees, and 90 degrees) was used to assess the minimum laminate thickness required for impact compliance. Results revealed that the sisal composite exhibited higher stiffness than jute. Furthermore, local reinforcement strategies were explored by applying targeted lay-ups to high-deformation regions (top tube and down tube). The findings showed that localized ply additions effectively reduced deformation while minimizing material usage, with the top tube exhibiting greater reinforcement efficiency. These results confirm the potential of natural fiber composites as viable, eco-friendly alternatives for bicycle frame applications.
Sandwich structures have significant potential for application in the aerospace, automotive, shipping, and engineering industries due to their lightweight, high specific strength, and high specific stiffness. This study examines the bending characteristics of a hexagonal honeycomb sandwich beam made of aluminum alloy (6061-T6) material. In this work, we create some circular elements into the hexagonal honeycomb corners. We used the finite element method to conduct three-point bending test. The dimensions of the cell are as follows: length (l = 5 mm), thickness (t = 0.4 mm), and the diameter of the circle hexagonal (& Oslash; CH) has four variations: 1 mm, 2 mm, 3 mm, and 4 mm. The numerical simulation includes a three-point bending test based on the ASTM C-393 standard. The results showed that the increase in compression displacement contributes to increased interaction among honeycomb cores, therefore increasing the bending deformation of sandwich beams. CH 1 demonstrates a significant 64% enhancement in reaction force when compared to the traditional honeycomb. The results of this study indicate that CH 1 is a highly effective option for applying the novel honeycomb sandwich beam. The traditional honeycomb structure demonstrates more dissipation of deformation energy compared to CH 1.
This study investigates the transient variation of oxygen concentration in an oxygen-free oven using two numerical methodologies and experimental validation. A two-dimensional model, based on the finite element method, is developed to analyze velocity fields and oxygen concentration distributions, incorporating convection and diffusion effects. Additionally, a zero-dimensional model is introduced to simplify the prediction of oxygen concentration under convection-dominant conditions. The models are validated against experimental data obtained from a semiconductor processing oven in case of different nitrogen flow rates. The results indicate that both models effectively predict oxygen concentration reduction, with deviations from experimental values remaining within 3.3%. Furthermore, the influence of air permeation is examined, and its significant impact on oxygen concentration dynamics is revealed. These findings provide valuable insights for optimizing oxygen-free oven designs and improving process reliability in industrial applications.
Biodegradable particleboards made from agricultural waste are emerging as sustainable alternatives to conventional wood-based materials. This study developed high-performance particleboards using alkaline-treated corn husk fibers and chitosan adhesive, a natural alternative to formaldehyde-based resins. Alkaline pretreatment improved fiber wettability by removing lignin, hemicellulose, and impurities, enhancing fiber-adhesive interaction. Chemical modifications were analyzed using FTIR and XRD, while contact angle measurements confirmed increased hydrophilicity. Mechanical testing showed significant improvements, with tensile and flexural strengths reaching 49.9 MPa and 56.8 MPa, respectively. SEM analysis revealed a well-bonded fiber-matrix interface, indicating strong adhesion. This approach demonstrates a scalable, eco-friendly method for producing biodegradable composites with superior mechanical performance. Future work will explore enhancing water resistance and assessing industrial-scale production feasibility.
This study presents an uncertainty model predictive control (UM-MPC) algorithm that considers the uncertainties inherent in both environmental conditions and model parameters. Its core aim is to bolster the tracking accuracy and control system stability of autonomous vehicles. Establishing a multi-cellular model to mitigate the impact of uncertain parameters in the model. Based on the characteristic that drivers adjust their attention concentration according to road changes, a dynamic rule for the weight matrix has been designed through a large amount of comparative experimental data, achieving a shift in the focus of the algorithm during rolling optimization. Additionally, an adaptive predictive adjustment function for weights is proposed, and the optimal solution is derived through offline analytical optimization and the improved Particle Swarm Optimization (PSO) algorithm. Through a Hardware-in-the-Loop platform, a comparative analysis was conducted with the Adaptive Model Predictive Control (AMPC) based on fuzzy rules, affirming the effectiveness of the algorithm.
This paper introduces a spindle imbalance detection method based on principal component analysis and a self-organizing map for machine tools under computer numerical control. By analyzing vibration signals collected during spindle operation, the method extracted eight key frequency-domain features, including spindle rotation and bearing characteristic frequencies. These features were refined through principal component analysis to eliminate collinearity while preserving essential information. A diagnostic model was established using a self-organizing map trained exclusively with healthy state data, enabling the autonomous monitoring of spindle health conditions. The method's effectiveness was validated through extensive experiments on a YCM_NDV102A vertical machining center. Experiments revealed accuracy of 99.7% and 100% in identifying normal spindle conditions and imbalance states, respectively. Overall, the proposed method performed similarly to a supervised learning method but did not require fault data for model training, making it more suitable for industrial applications.
The evolution of bicycle design has been driven by various user needs, influencing key aspects such as wheel size, handlebar length, and seat shape, all aimed at enhancing the rider's experience (Tomaszewski, 2021). Among these, the frame shape plays a crucial role in determining overall performance and comfort (Hsiao, 2015). Today, two predominant types of frames are in use: one-piece frames, commonly found in city bikes, racing bikes, and U-bikes, are designed for flat terrain or gentle slopes (Adsule, 2024). These frames are less effective at absorbing shocks and are unsuitable for handling rough impacts. In contrast, mountain bike frames, designed for steep mountain roads or rugged trails, feature a more complex structure divided into front and rear triangles. The front triangle connects the handlebars to the front wheel, while the rear triangle houses the braking system. This split-frame design improves impact absorption but introduces structural challenges, particularly in the rear triangle, which tends to be weaker due to its pivot system and disc brake integration. This study provides an in-depth analysis of the rear triangle's structural performance through three key tests: disc brake fatigue analysis, rear triangle fatigue analysis, and drop impact testing. Fatigue and impact simulations focus on identifying stress concentration points and displacement patterns to assess the frame's weaknesses. The results are compared with actual experimental conditions, with particular attention given to displacement and stress concentration, as they serve as indicators of potential frame vulnerabilities. Due to the significant external forces acting on it, the rear triangle is especially prone to noticeable force changes, making it an ideal focus for detailed analysis.
To reveal heat transfer mechanism of metal hydride hydrogen storage reactor (MHHSR) efficiently and accurately. Meyer wavelet finite element model (MWFEM) is built integrating MWSF to conventional finite element model, which can effectively implement heat transfer analysis of MHHSR. Firstly, heat transfer models of MHHSR is established, which contains governing equations of metal hydride bed, governing equations of heat exchange fluid, and corresponding heat transfer boundary conditions. Secondly, MWSF is considered as interpolation function to build MWFEM. Finally, MgH2 reactor is selected as object to conduct heat transfer analysis, effect of hydrogen supply pressure, heat exchange fluid temperature, heat exchange fluid velocity and porosity on heat transfer law of MgH2 reactor are achieved by proposed MWFEM, conventional finite element model, B-spline wavelet finite element model, Daubechies wavelet finite element model and experiment. Heat transfer mechanism of MgH2 reactor is acquired, results offer significant basis for optimal design of MgH2 reactor, accuracy and efficient of proposed MWFEM are validated.
In many engineering applications, compressed air without any moisture is critical. Heat regeneration-based adsorption dryers are commonly used to achieve ultra-low dew points in compressed air. This study experimentally examines the essential characteristics of three heat regeneration modes: internal heating, external heating, and compressor air heating. The calculations for the adsorption dryers are based on previous work, forming the foundation for a logical system comparison. The assessment is conducted over a common performance range, including a compressed air output of 1000 to 5000 m(3)/hr, operating pressures of 5 to 10 bar, and feed air temperatures of 25 to 45 degrees C. The results show that the characteristic specific energy requirements for internal heating, external heating, and compressor air heating are 0.176, 0.342, and 0 kW/m(3)/min, respectively, for electrical energy. For dried regeneration air, the energy requirements are 0.203, 0, and 0 kW/m3/min, respectively.
In this study, silica aerogel and short carbon fiber are added as reinforcements into NC826 epoxy resin to provide nano-macromolecular composite materials. The materials are subject to static compression and dynamic impact tests under various strain rates using a universal testing machine and Hopkinson bar. Field emission scanning electronic microscopy (FE SEM) is then used to obtain fractography to explore the mechanism of interfacial fracture and the dispersion of the reinforcements. In the static test, the highest stress was obtained from the epoxy resin composite material with 0.4wt% short carbon fiber; in the dynamic test, it was obtained from the material with 0.6wt% carbon fiber. The SEM fractography shows that the addition of reinforcements in an appropriate amount is effective in improving the strength of epoxy resin, preventing the growth of existing cracks, and the transferring the stress along the substrate. Excess reinforcements can cause poor dispersion, leading to aggregation and loss of strength.
This study presents a sol-gel-based strategy to synthesize amorphous SiO(2 )nanoparticles and incorporate them into PMMA to form corrosion-resistant composite coatings for Ti-6Al-4V substrates. The effects of synthesis parameters precursor concentration, catalyst concentration, reaction time, and temperature on particle size and dispersion were systematically investigated. Voronoi-based dispersion index (D-0.2) was used to quantify uniformity, showing that catalyst concentration played the most critical role in dispersion control. Well-dispersed SiO(2 )nanoparticles (D-0.2 > 50%) significantly improved electrochemical barrier performance, as confirmed by electrochemical impedance spectroscopy (EIS). The optimal composite, containing 5 wt% SiO2, exhibited impedance magnitudes in the range of 10(8)-10(9) Omegacm(2 )at low frequencies and stable phase angles (80-90 degrees), indicating dense coating morphology and strong dielectric behavior. In contrast, higher filler content (10 wt%) led to aggregation, structural defects, and diminished corrosion protection. The findings confirm that nanoparticle dispersion is a key factor in coating integrity and validate a practical design route for highperformance polymer-based anticorrosion coatings.
The vibrational stimuli, particularly when it approximates the resonant frequency of human spine, can subject the spine to significant forces, heightening the risks of damage and potentially causing devastating consequences. Although numerous biomechanical inquiries have delved into the resonant frequency and its corresponding mode shape of the human lumbar spine, the majority overlooked the influence of physiological compressive loading stemming from muscular contractions. Consequently, this research aimed to scrutinize the modal characteristics of the entire human lumbar spine under the influence of this loading. Initially, a computed tomography-based, 3-dimensional ligamentous finite element model of the human T12-pelvis spinal segment was created and validated. Subsequently, modal analysis was conducted on this model, incorporating varying levels of compressive preload applied via a follower load method, to determine its resonant frequency and corresponding mode shape in the vertical plane. The results showed that the T12-pelvis model exhibited a vertical resonant frequency of 6.49 Hz under no preload. Furthermore, the observed vertical mode shape showcased the lumbar spine engaging in not just vertical motion, but also anterior-posterior and rotational motions. Additionally, it was found that the modal responses were preload-dependent. As the preload increased, the vertical resonant frequency increased, whereas the vertical displacement of the vertebrae decreased. The present findings have the potential to enhance our comprehension of vibrational responses in the human lumbar spine, thus offering crucial insights in reducing injuries and discomfort among individuals exposed to vibration environment.
Ocean salinity varies from 33 to 37 g/L due to local geographic and climatic variations affecting ionic concentration, conductivity, microbial activity, and electron transfer rates. The impact of these natural salinity fluctuations on the performance of MFC remains underexplored. Hence, the present study investigates the influence of three salinity levels-lower (33 g/L, R33), optimum (35 g/L, R35), and higher (38 g/L, R38)-on power generation, bacterial viability, and biofilm formation in MFCs. Among the tested conditions, R35 exhibited the highest power and current density of 15.02 mW/m(2) and 103.59 mA/m(2), confirming that moderate salinity enhances microbial metabolism and electrochemical efficiency. At 160 mA/m(2), R33 displayed a second power peak with 16.5 mW/m(2), which indicates delayed concentration polarization and enhanced electron transport at higher current densities. Additionally, resulted in enhanced biofilm formation and the highest bacterial viability of 6.67 x 10(7) CFU/mL for R33. This indicates the gradual adaptation of Gram-negative bacteria and enhanced electron transfer rates. The power density varied from R33 and R35 by 17.3% and from R35 and R38 by 7.79%, which highlights the sensitivity of MFC performance to these narrow salinity changes. These results underscore the importance of salinity management during MFC operation in marine environments. Future work on microbial community analysis and adaptive salinity control strategies will help attain long-term stability and energy output in deep-sea environments.
The automated precision surface finishing processes are the demanding technologies to be developed, to improve some drawbacks of manual polishing. The objective of this study is to develop robot assisted constant force polishing of STAVAX mold steel using lab-made polishing tools mounted on a new polishing end effector. A new polishing end effector embedded with a force sensor has been designed and fabricated, so constant force polishing was possible on a 6-axis industrial robot. The Taguchi method was applied to determine the optimal polishing parameters of the new polishing tools for the STAVAX mold steel. For flat plane polishing using a cylindrical polishing tool with a diameter of 40 mm, the appropriate parameters were the particle size of 0.3 mu m, a rotation speed of 5,600 rev/min, a feed rate of 0.1 mm/min, and a polishing force of 6 N. The PID controller has been adopted in this work for constant
Pressure vessels are vital across many industries for containing fluids under high pressures and temperatures. Their design, fabrication, and maintenance require strict standards for safety and reliability, with welding playing a crucial role. Extensive research has optimized welding practices, enhancing weld quality and mechanical properties. Studies have explored the impact of welding parameters and filler materials on performance. For example, research on materials like stainless steel and Inconel has revealed differences in mechanical strength, corrosion resistance, and weld quality based on filler wire selection. Gas Tungsten Arc Welding (GTAW) has been highlighted as a precise and versatile technique for creating high-quality welds in various industrial applications. Despite advancements, a research gap exists regarding the use of stainless-steel electrodes with low alloy mild steel, specifically IS2062E350C. This study aims to address this gap, comparing different filler wires to enhance cost efficiency and weld performance.
The article uses a thermodynamic investigation of the steam reforming of methanol (SRM) using either urea water solution (UWS) or pure water. This process examines hydrogen-rich gas production, hydrogen yield, and carbon creation. The SRM with UWS produces a higher hydrogen-rich gas and achieves greater reforming efficiency than SRM without UWS. With a water-to-methanol ratio of 5 at 700 degrees C using UWS, the maximum hydrogen yield reaches 4.4 mole/mole MeOH, resulting in the highest reforming efficiency of 117.9%. As the temperature exceeds 250 degrees C, carbon production declines significantly despite adding UWS. Although the carbon production with UWS is greater than that of non-used UWS, there is a slight difference. Comparing hydrogen and CO concentration with reference results for no UWS SRM validates the analysis's reliability. The results supported by an experiment show that the hydrogen-rich gas yield can increase when the UWS as a reactant replaces pure steam for methanol-reforming.
This study applied a dual-optical-fiber real-time measurement system to precision surface grinding of FC-300 gray cast iron, focusing on internal strain and temperature variations. The system uses attached and free fibers to measure force-and heat-induced strains, respectively, with decoupling achieved through wavelength shift theory. In 14 grinding trials, the maximum temperature-induced strain reached 500 mu epsilon for workpiece A-2 and 1200 mu epsilon for workpiece B-2, while force-induced compressive strains peaked at-400 mu epsilon and-615.63 mu epsilon, respectively. These findings demonstrate the system's accuracy in capturing realtime strain variations, optimizing machining processes, and its potential for industrial applications.
