Search Results (992)

Silicon carbide (SiC) ceramics have gained significant attention in advanced engineering applications because of their superior mechanical properties, resistance to wear and corrosion, and thermal stability. However, the precision machining of these materials is extremely challenging because of their intrinsic hardness and brittleness. Wire Electrical Discharge Machining (WEDM) has become increasingly popular as a viable technique for processing SiC ceramics because of its ability to produce intricate geometries and high-quality surface finishes. In this review paper, a comprehensive overview of WEDM technology applied to SiC ceramics is presented, emphasizing the influence of process parameters, wire materials, and dielectric fluids on cutting efficiency and quality. This research explores recent experimental findings related to Wire Electrical Discharge Machining (WEDM) and highlights the challenges in reducing material damage. It also presents strategies to improve machining performance. Additionally, potential future directions are discussed, providing a roadmap for further research and the application of WEDM in processing silicon carbide (SiC) and its variants, including solid silicon carbide (SSiC) and silicon-infiltrated silicon carbide (SiSiC). Full article

Polytetrafluoroethylene (PTFE) is one of the alternative materials suitable for seawater-lubricated bearings, favored for its excellent corrosion resistance and good self-lubricating properties. As marine equipment develops towards higher load, higher reliability, and longer service life, more stringent requirements are imposed on the wear resistance of bearing materials. However, traditional PTFE materials struggle to meet the performance requirements for long-term stable operation in modern marine environments. To improve the wear resistance of PTFE, this study used alumina (Al₂O₃) particles with three different particle sizes (50 nm, 3 μm, and 80 μm) as fillers and prepared Al₂O₃/PTFE composites via the cold pressing and sintering process. Tribological performance tests were conducted using a ball-on-disk reciprocating friction and wear tester, with Cr12 steel balls as counterparts, under an artificial seawater lubrication environment, applying a normal load of 10 N for 40 min. The microstructure and wear scar morphology were characterized by scanning electron microscopy (SEM), and mechanical properties were measured using a Shore hardness tester. A systematic study was carried out on the microstructure, mechanical properties, friction coefficient, wear rate, and limiting PV value of the composites. The results show that the particle size of Al₂O₃ particles significantly affects the mechanical properties, friction coefficient, wear rate, and limiting PV value of the composites. The 50 nm Al₂O₃/PTFE formed a uniformly spread friction film and transfer film during the friction process, which has better friction and wear reduction performance and load bearing capacity. The 80 μm Al₂O₃ group exhibited poor friction properties despite higher hardness. The nanoscale Al₂O₃ filler was superior in improving the wear resistance, stabilizing the coefficient of friction, and prolonging the service life of the material, and demonstrated good seawater lubrication bearing suitability. This study provides theoretical support and an experimental basis for the design optimization and engineering application of PTFE-based composites in harsh marine environments. Full article

With the increased power density of internal combustion engines (ICE) and growing demands for lightweight design, the connecting rod big-end bearings are subjected to significant alternating loads. Consequently, the interference–fit interfaces become susceptible to fretting damage, which can markedly shorten engine service life and impair reliability. In the present study, the effects of the big end manufacturing process, bolt preload, and bearing bush interference fit are considered to develop a coupled lubrication–dynamic model of the connecting rod big-end bearing. This model investigates the fretting damage issue in the bearing bush of a marine diesel engine’s connecting rod big end. The results indicate that the relatively low stiffness of the big end is the primary cause of bearing bush fretting damage. Interference fit markedly affects fretting wear on the bush back, whereas the influence of bolt preload is secondary; nevertheless, a decrease in either parameter enlarges the fretting distance. Based on these findings, an optimized design scheme is proposed. Full article

The widespread application of granite manufactured sand (GS) concrete in pavement engineering is limited by issues such as suboptimal particle size distribution and an unclear optimal rock powder content. Furthermore, research on the long-term evolution of the skid resistance characteristics of GS concrete remains relatively scarce. This knowledge gap makes it difficult to accurately assess the skid resistance performance of GS concrete in practical engineering applications, thereby compromising traffic safety. To address this research gap, this study utilized a self-developed indoor abrasion tester for pavement concrete to assess the skid resistance of GS concrete. Three-dimensional laser scanning was employed to acquire the concrete’s surface texture parameters. Using the friction coefficient and texture parameters as skid resistance evaluation indicators, and combining these with changes in the concrete’s surface morphology, the study explores how effective sand content, stone powder content, and fine aggregate lithology affect the long-term skid resistance of GS concrete pavements and reveals the evolution trends of their long-term skid resistance. Research results show that as the number of wear cycles increases, low and high effective sand content affect the surface friction coefficient of specimens in opposite ways. Specimens with 95% effective sand content exhibit superior skid resistance. Stone powder content influences the friction coefficient in three distinct variation patterns, showing no clear overall trend. Nevertheless, specimens with 5% stone powder content demonstrate better skid resistance. Among different fine aggregate lithologies, GS yields a higher friction coefficient than river sand (RS), while limestone manufactured sand (LS) shows significant friction coefficient fluctuations across different wear cycles. Adding stone powder substantially enhances mortar strength and delays groove collapse edge formation. Moreover, higher effective sand content and proper stone powder content mitigate bleeding, thereby improving mortar performance. Full article

MXenes, a novel class of two-dimensional (2D) transition metal carbides and nitrides, have garnered significant attention due to their exceptional thermal conductivity, electrical properties, and mechanical strength. This review offers a comprehensive overview of MXenes, focusing on their synthesis methods, material properties, tribological performance, and potential challenges and opportunities. Typically synthesized through the selective etching of layered precursors, MXenes offer highly tunable structures, allowing for precise tailoring for specific functionalities. Their outstanding properties, such as high electrical conductivity, chemical versatility, mechanical durability, and intrinsic lubricity, make them promising candidates for various applications, including energy storage, electromagnetic shielding, water purification, biosensing, biomedicine, and advanced tribological systems. While many of these applications are briefly acknowledged, this review primarily emphasizes MXenes’ potential in tribological applications, where recent studies have highlighted their promise as solid lubricants and tribological additives due to their low shear strength, layered structure, and ability to form protective tribofilms under sliding contact. However, challenges such as oxidation resistance, long-term stability, and performance under extreme environments continue to impede their full potential. With less than a decade of focused research, the field is still evolving, but MXenes hold tremendous promise for revolutionizing modern material science, especially in next-generation lubrication and wear-resistant systems. This review explores both the opportunities and challenges associated with MXenes, emphasizing their emerging role in tribology alongside their broader engineering applications. Full article

As brain–computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems. Full article

In response to the demand for advanced materials in extreme environments, researchers have developed a variety of bulk and thin-film materials. One of the best-known processes for altering the mechanical and tribological properties of materials is surface engineering techniques. These involve various approaches to synthesize thin-film coatings, along with post-deposition treatments. The need for self-lubricating materials in extreme situations such as high-temperature applications, cryogenic temperatures, and vacuum systems has attracted the attention of researchers. They have fabricated several types of thin films using CVD and PVD techniques to meet this demand. Among the various techniques used for fabricating self-lubricating coatings, sputtering stands out as a special one. It contributes to developing smooth, homogeneous, and crack-free dense microstructures, which further enhance the coatings’ properties. This review explains the need for self-lubricating materials and the different techniques used to synthesize them. It discusses and summarizes the concept of synthesizing various types of self-lubricating films. It shows the different types of self-lubricating material systems, like transition metal-based nitrides and carbides, diamond-like carbon-based materials, and so on. This work also reflects the governing factors like the deposition temperature, doping elements, thickness of the film, deposition pressure, gas flow rate, etc., that influence the deposition results and, consequently, the properties of the film, as well as their advanced applications in different areas. This work reflects the self-lubricating properties of different kinds of films exposed to various environments in terms of their coefficient of friction and wear rate, emphasizing how the friction coefficient affects the wear rate. Full article

Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R² = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Full article

Variations in the drive motor’s load inertia during wafer transfer robot arm motion critically degrade end-effector trajectory accuracy. To address this challenge, this study proposes an anti-disturbance control strategy integrating Radial Basis Function Neural Network (RBFNN) and event-triggered mechanisms. Firstly, dynamic simulations reveal that nonlinear load inertia growth increases joint reaction forces and diminishes trajectory precision. The RBFNN dynamically approximates system nonlinearities, while an adaptive law updates its weights online to compensate for load variations and external disturbances. Secondly, an event-triggered mechanism is introduced, updating the controller only when specific conditions are met, thereby reducing communication burden and actuator wear. Subsequently, Lyapunov stability analysis proves the closed-loop system is Uniformly Ultimately Bounded (UUB) and prevents Zeno behavior. Finally, simulations on a planar 2-DOF manipulator demonstrate significantly enhanced trajectory tracking accuracy under variable loads. Critically, the adaptive neural network control method reduces trajectory tracking error by 50% and decreases controller update frequency by 84.7%. This work thus provides both theoretical foundations and engineering references for high-precision wafer transfer robot control. Full article

This paper presents the design and operating principle of a mechanical device for machining a cylinder liner flange seat in a diesel engine damaged due to cavitation or wear. Mounted on the engine block, the device allows the removal of damaged material and the formation of an undercut for the installation of repair rings. The gear ratios used allow for manual driving of the device. The device allows engine repair without the need for disassembly, which provides significant economic benefits by shortening repair time, reducing repair costs, and extending engine life, which is also important for environmental protection. Full article

M50 steel is widely used in the manufacturing of high-end bearing components for aero-engine shafts, where an excellent surface performance is required to withstand harsh service conditions. In this study, plasma carburizing at different temperatures varying from 410 to 570 °C was performed on pre-nitrided M50 steel to investigate the influence of the temperature on the structural evolution and mechanical behavior of the self-lubricating functional layer. The microstructure, phase composition, hardness, and wear resistance of the carburized samples were fully characterized using scanning electron microscopy (SEM), Transmission electron microscopy (TEM), Raman spectroscopy, a nano-indenter, and other analytical techniques. The carbon-rich film with nano-domains contains a significant amount of sp³ bonds at low carburizing temperatures, exhibiting a Diamond-like carbon (DLC) film character. With the rise in the carburizing temperature, the initially distinct interface between the carbon-rich film and the compound layer gradually disappears as the nitrides are progressively replaced by carbides; the sp³ bond of the film is decreased, which reduces the hardness and wear resistance. Samples carburized at 490 °C with a homogeneous surface layer consisting of DLC film and a compound layer showed a low friction coefficient (about 0.22) and a 60% reduction in the wear rate compared with the nitrided specimen. The formation of a surface carbon-enriched layer also plays a role in avoiding oxidative wear. Full article

Thermodynamic free energy is used to elucidate the significance of energy dissipation-induced temperature rise on the performance, reliability, and durability of all systems, biological, chemical and physical. Transformation (a measure of reliability) and degradation (a measure of durability) are distinguished. The temperature rise mechanism is characterized by the microstructurothermal (MST) energy/entropy. A framework to quantify the contributions of the MST entropy to system transformation and degradation is introduced and demonstrated using diverse multi-physics systems: cardiovascular strain in humans, charge capacity of batteries, tribological wear of journal bearings, and shear strength of lubricating greases. Various levels of temperature-induced degradation are observed in the systems. Thermal degradation rate increases with process and energy dissipation rates. The benefits of active cooling on systems and materials are shown. This article is recommended to engineers, scientists, designers, medical doctors, and other system analysts for use in dissipation/degradation characterization and minimization. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

Complex engineering systems—such as ultra-long horizontal wells in energy exploitation and distributed rail transit infrastructure—operate under harsh physical and environmental conditions, where accurate physical modeling and real-time parameter estimation are essential for ensuring safety, efficiency, and reliability. Traditional empirical and black-box data-driven approaches often fail to account for the underlying physical mechanisms, thereby limiting interpretability and generalizability. To address this, we propose a unified framework that integrates physics-informed scenario-based modeling with data-driven parameter inversion. In the first stage, critical system parameters—such as friction coefficients in drill string movement or contact forces in rail–wheel interactions—are explicitly formulated based on mechanical theory, leveraging symmetries and boundary conditions to improve model structure and reduce computational complexity. In the second stage, model parameters are identified or updated through inverse modeling using historical or real-time field data, enhancing predictive performance and engineering insight. The proposed methodology is demonstrated through two representative cases. The first involves friction estimation during tripping operations in the SU77-XX-32H5 ultra-long horizontal well of the Sulige Gas Field, where a mechanical load model is constructed and field-calibrated. The second applies the framework to rail transit systems, where wheel–rail friction is estimated from dynamic response signals to support condition monitoring and wear prediction. The results from both scenarios confirm that incorporating physical symmetry and data-driven inversion significantly enhances the accuracy, robustness, and interpretability of engineering analyses across domains. Full article

The cable crane is the core hoisting equipment for high arch dam construction, and its hoisting trajectory is critical for both operational efficiency and safety. However, current trajectory planning does not adequately consider the underactuated characteristics of the cable crane. For instance, sudden stops or abrupt changes in direction can easily induce large swings of the bucket, causing safety risks and equipment wear. To address this issue, this paper developed a trajectory planning model for obstacle avoidance with smooth transitions in cable crane hoisting for arch dams and solved the high-dimensional optimization problem using a path–velocity decoupling strategy. First, a shortest path with geometrical conciseness and free collision was generated based on an improved A* algorithm to reduce the frequency of directional changes. Next, for different hoisting scenarios, segmented S-curve and polynomial velocity functions were proposed to ensure smooth velocity transitions. Then, an orthogonal experimental design was employed to generate a cluster of candidate trajectories that meet kinematic constraints, from which the optimal trajectory was selected using a multi-objective evaluation function. The results demonstrate that the motion trajectory planned using the proposed method is notably smoother. Compared with the traditional trapezoidal velocity method, it reduces the maximum swing amplitude of the bucket by 40.78% at a modest time cost. In real-time obstacle avoidance scenarios, the approach outperforms emergency-stop strategies, reducing the bucket’s maximum swing amplitude by 30.48%. This work will provide a reference for engineers to optimize the trajectory of large lifting equipment in construction fields such as high arch dams and bridges. Full article