Search Results (237)

To address the problems of coarse grains and unsatisfactory mechanical properties of as-cast Mg-8.5Al-1Zn alloy, which hinder its application in dissolvable bridge plugs, this study took the alloy as the research object and subjected it to plastic deformation via hot extrusion with an extrusion ratio of 12. Through the use of Combined Electron Backscatter Diffraction (EBSD) and Transmission Electron Microscopy (TEM) Testing and Characterization Techniques, the macroscopic mechanical properties, microstructural evolution, and extrusion deformation mechanism of the alloy in both as-cast and as-extruded states were systematically investigated. The results indicate that hot extrusion deformation significantly enhances the comprehensive mechanical properties of the alloy. Compared with the as-cast alloy, the tensile strength, yield strength, and elongation of the as-extruded alloy are increased by 104.0%, 314.9%, and 166.7%, respectively, with the static toughness increasing by 809.1%. The as-cast alloy exhibits coarse grains, Al element segregation, and high-density dislocations. After hot extrusion, dynamic recrystallization dominates the grain refinement, reducing the grain size by approximately 60%. Solute atoms precipitate to form multiphase structures and coherent nano-scale precipitates, along with the formation of tensile twins and a weakened bimodal texture. The improved yield strength of the as-extruded alloy stems from the synergistic effect of multiple strengthening mechanisms, among which precipitation strengthening induced by nano-precipitates is the primary contributor. The enhanced plasticity is attributed to grain refinement and texture regulation. This study clarifies the extrusion deformation mechanism of the Mg-8.5Al-1Zn alloy for dissolvable bridge plugs and verifies the rationality of the hot extrusion process with an extrusion ratio of 12, providing technical support for its industrial application in dissolvable bridge plugs and the performance regulation of similar dissolvable magnesium alloys. Full article

The magnetic properties of materials similar to Nd-Fe-B permanent magnets are highly sensitive to microstructure. Using Hybrid Monte Carlo micromagnetics simulations, we systematically investigate how grain boundary (GB) and grain crystallographic orientation affect coercivity (H_c) and remanence (M_r). A polycrystalline model with independently adjustable microstructural parameters is constructed via Voronoi tessellation. Our results show that increasing GB width from 2 nm to 10 nm reduces Hc by 32% and M_r by 16%. Grain boundary acts as both a nucleation site and pinning center: a wider GB facilitates reverse domain nucleation, especially at the triple junctions. However, domain wall propagation is underpinned by GB during the propagation process. For a thick GB, H_c decreases with increasing GB saturation magnetization (M_s′), because the thick weakly magnetic layer weakens exchange coupling between adjacent grains, shifting the reversal behavior from collective switching to more localized nucleation. Increasing the average easy-axis tilt angle reduces H_c, as the misalignment lowers the effective anisotropy component along the applied field direction, facilitating magnetization reversal. These findings confirm the importance of GB and texture control in optimizing the magnetic performance of Nd-Fe-B permanent magnets, offering references for experimental investigations. Full article

This study compares the microstructural evolution and mechanical properties of TC4 (Ti-6Al-4V) titanium alloy joints welded by Tungsten Inert Gas (TIG) and laser processes, following a post-weld annealing treatment at 650 °C for 2 h. Distinct microstructures were obtained: the TIG-welded joint developed a heterogeneous mixture of short-rod α and lamellar β, while the laser-welded joint formed a more homogeneous equiaxed α structure with uniformly distributed β-phase nanoparticles. Electron backscatter diffraction (EBSD) results confirmed that the annealing treatment significantly weakened the strong welding-induced texture and disrupted the epitaxial growth mode of columnar grains. Mechanical testing demonstrated that annealing improved the strength-toughness balance, but the extent and mechanism differed between the two processes. For the TIG-welded joint, the ultimate tensile strength slightly decreased, while elongation and impact toughness increased by 18% and 10.4%, respectively. In contrast, the laser-welded joint maintained its original strength while achieving greater improvements in ductility and toughness, with elongation and impact toughness increasing by 20% and 15.2%, respectively. This divergence is attributed to insufficient recrystallization and the persistence of residual coarse grains, limiting the TIG joint’s performance. However, in the laser-welded joint, the pinning effect of β-phase nanoparticles and associated grain refinement enhanced ductility without compromising strength. Full article

The demand for miniaturized high-temperature components necessitates advanced additive manufacturing techniques, yet the microstructural and mechanical consequences of scaling down the laser powder bed fusion (LPBF) process remain poorly understood. In this study, we systematically investigate the scaling effects of micro laser powder bed fusion (μ-LPBF) versus conventional LPBF on the phase transformation kinetics and performance of the near-α Ti65 alloy. Results demonstrate that μ-LPBF significantly enhances surface integrity, reducing the arithmetic mean roughness (Ra) by 59.5%. Microstructural characterization reveals that the extreme cooling rates intrinsic to the microscale melt pool induce a massive refinement of hierarchical α′ martensite and promote a highly randomized variant selection. Consequently, the strong building-direction crystallographic texture typical of LPBF is substantially weakened, and the proportion of high-angle grain boundaries increases to 91.6%. This microstructural homogenization effectively mitigates mechanical anisotropy, reducing the directional variance in the Schmid factor by 35%. In terms of mechanical properties, μ-LPBF demonstrates exceptional strengthening at both room temperature and 600 °C, achieving a room-temperature yield strength of 1297 MPa and an ultimate tensile strength of 1514 MPa, which represent increases of 16.5% and 8.6%, respectively, compared to those of conventional LPBF. These findings provide critical insights into defect suppression and multiscale microstructural control under extreme thermal gradients, paving the way for the fabrication of isotropic, high-strength micro devices. Full article

This study evaluated how a partial substitution of pea protein isolate (PPI) with brewer’s spent grain (BSG) or barley rootlets (BRs) affects high-moisture meat analogues (HMMAs). PPI was substituted with 10% and 20% with BSG or BRs, respectively. Extrudates were produced on a co-rotating twin-screw extruder at maximum temperatures of 140 °C and 160 °C. Extrudates were assessed for colour, moisture, firmness and fibre morphology. Furthermore, the technofunctional and nutritional properties of the raw materials were determined. Extrudates with BSG produced the darkest colour, whereas PPI and BR formulations exhibited the lightest. A stronger reddish tint was observed at 160 °C, while the colour within the yellow–blue spectrum was largely temperature-independent. Firmness was generally higher at 160 °C, consistent with lower end-product moisture. Side stream addition lowered protein content and weakened fibre formation, with the effect most pronounced for BRs. Overall, formulation was the dominant factor influencing lightness, while temperature modestly increased redness and firmness. Preliminary sensory evaluation supported these trends. Extrudates produced at 140 °C were perceived as having a more fibrous structure. Higher substitution levels resulted in a weaker, more crumbly texture. With respect to the environmental assessment, a 20% replacement of PPI with BRs or BSG reduced overall environmental impacts by up to 19% and climate impacts by up to 16%. With regard to the novel food status, the EU Novel Food Status Catalogue classifies BSG as not novel, whereas BRs are not novel only when used in food supplements. Any other food uses, other than as, or in, food supplements, might considered to be novel and consequently might need to be authorised under the novel food regulation framework prior to market placement. Full article

This study systematically investigates the hot deformation behavior and microstructure evolution of Ti-3Al-2.5V-0.5Ni alloy under compression at temperatures ranging from 800 °C to 1010 °C and strain rates ranging from 0.1 s⁻¹ to 10 s⁻¹, with a maximum deformation of 75% (with a corresponding true strain of 1.4). An Arrhenius-type constitutive equation was developed, and a hot processing map was established using a dynamic material model (DMM). Microstructural evolution was characterized using electron backscatter diffraction (EBSD). A hot processing map delineated stable and unstable regions. Regions with high power dissipation efficiency (η) were identified at deformation temperatures of 850–880 °C with strain rates of 0.1–10 s⁻¹, and at 940–960 °C with strain rates of 1.5–10 s⁻¹. These regions show high recrystallization fraction and good processing performance. The instability zone was observed at about 900 °C and high strain rate, which should be avoided during processing. The microstructure analysis of different power dissipation efficiency regions was carried out in detail. The results show that the power dissipation efficiency is about 0.38 at the deformation temperature of 950 °C and the strain rate of 0.1 s⁻¹, accompanied by high dynamic recrystallization. However, when the deformation condition is 800 °C and 10 s⁻¹, the power dissipation efficiency is lower than 0.18, the degree of recrystallization is limited, and a large number of dislocations accumulate. In summary, the large strain rolling of Ti-3Al-2.5V-0.5Ni alloy should be processed in the high-temperature α + β phase region (850–900 °C) and low-to-medium strain rate range of 0.1–5 s⁻¹. The process conditions can promote high recrystallization fraction, good processability, and weakened crystallographic texture, thereby minimizing the anisotropy of the final sheet. This study provides theoretical guidance for the optimization of industrial hot processing parameters of the alloy. Full article

Infrared and visible image fusion aims to integrate complementary information from heterogeneous images captured by different optical sensors based on distinct imaging principles; however, existing methods often exhibit modality bias, leading to weakened targets or the loss of crucial texture details. To address this, we propose MBFTFuse, an adversarial fusion network based on modality balancing and feature tracing, which consists of a triple-path generator and dual discriminators. The architecture employs a generator with a triple-path structure: a central modality-balancing path for deep feature fusion and dual edge feature-tracing paths for modality-specific enhancement. Specifically, a multi-cognitive modality-balancing module is introduced to achieve feature weight equilibrium, while a Feature-Tracing Attention Module self-enhances single-modality features to compensate for information loss in the fusion results. Furthermore, a pixel loss based on intensity histograms is designed to optimize inter-modal balance at the pixel level. Comparative experiments against nine state-of-the-art methods across three public datasets demonstrate that MBFTFuse effectively highlights infrared targets while preserving intricate visible textures. The superior performance of this method in both quantitative metrics and downstream object detection tasks contributes to extending the boundaries of sensor-driven computer vision technologies. Full article

The interaction between circular piers and turbulent open-channel flow generates complex three-dimensional structures, including horseshoe vortices at the pier base and wake vortices downstream. These structures increase vertical velocities, pressure fluctuations, and shear stresses, contributing to erosion and structural instability. Although these phenomena have been widely studied, limited attention has been given to surface geometric modifications as a flow-control strategy. This study employs Large Eddy Simulation (LES) to evaluate the influence of a hexagonal dimple pattern on circular piles in a free-surface channel. The dimples were defined by varying diameter, depth, and spacing to reduce vertical velocity and alter vortex formation. The computational domain represents a 0.40 m wide, 12 m long, and 1.2 m high rectangular channel, with an inlet mass flow of 9.4 kg/s and 0.10 m water depth. Model validation against particle image velocimetry (PIV) data showed 99% correlation, confirming numerical accuracy. Results demonstrate that textured surfaces modify flow dynamics by enhancing kinetic energy dissipation and generating micro-vortices that weaken dominant structures. The optimal configuration (6 mm diameter, 2 mm depth, 1 mm spacing) reduced downward vertical velocity by 42% and wake vortex shedding frequency by 24%, indicating improved hydraulic stability and erosion mitigation potential. Full article

Landslide hazards occur frequently in the Ya’an region; therefore, accurately identifying and delineating potential landslide areas is crucial for disaster prevention and mitigation. Although deep learning-based detection methods using optical remote sensing imagery are widely adopted, the complex terrain and diverse land cover in this area often result in blurred boundaries and weakened textural features, making it difficult to precisely define spatial extents. To overcome these challenges, this study proposes an improved YOLOv11 model for landslide detection. Building on the YOLOv11 baseline, we designed a novel Multi-Scale Detail Enhancement module and integrated it into the neck network to effectively aggregate shallow-level details with deep-level semantic information, thereby enhancing the model’s ability to represent ambiguous boundaries. Additionally, we incorporated the lightweight SimAM attention mechanism into the backbone network. This mechanism dynamically suppresses background noise based on an energy minimization principle, improving feature discriminability within landslide regions and enabling precise boundary boxes. We conducted validation experiments in the Ya’an region using a custom dataset constructed from high-resolution UAV orthoimagery, comparing our method against mainstream models such as YOLOv8 and YOLOv10. The results show that the proposed improved YOLOv11 model achieves a precision of 90.2%, a recall of 84.8%, and an mAP of 92.7%. This enhanced performance demonstrates the model’s effectiveness in detecting landslides under complex terrain conditions, providing a practical technical reference for efficient hazard screening and dynamic monitoring. Full article

Ice accretion on aircraft, wind-turbine blades, power networks, civil infrastructure, and exposed sensors poses severe safety risks and economic costs. Passive icephobic surfaces mitigate icing by delaying heterogeneous nucleation, altering droplet impact/solidification and wetting transitions, and/or weakening the ice–substrate bond so that accreted ice sheds under modest aerodynamic, gravitational, or vibrational loads. This review synthesizes recent progress using a unified mechanism framework linking (i) nucleation and early freezing, (ii) droplet dynamics during impact or condensation/frosting, and (iii) ice accretion and removal governed by interfacial fracture. Smooth low-surface-energy coatings, textured (superhydrophobic) surfaces, slippery liquid-infused porous surfaces (SLIPS), and low-interfacial-toughness strategies are critically compared in terms of achievable performance ranges, failure modes, durability limits, fabrication scalability, and test-method dependence. Ice-adhesion measurement approaches (push-off, pull-off/tensile, centrifugal) are assessed and a minimum reporting checklist is provided to improve comparability. Case studies across aviation, wind energy, power infrastructure, sensors, and emerging civil-engineering coatings highlight that durability and scale-dependent failure modes remain the dominant barriers to durable, energy-free icing mitigation. The review concludes with priorities for eco-friendly chemistries, self-healing or renewable layers, standardized testing/reporting, and data-driven (machine learning-assisted) optimization to accelerate translation into durable passive ice-mitigation technologies. Full article

Infrared small target detection remains challenging in applications such as long-range surveillance and early warning due to the fact that infrared images rely on thermal radiation, which results in limited texture cues and a low signal-to-noise ratio for the targets. Although recent deep networks have improved representation capability, they often exhibit two persistent limitations. Fine target details are gradually weakened through successive downsampling, and edge-related priors are not sufficiently exploited to stabilize target responses under background interference. To alleviate these issues, an Edge-Prior Guided Dual-Branch Enhancement Network (EGDENet) is proposed, a dual-branch framework that injects edge priors into feature learning for infrared small target detection. An auxiliary edge-aware branch is introduced to complement the main encoder–decoder stream. Specifically, a Multi-directional Sobel Edge Extraction (MSEE) module is designed to adaptively reweight multi-directional edge responses, thereby strengthening boundary-sensitive representations. Furthermore, a Difference-Aware Gated Fusion (DAGF) module leverages Gated Spatial Convolution to capture subtle variations in the features and employs depthwise separable convolution along with adaptive enhancement to effectively integrate the extracted edge information. In addition, an Edge Pixel Integration (EPI) Loss is present to couple edge sensitivity with pixel-wise supervision. This loss improves the edge sensitivity of infrared small targets. The proposed EGDENet is evaluated on three benchmark datasets: NUAA-SIRST, IRSTD-1K, and SIRST-Aug. The experimental results show that our method outperforms or matches the performance of state-of-the-art methods. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

Laser powder bed fusion (LPBF) of AA7075 is severely constrained by a narrow process window and susceptibility to defect formation (hot cracking and porosity), which often dominates performance. In this study, 5 wt.% AlCoCrFeNi_2.1 high-entropy alloy (HEA) particles, volumetric energy density (VED = 74–222 J·mm⁻³), and subsequent T6 heat treatment were systematically investigated to reveal their combined effects on defect structure, crystallographic texture/substructure, and tensile behaviour. Quantitative EBSD shows a measurable grain refinement in the as-built state (average grain size 13.44 → 11.80 µm, ~12%) accompanied by a pronounced weakening of the <001> fibre texture (maximum MRD 4.94 → 2.38), indicating disrupted epitaxial growth and a more dispersed orientation distribution. After T6, the reinforced alloy retains a higher low-angle boundary fraction (31.62% vs. 24.17% in unreinforced AA7075) and a higher kernel average misorientation (0.80° vs. 0.60°), consistent with particle-stabilised substructure retention and retarded recovery. Across all VEDs, AA7075-HEA exhibits higher microhardness (compared with AA7075, the addition of HEA increases the hardness by roughly 20–50 HV) and tensile strength, with the intermediate VED (140.74 J·mm⁻³, T6 states) yielding the best performance. While macroscopic cracking is not fully eliminated, the results clarify that HEA-enabled texture/substructure modifications can contribute to enhanced defect tolerance and are more effectively translated into tensile performance when the as-built defect severity is controlled. These findings provide quantitative insights into defect–microstructure–property coupling in LPBF AA7075-HEA composites from as-built to T6 states. Full article

The effects of different finishing rolling temperatures on the microstructure and mechanical properties of a 580HE hole expansion steel were systematically investigated using optical microscopy, scanning electron microscopy, electron backscatter diffraction, and transmission electron microscopy. The results show that the yield strength increases with decreasing finishing rolling temperature, whereas the tensile strength and total elongation exhibit relatively small variations. Significant changes in phase fraction, grain size, spatial distribution, and NbC precipitation behavior are observed under different finishing rolling temperatures. The microstructure mainly consists of polygonal ferrite and granular bainite, while acicular ferrite is formed at higher finishing rolling temperatures. With decreasing finishing rolling temperature, the ferrite and bainite grains are markedly refined and become more uniformly distributed. Meanwhile, the ferrite fraction slightly increases, the crystallographic texture is weakened, and, more importantly, the number density of precipitates increases while their size is significantly reduced. The hole expansion ratio increases noticeably with decreasing finishing rolling temperature, which is mainly attributed to grain refinement, improved microstructural and strain homogeneity, and the selective strengthening effect of fine NbC precipitates. These factors effectively reduce stress concentration and hardness mismatch between soft and hard phases, thereby delaying crack initiation during hole expansion. Full article

Reliable vision-based sensing of protective equipment wearing compliance is essential for safety monitoring in underground mining environments, where complex lighting conditions, similar background textures, and large variations in the scale of wearable items significantly degrade detection performance. To address these challenges, this study proposes a vision-based protective equipment wearing compliance detection method for underground personnel based on CGALS-YOLO. Traditional object detection models often introduce substantial redundant background information during multi-scale feature fusion, which weakens the perception of key wearing regions, particularly for small-scale targets. To alleviate this issue, a content-guided feature fusion (CGAFusion) module is incorporated into the neck of the YOLOv8 network, enabling adaptive fusion of same-scale multi-path features through the collaborative effects of channel, spatial, and pixel attention mechanisms. This design enhances target-related feature representation while suppressing background interference in complex underground scenes. Furthermore, to reduce parameter redundancy and improve cross-scale discrimination consistency in the detection head, a lightweight shared convolution detection (LSCD) structure is introduced. By employing cross-scale shared convolution parameters, group normalization, and scale-adaptive regression, the proposed model achieves a parameter reduction of approximately 23.9% while lowering computational complexity and maintaining stable multi-scale detection performance. Experimental results on an underground protective equipment wearing compliance dataset demonstrate that CGALS-YOLO improves detection accuracy by approximately 4.6% and recall by 3.1% compared with the baseline YOLOv8n, achieving an mAP@0.5 of 89.4%. These results validate the effectiveness and practical applicability of the proposed method for real-time vision-based safety monitoring in underground environments. Full article