Hasil untuk "Mechanical engineering and machinery"

Ruqiang Yan, Jiaxin Ren, Jingcheng Wen et al.

Alexander Chidara, Kai Cheng, David Gallear

Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed on machinery-driven solutions—including high-efficiency shredders, granulators, extrusion moulders, and advanced sorting systems employing hyperspectral imaging and robotics. This review further explores chemical recycling technologies, such as pyrolysis, gasification, and supercritical fluid extraction, for managing contamination and additive removal. The integration of Industry 4.0 technologies, notably digital twins and artificial intelligence, is highlighted for its role in predictive maintenance, real-time quality assurance, and process optimisation. A combined PRISMA approach and ontological mapping are applied to classify technological pathways and lifecycle optimisation strategies. Critical engineering constraints—including thermal degradation, additive leaching, and feedstock heterogeneity—are examined alongside emerging innovations, like additive manufacturing and microwave-assisted depolymerisation, offering scalable, low-emission solutions. Regulatory instruments, such as REACH and Extended Producer Responsibility (EPR), are analysed for their influence on machinery compliance and design standards. Drawing from sustainable manufacturing frameworks, this study also promotes energy efficiency, eco-designs, and modular integration in recycling systems. This paper concludes by proposing a digitally optimized, machinery-integrated recycling model aligned with circular economy principles to support the development of future-ready PVC reprocessing infrastructures. This review serves as a comprehensive resource for researchers, practitioners, and policymakers, advancing sustainable polymer recycling.

LIN Xinqiang

ObjectiveIn order to achieve the capability of whole process unmanned operation of the Robo-Sweep intelligent cleaning robot, it is necessary to develop a set of unloading mechanisms that meet various needs.MethodsFirstly, according to the overall design scheme of the whole machine, the preliminary design of the unloading mechanism was carried out through the graphical method. Secondly, based on Adams software, a parameterized virtual prototype model of the unloading mechanism was established to perform kinematic and dynamic simulations of the unloading mechanism. Finally, taking the maximum thrust of the linear actuator during the lifting and tilting process as the optimization objective, through sensitivity analysis, the hinge joints that have a greater impact on the optimization objective were selected as design variables, and performance constraints and interference constraint functions were set. By using its parameter optimization design function, the optimal design values of the hinge joint positions of the unloading mechanism were determined, which in turn provides a basis for the selection of linear actuators.ResultsThrough comparing the data before and after the optimization, the maximum driving force of the lifting linear actuator is reduced by 17.6%, the maximum driving force of the tilting linear actuator is reduced by 5.8%, and the maximum acceleration of the garbage bin at the beginning of tilting is reduced by 26.9%.

WEN Shicong, SUN Junyu, SHI Fuxi

ObjectiveThis study investigates the effects of hemispherical surface textures on cavitation in high-speed rotating friction pairs, analyzing pressure disturbances at texture boundaries and their impact on oil film stability to enhance lubrication performance and reduce cavitation.MethodsBy combining simulation and test, the influence of surface texture on friction lubrication characteristics was analyzed. Firstly, multiphase flow, turbulence, and cavitation models were established. Then, finite element analysis was conducted to examine cavitation characteristics (velocity field, pressure field, and mass transfer rate) under different texture ratios. Finally, a visualization test platform was built, using high-speed cameras to observe cavitation bubble dynamics on textured and non-textured counterfaces at varying rotational speeds.ResultsSimulation and test results reveal that cavitation volume fraction and texture ratio are not positively correlated. The onset and collapse of cavitation is extremely short, and once the conditions for cavitation are met, full vacuoles can be produced within 0.015 s. The velocities of gas and liquid phases in cavitation region are always the same, and process of occurring cavitation is accompanied by gas-liquid mass transfer. The results verify that the cavitation gas originates from phase transition of liquid; the uneven surface in flow field is the focal trigger for cavitation. Simulation analysis and test results show that smooth surface does not produce cavitation, while the textured surface of the friction sub-surface is prone to intense cavitation. Therefore, surface roughness and location of texture distribution should be given priority when processing microtextures.

Hussein A. Kazem, Miqdam T. Chaichan, Ali H.A. Al-Waeli et al.

Integrating the photovoltaic/thermal (PV/T) system in green hydrogen production is an improvement in sustainable energy technologies. In PV/T systems, solar energy is converted into electricity and thermal energy simultaneously using hot water or air together with electricity. This dual use saves a significant amount of energy and officially fights greenhouse gases. Different cooling techniques have been proposed in the literature for improving the overall performance of the PV/T systems; employing different types of agents including nanofluids and phase change materials. Hydrogen is the lightest and most abundant element in the universe and has later turned into a flexible energy carrier for transportation and other industrial applications. Issues, including the processes of Hydrogen manufacturing, preservation as well as some risks act as barriers. This paper provides an analysis of several recent publications on the efficiency of using PV/T technology in the process of green hydrogen production and indicates the potential for its increased efficiency as compared to conventional systems that rely on fossil fuels. Due to the effective integration of solar energy, the PV/T system can play an important role in the reduction of the levelized cost of hydrogen (LCOH) and hence play an important part in reducing the economic calculations of the decarbonized energy system.

CHEN Ao, LI Chen, YAN Jiayun et al.

Standardization is one of the key steps to analyze locomotive overhaul data with a focus on reliability-centered maintenance (RCM). However, traditional manual methods encounter challenges such as small sample sizes, non-standardized data formats, analytical complexities, and high labour costs, hindering the achievement of data standardization. Large language models (LLM), featuring powerful performance in natural language processing comprehension and handling complex tasks, have made great academic and industrial progress in recent years. This study initially investigated the application performance of LLMs in information extraction from locomotive overhaul data, with the following three reveals, as the universal information extraction (UIE) LLM is suitable for information extraction in the field of locomotive overhaul; expanding the size of locomotive data helps improve the UIE performance in information extraction from locomotive overhaul data; balancing the types of fault labels does not notably help improve this performance. Subsequent explorations concentrated on difficulties in data annotation. The script writing method was utilized for automated annotation of data, and ChatGLM was leveraged to standardize locomotive overhaul data, yielding Bleu-4, Rouge-1, Rouge-2, and Rouge-L metrics of 86.87%, 89.60%, 87.54%, and 94.26%, respectively, in alignment with the requirements of engineering applications. Further developments introduced an auxiliary data standardization pre-processing tool to streamline the standardization process by encapsulating the LLM.

Eduardo Tomanik, Wania Christinelli, R. Souza et al.

Graphene-based materials have great potential for tribological applications. Graphene’s unique properties such as low shear resistance, high stiffness, and thermal conductivity make it an attractive material for improving the properties of lubricants in a wide range of industrial applications, from vehicles to house refrigerators and industrial machinery such as gearboxes, large compressors, etc. The current review aims to give an engineering perspective, attributing more importance to commercially available graphene and fully formulated lubricants instead of laboratory-scaled produced graphene and base oils without additives. The use of lubricants with graphene-based additives has produced e.g., an increase in mechanical efficiency, consequently reducing energy consumption and CO2 emissions by up to 20% for domestic refrigerators and up to 6% for ICE vehicles. Potential effects, other than purely friction reduction, contributing to such benefits are also briefly covered and discussed.

T. Kapitaniak, M. Šofer, B. Błachowski et al.

1 Division of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90-924 Łódź, Poland 2 Department of Applied Mechanics, Faculty of Mechanical Engineering, VŠB – Technical University of Ostrava, 17. Listopadu 15/2127, 708 33 Ostrava-Poruba, Czech Republic 3 Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawińskiego 5b, 02-106 Warsaw, Poland 4 Department of Mechanics and Fundamentals of Machinery Design, Faculty of Mechanical Engineering and Computer Science, Częstochowa University of Technology, al. Armii Krajowej 21, 42-201 Częstochowa, Poland

MA Jing, CAO Du, MA Lili

Aero-engine´s characteristics vary with flight conditions and operating states. In complex operating environments, both model uncertainty and controller parameter variation exist simultaneously, which greatly affect the control performance in the whole flight envelope. Therefore, a robust elastic adaptive control method based on parameter perturbation model is proposed in this paper. The structural model of aero-engine parameter perturbation is established. Then, aiming at the uncertainty of the controlled object model and the perturbation of controller gain, the robust resilient adaptive control law is designed when the gain perturbation is bounded but the upper bound is unknown by using Lyapunov stability theory and linear matrix inequality constraints, and the controller design problem is transformed into a feasible solution problem of linear matrix inequalities. The controller design only depends on the existence of the solution matrix of linear matrix inequalities, and the stability of the algorithm is proved. On this basis, the control simulation of different operating states of the engine in the flight envelope is carried out. The simulation results show that the adjustment time is less than 1.8 s and the overshoot is less than 5%, which indicates good stability and control performance of the designed controller.

Colani T. Fakude, Refiloe P. Modise, Aderemi B. Haruna et al.

Drug abuse has proliferated at an unprecedented rate worldwide, posing significant public health challenges that directly impact society, criminality, and the economy. This review presents the application of nanomaterials for qualitative and quantitative electrocatalytic analysis of drugs of abuse, mostly opioids (such as heroin (HER), morphine (MOR), codeine (COD), fentanyl (FEN), and tramadol (TR)), and addictive stimulants (such as cocaine (COC) and methamphetamine (MAM)) via direct oxidation. Electroanalytical techniques have attracted attention for generating point-of-use sensors because of their low cost, portability, ease of use, and the possibility of miniaturization. Electroanalytical-based devices can assist first responders with tools to identify unknown powders and to treat victims of drug abuse. Based on the drug therapeutic and usage purposes, research advances in drug electroanalysis can be classified and discussed with special emphasis on the electrochemical reaction mechanism of the drug. Therefore, this review discusses sensor enhancement based on the electrocatalytic properties introduced by various strategies, such as surface nanostructuring, the use of conducting polymers, and anodization of electrode surfaces Finally, a critical outlook is presented with recommendations and prospects for future development.

Hao Ma, Xiaoling Duan, Shulong Wang et al.

GaN JBS diodes exhibit excellent performance in power electronics. However, device performance is affected by multiple parameters of the P+ region, and the traditional TCAD simulation method is complex and time-consuming. In this study, we used a neural network machine learning method to predict the performance of a GaN JBS diode. First, 3018 groups of sample data composed of device structure and performance parameters were obtained using TCAD tools. The data were then input into the established neural network for training, which could quickly predict the device performance. The final prediction results show that the mean relative errors of the on-state resistance and reverse breakdown voltage are 0.048 and 0.028, respectively. The predicted value has an excellent fitting effect. This method can quickly design GaN JBS diodes with target performance and accelerate research on GaN JBS diode performance prediction.

Kang Zheng, Muyang Gan

In the information age, China's Internet technology has developed rapidly and is widely used in all areas of life. He also played an important role in the design and manufacture of industrial machinery, which advanced the development of China's manufacturing industry. With the increasing demand for the development of China's industrial sector, it is necessary to deepen the use of Internet of Things technology in production to accelerate the technological innovation and technological process of China's industrial sector. Starting with the specifics of Internet of Things technology, this article briefly describes its applications in engineering and manufacturing.

Y. Lei

Guang Zhu, Q. Cao, Zhenkun Wang et al.

The effective solution to avoid machinery damage caused by resonance has been perplexing the field of engineering as a core research direction since the resonance phenomenon was discovered by Euler in 1750. Numerous attempts have been performed to reduce the influence of resonance since the earlier of last century, by introducing a nonlinear structure or a closed-loop control system. However, the existed methodologies cannot eliminate resonance completely even extra problems were introduced inevitably, which means the technical choke-point of resonance-free remains unsolved. Here we propose a designable archetype model, which establishes a mapping between the mechanical properties and its structure. A general inverse method for structure construction is proposed based upon the required property for the system with quasi-zero stiffness of any designed finite order and the zero-stiffness properties. It is shown that an ellipse trajectory tracking of the designed model is the sufficient and necessary condition to satisfy the zero-stiffness property. Theoretical analysis shows that no resonant response happens in a zero-stiffness system to the full-band frequency excitation, or equivalently, the system can completely isolate the energy transfer between the load and environment, when the damping ratio approaches zero. Finally, an experimental rig for the prototype structure is built up according to the sufficient and necessary condition of the zero-stiffness system, for which the special dynamic behaviours are verified through experiments of frequency-sweep and random-vibration as well. Experimental results show that the prototype of the initial vibration isolation frequency of zero-stiffness system is much lower than 0.37 Hz, and the vibration attenuation of the proposed model is about 16.86 dB, 45.63 dB, and 112.37 dB at frequencies of 0.37 Hz, 1 Hz, and 10 Hz, respectively. The distinguished geometric structure of the zero-stiffness system leads to a new inspiration for the design of resonance-free in metamaterial unit and the inverse method can even adapt the design for a more targeted applications based on an arbitrary complex dynamic requirement.

N. Dreher, Iago Oliveira de Almeida, G. Storti et al.

Guifan Zhou

With the improvement of the complexity and reliability of mechanical equipment, it has been difficult for the commonly used variational modal decomposition method of vibration signal of rotating machinery to meet the current practical engineering requirements. In order to further improve the adaptability, processing efficiency, and robustness of rotating machinery fault diagnosis methods, a collaborative hybrid element heuristic to multiobjective optimization algorithm is introduced in this paper. Combined with variational modal decomposition (VMD) method, the fault diagnosis method of rolling bearing under complex working conditions is studied. This paper mainly uses a collaborative hybrid metaheuristic algorithm to improve the nondominated sorting genetic algorithm II (NSGA II) and multiobjective particle swarm optimization (MOPSO), which improves the convergence efficiency of multiobjective optimization method and solves the problem of uneven distribution of optimal solutions. Then, the improved multiobjective optimization algorithm is combined with VMD to solve the problem of parameter selection of the VMD method under complex working conditions of rotating machinery. At the same time, the variation relationship between various signal features and VMD decomposition results is compared and studied, and the features with good effect are taken as the objective function of the optimization algorithm; the ability of denoising and feature extraction of VMD in rotating machinery fault diagnosis is improved. In this paper, the proposed method is explored by using analog signals and experimental data of rolling bearings. Through comparison, the improvement of adaptive ability, operation speed, and robustness of the proposed method in rotating machinery fault diagnosis is verified.

Decai Li, Mian Zhang, Tianbo Kang et al.

Abstract Health services for rotating machinery are essential to ensure safe industrial production. In recent years, deep learning (DL) methods based on vibration analysis have been continuously developed in rotating mechanical fault diagnosis (MFD). However, current diagnostic models based on DL facing three major challenges: (1) Convolutional neural network (CNN)-based DL algorithms lose spatial information of features, which may inevitably cause recognition errors; (2) Sufficient faulty training samples must be required to guarantee the learning capability of DL models, which may not be satisfied in actual engineering; (3) The poor generality of existing DL-based models make it difficult for different rotating machines or even different components of a mechanical device for diagnostic tasks. Toward the above problems, a revised DL model, namely dual convolution-capsule network (DC-CN), is introduced to diagnose rotating machinery faults under minor sample conditions. The DC-CN well merits the advantages of CNN and the capsule network (CN), which fully retains spatial information while digging in-depth fault features. Therefore, DC-CN achieves an ideal balance between diagnostic effectiveness and data amount. Experiments including different rotating machinery and different fault components are conducted to validate the diagnostic performance of the proposed model, and the t-SNE dimensionality reduction algorithm is introduced to visualize the extracted features. The proposed DC-CN demonstrated the best diagnostic capability than other DL models in different rotating machinery: 1. For rolling bearing, DC-CN achieved 99.78%, 98.22%, 100%, and 100% under 4 different loading conditions; 2. For the planetary gearbox, DC-CN reached 96.91% and 98.22% under different rotational conditions, including 8 health scenarios of different gear types; 3. DC-CN has excellent performance under small sample conditions.

G. Liang, Maria Visitacion N. Gumabay, Qinghua Zhang et al.

In this study, the deep learning algorithm was used to diagnose the fault of rotating machinery. After the convolutional neural network was used to mine the fault characteristics from the historical fault data of centrifugal multistage impeller blower, a fault diagnosis model was constructed. The model diagnoses one-dimensional signal data, with two convolution layers, two pooling layers, three activation function layers, and one fully connected layer. Adam optimization function is used as the optimizer, and the Relu function is used as the activation function. Using this model, a smart fault diagnosis system with real-time monitoring and offline diagnosis functions is developed. The system obtains vibration data from the rotating machinery every minute, and can diagnose the machine immediately. The system can help laboratory administrators to quickly find potential mechanical faults, reduce accidents, prolong the service life of the system, save maintenance costs, and improve the level of intelligent management.

Yuji YAHAGI

Vortex structures behind two highly heated cylinders of equal diameter in tandem arrangements have been investigated experimentally. The experiments were performed under the following conditions: cylinders diameter, D = 4 mm; mean flow velocity of air, U∞ = 1.0 m/s; Reynolds number, Re = 250; cylinders spacing ratio, S/D = 1.0~10.0; and cylinder heat flux, q = 0~72.6 kW/m2. Two distinct flow structures are formed in the region of the cylinder clearance which depends on the S/D and the cylinder surface temperature, Tw. One is a quasi-stationary twin vortex at the small S/D condition (S/D<3.0~5.0) and the other is a shedding Karman vortex for large S/D condition (S/D>3.0~5.0). Behind the downstream cylinder, the Karman vortex street is formed in all conditions. The critical S/D changing to the Karman vortex increases with increasing the temperature of the upstream cylinder. The Strouhal number St under the twin vortex forming is in the range of 0.150 to 0.155 regardless of the S/D and heating conditions, while the St of the Karman vortex formed behind the downstream cylinder is decreased significantly as the S/D increases. For the large S/D, the Karman vortex is formed behind both of the cylinders then the upstream St agreed with the downstream St. St of the Karman vortex coincides with St in the single-cylinder condition taken into account of the cylinder heating conditions. For the small S/D and the upstream cylinder in a highly heated condition, the twin vortex structure behind the upstream cylinder plays a key role in the downstream shedding Karman vortex structure.

Aixin Feng, Yacheng Wei, Bingjie Liu et al.

The study investigated the microstructure and mechanical properties of composite strengthened high-chromium cast iron by laser quenching and laser shock peening. Observation of microstructure, measurement of microhardness, residual stress and FWHM (full width at half maximum), impact toughness and wear tests were carried out on untreated, laser quenched, laser quench-laser shock peened high-chromium cast iron specimens. The research results showed that the laser shock peening will not produce new phases in high-chromium cast iron, and can further promote the transformation of retained austenite into martensite. The grains inside the laser quench-laser shock peened sample were obviously refined, and the carbides were uniformly dispersed. The laser quench-laser shock peened specimen produced large residual compressive stress and FWHM value on the surface, reaching −432.49 MPa and 2.124°, respectively. The problem of residual stress difference between the hardened zone and the transition zone caused by laser quenching was eliminated, and the tensile stress was all converted into compressive stress, which further increases the dislocation density and improves the micro-hardness. The impact toughness value of laser quench-laser shock peened specimen was 4.43 J/cm2, which was 16.27% higher than that of the untreated sample. The friction coefficient and wear rate were significantly reduced, and the wear scar was shallow and narrow, showing weak abrasive wear and oxidative wear, the impact toughness and wear resistance of high-chromium cast iron were improved. The results obtained from this study could be used as reference in future research and applications of laser strengthened high-chromium cast iron.