برداشت انرژی ارتعاشی، یکی از روشهایی است که به منظور تأمین انرژی برای وسایل الکترونیکی که توان مصرفی بسیار کمی دارند (نظیر سنسورها)، مورد استفاده قرار میگیرد. با ظهور پیزوالکتریکها و به دلیل خواصی که مواد پیزوالکتریک دارا بودند، به سرعت به عنوان یکی از مرسومترین مواد جهت برداشت انرژی ارتعاشی معرفی شدند. هماکنون برداشت انرژی ارتعاشی با ماده پیزوالکتریک میتواند بیش از 300 میکرو وات بر سانتیمتر مربع توان تولید کند. یکی از انواع مواد پیزو الکتریک، پیزوپلیمرها میباشند. در این کار به کمک مواد پیزو پلیمری EAPap که فیلم نازکی از سلولز میباشند، تیرهای یکسردرگیر برداشتکننده انرژی ساخته شدهاند. با تغییر محل قرارگیری لایه پیزوالکتریک در طول تیر یکسردرگیر، تغییرات ولتاژ، جریان و توان خروجی بررسی شده است. مشاهده می گردد که با تغییر مکان پیزوالکتریک بر روی تیر و نزدیک تر شدن به انتهای تیر یکسردرگیر، به دلیل افزایش میزان کرنش، توان، جریان و ولتاژ خروجی نیز افزایش مییابد.
This study investigates the use of Orobanche plant extract, a parasitic weed that negatively impacts crops, as an innovative and sustainable natural dye for wool fibers. This dual-purpose approach seeks to repurpose an agricultural nuisance into a valuable dye source, thereby addressing the environmental challenges associated with synthetic dyes. The research examines the effects of dyeing conditions and various metal mordants on the color characteristics, fastness properties, and mechanical performance of the dyed wool. The selection of the Orobanche plant is supported by its rich content of phenylpropanoid glycosides, flavonoids, and anthocyanins, which offer a wide range of colors. UV–Vis spectroscopy analysis confirmed the presence of key chromophores, indicating the extract's suitability for dyeing applications. Comprehensive color assessment and fastness evaluation demonstrated the influence of factors such as pH, mordant type, and dye concentration on color strength and hue angle. Fastness properties showed that copper mordant provided the best light fastness, while aluminum was the least effective. Mechanical analysis showed that tin mordant significantly reduced fiber tenacity due to its impact on disulfide bonds, whereas aluminum and iron mordants had minimal effects on strength. This research establishes Orobanche extract as an eco-friendly dye source that, when optimized, can produce wool textiles with desirable color properties, durability, and mechanical integrity. The findings promote sustainable dyeing practices by transforming a problematic weed into an eco-conscious alternative to synthetic dyes, contributing to environmental conservation and waste reduction.
Renewable energy sources, Environmental engineering
The results of the study of the friction and wear characteristics of the developed nanostructured glass composite self-lubricating coatings are presented, the structural components which through magnesium carbide have a qualitative effect on the graphitization process due to the formation of a surface layer of carbide α-graphite, which, when combined with surface oxides characterized by low shear resistance, performs the role of solid lubricants under friction conditions. The positive role of the glass phase in the form of aluminoborosilicate, which affects the tribotechnical properties of coatings, has been established. It has been determined that the increase in adhesion strength is achieved by forming a surface layer of vitreous sodium silicate during spraying. It has been found that the intercalation of the graphite layer with dispersed particles of the surface structure does not significantly affect the tribotechnical characteristics. The developed nanostructured glass composite coatings showed high antifriction characteristics
The intelligent fault diagnosis of rotating mechanical equipment usually requires a large amount of labeled sample data. However, in practical industrial applications, acquiring enough data is both challenging and expensive in terms of time and cost. Moreover, different types of rotating mechanical equipment with different unique mechanical properties, require separate training of diagnostic models for each case. To address the challenges of limited fault samples and the lack of generalizability in prediction models for practical engineering applications, we propose a Multi-Attention Meta Transformer method for few-shot unsupervised rotating machinery fault diagnosis (MMT-FD). This framework extracts potential fault representations from unlabeled data and demonstrates strong generalization capabilities, making it suitable for diagnosing faults across various types of mechanical equipment. The MMT-FD framework integrates a time-frequency domain encoder and a meta-learning generalization model. The time-frequency domain encoder predicts status representations generated through random augmentations in the time-frequency domain. These enhanced data are then fed into a meta-learning network for classification and generalization training, followed by fine-tuning using a limited amount of labeled data. The model is iteratively optimized using a small number of contrastive learning iterations, resulting in high efficiency. To validate the framework, we conducted experiments on a bearing fault dataset and rotor test bench data. The results demonstrate that the MMT-FD model achieves 99\% fault diagnosis accuracy with only 1\% of labeled sample data, exhibiting robust generalization capabilities.
Abstract The article deals with determining the operating state of machinery and its components by processing trend characteristics through measuring mechanical vibration, which determine the end of the lifetime cycle of machine components. It focuses mainly on the area of spinning headstocks and proposes a methodology for permanent monitoring of the dynamic behaviour of the bearings of spinning units. The elaborate the methodology for monitoring bearings leads to an increase in the service lifetime of machinery components. It analyses the causes and frequency and time distribution of vibration.
S. I. Kambulov, I. V. Bozhko, G. G. Parkhomenko
et al.
The paper emphasizes that the primary objective of fallow field tillage in summer and pre-sowing soil preparation is to create the most favorable conditions for moisture retention and accumulation within soil layers. (Research purpose) The study aimed to investigate the process of moisture accumulation within soil layers as influenced by the type of working bodies used for continuous tillage of fallow fields. (Materials and methods) The research was conducted in field conditions using an experimental model of a steam cultivator equipped with a roller having a working width of 3 meters, as well as a standard KSOP-4 cultivator for continuous tillage. (Results and discussion) Observations from June to August confirmed that the experimental steam cultivator with a roller effectively prevented the displacement of wet soil layers to the surface, maintaining a volumetric moisture content of 16.42-17.37 percent in the 5-centimeter layer. Moisture accumulation was recorded at various soil depths, with volumetric moisture levels recorded at 28.40-30.48 in the 10-centimeter layer, 30.18-32.82 percent in the 15-centimeter layer, and 26.90-29.38 percent in the 20-centimeter layer. For comparison, continuous tillage using a standard cultivator resulted in the displacement of wet soil layers to the surface, with volumetric moisture levels of 22.62-25.14 percent in the 5-centimeter layer. Moisture accumulation in deeper soil layers decreased, showing 18.57-21.57 percent in the 10-centimeter layer, 14.09-15.58 percent in the 15-centimeter layer, and 22.75-22.21 in the 20-centimeter layer. (Conclusions) The study demonstrated that using specific working bodies for continuous soil cultivation in summer ensures moisture retention within the soil layers. This approach facilitates shallow cultivation to a depth of 4-6 centimeters without exposing wet layers to the surface.
In the field of in situ measurement of high-temperature pressure, fiber-optic Fabry–Perot pressure sensors have been extensively studied and applied in recent years thanks to their compact size and excellent anti-interference and anti-shock capabilities. However, such sensors have high technological difficulty, limited pressure measurement range, and low sensitivity. This paper proposes a fiber-optic Fabry–Perot pressure sensor based on a membrane-hole-base structure. The sensitive core was fabricated by laser cutting technology and direct bonding technology of three-layer sapphire and develops a supporting large-cavity-length demodulation algorithm for the sensor’s Fabry–Perot cavity. The sensor exhibits enhanced sensitivity, a simplified structure, convenient preparation procedures, as well as improved pressure resistance and anti-harsh environment capabilities, and has large-range pressure sensing capability of 0–10 MPa in the temperature range of 20–370 °C. The sensor sensitivity is 918.9 nm/MPa, the temperature coefficient is 0.0695 nm/(MPa∙°C), and the error over the full temperature range is better than 2.312%.
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley
et al.
In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and computational technologies, data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing. Timely and accurately identifying faulty machine signals is vital in industrial applications for which many relevant solutions have been proposed and are reviewed in many articles. Despite the availability of numerous solutions and reviews on MFD, existing works often lack several aspects. Most of the available literature has limited applicability in a wide range of manufacturing settings due to their concentration on a particular type of equipment or method of analysis. Additionally, discussions regarding the challenges associated with implementing data-driven approaches, such as dealing with noisy data, selecting appropriate features, and adapting models to accommodate new or unforeseen faults, are often superficial or completely overlooked. Thus, this survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults, highlights their strengths and limitations, provides a review of the methods used for condition-based analyses, comprehensively discusses the available machinery fault datasets, introduces future researchers to the possible challenges they have to encounter while using these approaches for MFD and recommends the probable solutions to mitigate those problems. The future research prospects are also pointed out for a better understanding of the field. We believe this article will help researchers and contribute to the further development of the field.
Nur I. Zulkafli, Mohamad F. Sukri, Musthafah Mohd Tahir
et al.
The integrated optimisation modelling for the chiller-air handling units system is developed for increasing the efficiency and energy utilisation of system. A building management system in the chillers network controls the cooling load to ensure the specified desired set temperature of the cooling air within the building can be satisfied. Unfortunately, the desired set point temperature of the cooling air is a fixed value and does not vary with the dynamic change of cooling demand with different ambient temperatures. Therefore, the power consumption of the chillers and the building cooling requirement with a wide range of different ambient temperatures is properly modelled by optimising the performance of chillers, air handling units, cooling towers, and water pumps. The linear programming model for the system is established to model a real representation of the chiller-air handling unit system. The result shows that the optimal coefficient of performance is greater by about 7%–10% than the current chiller system. The optimal power consumption of the chiller system reduces to 3%. Overall, the optimal decision solutions could be used as the potential improvement strategy to control the desired set point values in the building management system for efficient chiller-AHU system.
Renewable energy sources, Environmental engineering
Inerters, a new type of mass element, have been successfully applied in various fields, such as in automotive and civil engineering. The development of a new element, named a mechatronic inerter, which consists of a ball-screw inerter and permanent magnet electric machinery, proves the feasibility of adopting electrical element impedances to simulate corresponding mechanical elements. In this paper, the structures of the bridge electrical network and series-parallel electrical network and their impedance characteristics are first introduced. Then, a seven-degree-of-freedom vehicle model is established. In addition, by comparison with passive suspension, a bridge network and a series-parallel network with various basic topologies are used to improve the vibration isolation performance of mechatronic inertial suspension, and the advantages of the bridge network (a) are demonstrated. Finally, a bridge electrical network (a) was designed and a real vehicle test was carried out. The test results showed that the mechatronic inertial suspension based on the bridge network (a) was superior to the passive suspension; the RMS (root-mean-square) values of the suspension working space and dynamic tire load of the left rear wheel suspension were reduced by 21.1% and 6.3%, respectively; and the RMS value of the centroid acceleration was improved by 1.8%.
M. Rafidya Bintang Ramadhan, Muhamad Saifuddin Salim, Elvira Wahyu Arum Fanani
et al.
This research examines the strength of the UPRs-Cantula composite with the
addition of filler microcrystalline cellulose (MCC). Composites were created
with a volume fraction of 30% Vf and a 45° angle. This angle variation received
the same treatment as the others, including untreated, alkaline, and fumigation.
The treatment time for alkali treatment was 6 hours, while the treatment time
for fumigation was 10 hours. The strength of each angle variation was
determined, as well as its treatment of tensile strength, modulus of elasticity,
and Poisson's ratio UPRs-cantula composites. According to the results, the
alkali treatment produced the highest tensile strength and elastic modulus
values. The highest Poisson ratio value was discovered without treatment at a
45°. The alkaline treatment yielded the highest tensile strength and modulus of
elasticity test results. The pullout fiber fracture dominated the untreated
composite fracture, whereas the fiber breakage fracture dominated the alkaline
treatment.
Tirth Shah, Christian Brendel, Vittorio Peano
et al.
Mechanical vibrations are being harnessed for a variety of purposes and at many length scales, from the macroscopic world down to the nanoscale. The considerable design freedom in mechanical structures allows to engineer new functionalities. In recent years, this has been exploited to generate setups that offer topologically protected transport of vibrational waves, both in the solid state and in fluids. Borrowing concepts from electronic physics and being cross-fertilized by concurrent studies for cold atoms and electromagnetic waves, this field of topological transport in engineered mechanical systems offers a rich variety of phenomena and platforms. In this review, we provide a unifying overview of the various ideas employed in this area, summarize the different approaches and experimental implementations, and comment on the challenges as well as the prospects.
Background: Software development results in the production of various types of artifacts: source code, version control system metadata, bug reports, mailing list conversations, test data, etc. Empirical software engineering (ESE) has thrived mining those artifacts to uncover the inner workings of software development and improve its practices. But which artifacts are studied in the field is a moving target, which we study empirically in this paper.Aims: We quantitatively characterize the most frequently mined and co-mined software artifacts in ESE research and the research purposes they support.Method: We conduct a meta-analysis of artifact mining studies published in 11 top conferences in ESE, for a total of 9621 papers. We use natural language processing (NLP) techniques to characterize the types of software artifacts that are most often mined and their evolution over a 16-year period (2004-2020). We analyze the combinations of artifact types that are most often mined together, as well as the relationship between study purposes and mined artifacts.Results: We find that: (1) mining happens in the vast majority of analyzed papers, (2) source code and test data are the most mined artifacts, (3) there is an increasing interest in mining novel artifacts, together with source code, (4) researchers are most interested in the evaluation of software systems and use all possible empirical signals to support that goal.
Omar Rodríguez-Abreo, J. Rodríguez-Reséndíz, L. A. Montoya-Santiyanes
et al.
Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.
Robots that incorporate social norms in their behaviors are seen as more supportive, friendly, and understanding. Since it is impossible to manually specify the most appropriate behavior for all possible situations, robots need to be able to learn it through trial and error, by observing interactions between humans, or by utilizing theoretical knowledge available in natural language. In contrast to the former two approaches, the latter has not received much attention because understanding natural language is non-trivial and requires proper grounding mechanisms to link words to corresponding perceptual information. Previous grounding studies have mostly focused on grounding of concepts relevant to object manipulation, while grounding of more abstract concepts relevant to the learning of social norms has so far not been investigated. Therefore, this paper presents an unsupervised cross-situational learning based online grounding framework to ground emotion types, emotion intensities and genders. The proposed framework is evaluated through a simulated human–agent interaction scenario and compared to an existing unsupervised Bayesian grounding framework. The obtained results show that the proposed framework is able to ground words, including synonyms, through their corresponding perceptual features in an unsupervised and open-ended manner, while outperfoming the baseline in terms of grounding accuracy, transparency, and deployability.
Ali Kanj, Rhinithaa P. Thanalakshme, Chengzhang Li
et al.
We present an analytical and experimental study of torsional magnetic mechanism where the restoring torque is due to magnetic field interactions between rotating and fixed permanent magnets. The oscillator consists of a ball bearing-supported permanent magnet, called the rotor, placed between two fixed permanent magnets called the stators. Perturbing the rotor from its equilibrium angle induces a restoring magnetic torque whose effect is modeled as a torsional spring. This restoring effect is accompanied by dissipation mechanisms arising from structural viscoelasticity, air and electromagnetic damping, as well as friction in the ball bearings. To investigate the system dynamics, we constructed an experimental setup capable of mechanical, electrical and magnetic measurements. For various rotor-stator gaps in this setup, we validated an analytical model that assumes viscous and dry (Coulomb) damping during the rotor free response. Moreover, we forced the rotor by a neighboring electromagnetic coil into high amplitude oscillations. We observed unusual resonator nonlinearity: at large rotor-stator gaps, the oscillations are softening; at reduced gaps, the oscillations stiffen-then-soften. The developed reduced-order models capture the nonlinear effects of the rotor-to-stator and the rotor-to-coil distances. These magnetic oscillators are promising in low-frequency electromagnetic signal transmission and in designing magneto-elastic metamaterials with tailorable nonlinearity.
The mechanical performance of reinforced composites is strongly influenced at different scales by the stress transferred at the matrix-fibre interfaces and at any surface where material discontinuity occurs. In particular, the mechanical response of elastomeric composites where the reinforcement is composed by cords with helically wound fibres is heavily compromised by fatigue and delamination phenomena occurring at cord-rubber as well as at the ply interfaces, since rubber and polymeric matrices are mainly vulnerable to the accumulation of deviatoric energy due to the shear stresses transferred across the surfaces. Despite the large diffusion of composites in a vast field of applications and the mature knowledge of their behaviour, some key mechanical aspects underlying failure mechanisms are still partially unclear. For example, stress amplification and strain localization are often difficult to predict by means of analytical solutions and averaging techniques that usually conceal stress gradients. In this work, we analyse coupling between torsional and tensile loads in twisted cords, which are adopted in many cases to reinforce composites and rubbers in tire applications. We provide a model characterized by an enriched cord-matrix mechanical interplay able to theoretically explain and predict actual stress distributions responsible for the onset of delamination and fatigue-guided phenomena that are experimentally observed in these composites. In particular, we demonstrate that the assumption of a monoclinic/trigonal behaviour for the mechanical response of the hierarchical strands allows to estimate, by means of analytical formulas and a homogenization approach, hitherto neglected shear stresses at the matrix-reinforcement interface. These stresses are transferred to the neighbouring regions, leading to post-elastic behaviour and failure events.
Models are fundamentally crucial to many scientific fields, including software engineering, systems engineering, enterprise modeling, and business modeling. This paper focuses on diagrammatic conceptual modeling, as opposed to mathematical or computational models, wherein a conceptual model is a translation of reality processes into an abstract mechanism that has similar structure and parallel events of the external processes. Although various modeling approaches exist, including UML (Unified Modeling Language) in software engineering and its dialect, SysML (System Modeling Language), in systems engineering, several difficulties arise in such models, including the problem of model multiplicity that is related to the lack an integrated view of structure and behavior. This paper generalizes conceptual modeling to be applied in organizations at large. According to authorities, the so-called organization theory portrays organizations as machine-like systems. As a machine, an organization coordinates its parts to transform inputs into outputs. Therefore, we synthesize the notion of an organization as a machine and apply a new modeling methodology called thinging machine (TM) to real engineering operations. The results show the viability of the TM methodology serving as a foundation for high-level modelling of systems.