Search Results (6,537)

In this study, the effects of friction stir welding (FSW) parameters on the mechanical properties and formability of pre-hardened (PH) 2219 aluminum alloy welds were systematically investigated through tensile testing and Erichsen tests. Energy dispersive spectrometry (EDS), electron back scatter diffraction (EBSD), and a transmission electron microscope (TEM) were employed to characterize the microstructure of the PH alloy weld joints, revealing the strength–ductility synergy mechanism of the PH welded sheets. Experimental results indicated that with respect to mechanical properties, when the welding rotational speed was fixed at 1000 rpm, increasing the forward speed from 50 mm/min to 150 mm/min reduced the ultimate tensile strength (UTS) by 6.3% and decreased the EL by 21.4%. When the forward speed was fixed at 50 mm/min, increasing the rotational speed from 500 rpm to 1500 rpm resulted in only a 0.4% variation in UTS and maintained a stable EL, demonstrating that forward speed is the dominant parameter affecting mechanical properties. In terms of formability, at a lower forward speed (50 mm/min), the Erichsen value exhibited a single-peak trend with increasing rotational speeds. At higher forward speeds (100 or 150 mm/min), the Erichsen value was insensitive to changes in rotational speed. When the rotational speed was fixed at 1500 rpm, increasing the forward speed from 50 mm/min to 150 mm/min reduced the Erichsen value by 21.3%. Microstructural strengthening mechanism: In the weld zone, the cooperative precipitation of the θ″ and θ′ phases effectively hindered dislocation motion. Simultaneously, the high geometric compatibility factor promoted the activation of multiple slip systems, and dislocation rearrangement subsequently led to the formation of sub-grain boundaries, thereby achieving strength–ductility cooperation. These findings provide theoretical support for the performance-driven welding process design of high-strength aluminum alloy components in aerospace applications. Full article

Additively manufactured lattice structures have become a staple of optimized structural parts and are increasingly common in biomedical and chemical applications that require consideration of flow through porous architectures. However, design principles governing transport performance trail those established for mechanical optimization. Here, we introduce two complementary design frameworks that modify symmetry at both the unit cell and part scales to systematically tune internal transport. These approaches are further extended into patterned lattice structures, where multiple unit cell designs can be combined in one, two, or three dimensions to further regulate the internal flow. We find that identical global lattice geometries can arise from different unit cell basis and voxel plane orientations, with minimal changes in bulk geometric properties. Yet in parts with diameters of 12–35 mm, hydraulic diameters of 1–4 mm, and porosities ~80%, these design selections significantly affect the hydraulic tortuosity and fluid transport behavior. We further demonstrate performance from select designs that yield a new class of anisotropic lattices with strong sensitivity to flow direction that is tuned by the projected area perpendicular to flow. Collectively, these symmetry-informed, multi-order combinatorial design approaches enable predictable, direction-dependent transport design and expand the functional potential of lattice architectures across disciplines. Full article

Small object detection in UAV remote sensing imagery has long faced significant challenges. Existing Transformer-based detectors still suffer from feature degradation and insufficient multi-scale information fusion when handling small objects with sparse pixels and complex backgrounds. To address this, we propose MSF-DETR, a Transformer-based detector with multi-scale perception and cross-spatial-frequency domain fusion. Specifically, we design a multi-scale perception attention feature extraction network that integrates a Poly Kernel Inception module with a bidirectional contextual anchor attention mechanism via a dual-pathway fusion block, enabling simultaneous capture of multi-granularity features and long-range semantic dependencies. We further develop a feature alignment and cross-spatial-frequency enhancement pyramid that enriches shallow-layer spatial details through feature reorganization and leverages a spatial-frequency dual-domain collaborative strategy to capture both local textures and global spectral dependencies. Cross-scale dynamic intensity modulation combined with decoupled lightweight downsampling further effectively suppresses semantic noise, corrects feature misalignment, and preserves critical edge details. Finally, a Shape-NWD loss is devised to incorporate geometric and scale constraints, effectively alleviating the positional sensitivity of IoU for small targets. Extensive experiments on three public benchmarks demonstrate the superior performance of MSF-DETR; notably, on the VisDrone dataset, it achieves improvements of 7.45% and 8.71% in mAP50 and mAP50:95 over the baseline. Full article

Underground mine tunnels are often characterized by extremely uneven illumination, weak surface textures, and frequent dynamic interference, which severely undermine multi-view photometric consistency and easily induce floating artifacts and spatial divergence in conventional vision-based 3D Gaussian Splatting (3DGS). To address these issues, we propose a LiDAR-guided 3DGS framework for underground tunnel reconstruction based on dynamic-object removal and differentiable unsigned distance field (UDF) regularization. First, a dynamic foreground removal strategy with background restoration is introduced to remove transient foreground disturbances and restore static supervision consistency. Second, LiDAR point clouds are leveraged to initialize Gaussian primitives with a reliable geometric skeleton in weak-texture regions. More importantly, LiDAR priors are further converted into a differentiable UDF field and serve as a persistent geometric constraint. A dual-track mechanism is designed, where continuous geometric attraction pulls mildly deviated Gaussians back toward the physical surface and periodic out-of-bound culling removes severely drifting primitives. Experiments on real underground tunnel and chamber scenes show a clear scene-dependent behavior of the proposed method. In the tunnel scene, the method achieves the best SSIM together with competitive PSNR and LPIPS, while also reducing redundant out-of-bound primitives and improving geometric cleanliness. In the chamber scene, however, its advantages under global full-reference metrics are less evident. These results suggest that the proposed LiDAR-guided and differentiable UDF-regularized framework is particularly beneficial for weak-texture tunnel environments, while further improvement is still needed for chamber scenes with more complex appearance variations. Full article

Compliant displacement amplification mechanisms are widely used in MEMSs and micro-actuated systems to enhance the limited stroke of micro-actuators. However, systematic integration of instantaneous center building block (IC-BB)-based conceptual design and structured post-synthesis optimization for symmetric single-input dual-output compliant displacement amplification mechanisms (SIDO-CDAMs) remains limited in the literature. In this work, a symmetric SIDO-CDAM is first conceptually synthesized using the IC-BB approach by employing only compliant dyad building blocks (CDBs), resulting in a mechanism that produces dual outputs in the same direction. The synthesized conceptual mechanism is subsequently realized with necessary geometric refinements and modeled to validate the conceptual design. A two-stage post-synthesis optimization framework is then proposed to enhance geometrical advantage (GA) while reducing stiffness. In Stage-1, Taguchi design of experiments combined with analysis of variance (ANOVA) is used to screen design parameters, identify the dominant factor, and fix it at its optimal level to eliminate masking effects. In Stage-2, a reduced Taguchi design integrated with gray relational analysis (GRA) is applied for multi-response optimization based on finite element analysis (FEA). Regression models and FEA-based confirmation tests are employed to validate the optimized design. The results demonstrate a significant improvement in displacement amplification with a simultaneous reduction in stiffness compared to the base design. The proposed IC-BB-based conceptual synthesis, coupled with structured post-synthesis optimization, provides a robust and computationally efficient framework for the development of micro-actuation and precision engineering applications. Full article

This study presents a hybrid Non-Uniform Rational B-Spline (NURBS) methodology for the geometric reconstruction of topology-optimized structural components. NURBS are employed exclusively as a post-processing tool; all structural analyses are performed using standard finite elements (SOLID187 elements, ANSYS Mechanical R19.2), and isogeometric analysis (IGA) is not used. The methodology is validated on an Al 6061-T6 electric scooter neck under a 600 N static load. Two SIMP optimization iterations followed by a hybrid NURBS reconstruction reduce the component mass from 1.247 kg to 0.531 kg, achieving a 57.4% mass reduction. Finite element re-validation of the reconstructed geometry yields a maximum von Mises stress of 126.45 MPa (safety factor, SF = 2.18, exceeding the 2.0 requirement), a maximum deflection of 2.31 mm, and a first natural frequency of 127 Hz. Mesh convergence between the 2.5 mm and 1.25 mm refinements is Δ = 0.90%. Relative to the direct SIMP output (201 MPa), NURBS reconstruction reduces the peak stress by 37%, demonstrating that geometric post-processing is not a neutral step but a critical determinant of structural performance. Both fully automated STL reconstruction and edge-based NURBS reconstruction failed for this geometry class due to non-manifold topology and patch discontinuities, respectively. The proposed hybrid region-decomposition approach is the only method that has produced a watertight, FEA-compatible CAD model. Full article

In recent years, object detection using aerial images captured by unmanned aerial vehicles (UAVs) has become a research hotspot. However, due to the high resolution of UAV imagery, large variations in object scales, and the predominance of small targets, achieving fast and accurate object classification remains a significant challenge. To address these issues, this paper proposes a lightweight feature enhancement attention network, termed LFEA-YOLO. First, a multi-scale feature fusion (MSFF) module is introduced, which establishes a cross-scale feature interaction mechanism to effectively integrate semantic information with spatial details while reducing the computational overhead caused by redundant features. Second, deformable convolution networks (DCNs) are incorporated into the neck network to construct the DCNv4-C2F module. Leveraging its adaptive spatial sampling capability, this module dynamically adjusts the receptive field of convolution kernels, thereby overcoming the limitations of traditional fixed-grid sampling and enhancing the network’s ability to model geometric variations of small-scale objects. Finally, an attention-enhanced detection head (AE-Head) is designed, integrating a large separable kernel attention (LSKA) mechanism to dynamically emphasize discriminative features in key target regions. Experimental results on the VisDrone2019 and DOTAv1.0 datasets demonstrate significant improvements over the baseline model, with ${\mathrm{mAP}}_{50}$ and ${\mathrm{mAP}}_{50:95}$ increasing by 10.6% and 7.8%, respectively. Compared with several recently proposed models, LFEA-YOLO also exhibits clear advantages, validating the effectiveness of the proposed approach. Full article

Although fault-controlled instability of underground excavation has been widely studied, systematic analyses of how key fault geometric and mechanical parameters affect surrounding-rock behavior in deep hard-rock mine roadways remain limited. This study takes a deep roadway as the engineering background and uses numerical simulation to investigate the effects of fault thickness, fault dip angle, fault mechanical properties, and contact parameters on the initial deformation state, post-excavation deformation, and plastic-zone evolution of surrounding rock. The results indicate that the surrounding rock is already in a non-uniform initial state controlled by fault disturbance prior to excavation. Increasing fault thickness expands the initial high-deformation zone; fault dip angle mainly changes the spatial distribution pattern of the initial deformation field; and increasing either the fault mechanical parameters or the contact parameters reduces deformation concentration in the vicinity of the fault. After roadway excavation, deformation is mainly concentrated in the fault–roadway intersection zone, and roof deformation along the roadway axis shows distinct local peaks and an asymmetric distribution. The maximum roof deformation continues to increase with the increase of fault thickness (the deformation increases by 218% from 1 m to 5 m), and smaller fault dip angle conditions are prone to local large deformation.. In contrast, higher fault mechanical parameters and contact parameters can both effectively suppress roof deformation, with the contact parameters exerting more significant control (as the contact parameter increased from C1 to C5, the maximum roof deformation decreased by approximately 75%). The plastic zone mainly develops at the fault–roadway intersection and is dominated by shear plasticity, accompanied by tensile plasticity. Increasing fault thickness significantly enlarges the plastic-zone volume and strengthens the shear-dominated failure characteristic; fault dip angle mainly controls the propagation direction and morphology of the plastic zone; and increasing the fault mechanical parameters and contact parameters both help reduce the extent of the plastic zone. These findings can provide a theoretical basis for zoned support design and differentiated stability control of roadways crossing faults in deep metal mines. Full article

During seafloor terrain mapping missions conducted by AUVs, positioning error accumulation occurs inevitably over long distances due to the unavailability of global satellite navigation signals underwater. Moreover, the alternating distribution of flat and undulating regions on the seafloor renders single-constraint-based bathymetric SLAM methods prone to performance degradation in complex environments. To address these challenges, this paper proposes a multi-constraint hybrid strategy SLAM framework for AUV-based seafloor terrain mapping, grounded in an analysis of error accumulation mechanisms and constraint failure characteristics. The framework establishes a hierarchical and progressive constraint architecture to enable collaborative optimization across different spatial scales and topographic conditions. At the foundational pose estimation stage, multi-source trajectory information is fused to ensure continuity and stability in pose computation. In the local consistency constraint stage, an improved point cloud registration method combined with a neighborhood survey-line constraint mechanism is introduced to enhance geometric consistency among survey lines in feature-sparse regions. At the global optimization stage, a loop closure detection strategy is designed based on topographic statistical features, incorporating adaptive thresholds and correlation metrics to achieve robust introduction of global constraints. By flexibly integrating direct registration and feature-matching strategies according to topographic characteristics, the framework fully leverages the advantages of multi-constraint cooperative optimization. The proposed method is validated by the field data. Experimental results on real lake-trial data show that, relative to the baseline configurations evaluated under identical noise-injection conditions, the MCHS-SLAM framework yields more concentrated consistency-error distributions with markedly shorter large-error tails, and exhibits improved error suppression relative to the reference trajectory. This work presents a methodological framework for high-quality seafloor terrain mapping under heterogeneous terrain conditions, providing a basis for future extensions toward onboard real-time deployment. Full article

Tree detection is a core task in forest inventory and mapping, yet reliable stem identification remains difficult in dense and structurally complex forests. This study systematically reviews the literature on terrestrial laser scanning (TLS)-based tree detection to summarize methodological development, identify persistent challenges, and highlight research gaps. Records were retrieved from Scopus and Web of Science (WoS). Following PRISMA 2020, 39 articles were included and analyzed using Bibliometrix v 5.2.1 package in R Studio 2026.01.1 and qualitative content coding. The reviewed studies were published between 2011 and 2025 in 20 peer-reviewed journals and involved 169 authors from 73 institutions across 24 countries. The literature was organized into three developmental phases: foundational development (2011–2015), rapid growth (2016–2020), and refinement and integration (2021–2025). Across these phases, methods evolved from geometric fitting and clustering to voxel-based and increasingly integrated workflows. Reported performance varied markedly with scan configuration, forest structure, and algorithm design, ranging from very low detection rates to near-complete detection under favorable conditions. Overall, TLS shows strong potential for forest inventory; however, dense stands, multilayered forests, and regeneration-rich environments remain major challenges. Full article

The purpose of this research is to determine which factors contribute to extending the read range of transponders equipped with different coupling-circuit topologies operating within selected RFID frequency bands. The analysis covered transponders that varied in both the configuration of their coupling circuits and their geometric dimensions. To accomplish this, transponder models were created using the EMCoS Studio electromagnetic simulation environment. Each model was subjected to simulations that yielded the mutual inductance and the voltage induced at the chip terminals. This study examines how the impedance of the embroidered antenna, the impedance of the chip’s coupling circuit, and the magnetic flux density affect the resulting chip voltage. In several of the investigated configurations, the peak chip voltage appeared outside the frequency range normally associated with RFID systems. The frequency at which this maximum occurred was dependent on the mutual inductance value. Understanding how individual parameters influence mutual inductance makes it possible to shift the voltage peak into a target operating band. Numerical simulation results, combined with the transponder’s mathematical model, enabled the calculation of the mutual inductance and the terminal voltage—quantities that directly determine the achievable read range. This study focuses on factors such as the resonant frequencies of the antenna and coupling circuit, their impedances, and the characteristics of the magnetic field. The findings show that tuning these parameters can affect not only the location of the voltage maximum, but also its amplitude. This effect introduces additional complexity in designing and selecting suitable transponder configurations. Full article

Real-time detection of apple leaf diseases in orchard environments faces ongoing challenges, particularly in preserving fine-grained disease features with limited computing resources. To address these issues, we propose a high-precision lightweight model based on YOLOv10n, called YOLO-ALD. First, we introduce Spatial and Channel Reconstruction Convolution into deeper backbone networks to replace standard downsampling layers and convolutions. This suppresses spatial and channel redundancy caused by environmental noise and optimizes feature representation. Second, we design a new C2f-Faster-SimAM module for the neck network. This module combines the inference efficiency of FasterNet with a parameter-free 3D attention mechanism to adaptively focus on early lesions, effectively distinguishing them from leaf veins without increasing model complexity. Third, in the detection head section, we use the Focaler-ShapeIoU loss function to optimize bounding box regression. It utilizes a dynamic focusing mechanism and geometric constraints to ensure the localization accuracy of irregular shapes and hard-to-detect samples. Experimental results on our self-built dataset covering four specific diseases and healthy leaves showed that, compared with YOLOv10n, the mAP@0.5 of YOLO-ALD reached 92.1%, achieving a 2.1% increase. In addition, the model has an inference speed of 105 FPS, with only 2.1 M parameters and 5.6 GFLOPs. Therefore, YOLO-ALD achieves a good balance between efficiency and robustness, showing strong theoretical potential for resource-constrained mobile agriculture diagnosis. Full article

Unmanned aerial vehicle (UAV) aerial object detection is increasingly important for traffic monitoring, emergency rescue, and environmental perception. However, vehicle detection in heavy rain, dense fog, blizzards, and backlit night scenes suffers from target information loss, feature misalignment, and unstable performance. We, therefore, construct a new severe-weather UAV dataset, Severe-Weather UAV (SWUAV), and propose the real-time Dynamic AlignAir Network (DANet). SWUAV contains 18,195 red–green–blue (RGB) aerial images covering 12 adverse weather/illumination conditions with 236,392 vehicle instances. After the high-resolution backbone features, we insert a cross-scale adaptive alignment module that performs adaptive channel calibration, contrastive self-attention, and geometric/semantic remapping to reduce scale drift/mismatch, suppress noise, and strengthen degraded target cues; we then design a dynamic adaptive alignment head (DAAH) with a shared encoder and a deformable regression branch to mitigate classification–regression mismatch under adverse conditions while further reducing complexity. On SWUAV, DANet raises the YOLOv11-s baseline average precision (AP)/AP50 (AP at intersection over union, IoU = 0.50) from 43.9%/62.6% to 46.9%/64.8%, with only 8.65 M parameters, 22.7 giga floating-point operations (GFLOPs), and a 323.47 frames-per-second (FPS) end-to-end throughput (3.09 ms per image at batch size 16), outperforming EdgeYOLO-s and RT-DETR. The dataset and code are publicly available. Full article

This communication discusses the problem of depositing equiatomic metal alloy films. It is shown that this problem can be solved using a magnetron equipped with a target constructed using a new “multilayer target” paradigm. This target, sputtered in an argon environment, consists of several parallel metal plates mounted on the magnetron axis. A method based on the equality of the sputtered fluxes generated by the plates is proposed for calculating the geometric dimensions of the plates. This equality leads to a system of algebraic equations, which are proposed to be solved under the assumption of a uniform discharge current density distribution in the sputtering region of the target. The communication describes two types of targets in which the plates have slots of different shapes. In one case, the slots are shaped as sectors of a ring with a given angle. In the other, the plates are shaped as rings. As examples, the geometric dimensions of targets for a balanced magnetron system intended for the deposition of films of equiatomic Ti_0.33Ta_0.33Nb_0.33 and Ti_0.25Ta_0.25Nb_0.25Mo_0.25 alloys are calculated. The presentation is accompanied by the results of individual experiments. This report is preliminary in nature; experimental verification is ongoing. The application of the new paradigm in magnetron target design facilitates the fabrication of films of nanostructured medium- and high-entropy alloys with specified chemical compositions, which is the central theme of the Special Issue devoted to functional nanomaterials. Full article

This study presents a surrogate-assisted evolutionary optimization framework for parametric design under limited data conditions, integrating computational fluid dynamics (CFD), machine learning, and evolutionary algorithms to optimize spiked blunt body geometries in supersonic flow. A dataset of CFD simulations covering a range of Mach numbers and geometric ratios, including spike length () and diameter (), was used to train regression-based surrogate models.Among the evaluated models, the Gradient Boosting Regressor (GBR) achieved the highest predictive accuracy (, RMSE = 0.00775), effectively capturing the nonlinear relationship between flow conditions, geometry, and drag coefficient (). The trained surrogate model was coupled with three evolutionary algorithms—Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and Genetic Algorithm (GA)—to identify optimal geometric configurations across different Mach regimes. To validate the proposed framework, the optimal solutions obtained from the surrogate-based optimization were re-evaluated using CFD simulations. A strong agreement between predicted and simulated drag coefficients was observed, confirming the reliability of the surrogate model for guiding optimization within the explored design space. The results reveal consistent geometric trends, with the optimal spike length ratio decreasing as Mach number increases, while the diameter ratio converges to a narrow range around . Additionally, SHapley Additive exPlanations (SHAP) analysis identified as the most influential parameter affecting drag, followed by Mach number and , supporting the physical interpretation of the flow behavior. Overall, the proposed framework demonstrates that the integration of CFD, machine learning, and evolutionary algorithms provides an efficient and reliable approach for geometric optimization in supersonic applications, enabling accurate design exploration with a limited number of high-fidelity simulations. Full article