Search Results (617)

As the core actuation component of intelligent precision spraying systems, the variable-rate nozzle is essential for achieving on-demand agricultural spraying; improving the use efficiency of water, fertilizers and pesticides; and reducing environmental pollution. This paper systematically reviews the development of agricultural variable-rate nozzles, from early mechanical profiling structures to modern intelligent control technologies based on Pulse Width Modulation (PWM). First, the existing variable-rate nozzles are classified into three major categories: electromagnetic-integrated type, centrifugal type, and variable-diameter type. A comparative analysis is conducted from three dimensions of working principle, performance characteristics and application scenarios, to delineate the respective advantages and limitations of each nozzle category. Second, the paper examines key technological advances in three areas: high-frequency solenoid valves, PWM control, and pressure and flow stabilization. It identifies the nonlinear response of solenoid valves, flow distortion under low duty cycles, and water hammer pressure fluctuation induced by high-speed switching as the three core technical bottlenecks at the current stage. Subsequently, the latest achievements and typical methodologies of variable-rate nozzles in structural design, simulation and experimental analysis are systematically reviewed, and their application performance in scenarios including field crops, orchards, protected agriculture and beyond are summarized. Finally, the remaining open issues in this field are put forward. It is suggested that future research should focus on key breakthroughs in the development of corrosion and wear-resistant high-frequency solenoid valves, the formation mechanism and suppression methods of pressure fluctuation, as well as adaptive algorithms based on machine learning or Model Predictive Control (MPC), to promote the leapfrog development of agricultural variable-rate nozzle technology from single variable control to multi-factor coupling optimization. All references cited in this paper are from articles published after the year 2000. Among them, the literature published in the last decade accounts for 86.6%, and literature published in the last five years accounts for 58.9%. Full article

Thermal overload in internal combustion engines may progressively destabilize lubricant-film integrity and promote severe tribological deterioration within highly stressed contact interfaces. This study investigates the thermo-tribological degradation sequence of a high-mileage gasoline engine subjected to prolonged idle operation under impaired cooling conditions, ultimately resulting in engine seizure. The investigated engine had accumulated 356,724 km, while the lubricant had remained in service for approximately 26,724 km prior to the experiment. The post-failure investigation combined teardown inspection, geometrical camshaft assessment, reverse gravimetric reconstruction, hydraulic tappet surface profiling, XRF surface characterization, laboratory oil analysis, and SEM/EDS evaluation of wear debris. The results demonstrated strongly localized degradation concentrated primarily within the cam–tappet interfaces. Severe non-uniform camshaft wear was accompanied by pronounced hydraulic tappet surface damage and evidence of unstable boundary-lubrication conditions. Laboratory oil analysis revealed elevated wear-metal concentrations, depletion of the alkaline reserve, increased oxidation indicators, and a final Class D oil condition assessment. SEM/EDS characterization identified Fe-bearing wear debris associated with sustained material removal and debris recirculation during the final degradation stage. The combined evidence supports a coupled thermo-tribological degradation mechanism involving lubricant deterioration, boundary-lubrication instability, adhesive wear acceleration, oxidative surface degradation, and debris-assisted surface damage preceding final engine seizure. The present case study provides experimentally documented evidence of lubrication collapse under real-engine thermal runaway conditions and highlights the critical role of lubricant condition in maintaining tribological stability under severe thermal loading. Full article

Signals generated during tool wear are nonlinear, non-stationary, and easily affected by machining noise, which makes reliable tool condition monitoring difficult in intelligent manufacturing. To address this issue, this study proposes a tool wear degree classification framework, FCTrans-CA, that fuses time-domain and frequency-domain information through a lightweight cross-attention (CA) bridge. Fast Fourier transform (FFT) is first used to obtain frequency-domain representations. The raw time-domain signals are processed by a multi-scale one-dimensional convolutional neural network (MS-CNN) to extract temporal wear features, while the FFT-derived representations provide complementary spectral cues. These two feature streams are fused by an asymmetric CA module in which frequency-domain features guide the selection of wear-sensitive temporal features. K-means clustering is used to divide the measured flank wear (VB) trajectory of each tool into initial-, normal-, and severe-wear stages, thereby reducing subjectivity in label generation. Experiments on the PHM2010 milling dataset show that FCTrans-CA achieves 99.43% classification accuracy on 40,648 test samples. The results indicate that cross-domain feature interaction improves the separability of wear states and provides a reproducible data-driven route for tool wear monitoring. Full article

The fashion industry generates a major environmental impact, requiring integrated sustainable approaches. This study integrates six thematic axes—sustainability, modular design, natural materials, eco-friendly dyeing, multidimensional comfort and consumer perception, and radial composition—into an integrative framework for sustainable design. The mixed-methods methodology comprises four stages: (I) a quantitative stage (questionnaire, n = 150); (II) an experimental stage (testing of comfort characteristics of linen fabrics according to ISO standards, and indigo dyeing through three techniques: uniform, tie-dye, and shibori); (III) a digital design stage in CLO3D and physical fabrication of the prototype; (IV) a prototype testing and validation stage (panel, n = 20). The prototype provides functional adaptability through 8 design configurations, versatility through reversibility, and aesthetic diversity through the radial composition, yielding 16 distinct wearing modes within a single product. Panel evaluation confirms high prototype acceptance (M = 4.81–4.95), and physical interaction with the prototype significantly increases purchase intention compared with conceptual evaluation (M_pre = 3.54; M_post = 4.60; d = 2.04; p < 0.001). The contribution validates a framework that integrates six dimensions of sustainable fashion into a coherent clothing design model, demonstrating design’s role as a practical instrument in the sustainability transition, with applied implications for designers and researchers. Full article

Tool wear significantly impacts the productivity and efficiency of sheet metal stamping operations, particularly in high-volume progressive die applications. This study presents an iterative calibration framework for Archard’s wear model, tailored to industrial stamping processes. The proposed methodology integrates finite element simulations with experimentally measured wear data obtained from production components, enabling data-driven calibration of the wear coefficient $K_{sim}$. The framework achieves high predictive accuracy, with deviations of 1.4–3.7% between simulated and optically measured wear depths and localization, after more than 15 million strokes. Rapid convergence is obtained within two to three calibration cycles, significantly reducing computational effort while maintaining physical fidelity. The simulation setup incorporates detailed modelling of contact pressure, sliding velocity, and stress distribution, validated using optical surface measurement systems and coordinate-based metrology. Beyond the specific industrial case, the framework demonstrates transferability to other sheet metal forming processes, such as bending, blanking, and coining, by leveraging physically based parameter adaptation across comparable pressure–velocity regimes. The approach enables predictive wear modeling in data-scarce environments and supports early-stage tool design workflows. Overall, the proposed methodology bridges the gap between empirical calibration and generalized simulation, contributing both methodological rigour and practical applicability to manufacturing science. Full article

Contact lens wear is widely used for vision correction by millions of individuals worldwide; however, it remains associated with a spectrum of ocular complications ranging from mild inflammatory conditions to vision-threatening infections. Common contact lens-related complications are predominantly noninfectious, including contact lens discomfort, dry eye syndromes, and papillary conjunctivitis. These conditions are typically mild and manageable with conservative measures. In contrast, corneal inflammatory conditions, such as contact lens-induced acute red eye and peripheral ulcers, represent an intermediate spectrum and may clinically overlap with early infection, creating diagnostic uncertainty. The most serious complication is microbial keratitis, a vision-threatening infection that remains challenging to recognize in its early stages due to its variable and often subtle presentation. Delayed identification may lead to rapid progression and significant visual morbidity. Patients with contact lens-related complaints often present to frontline settings, where early recognition is essential. Distinguishing benign from infectious conditions can be challenging; a risk-based approach with prompt triage and referral, along with proper lens hygiene and patient education, is key. Full article

In hybrid rolling bearings operating under extreme high-temperature and high-load conditions, steel rolling elements are prone to early failure, which has accelerated the widespread adoption of ceramic materials. To address the limitations of conventional studies, which have focused mainly on macroscopic wear parameters while neglecting subsurface failure mechanisms and the relationship among sintering process, microstructure, and fatigue performance, this work systematically compares the tribological behavior of Si₃N₄ ceramic balls fabricated by high-pressure electric resistance hot-pressing (REHP) and B₄C ceramic balls prepared by conventional hot pressing (HP) against 52100 steel counterparts. The central innovation of this study lies in clarifying, based on Hertzian contact theory and Lundberg-Palmgren life theory, that subsurface orthogonal shear stress, rather than surface compressive stress, is the fundamental driving force for contact fatigue failure of ceramic balls. In addition, two distinct damage evolution modes are revealed: B₄C exhibits early-stage brittle fracture and large-scale spalling, whereas REHP-Si₃N₄ is characterized by microcrack initiation and slow crack propagation. Moreover, the intrinsic mechanism by which the REHP process significantly enhances the contact fatigue life of ceramics is elucidated; namely, it refines grain size, eliminates residual porosity, and increases densification. The results show that, under the same high-load conditions, the mass loss of REHP-Si₃N₄ ceramic balls is only 35.7% of that of HP-B₄C, while the service life is extended by 20%. This work provides a key theoretical basis for ceramic material selection and sintering process optimization in high-performance hybrid bearings. Full article

Aluminum electrolysis is an energy-intensive process in which the cathode voltage drop (CVD) and horizontal current in the molten aluminum layer directly affect energy consumption and cell stability. In this study, a three-dimensional electro-thermal model of a 400 kA prebaked aluminum electrolysis cell was established to optimize copper-embedded cathode collector bars. Using a staged parameter-screening and integrated optimization strategy, the effects of copper rod longitudinal position, diameter, and embedded length on CVD, horizontal current density, cathode surface current uniformity, and thermal response were systematically evaluated. Under the present modeling conditions, the configuration with a longitudinal position of 1.0 m, diameter of 0.05 m, and embedded length of 1.0 m provided a favorable balance between electrical performance and copper consumption. This design reduced the equivalent voltage drop by 142.7 mV and decreased the average horizontal current density in the molten aluminum layer to approximately 4900 A/m². The peak cathode surface current density was also reduced, corresponding to a predicted cathode service-life increase of approximately 13.2% based on a relative wear model. A preliminary economic analysis indicated that an initial investment of CNY 424,000 could yield conservative annual electricity cost savings of approximately CNY 114,000, with a simple payback period of about 3.7 years. These results provide quantitative guidance for the structural design and industrial evaluation of copper-embedded cathode collector bars. Full article

In precision mold manufacturing, the machining of HRC52 hardened steel causes severe tool wear and high noise in multi-source sensor signals, making accurate remaining useful life (RUL) prognostics challenging. To address this, we propose a hybrid model based on a two-stage VB-to-RUL estimation strategy. The network first performs high-fidelity flank wear (VB) trajectory tracking; the RUL is then deduced via threshold mapping. The model integrates three components: a one-dimensional deep convolutional neural network (DCNN), a low-resolution self-attention (LRSA) module with 1D-to-2D spatiotemporal reconstruction, and a multi-channel bidirectional long short-term memory network (McBiLSTM). A Gaussian smoothing filter is first applied to denoise the 50 kHz signals, followed by physical-period sliding windows for feature extraction. A multi-strategy fusion pooling layer (mean, max, and last-quarter features) further improves prediction accuracy. Using the PHM 2010 milling cutter dataset under leave-one-out cross-validation, the proposed model achieves a mean absolute percentage error (MAPE) of 1.45% and a root mean square error (RMSE) of 2.76 μm, reducing prediction error by up to 75.6% compared to Transformer, LSTM, and GRU baselines. These results demonstrate that the model effectively extracts degradation features even during the accelerated wear stage, providing a potential solution for tool health monitoring and predictive maintenance under complex cutting conditions. Full article

The development of protective coatings with simultaneously enhanced mechanical, tribological, and antioxidant properties remains a major challenge for micro-electro-mechanical systems operating under harsh environments. In this work, HfC_x/a-C:H coatings with varying carbon contents were deposited by magnetron sputtering. Increasing the C₂H₂ flow rate from 12 to 20 sccm drove the coating structure to undergo two-stage evolution, from a composite structure dominated by HfC nanograins with a-C:H distributed at triple junctions of HfC_x grain boundaries to a typical nanocomposite structure with ~8 nm HfC_x nanograins embedded in a continuous a-C:H matrix. The coating deposited at 18 sccm exhibited the highest hardness (31.3 GPa) and effective Young’s modulus (392.3 GPa), owing to enhanced interface-mediated strengthening effect induced by the optimized nanocomposite structure. The coating prepared at 20 sccm showed the lowest friction coefficient (0.28), the lowest wear rate (6.82 × 10⁻⁶ mm³/N·m), and the best oxidation resistance. These improvements were supported by the enhanced mechanical properties and a-C:H fraction, the increased interface density and tortuosity, and the regulation of oxidation kinetics by the a-C:H matrix. This work provides an effective strategy for designing multi-functional protective coatings with balanced mechanical, tribological, and oxidation performance. Full article

The thermochemical diffusion treatment of boronitrocarburizing in a single stage was conducted on AISI 1018 steel using the powder-packing method. The treatment was performed at temperatures of 1123 K, 1173 K, and 1223 K for 8 h. The specimens were characterized using Scanning Electron Microscopy (SEM), Energy-Dispersive Spectroscopy (EDS) and X-ray diffraction (XRD) enabling a superficial elemental analysis of B, N, and C diffusion into the substrate. The tribological effects of friction and wear under dry conditions were analyzed through a pin-on-disc test, employing an aluminum oxide (Al₂O₃) sphere and a profilometer to measure mass loss. The study concluded that the sample treated at 1173 K exhibited the best tribological performance, showing the lowest coefficient of friction ($\mu \approx 0.1216$), while the samples treated at 1123 K and 1223 K exhibited coefficients of friction of $\mu \approx 0.1611$ and $\mu \approx 0.1856$, respectively. All treated samples showed a reduction in the coefficient of friction compared to the control sample ($\mu \approx 0.558$). Full article

As a core crushing equipment in mining, building materials, and related industries, the cone crusher relies heavily on the optimal design and health state prediction of its mantle liner to enhance equipment reliability and reduce maintenance costs. This paper proposes a comprehensive approach integrating dynamic modeling, intelligent optimization, and health prognosis. First, a virtual prototype model is established based on laminated crushing theory and multibody dynamics simulation to analyze the motion and force characteristics of the mantle liner. Second, for the two key parameters—counterweight mass and motor speed—an improved butterfly optimization algorithm (IBOA) incorporating Cauchy mutation and an adaptive weight is proposed to achieve efficient global optimization. Furthermore, vibration signal features are extracted at different wear stages; a comprehensive health indicator curve is constructed by combining PCA dimensionality reduction with adaptive feature fusion (ASFF), and the Weibull degradation model is employed for life extrapolation prediction. Finally, fuzzy C-means (FCM) clustering is applied to autonomously partition the health states. Parameter optimization reduces the standard deviation of the force acting on the mantle liner by approximately 15.4%, markedly improving system operational stability. Health prognosis reveals that the liner enters a faulty state after 785 h, and the health condition is effectively classified into four stages: healthy, good, degraded, and faulty. The results demonstrate that the proposed optimization and health prognosis methods can effectively improve the operational efficiency and reliability of cone crushers, exhibit favorable engineering applicability, and provide a quantitative basis for condition monitoring and maintenance decision-making. Full article

Under long-term service conditions, vibration signals of water-lubricated stern bearings exhibit strong nonlinearity, nonstationarity, and multicomponent coupling, which makes accurate feature extraction challenging. To address this issue, this study proposes a progressive EEMD-TFMST-based analysis framework that combines spectral localization, adaptive signal decomposition, noise suppression, and high-resolution time–frequency characterization. Rotational-speed tests and long-duration wear tests were conducted using an SSB-100 test rig, and the lubrication regimes were identified based on friction coefficient variations. The results show that the dominant vibration features are strongly dependent on the lubrication regime and wear stage. With increasing rotational speed, the vibration response evolves from isolated peaks near 400 and 600 Hz under boundary lubrication to enhanced 300–400 Hz components under mixed lubrication, and further to broadband responses within 0–1000 Hz under hydrodynamic lubrication, with dominant peaks mainly concentrated in the 300–500 Hz range. With increasing rotational speed, the lubrication regime gradually changes from boundary lubrication to hydrodynamic lubrication, accompanied by a transition of vibration energy from single-IMF concentration to broadband distribution across multiple IMF components. Long-term operation induces stage-dependent changes in lubrication and vibration behavior: moderate wear improves vibration stability, whereas excessive wear deteriorates lubrication, increases the proportion of mixed lubrication, and promotes energy migration toward lower frequencies with additional high-frequency excitation. Under prolonged high-speed operation, lubrication degradation further induces broadband vibration. The proposed method enables accurate quantification of vibration features and provides a useful basis for service-performance evaluation and early fault warning of water-lubricated stern bearings. Full article

This study examines the development of a steady state in the cyclic wear process for various combinations of metallic and inorganic materials. Energy dissipation is widely acknowledged as a significant parameter in wear mechanisms. However, at the nanoscale, the linear correlation between energy dissipation and wear progression is not consistently applicable. In this study, experimental observations of cyclic wear between scanning probe microscopy (SPM) cantilevers and substrate displacement were conducted. Substrate vibrations were monitored using a laser Doppler vibrometer, which facilitated the direct estimation of energy dissipation at nanocontacts during cyclic loading. The wear rates of the substrates decreased with an increase in the number of cyclic loadings, indicating the formation of a transfer steady state at the interface. Symmetric contact mode, based on the viscoelastic behavior of the contact, and asymmetric mode, based on adhesion between the interfaces, are commonly observed. The asymmetric mode evolved in the later stages of cyclic wear, suggesting the transfer of the steady state between the interfaces. A linear relationship between energy dissipation and wear rates was still observed for metallic substrates, whereas a steady state was observed for inorganic materials. This difference can be attributed to material exchange at the interfaces. Full article

In factory workshops, wearing safety helmets is vital for worker safety. However, current deep learning-based detection methods are often hindered by large model parameters and high computational demands, limiting their deployment in resource-constrained settings. This article introduces YOLO-AFL, a novel lightweight model designed to solve these problems. The algorithm introduces several key optimizations to improve performance without increasing computational load. Firstly, the K-Means++ algorithm is applied during the anchor box preprocessing stage, along with a new distance metric (1 − AIoU), which enhances anchor box size estimation and boosts performance without additional overhead. Secondly, by introducing a lightweight PConv operation into the C3 module, the complexity of the model is significantly reduced. Finally, a dual attention network (LDA-GC) is designed to compensate for any accuracy loss caused by the model’s simplifications. Experimental results on a custom dataset show that the proposed algorithm achieves an mAP50 of 94.1%. Compared to the baseline model, it reduces the number of parameters by 19.1% and decreases computational complexity by 16.9%, demonstrating its superior performance and efficiency in safety helmet wearing detection. Full article