Texture Boundary Research Articles

Tribological Analysis of Surface Textures in Porcelain Tiles for Enhancing Pedestrian Walkway Safety

Due to their durability and aesthetic appeal, porcelain tiles have become a staple in modern construction and interior design. However, the persistent challenge of mitigating slip hazards, particularly in moist conditions, remains a critical concern for pedestrian safety. This study investigates the slip resistance performance of porcelain tiles, focusing on the influence of varying surface textures. The study clarifies the intricate relationship between surface texture and slip resistance through comprehensive dynamic friction tests involving four distinct porcelain tile samples and three different shoe samples conducted under arid, damp, and lathered conditions. While dry conditions showcase robust traction, moist environments underscore the significance of surface textures in enhancing grip. Moreover, the study introduces an operational scale of surface roughness, delineating optimal texture boundaries for maximal slip resistance. The implications of these findings extend beyond mere material characterisation, offering actionable insights for architects, designers, and safety professionals to enhance pedestrian safety in diverse settings. By bridging the gap between material science and real-world safety concerns, this research paves the way for innovative porcelain tile designs that prioritise safety without compromising aesthetic appeal.

A new understanding of hydrogen-assisted cracking of coarse-grained heat-affected zone in X65 pipeline steel through {110} lath bainite boundary texture

The susceptibility of subzones of the coarse-grained heat-affected zone (CGHAZ) in X65 pipeline steel to hydrogen embrittlement was studied through an in situ crack-tip opening displacement test in an H2S-containing environment. The intercritically reheated CGHAZ (IC-CGHAZ) exhibited the highest embrittlement factor of 89.4%, compared to 64.0% for the sub-critically reheated CGHAZ (SC-CGHAZ). Hydrogen transportation by dislocations to massive-slender martensite/austenite (M/A) constituents initiated cracks in IC-CGHAZ. Deterioration of lath bainite (LB) boundaries, with separation of M/A-ferrite interface, accelerated crack propagation across prior austenite grains and promoted hydrogen-assisted degradation of fracture toughness. Reheated CGHAZ at a peak temperature of 620 °C led to an increased intensity of the {110} LB boundary texture by up to 52% in SC-CGHAZ. This enhanced texture facilitated dislocation slipping along {110} LB boundary planes, thereby promoting deformation compatibility and preventing the deterioration of LB boundaries in the presence of hydrogen.

Evolution of microstructure, mechanical properties, and corrosion behavior of Al-xMg-Mn alloy plates by rolling treatment

This article presents a systematic study on the synergistic effects of Mg element and rolling on the microstructure, mechanical properties, and corrosion resistance of Al-Mg-Mn alloy. The presence of Mg element leads to the formation of the β-phase (Al3Mg2). In the as-rolled alloy with high Mg content, the continuity of β-phase distributed at grain boundaries or fiber texture boundaries is weakened and its size is refined. This refinement is beneficial for improving the hardness and tensile properties of the alloy. However, it slightly reduces the electrical conductivity, elongation, and corrosion resistance of the alloy. The reasons for these changes in properties are analyzed from the perspective of dislocation motion. Comparing the hot-rolled state alloy with the cold-rolled state alloy, it is found that the corrosion resistance of the latter is improved. This improvement can be attributed to the difficulty of forming a continuous network of β-phase at grain boundaries, which significantly reduces the alloy's corrosion sensitivity. Additionally, the higher strength of the alloy can be attributed to dispersion strengthening and grain boundary strengthening. The objective of this work is to provide a strategy for obtaining Al-Mg-Mn alloy sheets with excellent performance

TEXTURE-BASED SEPARATION TO REFINE BUILDING MESHES

Abstract. In this paper, we present a texture-based separation approach to refine building meshes, which aims to address the challenges of detecting and isolating different objects in an indoor scene mesh. We propose a novel segmentation model based on the materials of the different parts of the scene. The proposed approach uses factorization-based texture segmentation to separate the different materials in the meshes and detect the edges on the segmented texture. To prevent large faces containing multiple materials from being segmented wrongly, the mesh is cut along the texture boundaries and new faces with a single material are created. Finally, we segment the new faces based on the material index and neighbouring faces using a region-growing algorithm. We evaluate the proposed approach on the Matterport indoor dataset, which shows that our approach performs well on detecting boundaries between distinct materials, but over-segments on complex shapes. Our proposed method improves the segmentation of large flat surfaces like posters or rugs.

Wavelet-Based Self-Attention GAN With Collaborative Feature Fusion for Image Inpainting

Image inpainting is a significant task in the applications of computer vision, that aims to fill in damaged regions with visually realistic contents. With the development of deep learning, generative adversarial network (GAN)-based image inpainting approaches have achieved remarkable progress. However, these methods only utilize one-sided structure information to assist inpainting, which can not achieve satisfactory results, especially when synthesizing large-area missing complex images. In order to tackle this problem, a wavelet-based self-attention GAN (WSA-GAN) with collaborative feature fusion is proposed, which is embedded with a wavelet-based self-attention (WSA) and a collaborative feature fusion (CFF). The WSA is designed to conduct long-range dependence among multi-scale frequency information to highlight significant structure details for better generating texture boundaries. The CFF is presented to couple channel-guided space and space-affected channel streams to facilitate the interaction of spatial and channel features, which can effectively avoid potential domain conflicts. Besides, a novel wavelet consistency loss and a hierarchical pyramid feature matching (PFM) discriminator are introduced to stabilize the model training. Extensive experiments on three public datasets, including Paris StreetView, CelebA-HQ and Places, demonstrate that the proposed method outperforms the state-of-the-art methods both quantitatively and qualitatively.

Crystallographic texture and grain boundary character development of hot-press sintered tungsten heavy alloy: The effect of rhenium concentration

In this study, the effects of rhenium on the evolution of microstructure and texture in hot press sintered tungsten heavy alloy (93W-4.9Ni-2.1Cu) is examined. Rhenium is added (i.e., increased rhenium from 0 to 1 wt % with interval of 0.25 wt %) to a conventional tungsten heavy alloy while maintaining a constant Ni: Cu (7:3) ratio. The samples were prepared using a vacuum hot press sintering method with parameters including 50 MPa pressure, 10 °C/min heating rate, and 1450 °C temperature for a 20-min holding time. The impact of rhenium on densification, crystallographic texture development, and grain boundary features of Re alloyed WHA was studied using electron backscattered diffraction (EBSD). The development of grain boundary texture was the result of a Ʃ3 CSL increase in sintered compact. The base alloy exhibits fibre texture in the (001) plane with the <111> direction and a higher proportion of low-angle grain boundaries. Reduced kinetics of grain growth during sintering led to a higher degree of densification and randomised weak fibre texture in rhenium-alloyed samples. The deterioration of texture in the (001) <111> direction in rhenium-added alloys was caused by recrystallization following recovery during sintering. Metallographic studies reveal that an increase in rhenium in the WHA's stoichiometry resulted in particle refinement during sintering, thereby enhancing the WHA's properties. Maximum micro-hardness (432 HV0.5), electrical conductivity (23.74 % of IACS), and relative density (99.90 %) were observed in a 93W-4.9Ni-2.1Cu (Re 1 wt% alloy).

Multiscale evidence for weathering and the preservation of carbonaceous material in an Antarctic micrometeorite

Micrometeorites (MMs) recovered from the Earth’s surface may have undergone significant changes prior to their collection, including weathering while residing in the terrestrial environment. These alteration processes, such as the precipitation of hydrous phases, may overprint both primary and atmospheric entry features, obscuring pre-existing material properties. In addition, weathering exerts a prominent control on the geochemical interactions, such as species mobility, between extraterrestrial material and the terrestrial environment, particularly in Antarctica. In this study, we have characterised the textural and compositional consequences of weathering on an unmelted, fine-grained, Antarctic MM, which includes the mapping of nanometre-scale features using atom probe tomography. In particular, we investigate geochemical behaviour across textural boundaries in the MM and observe nanoscale elemental heterogeneity within complex alteration assemblages. In one sample region, a compositional boundary is highlighted by distinct elemental differences, consistent with a weathering encrustation of mixed mineralogies, while analyses in other targeted regions show evidence for nanoscale elemental networks, as well as a grain boundary adjacent to a carbon-rich region. We discuss our findings in the context of terrestrial weathering as a dominant cause for the nanoscale features observed. Weathering processes responsible for these features include the leaching of extraterrestrial material, precipitation of secondary alteration products with associated layering, and the influence of mechanical stress on pre-existing weaknesses. From these results, we derive a weathering sequence to explain the formation of the alteration product assemblage and highlight the controls on MM geochemistry in the terrestrial environment. Our observations show that nanoscale carbonaceous material may be preserved in oxy-hydroxides under icy conditions, which can also act as tracers for local environmental changes.

Measurement of traction properties of ceramic tiles and its attention for preventing pedestrian falls

Ceramic tiles are generally located in various community and industry venues because of their durability and ability to withstand blemishes, smells, and microorganisms. Thus, they are ideal for bathrooms, kitchens, entryways, laundry rooms, and utility areas. However, they can be extremely slippery in lubricated and even dusty environments. Regardless of the significance of this issue, the care aspects of ceramic tiles’ traction properties should have been systematically studied to avoid slip and fall incidence. The present study evaluates the slip resistance performance of ceramic tiles with different textures and identifies their controls on pedestrian fall prevention. For the ceramic tiles, the three most popular types of surface textures: smooth, moderately rough, and very rough, were chosen. Dynamic friction tests measured traction properties between the ceramic tiles and three shoes in four environments. The measurement results identified that the ceramic tiles exhibited high traction functioning under clean and dry conditions. Whilst the textures of ceramic tiles played a considerable role in soapsuds-covered moderately risky and oil-covered highly hazardous conditions. The current study also proposed a concept of an operational range of surface roughness and identified ideal boundaries of surface textures against diverse levels of slippery circumstances. The outcomes of the present study may be applied to enhance the surface design of ceramic tiles to preclude fall hazards in our community and workplaces.

Cu-Ag-Au Microspherules in Igneous Rocks: Morphology, Composition, Diagnostic Criteria and Possible Origin

Igneous rocks from the Russian Far East contain Cu-Ag-Au microspherules with distinct exterior and interior structures, compositions and assemblages of Cu-rich micro-inclusions. Natural microspherules are compared in this study with technogenic Cu-Ag-Au microspherules, which are experimentally produced and extracted from gold scrap jewelry. The following set of diagnostic criteria are considered to distinguish natural from technogenic microspherules on a genetic basis as follows: (1) compacted-related features versus cellular appearance of the exterior; (2) lack of exsolution- or crystallization-related features in natural and domain-type internal structure in technogenic microspherules; (3) absence of spherical copper-oxide inclusions along with meniscus-type textural boundaries in technogenic mcirospherules; (4) pure copper-oxide composition of inclusions versus the common presence of Fe, Ni, Zn, Cu and Na in natural microspherules. The diagnostic characteristics of natural Cu-Ag-Au microspherules suggest extremely fast cooling rates during their formation, which is possible during violent explosive volcanic eruptions or injection of partially molten, pulverized metal alloys into shallow intra-crustal cavities and fault-related tectonic gashes.

Neural correlates of perceptual texture change during active touch.

Texture changes occur frequently during real-world haptic explorations, but the neural processes that encode perceptual texture change remain relatively unknown. The present study examines cortical oscillatory changes during transitions between different surface textures during active touch. Participants explored two differing textures whilst oscillatory brain activity and finger position data were recorded using 129-channel electroencephalography and a purpose-built touch sensor. These data streams were fused to calculate epochs relative to the time when the moving finger crossed the textural boundary on a 3D-printed sample. Changes in oscillatory band power in alpha (8-12 Hz), beta (16-24 Hz) and theta (4-7 Hz) frequency bands were investigated. Alpha-band power reduced over bilateral sensorimotor areas during the transition period relative to ongoing texture processing, indicating that alpha-band activity is modulated by perceptual texture change during complex ongoing tactile exploration. Further, reduced beta-band power was observed in central sensorimotor areas when participants transitioned from rough to smooth relative to transitioning from smooth to rough textures, supporting previous research that beta-band activity is mediated by high-frequency vibrotactile cues. The present findings suggest that perceptual texture change is encoded in the brain in alpha-band oscillatory activity whilst completing continuous naturalistic movements across textures.

FHENet: Lightweight Feature Hierarchical Exploration Network for Real-Time Rail Surface Defect Inspection in RGB-D Images

In recent years, computer vision systems have been increasingly applied to rail defect inspection. Rail defects should be identified quickly and accurately to ensure safe, stable, and fast train operations and thereby reduce the incidence of accidents and economic losses. As most existing methods focus on accuracy, they cannot be deployed on mobile devices with limited computational resources. To solve this problem, we propose a lightweight feature hierarchical exploration network (FHENet) for real-time rail surface defect inspection. First, boundary textures of rail defects are acquired based on the maximum function and maximum pooling in a novel boundary extraction module (BEM), which improves boundary prediction while avoiding heavy computations. Second, a novel cross-modality exploration module (CEM) complements prominent regions through basic operations to avoid complex inference while providing high detection performance. Third, a novel multifeature integration module (MIM) optimizes representative feature regions by using simple operations to avoid complex computations. Results from extensive experiments demonstrate the superiority of the proposed FHENet to 14 state-of-the-art methods. Regarding efficiency, FHENet outperforms the state-of-the-art methods with only 5.26 M parameters and a processing speed of 60.33 frames/s. The FHENet code and results are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hjklearn/Rail-Defect-Detection</uri> .

Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System

The present work numerically investigates the flow behavior of Newtonian fluid between rotating disk systems. Rotating disk flow is an embranchment of fundamental flow research, which forms the theoretical basis for the flow media in many disk-type hydraulic machinery, and has been widely applied in energy, aerospace, vehicles, medical, and chemical areas. The Reynolds equation model of the rotating disk flow is established based on a series of assumptions, and considers the characteristics of hexahedral surface textures on the friction disk, and the control equation is dimensionless. The velocity, pressure, and pressure coefficient in oil film are solved by finite difference algorithm, and the shear cavitation incipient position is predicted. Graphical visualization and numerical data about cavitation characteristics are also presented. Furthermore, the cavitation process is researched in CFX and the experiment, eventually verifying the correctness of the numerical analysis. The pressure perturbation caused by texture boundaries and the low pressure at the vortex center induce the initial generation of cavitation, and cavitation effect can obviously enhance lubricating performance. The cavitation region extends from micro-dimples towards non-textured region when texture rate increases. This study can guide design of surface structures such as mechanical seals, wet clutches, and disk turbines.

Microstructure characterization of polycrystalline yttrium iron garnets (YIGs) sintered from the calcined sol-gel powder

We present the results of microstructural characterization of the polycrystalline yttrium iron garnet (YIG) during high temperature sintering. Three YIG samples, compacted from the calcined sol-gel powder, are annealed at 1400 °C for 350 min, 1050 min and 1750 min, respectively. During the annealing, the polycrystalline YIG coarsens via normal grain growth, developing self-similar steady-states in grain size distribution, grain texture, and grain boundary texture. However, the 1050-min and 1750-min samples feature x-ray peaks with narrower full width at half-maximum (FWHM), and smaller local misorientation than the 350-min one, signifying that the strain still decreases after annealing for 350 min at 1400 °C. EPMA analyses show that more uniform distribution of the elements is found in 1750-min sample than in 350-min sample. Notably, little segregation of metal elements is observed on the grain boundary of the current YIG samples, as opposed to previously reported conventionally sintered YIGs using yttrium-oxide and iron-oxide powders, often featuring either second phases or segregation of Fe on YIG grain boundaries. • The high-density polycrystalline yttrium iron garnet (YIG) is sintered using calcined sol-gel powders. • Microstructure coarsens via normal grain growth, maintaining the self-similarity in grain size distribution, grain texture, and grain boundary misorientation and texture. • X-ray and KAM measurements suggest that the lattice strain reduces during annealing. • Considering observed slow growth kinetics, both sharpening of the X-ray peaks and decrease in the KAM may result from the homogenization of elements during annealing, producing the concurrent reduction in the lattice strain.

Cavitation Morphology Study between Hemispherical Textured Rotating Friction Pairs

A non-direct contact rotary interface uses a viscous fluid as the lubricant working medium. Because the oil film friction coefficient formed is extremely small, so it has great application potential in sealing, fluid transmission, thermosolutal convection, and bionics. Research on mechanical seals, wet clutches, and dynamic load bearing have proven that micro-textures can effectively improve friction and lubrication performance. However, when the fluid flows through the texture boundary, pressure disturbances can induce hydrodynamic cavitation. A pair of rotating disks are selected as our research objects. From the simulation and experiment research, we found that cavitation volume does not always increase with an increase in the texture rate, and cavitation always occurs preferentially at the outer diameter, so it is necessary to avoid machining the texture structure at the outer diameter of the mechanical seal end. Once the conditions for cavitation are met, a complete cavity is formed in approximately 0.015 s. The study also verifies that the cavitation gas originates from the phase change of the oil.

Labyrinthine structure Al CrFeNi (x ≥ 1) eutectic high entropy alloys with duplex reinforced phases

In order to optimize component and ductility of AlCrFeNi eutectic high-entropy alloys (EHEAs), this study designs ultra-high aluminum content AlxCrFeNi (x = 1.0, 1.25, 1.5, 1.75) EHEAs with BCC+B2 duplex reinforced phases, in turn, all specimens should be heat-treated at 1000 °C for 5 h. Different Al addition displays various texture partitioning behaviors in AlxCrFeNi. The structure of EHEAs is coupled with sunflower core and coarsened chunk structure when 1.25 ≤ x ≤ 1.5. Unfortunately, they exhibit poor mechanical properties. Nevertheless, the microstructure for AlCrFeNi and Al1.75CrFeNi is composed of complete labyrinth texture, while Al1.75CrFeNi has stronger grain boundary textures than AlCrFeNi. Compared with AlCrFeNi, Al1.75CrFeNi EHEA's compressive ultimate strength increased by 195.9 MPa, while the elongation remained about 19%. The Vickers hardness enhanced from 457.9HV to 482.9 HV. The strengthening mechanisms and fracture mechanism are evaluated for the series of EHEAs based on the correlation between microstructure and mechanical properties.

Texture classification-based feature processing for violence-based anomaly detection in crowded environments

Anomaly detection from video surveillance inputs helps to improve security in crowded places and outdoors. The captured image is analyzed to identify human faces, objects, and abnormal events through computer-aided analytics. This article proposes a Texture-Classification-based Feature Processing (TCFP) technique for distinguishing anomalies in captured video inputs. The anomalies are identified as events from the sequence frames wherein the dynamic inputs are distinguished using their features. Deep learning is employed for temporal training features based on frame characteristics in this distinguishing process. The input frame is segregated using textural boundaries separated using non-dimensional features. The learning process trains dimensional and non-dimensional features for identifying anomalies and maximizing detection accuracy. The textural boundaries are defined using the non-dimensional vectors present in the frame series in the different face classifications. Therefore, the errors are confined within selective boundaries without impacting the preceding feature. This improves the F1score with less processing time.

Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy

White matter lesions (WML) underlie multiple brain disorders, and automatic WML segmentation is crucial to evaluate the natural disease course and effectiveness of clinical interventions, including drug discovery. Although recent research has achieved tremendous progress in WML segmentation, accurate detection of subtle WML present early in the disease course remains particularly challenging. Here we propose an approach to automatic WML segmentation of mild WML loads using an intensity standardisation technique, gray level co-occurrence matrix (GLCM) embedded clustering technique, and random forest (RF) classifier to extract texture features and identify morphology specific to true WML. We precisely define their boundaries through a local outlier factor (LOF) algorithm that identifies edge pixels by local density deviation relative to its neighbors. The automated approach was validated on 32 human subjects, demonstrating strong agreement and correlation (excluding one outlier) with manual delineation by a neuroradiologist through Intra-Class Correlation (ICC = 0.881, 95% CI 0.769, 0.941) and Pearson correlation (r = 0.895, p-value < 0.001), respectively, and outperforming three leading algorithms (Trimmed Mean Outlier Detection, Lesion Prediction Algorithm, and SALEM-LS) in five of the six established key metrics defined in the MICCAI Grand Challenge. By facilitating more accurate segmentation of subtle WML, this approach may enable earlier diagnosis and intervention.

INSIGHTS INTO THE ORIGIN OF SHALLOW MARINE DOLOSTONES, LONGWANGMIAO FORMATION (LOWER CAMBRIAN), NORTHEAST SICHUAN BASIN, CHINA: PETROGRAPHIC AND GEOCHEMICAL CONSTRAINTS

For a better understanding of the origin of Early Cambrian dolomites in the Sichuan Basin, which serve as valuable oil and gas reservoirs, we present and interpret complex multiparameter petrographic and geochemical analyses of dolomites in the Cambrian Longwangmiao Formation at the Yangsiqiao section. Our data show that these dolomites mainly occur in the middle and upper parts of the Longwangmiao Formation, and we identify two types of dolomites: micrite and fine-medium crystalline dolomites. The micritic dolomites, occasionally coupled with quartz and mica minerals, show dirty crystal surfaces, nonautogenous crystal shapes, and dark cathodoluminescence and mainly occur in pure micrite and granular dolostones, which retain the original sedimentary texture. Contrarily the fine-medium crystalline dolomites, mainly developed in crystalline and grain dolostones and generally coupled with residual calcites and altered sedimentary textures, have a dark heart-bright boundary texture under a polarized microscope and occasional zonal texture under cathode luminescence. Furthermore, the micritic dolomites have CaO and MgO contents approximately similar to those of the ideal stoichiometric dolomite, relatively high Sr concentration, Σ rare-earth element (REE) +Y concentrations, and δ13C and δ18O values located in the corresponding range of dolomite deposited from Early Cambrian marine water. However, light-REE enrichment (LREE), heavy-REE (HREE) depletion patterns, and high Al2O3 and SiO2 contents distinguish these micritic dolomites from typical marine carbonate deposits. Notably, the fine-medium crystalline dolomites have higher CaO content and lower MgO, Al2O3 and SiO2 contents, lower Sr and higher Fe and Mn concentrations than those of micritic dolomites. The ΣREE+Y concentrations and REE patterns of these fine-medium crystalline dolomites belong to the typical marine carbonate deposits. These features indicate considerable differences in origins between micrite and fine-medium crystalline dolomites. The micritic dolomites show a significant genetic response to the precipitation of seawater in shallow marine with the injection of terrestrial water during the syngenetic period, while the fine-medium crystalline dolomites have a typical response to the secondary metasomatism products of original calcites at the shallow burial stage.

Rate-Distortion Optimized Graph for Point Cloud Attribute Coding

Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point cloud attribute compression. Existing researches mainly utilize geometry distance to define graph structure for coding attribute (e.g., color), which may distribute high weights to the edges connecting points across texture boundaries. In this case, these geometry-based graphs cannot model attribute differences between points adequately, thus limiting the compression efficiency of GFT. Hence, we firstly utilize attribute itself to refine the distance-based weight values by setting penalty function, which smoothens signal variations on graph and concentrates more energies in the low frequencies. Then, adjacency matrices acting as penalty function variables are transmitted to decoder with extra bit overheads. To balance the attribute smoothness on graph and the cost of coding adjacency matrices, we finally propose the graph based on Rate-Distortion (RD) optimization and find the optimal adjacency matrix. Experimental results show that our algorithm improves RD performance compared with competitive platforms. Moreover, additional experiments also analyze the gain source by evaluating the effectiveness of RD optimized graphs.

An enhancement of edge preservation in OAMNHA denoising using texture boundaries

An enhancement of edge preservation in OAMNHA denoising using texture boundaries