Search Results (1,314)

Multi-point flexible die (MPFD) exhibits broad application potentials for efficient and controllable forming of curved sheets due to its rapid reconfigurability. Nevertheless, the relatively poor surface accuracy and geometrical accuracy of the fiber-reinforced composite components formed by MPFDs limit the widespread application of this technology. In this study, a novel gas-assisted diaphragm forming (GADF) process based on MPFDs for curved basalt fiber/epoxy resin composite sheets was proposed. The precise control of temperature, pressure and MPFD configuration in the process was realized and verified. The effects of different process parameter configurations on dimple defects and geometrical accuracy were analyzed, and the mechanism of defect generation was investigated. A response surface-based forming accuracy prediction model was developed to analyze the influence of component structural parameters on geometrical accuracy. Based on the predictive model, compensation reconfiguration of MPFDs was carried out to achieve high-accuracy sheet forming. Results demonstrated that increasing pressure exacerbated the dimple while reducing shape accuracy. A moderate temperature (120 °C) was proved optimal for component forming, as both excessively low and high temperatures aggravated dimple and induced geometrical errors. Increasing interpolator thickness effectively reduced dimple defects, but excessive thickness adversely affected component geometrical accuracy. Considering both dimple suppression and geometrical accuracy, the optimal process parameters were determined to be 5 kPa, 120 °C, and 2 mm of interpolator thickness. Through MPFD modification based on the response surface model, the geometrical accuracy of the formed components was improved by 38.85%, achieving high-quality forming of the curved composite sheets. Full article

In this paper, we show that the sphere-in-contact model can predict long-range surface adsorption phenomena based on adsorbate-adsorbate repulsions and their geometric distance, assuming that their negative surface-induced charge is smeared on the surface of the adsorbate atoms. Additionally, it can be used to model collective surface diffusion mechanisms such as the domino-type surface diffusion of adsorbate rows on close-packed metal HCP and FCC surfaces. We have recently shown that the sphere-in-contact model can be used as an educational and research tool in various contexts, such as the visualization of carbon structures (e.g., graphene, carbon nanotubes, carbon nanocones, and graphite), heterogeneous catalysts, metal nanoparticles, and organic molecules. Here we present how it can be used to model the adsorbate structure of monoatomic elements on the hexagonal close-packed surface of HCP and FCC metals to study long-range ordering phenomena of monoatomic adsorbates on metals. We have used atoms of varying radius and color to represent the metal surface atoms and the adsorbate atoms. The study reveals that many surface configurations are possible for a fixed adsorbate coverage (θ) by the movement of the adsorbate atoms in response to surface adsorbate-adsorbate repulsions. The movement of the particles (e.g., particle diffusion) can be seen directly in the model, and this is caused by the user intervention. This has great educational and research value, as one can directly see how the adsorbate atoms reorder on the surface of a metal and therefore study diffusion mechanisms. We calculate the repulsive interaction energy of adsorbates using the sphere-in-contact model and can identify which surface-adsorbed configuration is the lowest energy. We find that at a surface coverage of 1/3 (0.333 ML), the most stable adsorbate configuration places adsorbates at the third nearest neighbor 3-fold hollow sites, forming a hexagonal pattern. We find that this model will be useful in the rational design of catalytic materials and material coatings with new technological applications where long-range ordering of surface adsorbates is essential and adsorbate interactions are mainly repulsive interatomic interactions. Full article

The use of a corrugated steel plate shear wall (CSPSW) lateral load-bearing system in a steel moment frame (SMF) significantly increases the system’s energy absorption and stiffness. However, the design of CSPSWs involves many parameters and details that greatly increase the complexity of the structure’s response. This study aims to evaluate the effectiveness of the geometric parameters of this system using modern optimization algorithms and an alternative mathematical technique, Response Surface Methodology (RSM). Five geometric parameters, namely crest width (a), diagonal section width (b), corrugation depth (c), sheet thickness (t), and aspect ratio of plate dimension (d), were analyzed to improve the performance of CSPSWs. Design of experiments (DOE) was performed using Design-Expert software, and the required response surface methodology models were designed based on the dimensions of the five variables. Structure weight per meter reduction was set as the optimization goal of the problem. The problem constraints were also defined based on an increase in load-bearing capacity and a reduction in the equivalent plastic strain (PEEQ) percentage in three safety levels 80%, 85% and 90%. Subsequently, the alternative equations developed by RSM to define the objective function and nonlinear constraints were also optimized using modern algorithms in MATLAB 2015. Results revealed a coefficient of determination (R²) of 0.9995 between the experimental and numerical findings and a 1% error between the values obtained from the optimization and reanalysis of the finite elements. Also, they showed an increase in the frame’s lateral load-bearing capacity with the CSPSW, along with a reduction in weight. Full article

High-fidelity 3D reconstruction of human-sized objects typically requires multi-sensor scanning systems that are expensive, complex, and rely on proprietary hardware configurations. Existing low-cost approaches often rely on handheld scanning, which is inherently unstructured and operator-dependent, leading to inconsistent coverage and variable reconstruction quality. This limitation necessitates the need for a controlled, repeatable, and affordable scanning method that can generate high-quality data without requiring multi-sensor hardware or external tracking markers. This study presents a marker-less scanning platform designed for human-scale reconstruction. The system consists of a single structured-light sensor mounted on a vertical linear actuator, synchronised with a motorised turntable that rotates the subject. This constrained kinematic setup ensures a repeatable cylindrical acquisition trajectory. To address the geometric ambiguity often found in vertical translational symmetry (i.e., where distinct elevation steps appear identical), the system employs a sensor-assisted initialisation strategy, where feedback from the rotary encoder and linear drive serves as constraints for the registration pipeline. The captured frames are reconstructed into a complete model through a two-step Iterative Closest Point (ICP) procedure that eliminates the vertical drift and model collapse (often referred to as “telescoping”) common in unconstrained scanning. To evaluate system performance, a modular anthropometric benchmark object representing a human-sized target (1.6 m) was scanned. The reconstructed model was assessed in terms of surface coverage and volumetric fidelity relative to a CAD reference. The results demonstrate high sampling stability, achieving a mean surface density of $0.760{\mathrm{points}/\mathrm{mm}}^2$ on front-facing surfaces. Geometric deviation analysis revealed a mean signed error of −1.54 mm ($\sigma =$ 2.27 mm), corresponding to a relative volumetric error of approximately 0.096% over the full vertical span. These findings confirm that a single-sensor system, when guided by precise kinematics, can mitigate the non-linear bending and drift artefacts of handheld acquisition, providing an accessible yet rigorously accurate alternative to industrial multi-sensor systems. Full article

A series of generalized fixed-bed physical model experiments were conducted to investigate the hydrodynamic effects of spur dike configuration and permeability. The study was carried out in a rectangular flume at a geometric scale of 1:40. A traditional impermeable spur dike, a novel impermeable spur dike with a curved geometry, and permeable spur dikes with varying porosities (p = 11.8%, 17.6%, and 23.2%) were systematically examined. Surface velocity and flow direction were measured using a large-scale surface flow field measurement system. Additionally, tracer-based experiments were conducted to characterize oil spill spreading pathways, areas, and rates. The results showed that the novel curved-profile spur dike alleviates upstream backwater effects and weakens downstream plunging flow compared to the conventional straight-profile spur dike, resulting in a more uniform surface flow structure. At low porosity (P = 11.8%), hydrodynamic behavior resembled that of impermeable structures. In contrast, at high porosity (P = 23.2%), upstream–downstream hydraulic connectivity was enhanced, and recirculation intensity was reduced. Regarding oil spill dispersion, spur dike promoted oil retention in the upstream region and lateral spreading around the spur dike head. The extent of the spreading area was strongly influenced by both the cross-sectional geometry and the porosity of the spur dike. Among the permeable cases, the largest spreading area was observed at an intermediate porosity (P = 17.6%). However, permeable spur dike generally exhibited smaller overall spreading areas compared to impermeable spur dike. Finally, an empirical model for predicting the oil spreading area was developed by incorporating flow velocity, water depth, and porosity. These findings provide a scientific basis for optimizing spur dike design and mitigating oil spill risks. Given the severe threat that oil pollution poses to aquatic environments, the retention capacity of spur dikes serves as a critical hydraulic barrier, thereby promoting environmental and ecological sustainability. Full article

Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing. Full article

Porous silicon (PSi), characterized by its high specific surface area and highly tunable morphology, presents significant potential across optoelectronics, energy storage, and biomedical applications. This review provides a systematic analysis of the synthesis methodologies, interfacial chemical engineering, and diverse applications of PSi. Initially, fabrication techniques are examined, contrasting the pore formation mechanisms of electrochemical anodization, metal-assisted chemical etching (MACE), and emerging vapor-phase etching methods, while elucidating the control of geometric parameters from microporous to macroporous scales. To address the thermodynamic instability of the hydride-terminated surface, this review systematically evaluates modification strategies such as thermal oxidation, hydrosilylation, carbonization, and atomic layer deposition (ALD). We critically analyze their efficacy in mitigating oxidative drift and enabling specific functionalization. Subsequently, the review summarizes current applications in sensing (refractive index and photoluminescence modulation), energy storage (lithium-ion battery anodes and supercapacitors), and microsystem technologies (radio frequency (RF) isolation, gettering, and micro-electro-mechanical systems (MEMS) sacrificial layers), emphasizing the critical role of structure–property relationships. Finally, an objective assessment is provided regarding the challenges in translating PSi technology to industrial scales, specifically addressing the trade-offs between biodegradability and stability, wafer-scale process uniformity, and the compatibility of wet-chemical processing with standard complementary metal–oxide–semiconductor (CMOS) integration flows. Full article

Morphological differences between sea-caught and farmed fish reflect environmental conditions and long-term domestication. However, standardized and objective quantification of these differences remains limited for many commercially important species. The large yellow croaker (Larimichthys crocea) represents a typical marine fish with clear contrasts between natural and aquaculture production systems. In this study, an image-based phenotyping workflow was developed to quantify external morphological traits of sea-caught and farmed L. crocea. Visible-light images were acquired under standardized conditions. A YOLOv11-based instance segmentation model was applied to automatically delineate major anatomical regions, including the body, head, eyes, pectoral fins, and tail. Surface areas and proportional indices were calculated following geometric calibration to ensure measurement consistency. The segmentation model achieved high accuracy on the test dataset (mAP@50 > 98%). Morphometric analyses revealed clear differences between the two groups. Farmed individuals exhibited larger body-related surface areas, whereas the relative proportions of pectoral fins and tail regions were reduced. Sea-caught fish showed higher proportional investment in locomotor structures, consistent with the physical demands of natural marine environments. These results indicate a shift in morphological allocation associated with aquaculture, characterized by enhanced trunk growth and reduced relative development of propulsion-related structures. The proposed workflow provides a rapid, non-invasive, and reproducible approach for fish morphometric analysis. It offers practical potential for phenotypic monitoring and stock assessment, while contributing quantitative evidence for domestication-driven morphological divergence in marine fishes. Full article

This paper presents a cover lens concept for camera modules based on surface acoustic waves (SAW) to mitigate the degradation of physical AI optical sensor field-of-view performance caused by surface contamination. The proposed approach utilizes a single-phase unidirectional transducer (SPUDT) that intentionally breaks left–right symmetry through a geometrically asymmetric electrode array to generate SAW, thereby removing droplet contamination. First, the acoustic streaming induced inside a single sessile droplet by the SAW was visualized, and the dynamic behavior of the droplet upon SAW actuation was observed using a high-speed camera. The internal flow developed into a recirculating vortex structure with directional deflection relative to the SAW propagation direction, indicating a symmetry-broken streaming pattern rather than a purely symmetric circulation. Upon the application of the SAW, the droplet was confirmed to move a total of 7.2 mm along the SAW propagation direction, accompanied by interfacial deformation and oscillation. Next, an analysis of transport trajectories for five sessile droplets dispensed at different y-coordinates ($y_1$–$y_5$) revealed that all droplets were transported along the x-axis regardless of their initial positions. Furthermore, the analysis of transport velocity as a function of droplet viscosity (1 $\mathrm{c}\mathrm{P}$ and 10 $\mathrm{c}\mathrm{P}$) and volume (2$\mathrm{\mu }\mathrm{L}$, 4$\mathrm{\mu }\mathrm{L}$, and 6 $\mathrm{\mu }\mathrm{L}$) demonstrated that the transport velocity gradually increased with driving voltage but decreased as viscosity increased under identical actuation conditions. Finally, the proposed cover lens was applied to an automotive front camera module to verify its effectiveness in improving object recognition performance by removing surface contamination. Based on its simple structure and driving principle, the proposed technology is deemed to be expandable as a surface contamination cleaning technology for various physical AI perception systems, including intelligent security cameras and drone camera lenses. Full article

Wall-climbing robots are increasingly required for applications in aerospace, high-altitude operations, and complex environmental monitoring, where they must maintain reliable adhesion and continuous mobility across surfaces with rapidly changing material properties and roughness. Achieving these demands requires lightweight systems with end effectors that integrate multi-surface adaptability and load-carrying capacity. Current single adhesion mechanisms are typically effective only under specific wall conditions, making it challenging to achieve stable, continuous adhesion and detachment on surfaces with significantly different roughness. To address this limitation, we propose a flexible, multi-mechanism coupled end effector driven by a pneumatic–cable hybrid system, integrating two complementary adhesion mechanisms—claw-based interlocking and vacuum suction—into a unified flexible structure. First, we develop the overall structural framework of the end effector and conduct finite element simulations to analyze key structural parameters of the telescopic cavity. We then establish a contact force model between the claw and vertical rough surfaces to clarify the interlocking adhesion mechanism and determine critical geometric parameters. Based on these analyses, a cable-driven adjustment mechanism is introduced to enable dynamic self-adaptation and assist with load-bearing during adhesion, enhancing the stability and load-carrying capacity under varying wall conditions. On rough surfaces, the end effector achieves reliable adhesion through claw interlocking, while on smooth surfaces, it maintains stable attachment through vacuum suction. Furthermore, it supports seamless switching between adhesion modes on different surfaces. When integrated into a wall-climbing robot, the system enables stable adhesion and detachment on both rough and smooth surfaces, providing a feasible solution for the lightweight, integrated design of end effectors for multi-surface adaptive wall-climbing robots. Full article

The monitoring and conservation of built heritage is a major challenge for the scientific community, given the continuous degradation caused by natural, anthropogenic and climatic factors. The generation of high-resolution 3D documentation is important in the diagnosis of deterioration in historic buildings and the planning of conservation and restoration efforts. The present study proposes an integrated, multi-source workflow combining terrestrial laser scanning (TLS), unmanned aerial vehicle (UAV) photogrammetry, and 3D camera interior scanning. This workflow was employed to document and evaluate the Casa Rusănescu monument in Craiova, Romania. The following processes were incorporated: coordinated acquisition, processing, alignment, evaluation of geometric consistency and deviation-based diagnosis. The diagnosis process include measuring the distance between data clouds and analyzing surface roughness, curvature, planarity and linearity. The workflow was designed to be applicable in real urban conditions, ensuring the coverage of façades, interiors and roof structures. The final, combined dataset contained over 235 million points and includes both interior and exterior geometries. This process helped identify various types of damage, such as cracks, exfoliation, plaster detachment, moisture-related changes, and geometric deformations. An additional AI-assisted validation step (Twinspect) was used to cross-check the degradation indicators derived from point-cloud analyses. The findings suggest that using multiple sensors improves spatial completeness, enhances anomaly detection, and establishes a reliable baseline prior to restoration interventions and long-term monitoring. This methodology facilitates the development of digital twins and GIS-based risk assessments, thereby providing a scalable solution for heritage preservation. Full article

Concrete 3D printing technology is emerging as a new way to transform the construction industry in the future. However, the existing concrete 3D printing technology still has different degrees of defects in the print molding process. The existing concrete 3D nozzles need to undergo a long motion trajectory when printing complex curved components, which leads to lower geometric accuracy of curved structures, as well as poorer overall molding quality of the printed components. The aim of this study is to design a reducer nozzle to effectively shorten the printing stroke and thus improve the printing accuracy. A reducing nozzle is proposed with multi-gear internal meshing and a rotating blade structure nozzle with an adjustable outlet caliber. The mechanical strength of the rotating blade of the nozzle and the distribution characteristics of the flow field inside the nozzle are verified through fluid–solid coupling analysis. Experimental comparison shows that compared with the existing concrete 3D printing nozzle, the variable-caliber nozzle significantly improves the surface quality of the specimen, which strongly promotes the practical application and development of concrete 3D printing technology in the engineering field. Full article

Detecting minute defects, such as spurs on the surface of a Printed Circuit Board (PCB), is extremely challenging due to their small size (average size < 20 pixels), sparse features, and high dependence on circuit topology context. The original Mosaic data augmentation method faces significant challenges with semantic adaptability when dealing with such tasks. Its unrestricted random cropping mechanism easily disrupts the topological structure of minute defects attached to the circuits, leading to the loss of key features. Moreover, a splicing strategy without domain constraints struggles to simulate real texture interference in industrial settings, making it difficult for the model to adapt to the complex and variable industrial inspection environment. To address these issues, this paper proposes a Context-aware and Domain-consistent Mosaic (CD-Mosaic) augmentation algorithm. This algorithm abandons pure randomness and constructs an adaptive augmentation framework that synergizes feature fidelity, geometric generalization, and texture perturbation. Geometrically, an intelligent sampling and dynamic integrity verification mechanism, driven by “utilization-centrality”, is designed to establish a controlled sample quality distribution. This prioritizes the preservation of the topological semantics of dominant samples to guide feature convergence. Meanwhile, an appropriate number of edge-truncated samples are strategically retained as geometric hard examples to enhance the model’s robustness against local occlusion. For texture, a dual-granularity visual perturbation strategy is proposed. Using a homologous texture library, a hard mask is generated in the background area to simulate foreign object interference, and a local transparency soft mask is applied in the defect area to simulate low signal-to-noise ratio imaging. This strategy synthesizes visual hard examples while maintaining photometric consistency. Experiments on an industrial-grade PCB dataset containing 2331 images demonstrate that the YOLOv11m model equipped with CD-Mosaic achieves a significant performance improvement. Compared with the native Mosaic baseline, the core metrics mAP@0.5 and Recall reach 0.923 and 86.1%, respectively, with a net increase of 8.3% and 8.8%; mAP@0.5:0.95 and AP_small, which characterize high-precision localization and small target detection capabilities, are improved to 0.529 (+3.0%) and 0.534 (+3.3%), respectively; the comprehensive metric F1-score jumps to 0.903 (+6.2%). The experiments prove that this method effectively solves the problem of missed detections of industrial minute defects by balancing sample quality and detection difficulty. Moreover, the inference speed of 84.9 FPS fully meets the requirements of industrial real-time detection. Full article

This paper introduces SIDe-HBIM (Single-Image Depth inference for HBIM), a semi-automated image-to-BIM pipeline aimed at improving the integration of architectural decorative elements into HBIM environments. The research addresses the difficulty of representing geometrically complex yet information-oriented heritage components when traditional survey techniques are impractical or disproportionate. Starting from a single photographic input, the methodology combines AI-based depth estimation, quantitative computational evaluation and parametric modeling to generate lightweight, morphologically coherent 3D elements suitable for non-photorealistic HBIM applications. Multiple image-to-depth models are processed in parallel and ranked through a weighted synthetic index based on geometric and structural indicators, after which the selected depthmap is converted into a continuous NURBS surface and integrated into a BIM environment. Application to three heterogeneous case studies from the Basilica of Santa Maria degli Angeli (Assisi) demonstrates that SIDe-HBIM is particularly effective for bas-reliefs and moderate-relief decorative apparatuses, offering a reproducible and efficient alternative for HBIM-oriented documentation. Full article

Efficient proppant transport in conglomerate reservoirs is severely constrained by rough fracture surfaces and strong geometric heterogeneity, leading to premature near-wellbore deposition and insufficient distal support. To address this challenge, this study aims to clarify the transport and deposition mechanisms of proppants in rough-wall fractures representative of the Mahu conglomerate reservoir. A large-scale visualized physical simulation system with an artificial rough fracture (20 m length × 4.5 m height) was developed based on dynamic similarity principles, enabling long-distance proppant transport observation under controlled pumping rate, fluid viscosity, proppant size, and sand concentration. Ten systematic experiments were conducted, and real-time particle motion and sand ridge evolution were captured using high-speed imaging and pressure monitoring. The results show that proppants form longitudinally layered sand ridges that evolve through three stages: leading-edge initiation, equilibrium-height growth, and distal extension. Viscosity and sand concentration primarily control propped-area continuity, while pumping rate governs transport distance and particle size affects structural stability. Rough fracture surfaces significantly intensify near-wellbore accumulation by enhancing energy dissipation and local flow heterogeneity. These findings provide mechanistic insights into proppant transport in rough fractures and offer quantitative guidance for optimizing fracturing parameters in conglomerate reservoirs. Full article