Search Results (981)

This work presents a fused-filament fabrication (FFF) hot end that combines an unrestricted-rotation C-axis with a rectangular-slot nozzle and an induction-heated melt sleeve. The architecture replaces the popular resistive cartridge and heater block design with an external coil that induces eddy-current heating in a thin-walled sleeve, threaded to the heat break and nozzle, reducing thermal mass and eliminating wired sensors across the rotating interface. A contactless infrared thermometer targets the nozzle tip; the temperature is regulated by frequency-modulating the inverter around resonance, yielding stable control. The hot end incorporates an LPBF-manufactured nozzle, which transitions from a circular inlet to a rectangular outlet to deposit broad, low-profile strands at constant layer height while preserving lateral resolution. The concept is validated on a desktop Cartesian platform retrofitted to coordinate yaw with XY motion. A twin-printer testbed compares the proposed hot end against a stock cartridge-heated system under matched materials and environments. With PLA, the induction-heated, rotating hot end enables printing at 170 °C with defect-free flow and delivers substantial reductions in job time (22–49%) and energy per part (9–39%). These results indicate that the proposed approach is a viable route to higher-throughput, lower-specific-energy material extrusion. Full article

Fault diagnosis in axial piston pumps is key to ensuring the proper operation of a hydraulic system. Slipper wear, as a typical fault in piston pumps, is challenging to accurately diagnose because the faults are very similar for different forms and degrees of wear. The achievement of accurate fault diagnosis of different forms and degrees of wear in the slipper will greatly improve the reliability of axial piston pump operation and, at the same time, provide new ideas for research into similar fault diagnosis problems in other rotating machinery. Therefore, a method of fault diagnosis based on the following symmetric dot pattern (SDP) and multi-channel densely connected convolutional networks (DenseNet) is proposed in this paper. The method applies an SDP transformation to transform the slipper failure signal into an SDP image, which achieves the fusion of triaxial vibration signals and enriches the signal features. The inception module is improved by replacing the original structure with larger convolutional kernels in multiple branches and decomposing the larger convolutional kernels. The inception module, the convolutional block attention module (CBAM), and the DropBlock method are introduced into DenseNet to improve feature extraction capability, computational efficiency, and model generalization ability. Experiments are performed on several slipper wear fault SDP image datasets, and all the indices produced by the proposed method are higher than those of the traditional convolutional neural networks, which fully proves the effectiveness and superiority of the procedure. Full article

This study systematically investigates the evolution of the flow field and cavitation behavior during the underwater launch of a rotating sphere. By comparing surface pressure distribution, cavitation evolution, flow separation locations, and re-entrant jet formation under various rotational conditions, this study reveals the significant influence of rotation on both the cavitation processes and sphere’s motion trajectory. It is found that under rotational conditions, cavity detachment tends to occur earlier on the front side, and the re-entrant jet develops more fully, reaching maximum length and intensity at a moderate angular velocity. In additionally, rotation alters the cavity interface and overall flow structure, resulting in noticeable differences in surface wetting, pressure distribution, and separation behavior between the front and rear sides. As the rotational speed increases, flow separation points become less distinct, and pressure fluctuations on the rear side intensify, indicating that rotation plays a critical role in modulating underwater cavitation dynamics. The findings provide theoretical insights into flow control and cavitation risk assessment for underwater launches of rotating bodies. Full article

This study presents the design and development of an intelligent end-effector integrated into a custom 7-degree-of-freedom (DOF) robotic arm for monitoring the health status of tomato plants during their growth stages. The robotic system combines five rotational and two prismatic joints, enabling both horizontal reach and vertical adaptability to inspect plants of varying heights without repositioning the robot’s base. The integrated vision module employs a YOLOv5 neural network trained with 7864 images of tomato leaves, including both healthy and diseased samples. Image preprocessing included normalization and data augmentation to enhance robustness under natural lighting conditions. The optimized model achieved a detection accuracy of 90.2% and a mean average precision (mAP) of 92.3%, demonstrating high reliability in real-time disease classification. The end-effector, fabricated using additive manufacturing, incorporates a Raspberry Pi 4 for onboard processing, allowing autonomous operation in agricultural environments. The experimental results validate the feasibility of combining a custom 7-DOF robotic structure with a deep learning-based detector for continuous plant monitoring. This research contributes to the field of agricultural robotics by providing a flexible and precise platform capable of early disease detection in dynamic cultivation conditions, promoting sustainable and data-driven crop management. Full article

The high-performance aeroengine operates under extreme loads. In engineering practice, the vibration problems caused by stator vibrations have become increasingly prominent, with impacts on the rotor dynamic behavior. This paper takes the rotor–stator system of aeroengines as the analysis object and studies the influence of stator modal vibration on the rotor dynamic behavior. The dynamic model of the rotor–stator system has been established, and the influence of the contact state of cylindrical roller bearings (CRBs) has been analyzed by considering bearing clearance. To precisely capture the transient contact state within the CRBs, a numerical method combining the Newmark-β method with the Event Function has been developed. The numerical calculation results show that the collision effect introduced by the bearing clearance will excite a localized stator mode at the supercritical state, which fundamentally alters the rotor dynamic behavior: generating prominent combination frequencies $f_M\pm f_r$ due to modulation between the rotor rotation $f_r$ and the stator vibration $f_M$. Moreover, good consistency between the experimental and calculated results has been obtained. This study demonstrates that the stator modal vibration can critically modify rotor dynamic behavior in supercritical operation, leading to potentially hazardous non-synchronous whirl. The integrated model and numerical method provide a robust framework for analyzing complex rotor–stator interactions, offering significant insights for vibration control and fault diagnosis in high-speed rotating machinery. Full article

Ship detection in remote sensing imagery is hindered by cluttered backgrounds, large variations in scale, and random orientations, limiting the performance of detectors designed for natural images. We propose RSDB-Net, a Rotation-Sensitive Dual-Branch Detection Network that introduces innovations in feature extraction, fusion, and detection. The Swin Transformer–CNN Backbone (STCBackbone) combines a Swin Transformer for global semantics with a CNN branch for local spatial detail, while the Feature Conversion and Coupling Module (FCCM) aligns and fuses heterogeneous features to handle multi-scale objects, and a Rotation-sensitive Cross-branch Fusion Head (RCFHead) enables bidirectional interaction between classification and localization, improving detection of randomly oriented targets. Additionally, an enhanced Feature Pyramid Network (eFPN) with learnable transposed convolutions restores semantic information while maintaining spatial alignment. Experiments on DOTA-v1.0 and HRSC2016 show that RSDB-Net performs better than the state of the art (SOTA), with mAP-ship values of 89.13% and 90.10% (+5.54% and +44.40% over the baseline, respectively), and reaches 72 FPS on an RTX 3090. RSDB-Net also demonstrates strong generalization and scalability, providing an effective solution for rotation-aware ship detection. Full article

This paper addresses the issue of fault diagnosis in high-speed train bogie bearings under complex working conditions and proposes a method for calculating the characteristic frequency of rolling bearings that takes into account the influence of radial clearance. By establishing a five-degree-of-freedom nonlinear dynamic model, this study systematically analyzes the modulation mechanism of radial clearance on the fault characteristic frequency of bearings and verifies the findings through an experimental platform. The results indicate that an increase in clearance not only leads to significant attenuation of the fault characteristic frequency amplitude, but also induces sideband modulation effects, thereby interfering with fault diagnosis accuracy. The experimental data show good agreement with the theoretical calculations, verifying the effectiveness of the proposed method. Specifically, the nonlinear stiffness-based characteristic frequency calculation reduces the prediction error from 6.9–5.7% under traditional theory to 2.3–3.4% across a wide range of rotational speeds. Meanwhile, the clearance-induced amplitude attenuation predicted by the model is also experimentally confirmed, with measured amplitude reductions of 35–42% as clearance increases from 0.2 μm to 0.5 μm. These results not only demonstrate the accuracy and engineering applicability of the method but also provide new theoretical foundations and practical references for health monitoring and early fault diagnosis of high-speed train bearings. Full article

Direct ink writing (DIW) deposits viscous, shear-responsive inks at low temperature, enabling hydrogels and cell-laden bioinks for biomedical fabrication. Access to DIW remains limited by the cost of dedicated systems and the complexity of custom motion control. Repurposing fused deposition modeling (FDM) printers lowers these barriers by using accurate motion stages, open firmware, and familiar workflows while preserving build volume. In this study, three DIW actuator designs were implemented on an FDM frame. The first used a gear-and-rail transmission that converted stepper rotation to plunger travel. The second used a direct trapezoidal-screw pusher that increased force but reduced build-space clearance. The third relocated actuation to a remote piston-driven module that decoupled force generation from the printhead. The final architecture integrates the remote piston with partitioned control, where the printer executes motion and a programmable logic controller (PLC) manages extrusion. This arrangement reduces carried mass, preserves build space, and enables precise volumetric dosing with fast response. On a standard desktop frame, the system achieved controllable deposition of an agar/alginate ink using off-the-shelf electronics and modest modifications. This approach promotes sustainable and accessible innovation by repurposing existing FDM printers with open-source hardware and modular components. The resulting platform supports biomimetic biofabrication by combining mechanical efficiency, environmental responsibility, and cost-effective design. Full article

In this work we demonstrate the laser-driven reconfiguration of stripe domains in a thick bismuth-substituted iron garnet film with the (210) crystallographic orientation exhibiting strong in-plane anisotropy. Under a weak in-plane external magnetic field (H_‖), laser irradiation leads to local “twisting” of the magnetic domains; domains with opposite magnetization rotate in different directions. The twisting angle increases linearly with the in-plane magnetic field (H_‖) (above a threshold of approximately 6 Oe) and also changes linearly with the average laser intensity, being fully reversible after the irradiation process. The magnitude of the domain rotation effect does not depend on the light polarization state or its orientation. After optical irradiation, the magnetization distribution in the sample returns to its initial state. It is also observed that moving the focused beam spot along the surface can lead to irreversible modifications in the domain topology in several ways: there is a shift in the dislocations in stripe domain structure (domain “heads”) across the beam transfer direction, expanding the area with a specific magnetization vector orientation, and the stabilization of domain wall positions by their pinning on crystallographic defects. The proposed analytical model based on a local reducing of the effective anisotropy fully describes the rotation type and angle of domains and domain walls, defining their possible trajectories and certain values of the area heating or local anisotropy modulation and the rotation angles. The experimental results and the theoretical model demonstrate a thermal origin of the laser-induced effect in this type of magnetic domain structure. Full article

This manuscript presents a study on the application of blind source separation (BSS) techniques, specifically the Independent Component Analysis (ICA) method, for the detection and identification of localized faults in rolling element bearings. Bearing defects typically manifest as distinct harmonics of characteristic fault frequencies, accompanied by modulation sidebands in the vibration signal spectrum. The accurate extraction and isolation of these components are crucial for reliable fault diagnosis, particularly in systems where multiple vibration sources overlap. In this work, a linear ICA algorithm was applied to vibration signals acquired from a simplified rotating machinery setup designed to emulate common bearing fault conditions. The study investigates the effect of ICA-based signal decomposition on the statistical distribution of selected diagnostic indicators and evaluates its ability to enhance the detectability of fault-related components. The experimental results demonstrate that the application of ICA significantly improves the separation of vibration sources, leading to a more distinct representation of fault signatures. The findings confirm the effectiveness of blind source separation methods in vibration-based diagnostics and highlight the potential of ICA as a complementary tool for improving the accuracy and robustness of bearing fault detection systems in rotating machinery. Full article

Background: Cognitive Reserve (CR) is a theoretical construct developed to explain individual differences in resilience to age-related cognitive decline. Empirical evidence supports its positive role across multiple cognitive domains. However, behavioral research has primarily focused on areas either vulnerable to aging, such as memory, or relatively preserved, such as language. In contrast, the relationship between CR and task-specific performance in domains like visuospatial processing—a domain critical for everyday functioning—remains underexplored. This study investigates whether CR, as measured by the Cognitive Reserve Index Questionnaire (CRIq), predicts performance in mental rotation tasks in healthy older adults. Methods: Participants (age 55–85) completed two tasks: (1) a hand laterality task, requiring judgments about whether a rotated hand image (palm or back view) was left or right; and (2) a letter-congruency task, in which participants determined whether simultaneously presented rotated letters were identical or mirror-reversed. Results: Generalized and linear mixed-effects models revealed a protective effect of cognitive reserve, with higher CRIq scores significantly predicting greater accuracy in both tasks. Efficiency benefits (i.e., shorter reaction times) were evident mainly in the easiest conditions, suggesting that CR supports processing resources more effectively under moderate rather than maximal task demands. This pattern indicates that cognitive reserve does not uniformly enhance performance but instead modulates the allocation of cognitive resources in a context-dependent manner. Conclusions: To our knowledge, this is the first study to demonstrate a modulatory role of CR on visuospatial abilities in healthy older adults. These findings open new avenues for investigating how CR may differentially affect performance across a broader spectrum of cognitive functions, including attention, executive control, and spatial processing. A better understanding of these mechanisms could inform targeted cognitive interventions to strengthen resilience and promote successful aging. Full article

Traditional calibration methods rely on precise targets and frequent manual intervention, making them time-consuming and unsuitable for large-scale deployment. Existing learning-based approaches, while automating the process, are typically limited to single LiDAR–camera pairs, resulting in poor scalability and high computational overhead. To address these limitations, we propose a lightweight calibration network with flexibility in the number of sensor pairs, making it capable of jointly calibrating multiple cameras and LiDARs in a single forward pass. Our method employs a frozen pre-trained Swin Transformer as a shared backbone to extract unified features from both RGB images and corresponding depth maps. Additionally, we introduce a cross-modal channel-wise attention module to enhance key feature alignment and suppress irrelevant noise. Moreover, to handle variations in viewpoint, we design a modular calibration head that independently estimates the extrinsics for each LiDAR–camera pair. Through large-scale experiments on the nuScenes dataset, we show that our model, requiring merely 78.79 M parameters, attains a mean translation error of 2.651 cm and a rotation error of $0.{246}^{\circ }$, achieving comparable performance to existing methods while significantly reducing the computational cost. Full article

Pumped-storage power plants play a vital role in power systems by providing peak load regulation, frequency control, and phase modulation services. The safety and stability of these plants critically depend on understanding transient processes during frequent unit start–stop cycles and operational transitions. This study employs 1D–3D coupled numerical simulations to investigate a pump–turbine unit’s external characteristics, pressure pulsations, and internal flow dynamics under turbine runaway conditions. At the runaway rotational speed of 650.9 r/min, large-scale vortices with intensities exceeding 500 s⁻¹ form at the inlet of specific runner blade passages, severely obstructing flow. Concurrently, the tailwater pipe vortex structure transitions from a central spiral pattern to a wall-attached configuration. The concurrent occurrence of these phenomena induces abrupt runner force variations and significant pressure pulsations, primarily comprising high-frequency high-amplitude pulsations at 1× and 2× blade frequency attributable to runner dynamic-static interference; broad-spectrum high-amplitude pulsations resulting from operational transitions; and low-frequency high-amplitude pulsations induced by the tailwater pipe vortex belt. Full article

We begin by theoretically analyzing the relationship between the rotation rates of the rotation of state of polarization (RSOP) and the principal state of polarization (PSP) in a fiber link where both polarization mode dispersion (PMD) and time-varying RSOP are present. The theoretical analysis is validated through numerical simulations. Our findings reveal that the rotation rates of both the input and output PSPs significantly differ from the channel’s RSOP rate in most scenarios. Moreover, under varying RSOP distribution scenarios within the channel, the relationships among the rotation rates of input PSP, output PSP and RSOP also differ, and therefore rotation rates of input or output PSPs can reflect the changes of RSOP, indicating that monitoring PSP rotation rate can enable a better understanding of RSOP. Furthermore, we propose a DSP-based algorithm for monitoring PSPs and their rotation rates. By jointly applying a sliding-window median filter and a modulus judgment procedure, the algorithm yields more accurate PSP trajectories and rotation rate estimates than the existing approaches in literature, while relying solely on the existing DSP module without requiring any additional hardware. The recovered PSP orientation and rotation rate information can then be fed into the CMA equalizer, enhancing its compensation performance and thereby improving the overall stability and performance of the coherent optical system. Full article

This study proposes an improved variable-speed control strategy for Wells turbines in floating oscillating water column (OWC) wave energy converters (WECs) to address efficiency loss caused by turbine stalling. By optimizing the $\varphi$ from the conventional critical value from 0.3 to 0.11, the system achieves maximum mechanical power output while avoiding stall effects. A dynamic rotor-speed controller is designed to modulate turbine rotation behavior in response to real-time airflow velocity. This approach is validated using numerical simulations and MATLAB/Simulink R2021b models under both regular and irregular wave conditions. Results show a 124% increase in turbine power compared to uncontrolled operation, with stable DC-link voltage (+0.2%) and reduce torque ripple. The strategy enhances energy conversion efficiency by 51.2% and ensures safe operation under mechanical speed limits (3000 rpm), thus offering a practical solution for offshore WEC systems. Full article