Search Results (448)

This study investigates the mechanical, morphological, and wear properties of SiO₂-filled tri-axial warp-knitted (TWK) glass fiber-reinforced vinyl ester matrix composites, with a focus on void fraction, tensile, flexural, hardness, and wear behavior. Adding SiO₂ fillers reduced void fractions, enhancing composite strength, with values ranging from 1.63% to 5.31%. Tensile tests revealed that composites with 5 wt% SiO₂ (GV1) exhibited superior tensile strength, Young’s modulus, and elongation due to enhanced fiber–matrix interaction. Conversely, composites with 10 wt% SiO₂ (GV2) showed decreased tensile performance, indicating increased brittleness. Flexural tests demonstrated that GV1 outperformed GV2, showcasing higher flexural strength, elastic modulus, and deflection, reflecting improved load-bearing capacity at optimal filler content. Shore D hardness tests confirmed that GV1 had the highest hardness among the specimens. SEM analysis revealed wear behavior under various loads and sliding distances. GV1 exhibited minimal wear loss at lower loads and distances, while higher loads caused significant matrix detachment and fiber damage. These findings highlight the importance of optimizing SiO₂ filler content to enhance epoxy composites’ mechanical and tribological performance. Full article

Although wind turbine installation vessels (WTIVs) are increasingly operating in deepwater complex geological areas with larger scales, systematic research on and experimental validation of platform jack-up stability remain insufficient. This study aimed to establish a comprehensive evaluation framework encompassing penetration depth, anti-overturning/sliding stability, and punch-through risk, thereby filling the gap in holistic platform stability analysis. An entire four-legged centrifuge test at 150× g was integrated with coupled Eulerian–Lagrangian (CEL) numerical simulations and theoretical methods to systematically investigate spudcan penetration mechanisms and global sliding/overturning evolution in clay/sand. The key findings reveal that soil properties critically influence penetration resistance and platform stability: Sand exhibited a six-times-higher ultimate bearing capacity than clay, yet its failure zone was 42% smaller. The sliding resistance in sand was 2–5 times greater than in clay, while the overturning behavior diverged significantly. Although the horizontal loads in clay were only 50% of those in sand, the tilt angles at equivalent sliding distances reached 8–10 times higher. Field validation at Guangdong Lemen Wind Farm confirmed the method’s reliability: penetration prediction errors of <5% and soil backflow/plugging effects were identified as critical control factors for punch-through risk assessment. Notably, the overturning safety factors for crane operation at 90° outreach and storm survival were equivalent, indicating operational load combinations dominate overturning risks. These results provide a theoretical and decision-making basis for the safe operation of large WTIVs, particularly applicable to engineering practices in complex stratified seabed areas. Full article

Light Detection and Ranging(LiDAR)-inertial simultaneous localization and mapping (SLAM) is a critical component in multi-sensor autonomous navigation systems, providing both accurate pose estimation and detailed environmental understanding. Despite its importance, existing optimization-based LiDAR-inertial SLAM methods often face key limitations: unreliable feature extraction, sensitivity to noise and sparsity, and the inclusion of redundant or low-quality feature correspondences. These weaknesses hinder their performance in complex or dynamic environments and fail to meet the reliability requirements of autonomous systems. To overcome these challenges, we propose a novel and accurate LiDAR-inertial SLAM framework with three major contributions. First, we employ a robust multi-category feature extraction method based on principal component analysis (PCA), which effectively filters out noisy and weakly structured points, ensuring stable feature representation. Second, to suppress outlier correspondences and enhance pose estimation reliability, we introduce a coarse-to-fine two-stage feature correspondence selection strategy that evaluates geometric consistency and structural contribution. Third, we develop an adaptive weighted pose estimation scheme that considers both distance and directional consistency, improving the robustness of feature matching under varying scene conditions. These components are jointly optimized within a sliding-window-based factor graph, integrating LiDAR feature factors, IMU pre-integration, and loop closure constraints. Extensive experiments on public datasets (KITTI, M2DGR) and a custom-collected dataset validate the proposed method’s effectiveness. Results show that our system consistently outperforms state-of-the-art approaches in accuracy and robustness, particularly in scenes with sparse structure, motion distortion, and dynamic interference, demonstrating its suitability for reliable real-world deployment. Full article

This study discusses the frictional wear performance of three 3D-printed materials, acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and high-impact polystyrene (HIPS), while evaluating different layer thickness levels. The materials were subjected to wear volume and rate tests by ball-on-disc wear tests at various thickness levels (0.1, 0.2, and 0.3 mm) and sliding distances. Lastly, SEM analysis was carried out to study the wear tracks and debris developed during the testing. Quantitatively, ABS maintained a mean wear volume below 0.15 mm³ across all test conditions (e.g., 0.05 ± 0.01 mm³ at 0.1 mm layer thickness and 150 m sliding distance), whereas PLA and HIPS recorded much higher averages of 1.5 mm³ and 3.0 mm³, respectively. With the increase in layer thickness, which caused an upward trend in the obtained results, the wear volume of the investigated materials also increased. ABS exhibited the smallest material loss of all three polymers; for example, at 0.1 mm layer thickness and a 150 m sliding distance, the mean wear volume was only 0.05 mm³, and even under the harshest condition tested (0.3 mm layer thickness, 300 m), the value remained below 0.15 mm³. PLA and HIPS showed higher wear volumes, while HIPS had the lowest resistance among the three materials. The multifunctional wear behavior difference contributed by material type was 59.76%, as shown through ANOVA, and that by layer thickness was 21.32%. Among the parameters investigated, material type had the largest control in wear behavior due to inherent variation in the structural characteristics of the material such as interlayer adhesion, toughness, and brittleness. For instance, the amorphous nature of ABS and its good layer adhesion provided significantly superior wear resistance compared to the brittle PLA and the poorly adhered HIPS. It is highlighted in this research that selecting appropriate material and layer thickness combinations can improve the durability of 3D-printed components. Full article

High-precision indoor positioning technology is increasingly prominent in its application value in emerging fields such as the Industrial Internet of Things, smart cities, and autonomous driving. 5G networks can transmit large-bandwidth signals and have the capability to transmit and receive signals with multiple antennas, enabling the simultaneous acquisition of angle and distance observation information, providing a solution for high-precision positioning. Differences in the types and quantities of observation information in complex environments lead to positioning scenarios having a multimodal nature; how to propose an effective observation model that covers multimodal scenarios for high-precision robust positioning is an urgent problem to be solved. This paper proposes a three-stage time–frequency synchronization method based on group peak time sequence tracing. Timing coarse synchronization is performed through a group peak accumulation timing coarse synchronization algorithm for multi-window joint estimation, frequency offset estimation is based on cyclic prefixes, and finally, fine timing synchronization based on the primary synchronization signal (PSS) sliding cross-correlation is used to synchronize 5G signals to chip-level accuracy. Then, a tracking loop is used to track the Positioning Reference Signal (PRS) to within-chip accuracy, obtaining accurate distance information. After obtaining distance and angle information, a high-precision positioning method for multimodal scenarios based on 5G heterogeneous measurement combination is proposed. Using high-precision angle observation values as intermediate variables, this algorithm can still solve a closed-form positioning solution under sparse observation conditions, enabling the positioning system to achieve good positioning performance even with limited redundant observation information. Full article

This paper investigates the tribological behavior of Stellite 6 coatings produced with high-velocity oxygen fuel (HVOF), with an emphasis on the transition between severe and mild wear regimes and the glaze layer formation. The development of these coatings involved two spray parameters modifying the oxygen fuel ratio and three post-heat treatment conditions at temperatures ranging between 600 °C and 1150 °C. The coatings were tested under conditions varying the normal load, temperature, sliding distance, and testing temperatures (up to 300 °C). The results show that the coating obtained from the HVOF process exhibited a microstructure different from the conventional bulk Co-alloys, significantly impacting the wear performance. The coating post-processing was essentialto enhance wear resistance at elevated temperatures. Full article

In recent days, aluminum-based hybrid composites have garnered more interest than monolithic alloys owing to their remarkable properties, encompassing a high strength-to-weight ratio, excellent corrosion resistance, and impressive wear durability. The present study attempts to optimize the multiple wear attribute characteristics of Al6061/SiC/Al₂O₃ hybrid composites using grey and entropy-based VIKOR techniques. The composites were produced by adding equal proportions of SiC/Al₂O₃ (0–12 wt.%) ceramics through the stir-casting process, using an ultrasonication setup. Dry sliding wear experiments were executed with tribometer variants, namely reinforcement content (wt.%), load (N), sliding velocity (v), and sliding distance (SD), following L₂₇ OA. The optimal combination of process variables for achieving high GRG values from grey analysis was found to be A₃-B₃-C₃-D₃. The S/N ratios and ANOVA results for GRG indicated that RF content (wt.%) is the predominant component determining multiple outcomes, followed by sliding distance, load, and sliding velocity. The multi-order regression model formulated for the VIKOR index (Q_i) displayed high significance and more accuracy, with a variance of 0.0216 and a coefficient of determination (R²), and adjusted R² values of 99.60% and 99.14%. Subsequent morphological studies indicated that plowing, abrasion, and adhesion mechanisms are the dominant modes of wear. Full article

This paper proposes a joint time–frequency analysis method that combines Rao detector with dynamic sliding thresholds to enhance the detection performance of electric source axial frequency magnetic field signals. For each signal-to-noise ratio (SNR) point, 1000 Monte Carlo simulations were independently conducted, with SNR ranging from 15 dB to −30 dB. The results show that the proposed method maintains high detection rates even at extremely low SNRs, achieving about 90% detection probability at −13 dB, significantly outperforming traditional energy detectors (with a threshold of 2 dB). Under conditions where the detection probability is ≥90% and the false alarm probability is 10⁻³, the SNR threshold for the Rao detector is reduced by 15 dB compared to energy detectors, greatly improving detection performance. Even at lower SNRs (−30 dB), the Rao detector still maintains a certain detection rate, while the detection rate of energy detectors rapidly drops to zero. Further analysis of the impact of different frequencies (1–5 Hz) and CPA distances (45–80 cm) on performance verifies the algorithm’s robustness and practicality in complex non-Gaussian noise environments. This method provides an effective technical solution for low SNR detection of ship axial frequency magnetic fields and has good potential for practical application. Full article

Laser-boronized parts manufactured by a selective laser melting process from 18Ni to 300 maraging steel are investigated in this study. Two main issues are addressed, namely (i) the possibility to restore the hardness of the heat-affected zone (HAZ) formed during laser processing and (ii) the effect of re-aging on the hardness and wear resistance of the laser-boronized layer with a hypoeutectic structure. Optical microscopy, scanning electron microscopy, energy-dispersive spectroscopy, X-ray diffraction analysis, microhardness measurements, the “ball-on-plate” dry sliding test, and the two-body dry abrasive wear test were employed to answer the questions. The results confirmed that HAZ is formed with the dissolution of intermetallides formed before and undergo full (near the molten pool) or partial (at some distance from the molten pool) iron–base matrix recrystallization. The hardness of HAZ (350–550 HK0.05) has been restored after re-aging to the 550–600 HK0.05 level. Moreover, a certain positive effect of re-aging on the laser-boronized layer with a hardness of ~470–750 HK0.2 is established, associated with structural transformations induced by aging in the iron-based solid solution phase. The hardness increased by ~9–25%. The wear resistance of the hardest boronized samples (~750 HK0.2) under dry sliding and dry abrasive wear conditions was ~5.8 times and 3.7 times higher than that of the aged control sample, while re-aging provided further improvement of these characteristics. The presented results provide insights into the effectiveness of laser-boronized layers having a hypoeutectic structure in terms of increasing the wear resistance of maraging steel. Full article

This study examined the impact of molybdenum disulfide (MoS₂) addition as a filler in epoxy composites reinforced with chopped basalt fibers (CBF), maintaining the basalt fiber content at a constant 40 wt. %. The investigation focused on physical, microstructural, mechanical, and sliding-wear properties. Testing revealed that tensile, impact, compressive, and flexural strengths improved with MoS₂ content from 0 to 8 wt. %. However, at 12 wt. % loading, these properties declined due to uneven dispersion and particle agglomeration. An increase in hardness was observed with rising MoS₂ content, with a maximum value of 98 HV at 16 wt. %. Wear testing was conducted using a Taguchi L16 orthogonal array, evaluating the effects of multiple parameters. The results indicated that MoS₂ content had the most significant influence on wear rate (WR), followed by applied load (P) and sliding distance (D), while sliding velocity (V) had minimal impact on specific wear rate (SWR) and coefficient of friction (COF). Scanning electron microscopy (SEM) was used to analyze wear mechanisms, and analysis of variance (ANOVA) confirmed the optimal conditions. Full article

The wear loss and frictional characteristics of magnesium-based hybrid composites reinforced with boron carbide (B₄C) particles and graphite filler were the main subjects of the investigation. Key parameters, including reinforcement content (0–10 wt%), applied load (5–30 N), sliding speed (0.5–3 m/s), and sliding distance (500–3000 m), were varied. Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. A comparison of model performances revealed that the Gradient Boosting Machine (GBM) provided superior accuracy compared to other machine learning models in predicting both wear loss and the coefficient of friction. Additionally, feature importance analysis indicated that the graphite weight percentage was the most significant influence in predicting the coefficient of friction and wear loss characteristics. Full article

Buffer stops have always been installed on blind tracks to mitigate the hazards associated with overruns due to insufficient or wrong braking. Conventional buffer stops fixed to the rails may absorb only limited energy while Energy-Absorbing Buffers Stops (EABS) dissipate higher energy hydraulically and/or by friction from sliding blocks clamped to the rail head. The assessment of EABS performances in terms of maximum stopping distance and maximum allowed deceleration is usually performed by using the common kinematic rules of motion and considering the overrunning train as a single mass hitting the buffer stop. This paper studies the dynamic characteristics of the collision of entire trains with a friction EABS applying a Longitudinal Train Dynamics (LTD) approach. Several realistic scenarios using the UIC approved TrainDy software were simulated considering various train compositions, with different types of vehicles (locomotives, freight wagons and passenger coaches) and different kinds of buffers. The results show that high dynamic loads are exerted on the vehicles within the train, while the average deceleration and the stopping distance are not greatly influenced when compared with a simpler Finite Element Method (FEM) approach that does not consider the train composition. The progressive application of the EABS braking force increases the stopping distance but can reduce the peak deceleration of about 50%. The results may be used to tune the design parameters of friction EABS according to the currently available specifications and standards for rolling stock structural assessment considering that no international standards for EABS exist currently. Full article

In remote conductivity control for water–fertilizer integration systems, challenges such as long-distance nonlinearities and variable parameters can lead to fertilization inaccuracies, including over-irrigation and uneven distribution, affecting both productivity and environmental sustainability. To mitigate these issues, this study proposes a variable-parameter sliding mode control (VSMC) strategy, combined with an adaptive observer based on Recursive Least Squares (RLS) to estimate system inertia and load torque in real time. This allows for dynamic adjustment of the sliding surface parameters, ensuring robust control even under varying operating conditions. Two parameter derivation approaches—analytical modeling and data-driven fitting—are evaluated. Field tests demonstrate that VSMC outperforms the Proportional–Integral (PI) and conventional sliding mode control (SMC) methods in maintaining target electrical conductivity (EC) levels. Specifically, for a target EC of 1.4 mS/cm, VSMC stabilizes the system to within 1.18–1.60 mS/cm in 95 s, with a 14.3% overshoot, well within agronomic tolerance. In regional irrigation trials, VSMC significantly improves fertilizer uniformity, reducing the standard deviation of potassium nitrate distribution from 2.14 (PI) to 0.59. The simulation and experimental results validate the effectiveness and robustness of the proposed method, highlighting its potential to enhance agronomic efficiency and reduce environmental impact. Full article

Shallow landslides are often unpredictable and seriously threaten surrounding infrastructure and the ecological environment. Traditional landslide prediction methods are time-consuming, labor-intensive, and inaccurate. Thus, there is an urgent need to enhance predictive techniques. To accurately predict the runout distance of shallow landslides, this study focuses on a shallow soil landslide in Tongnan District, Chongqing Municipality. We employ a genetic algorithm (GA) to identify the most hazardous sliding surface through multi-iteration optimization. We discretize the landslide body into slice units using the dynamic slicing method (DSM) to estimate the runout distance. The model’s effectiveness is evaluated based on the relative errors between predicted and actual values, exploring the effects of soil moisture content and slice number on the kinematic model. The results show that under saturated soil conditions, the GA-identified hazardous sliding surface closely matches the actual surface, with a stability coefficient of 0.9888. As the number of slices increases, velocity fluctuations within the slices become more evident. With 100 slices, the predicted movement time of the Tongnan landslide is 12 s, and the runout distance is 5.91 m, with a relative error of about 7.45%, indicating the model’s reliability. The GA-DSM method proposed in this study improves the accuracy of landslide runout prediction. It supports the setting of appropriate safety distances and the implementation of preventive engineering measures, such as the construction of retaining walls or drainage systems, to minimize the damage caused by landslides. Moreover, the method provides a comprehensive technical framework for monitoring and early warning of similar geological hazards. It can be extended and optimized for all types of landslides under different terrain and geological conditions. It also promotes landslide prediction theory, which is of high application value and significance for practical use. Full article

In recent decades, missile guidance and control have advanced significantly, with methods like pure pursuit (PP), command to line-of-sight (CLOS), and proportional navigation (PN) enabling accurate target interception in uncertain environments through line-of-sight (LOS) tracking. In this work, we propose a novel 3D sliding pure pursuit guidance (3DSPP) law for controlling a surface-to-air missile against a maneuvering target. The algorithm is compared with established guidance laws such as zero-effort miss distance “ZEM-PN” and “3D-PP”, with performance metrics including the miss distance $M_d$ and time of closest approach $t_{cap}$. The results demonstrate that the 3DSPP outperforms the conventional methods by achieving the lowest $M_d=$ 0.1497 m and the fastest $t_{cap}=$ 7.3853 s, ensuring more precise and rapid interception. The algorithm also exhibits superior robustness to noise and efficient energy management, making it a promising solution for real-world missile guidance systems. Full article