Search Results (2,684)

Secondary hypereutectic Al-Si alloys are attractive for sustainable manufacturing, yet their application is often limited by low strength and electrical conductivity due to impurity-induced microstructural defects. Achieving a balance between mechanical and conductive performance remains a significant challenge. In this work, nickel-coated carbon nanotubes (Ni-CNTs) were introduced into secondary Al-20Si alloys to tailor the microstructure and enhance properties through interfacial engineering. Composites containing 0 to 0.4 wt.% Ni-CNTs were fabricated by conventional casting and systematically characterized. The addition of 0.1 wt.% Ni-CNTs resulted in the best combination of properties, with a tensile strength of 170.13 MPa and electrical conductivity of 27.60% IACS. These improvements stem from refined α-Al dendrites, uniform eutectic Si distribution, and strong interfacial bonding. Strengthening was achieved through grain refinement, Orowan looping, dislocation generation from thermal mismatch, and the formation of reinforcing interfacial phases such as AlNi₃C₀.₉ and Al₄SiC₄. At higher Ni-CNT contents, property degradation occurred due to agglomeration and phase coarsening. This study presents an effective and scalable strategy for achieving strength–conductivity synergy in secondary aluminum alloys via nanoscale interfacial design, offering guidance for the development of multifunctional lightweight materials. Full article

The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ (x = 0.03–0.15) ceramics were fabricated via the traditional solid reaction method. Characterization results revealed that each component exhibited a pure perovskite structure, and the average grain size significantly diminishes with increasing x. The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramics exhibited prominent relaxor ferroelectric behavior, whose characteristic narrow hysteresis loops effectively enhanced the energy storage performance of the material. Most importantly, the composition with x = 0.10 demonstrated exceptional energy storage properties at 150 kV/cm, achieving a high recoverable energy storage density (W_rec = 1.91 J/cm³) and excellent energy efficiency (η = 90.87%). Under the equivalent electric field, this composition also displayed a superior pulsed discharge performance, including a high current density (871 A/cm²), a high power density (67.3 MW/cm³), an ultrafast discharge time (t_0.9 = 109 ns), and a discharged energy density of 1.47 J/cm³. These results demonstrate that the (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramic system establishes a promising design paradigm for the creation and refinement of next-generation dielectrics for pulse power applications. Full article

The high cost of hydrogen production is the primary factor limiting the development of the hydrogen energy industry chain. Additionally, due to the inefficiency of hydrogen production by water electrolysis technology, the development of high-performance catalysts is an effective means of producing low-cost hydrogen. In water electrolysis technology, the electrocatalytic activity of the electrode affects the kinetics of the oxygen evolution reaction (OER) and the hydrogen evolution rate. This study utilizes the liquid phase co-precipitation method to synthesize three types of Prussian blue analog (PBA) electrocatalytic materials: Fe/PBA(Fe₄[Fe(CN)₆]₃), Fe-Mn/PBA((Fe, Mn)₃[Fe(CN)₆]₂·nH₂O), and Fe-Mn-Co/PBA((Mn, Co, Fe)₃^II[Fe^III(CN)₆]₂·nH₂O). X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses show that Fe-Mn-Co/PBA has a smaller particle size and higher crystallinity, and its grain boundary defects provide more active sites for electrochemical reactions. The electrochemical test shows that Fe-Mn-Co/PBA exhibits the best electrochemical performance. The overpotential of the oxygen evolution reaction (OER) under 1 M alkaline electrolyte at 10/50 mA·cm⁻² is 270/350 mV, with a Tafel slope of 48 mV·dec⁻¹, and stable electrocatalytic activity is maintained at 5 mA·cm⁻². All of these are attributed to the synergistic effect of Fe, Mn, and Co metal ions, grain refinement, and the generation of grain boundary defects and internal stresses. Full article

To investigate the effect of liquid-phase laser ablation on the hardness of WC-Co cemented carbide, this study performed hardness testing, elemental distribution analysis, and XRD phase analysis. The influence of ablation times on the hardness, elemental distribution, and phase composition of WC-Co cemented carbide was examined, and a model describing the hardness evolution mechanism under liquid-phase laser ablation was proposed. The results demonstrated that the hardness of WC-Co cemented carbide increased with the number of ablations. After 14 ablation times, the maximum hardness reached 2800 HV, representing an increase of 51%–56% compared to the matrix hardness. As the number of ablations increased, the content of ditungsten carbide (W₂C) and tungsten carbide (WC) in the cemented carbide increased, the WC grain size decreased, the dislocation density increased, and the distribution became more uniform. The refinement of WC grains and the elevated dislocation density facilitated stronger intergranular bonding, thereby significantly enhancing the material’s hardness. This study provides theoretical guidance for improving the surface mechanical properties of WC-Co cemented carbide tools through liquid-phase laser ablation. Full article

Coarse-grained soils serve as essential fill materials in earth–rock dam engineering, where their mechanical properties critically influence dam deformation and stability, directly impacting project safety. Artificial intelligence (AI) techniques are emerging as powerful tools for predicting the mechanical properties of coarse-grained soils. However, AI-based prediction models for these properties face persistent challenges, particularly in parameter tuning—a process requiring substantial computational resources, extensive time, and specialized expertise. To address these limitations, this study proposes a novel prediction model that integrates Adaptive Simulated Annealing (ASA) with an improved Particle Swarm Optimization (PSO) algorithm to optimize the Elman Neural Network (ENN). The methodology encompasses three key aspects: First, the standard PSO algorithm is enhanced by dynamically adjusting its inertial weight and learning factors. The ASA algorithm is then employed to optimize the Adaptive PSO (APSO), effectively mitigating premature convergence and local optima entrapment during training, thereby ensuring convergence to the global optimum. Second, the refined PSO algorithm optimizes the ENN, overcoming its inherent limitations of slow convergence and susceptibility to local minima. Finally, validation through real-world engineering case studies demonstrates that the ASA-PSO-optimized ENN model achieves high accuracy in predicting the mechanical properties of coarse-grained soils. This model provides reliable constitutive parameters for stress–strain analysis in earth–rock dam engineering applications. Full article

Laser powder bed fusion (LPBF) is an advanced additive manufacturing technology that is gaining increasing interest for biomedical implants because it can produce dense, patient-specific metallic components with controlled microstructures. This study investigated the LPBF fabrication of 316L stainless steel, which is widely used in orthopedic and dental implants, and examined the effects of laser power and scanning speed on the microstructure and mechanical properties relevant to biomedical applications. The study achieved 99.97% density and refined columnar and cellular austenitic grains, with optimized molten pool morphology. The optimal LPBF parameters, 190 W laser power and 700 mm/s, produced a tensile strength of 762.83 MPa and hardness of 253.07 HV_0.2, which exceeded the values of conventional cast 316L stainless steel. These results demonstrated the potential of optimized LPBF 316L stainless steel for functional biomedical applications that require high mechanical integrity and biocompatibility. Full article

This work expands the processing window of the laser powder bed fusion (LPBF) processing of WE43 magnesium alloy by evaluating laser powers and scanning speeds up to 400 W and 1200 mm/s, and their effect on densification, microstructure, and electrochemical performance. Relative density of 99.9% was achieved for 300 W and 800 mm/s, showing that the use of high laser power is not a limitation for the manufacturing of Mg alloys, as has been usually considered. Microstructural characterisation revealed refined grains and the presence of RE-rich intermetallic particles, while microhardness increased with height due to thermal gradients. Electrochemical testing in 3.5 wt.% NaCl solution, a more aggressive media than those already used, indicated that the corrosion of samples with density values below 99% is conditioned by the porosity; however, above this value, in the WE43, the corrosion evolution is more related to the microstructure of the samples, according to electrochemical evaluation. This study demonstrates the viability of high-energy LPBF processing for WE43, offering optimised mechanical and corrosion properties for biomedical and structural applications. Full article

This study demonstrates the successful fabrication of high-performance reaction-bonded silicon carbide (RBSC) ceramics through an optimized liquid silicon infiltration (LSI) process employing multi-modal SiC particle gradation and nano-carbon black (0.6 µm) additives. By engineering porous preforms with hierarchical SiC distributions and tailored carbon sources, the resulting ceramics achieved a compressive strength of 2393 MPa and a flexural strength of 380 MPa, surpassing conventional RBSC systems. Microstructural analyses revealed homogeneous β-SiC formation and crack deflection mechanisms as key contributors to mechanical enhancement. Ultrafine SiC particles (0.5–2 µm) refined pore architectures and mediated capillary dynamics during infiltration, enabling nanoscale dispersion of residual silicon phases and minimizing interfacial defects. Compared to coarse-grained counterparts, the ultrafine SiC system exhibited a 23% increase in compressive strength, attributed to reduced sintering defects and enhanced load transfer efficiency. This work establishes a scalable strategy for designing RBSC ceramics for extreme mechanical environments, bridging material innovation with applications in high-stress structural components. Full article

Geographic object-based image analysis (GEOBIA) has demonstrated substantial utility in remote sensing tasks. However, its integration with deep learning remains largely confined to image-level classification. This is primarily due to the irregular shapes and fragmented boundaries of segmented objects, which limit its applicability in semantic segmentation. While convolutional neural networks (CNNs) excel at local feature extraction, they inherently struggle to capture long-range dependencies. In contrast, Transformer-based models are well suited for global context modeling but often lack fine-grained local detail. To overcome these limitations, we propose GOFENet (Geo-Object Feature Enhanced Network)—a hybrid semantic segmentation architecture that effectively fuses object-level priors into deep feature representations. GOFENet employs a dual-encoder design combining CNN and Swin Transformer architectures, enabling multi-scale feature fusion through skip connections to preserve both local and global semantics. An auxiliary branch incorporating cascaded atrous convolutions is introduced to inject information of segmented objects into the learning process. Furthermore, we develop a cross-channel selection module (CSM) for refined channel-wise attention, a feature enhancement module (FEM) to merge global and local representations, and a shallow–deep feature fusion module (SDFM) to integrate pixel- and object-level cues across scales. Experimental results on the GID and LoveDA datasets demonstrate that GOFENet achieves superior segmentation performance, with 66.02% mIoU and 51.92% mIoU, respectively. The model exhibits strong capability in delineating large-scale land cover features, producing sharper object boundaries and reducing classification noise, while preserving the integrity and discriminability of land cover categories. Full article

Aluminum matrix composites provide an ideal solution for lightweight brake disks, but conventional casting processes are prone to crack initiation due to inhomogeneous reinforcement dispersion, gas porosity, and inadequate toughness. To break the conventional trade-off between high wear resistance and low toughness of brake disks, this study fabricated a bimetallic structure of SiC_p/Al–Fe–V–Si aluminum matrix composite and cast ZL101 alloy using friction stir lap welding (FSLW). Then, the microstructural evolution, mechanical properties, and tribological behavior of the FSLW joints were studied by XRD, SEM, TEM, tensile testing, and tribological tests. The results showed that the FSLW process homogenized the distribution of SiC particle reinforcements in the SiC_p/Al–Fe–V–Si composites. The Al₁₂(Fe,V)₃Si heat-resistant phase was not decomposed or coarsened, and the mechanical properties were maintained. The FSLW process refined the grains of the ZL101 aluminum alloy through recrystallization and fragmented eutectic silicon, improving elongation to 22%. A metallurgical bond formed at the joint interface. Tensile fracture occurred within the ZL101 matrix, demonstrating that the interfacial bond strength exceeded the alloy’s load-bearing capacity. In addition, the composites exhibited significantly enhanced wear resistance after FSLW, with their wear rate reduced by approximately 40% compared to the as-received materials, which was attributed to the homogenized SiC particle distribution and the activation of an oxidative wear mechanism. Full article

Video temporal grounding (VTG) aims to localize a semantically relevant temporal segment within an untrimmed video based on a natural language query. The task continues to face challenges arising from cross-modal semantic misalignment, which is largely attributed to redundant visual content in sensor-acquired video streams, linguistic ambiguity, and discrepancies in modality-specific representations. Most existing approaches rely on intra-modal feature modeling, processing video and text independently throughout the representation learning stage. However, this isolation undermines semantic alignment by neglecting the potential of cross-modal interactions. In practice, a natural language query typically corresponds to spatiotemporal content in video signals collected through camera-based sensing systems, encompassing a particular sequence of frames and its associated salient subregions. We propose a text-guided visual representation optimization framework tailored to enhance semantic interpretation over video signals captured by visual sensors. This framework leverages textual information to focus on spatiotemporal video content, thereby narrowing the cross-modal gap. Built upon the unified cross-modal embedding space provided by CLIP, our model leverages video data from sensing devices to structure representations and introduces two dedicated modules to semantically refine visual representations across spatial and temporal dimensions. First, we design a Spatial Visual Representation Optimization (SVRO) module to learn spatial information within intra-frames. It selects salient patches related to the text, capturing more fine-grained visual details. Second, we introduce a Temporal Visual Representation Optimization (TVRO) module to learn temporal relations from inter-frames. Temporal triplet loss is employed in TVRO to enhance attention on text-relevant frames and capture clip semantics. Additionally, a self-supervised contrastive loss is introduced at the clip–text level to improve inter-clip discrimination by maximizing semantic variance during training. Experiments on Charades-STA, ActivityNet Captions, and TACoS, widely used benchmark datasets, demonstrate that our method outperforms state-of-the-art methods across multiple metrics. Full article

The development of technology has driven a rising need for high-accuracy and high-efficiency manufacturing of low-volume products. Incremental forming technology, characterized by die-free flexibility and low production costs, can effectively replace stamping processes for manufacturing customized small-batch products. However, high-performance aluminum alloys generally exhibit poor room-temperature plasticity but excellent high-temperature plasticity, necessitating the integration of thermal-assisted methods for manufacturing such products. However, the temperature of the forming region will excessively rise without temperature control, which will affect the forming performance of the material in hot incremental sheet forming of AA2024-T4 aluminum alloy. This study focuses on AA2024-T4 aluminum alloy and proposes a uniform temperature control method for the electric hot tube-assisted incremental sheet forming process, incorporating an adaptive fuzzy PID algorithm. The temperature difference of the forming region is lower than 6% under the various temperatures. On this basis, the forming limit angle and the microstructure state of the material are analyzed, and the grain feature of the material exhibits significantly refined grains and the uniform fine grain distribution under 180 °C with the temperature control of the adaptive fuzzy PID algorithm. Full article

This study investigated the influence of various annealing treatments on the microstructure and corrosion behavior of 20MnCr5 steel in a 3.5% NaCl solution. A combination of microstructural analysis, hardness testing, and electrochemical techniques was used to comprehensively characterize each condition. To enhance data interpretability, a correlation analysis was performed and visualized through a correlation diagram, enabling statistical assessment of the relationships between grain features, phase distribution, mechanical properties, and corrosion indicators. The results demonstrated that corrosion resistance in 20MnCr5 steel is not governed by a single parameter but by the interplay between grain size, morphology, and phase balance. Excessive pearlite content or coarse, irregular grains were consistently associated with higher corrosion rates and lower electrochemical stability. In contrast, a moderate phase ratio and equiaxed grain structure, achieved through normalization, resulted in better corrosion resistance, confirmed by the highest polarization resistance and lowest corrosion current density values among all samples. Although increased grain refinement improved the hardness, it did not always correlate with a better corrosion performance, especially when morphological uniformity was lacking. This highlights the importance of balancing mechanical and corrosion properties through carefully controlled thermal processing. Full article

The optical floating zone technique was utilized to purify chromium and a single-phase chromium–silicon alloy in this work. The impurity content (carbon, nitrogen, and oxygen) can be reduced by decreasing the withdrawal speed of samples during the zone refining process, and the coarsening of grains was also observed. The effect of the impurities on mechanical properties was determined by hardness measurements at room temperature, and the hardness of both chromium and the chromium–silicon alloy decreased with lower concentrations of nitrogen and oxygen. In contrast, brittle material behavior is observed in samples prepared by arc melting process with higher concentrations of impurities. To use chromium–silicon alloys for future high-temperature applications, their brittle behavior must be improved, which can be achieved by reducing their carbon, nitrogen, and oxygen concentrations. Full article

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article