Search Results (156)

The abrasion resistance of fabrics is one of the basic properties determining the utility performance and durability. The abrasion resistance of textile materials is measured using the Martindale device according to appropriate standards. The sample breakage method is the most commonly used of the three methods. The method is based on organoleptic assessment of fabric breakage. The method is time-consuming, and results may be subject to error resulting from the subjective nature of the assessment. The aim of the presented work was to check the possibility of the application of contactless 3D surface geometry measurement using an optical profilometer in an assessment of changes in fabrics’ surface due to the abrasion process. The obtained results confirmed that some parameters of the geometric structure of fabric surfaces, such as the highest height of the roughness profile Rz, the height of the highest pick of the roughness profile Rp, the depth of the lowest valley of the roughness profile Rv, the depth of the total height of the roughness profile Rt, and the kurtosis Rku, can be used to assess the abrasion resistance of fabrics. It is also stated that using the non-contact optical measurement of fabric surface geometry allows for an assessment of the directionality of surface texture. For this purpose, the autocorrelation function and angle distribution function can be applied. Full article

The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite imagery often exhibit weak texture characteristics. This leads to serious issues in reconstructing water surface DSMs with traditional dense matching methods, such as significant holes and abnormal undulations. These problems significantly impact the intelligent application of satellite DSM products. To address these issues, this study innovatively proposes a water region DSM reconstruction method, boundary plane-constrained surface water stereo reconstruction (BPC-SWSR). The algorithm constructs a water surface reconstruction model with constraints on the plane’s tilt angle and boundary, combining effective ground matching data from the shoreline and the plane constraints of the water surface. This method achieves the seamless planar reconstruction of the water region, effectively solving the technical challenges of low geometric accuracy in water surface DSMs. This article conducts experiments on 10 high-resolution satellite stereo image pairs, covering three types of water bodies: river, lake, and sea. Ground truth water surface elevations were obtained through a manual tie point selection followed by forward intersection and planar fitting in water surface areas, establishing a rigorous validation framework. The DSMs generated by the proposed algorithm were compared with those generated by state-of-the-art dense matching algorithms and the industry-leading software Reconstruction Master version 6.0. The proposed algorithm achieves a mean RMSE of 2.279 m and a variance of 0.6613 m² in water surface elevation estimation, significantly outperforming existing methods with average RMSE and a variance of 229.2 m and 522.5 m², respectively. This demonstrates the algorithm’s ability to generate more accurate and smoother water surface models. Furthermore, the algorithm still achieves excellent reconstruction results when processing different types of water areas, confirming its wide applicability in real-world scenarios. Full article

A relatively new GelSight Max measurement instrument was applied to the microtopographies of experimental hardened clay surfaces, both with and without fingerprint (dermatoglyph) impressions, and the surface of an archaeological pottery handle fragment with a preserved fingerprint (paleodermatoglyph). The experimental clay surfaces were documented in order to determine the instrument’s ability to capture these surfaces in three-dimensions by imprinting them onto an elastomeric tactile membrane. Fingerprints on the experimental hardened clay and the archaeological pottery fragment were mathematically documented to test this instrument’s ability to capture these impressions. The surface texture measurements of the hardened clay and the pottery fragment were mathematically compared using conventional topographic characterization parameters (height and hybrid), fractal dimensions (Das) with associated coefficients of determination (R²), and multiscalar geometric characterization parameters, particularly relative area (Srel), area-scale complexity (Asfc), relative length (RL), and length-scale complexity (Lsfc). The surfaces of the experimental hardened clay with and without fingerprints and the archaeological pottery handle fragment with a fingerprint can be discriminated using some conventional height parameters, as well as some multiscale geometric topographic characterization parameters. Specifically, relative area (Srel), area-scale complexity (Asfc), relative length (RL), and length-scale complexity (Lsfc) could all discriminate between the hardened clay block with and without fingerprints and the fingerprint on the archaeological pottery handle fragment at different scales of measurement. Mean square ratios (MSRs) above 90% and 95% confidence levels indicated that the discrimination of these multiscale geometric characterizations was significant. In sum, the GelSight Max has the potential to be a valuable instrument for archaeologists studying pottery and fingerprints. Full article

Estimating bed load in rivers is a critical aspect of river engineering. Numerous methods have been developed to quantify bed load transport, often yielding varying results depending on the bed surface texture and grain size. This study aims to investigate how vegetation on channel banks and bed material particle size influence bed load transport, bed shear stress, velocity distribution, and the Shields parameter. It also examines the impact of geometric changes in the channel cross-section on bed load transport capacity. To address these objectives, a novel simulation method was developed to analyze the effects of vegetated banks, bed material size, and channel geometry. Field investigations were carried out in two reaches of the Taleghan River in Iran—one with vegetated banks and one without. Complementary flume experiments were conducted at two scales, incorporating vegetation on the sidewalls. Results showed that Shields parameter distribution corresponded with bed load distribution across cross-sections. Increase in flow rate and the Shields parameter led to higher bedload transport rates. Near vegetated banks, flow velocity, shear stress, and bedload transport were significantly reduced, with velocity profiles showing distinct variations compared to non-vegetated sections. Full article

This review paper provides a comprehensive overview of selected 3D surface texture parameters defined by ISO 25178-2, with a focus on their metrological aspects in high-resolution measurements using atomic force microscopy (AFM). The parameters Sa, Sz, Sq, Sdq, and Sdr are analyzed in terms of their practical application, sensitivity to measurement conditions, and role in assessing surface functionality. Through a review of the literature and simulations of surface profiles with controlled geometric variations, the study demonstrates how the selected parameters respond to changes in step pitch, step width, slope, and lateral calibration errors. Experimental AFM measurements performed on a certified step height standard further illustrate the impact of calibration on the quality of measurement results. Special emphasis is placed on the importance of evaluating measurement uncertainty. The results confirm the need for rigorous instrument calibration and uncertainty assessment to ensure reliable and comparable surface characterization across different instruments and laboratories. Full article

Surface defect detection on steel strips is a critical step in quality control for industrial products. While existing research has made some progress in optimizing annotation strategies and improving efficiency, issues such as feature aliasing during the annotation process, the insufficient utilization of boundary information, and the inaccurate representation of complex defect patterns remain inadequately addressed. To tackle these challenges, this paper proposes an annotation optimization framework from the perspective of feature analysis. The framework decomposes defect features into geometric features and grayscale distribution features and, based on feature decoupling theory, classifies defects into three typical patterns: block, linear, and textured defects. For each pattern, the minimum annotation units that preserved essential features were designed, enabling the standardized representation of complex defects and precise boundary localization. Experiments on the NEU-DET dataset showed that this annotation framework improves the average mAP of six mainstream detection models by 4.9 percentage points, validating its effectiveness in enhancing the detection performance. Additionally, this paper introduces an Efficiency–Cost Ratio (ECR) evaluation metric to quantify the relationship between the annotation cost and performance improvement. The study found that block and linear defect detection achieved optimal performance with only 50% annotation effort. This research not only improved the performance of defect detection models but also quantified the annotation resource utilization efficiency, providing robust theoretical support and practical guidance for efficient defect detection in complex industrial scenarios. Full article

In intelligent building, efficient surface defect detection is crucial for structural safety and maintenance quality. Traditional methods face three challenges in complex scenarios: locating defect features accurately due to multi-scale texture and background interference, missing fine cracks because of their tiny size and low contrast, and the insufficient generalization of irregular defects due to complex geometric deformation. To address these issues, an improved version of the You Only Look Once (YOLOv8) algorithm is proposed for building surface defect detection. The dataset used in this study contains six common building surface defects, and the images are captured in diverse scenarios with different lighting conditions, building structures, and ages of material. Methodologically, the first step involves a normalization-based attention module (NAM). This module minimizes irrelevant features and redundant information and enhances the salient feature expression of cracks, delamination, and other defects, improving feature utilization. Second, for bottlenecks in fine crack detection, an explicit vision center (EVC) feature fusion module is introduced. It focuses on integrating specific details and overall context, improving the model’s effectiveness. Finally, the backbone network integrates deformable convolution net v2 (DCNV2) to capture the contour deformation features of targets like mesh cracks and spalling. Our experimental results indicate that the improved model outperforms YOLOv8, achieving a 3.9% higher mAP50 and a 4.2% better mAP50-95. Its performance reaches 156 FPS, suitable for real-time inspection in smart construction scenarios. Our model significantly improves defect detection accuracy and robustness in complex scenarios. The study offers a reliable solution for accurate multi-type defect detection on building surfaces. Full article

A three-dimensional (3D) analytical framework was developed to quantify stress concentration factors (SCFs) on engineering surfaces with arbitrary slight undulations, effectively addressing the limitations of existing two-dimensional (2D) models by rigorously integrating the effects of Poisson’s ratio ($v$) and surface texture directionality ($\theta$). Initially, the 3D analytical solutions for single-notched specimens under uniaxial loading, which account for the $v$ effect, were derived and compared with their 2D counterparts. The results demonstrate a clear positive correlation between $v$ and SCFs. Subsequently, the framework was extended to single-layer undulating surfaces, revealing anisotropic stress modulation governed by $\theta$. SCFs increase monotonically with $\theta$, a directional sensitivity that 2D solutions are unable to represent. A parametric analysis of cosine-wave surfaces further identified a nonlinear accuracy dependency on the amplitude–frequency product ($Af$). Finite element method (FEM) validation showed that the relative errors are less than 5% when $Af<0.05$, and they rise to 14.8% when $Af\approx 0.1$. Furthermore, application to machined surfaces validated the superior accuracy of the 3D solution, achieving approximately 10% improvement compared to 2D methods with errors controlled within 5%. Significantly, the texture direction perpendicular to the loading direction results in notably higher SCFs than the parallel direction, directly correlating texture orientation with stress concentration severity. This study provides a robust theoretical basis for surface topography optimization in engineering applications, with validated reliability across geometric and material parameters. Full article

In addition to ensuring the functionality of objects used in the household, transport or industry at large, applied design focuses on aesthetic qualities related to the external form and condition of a surface. At the same time, there is a trend for plastic, rubber or aluminium objects made by moulding (both injection and casting) to look as if they were made of natural materials. This effect is ensured by properly designed and manufactured surface textures in the mould seats. However, the working surfaces of the moulds often corrode as a result of inadequate maintenance and storage. The aim of this study was to find out how popular agents on the market dedicated to corrosion product removal would change the surface geometrical texture. During the prepared experimental plan, it was also decided to investigate the properties in this respect of one of the popular drinks (i.e., cola) which is sometimes used in workshop practice as an alternative corrosion product removal agent. Based on the results of the study, conclusions were drawn about the short- and long-term effects of the corrosion product removal agents. Full article

The development of microtextures has had a transformative impact on surface design in engineering, leading to substantial advancements in the performance, efficiency, and functionality of components and tools. This study presents an innovative methodology for fabricating SEDM electrodes. The methodology combines additive manufacturing by mask stereolithography with an optimized electroforming process to obtain high-precision copper shells. A key aspect of the study involved redesigning the electroforming equipment, enabling the independent examination of critical variables such as anode–cathode distance and electrolyte recirculation. This approach allowed precise analysis of their impact on metal deposition. This redesign enabled the assessment of the impact of electrolyte recirculation on the quality of the shells obtained. The findings indicate that continuous recirculation at 60% power effectively reduced thickness deviation by up to 32.5% compared to the worst-case scenario, achieving average thicknesses within the functional zone of approximately 110 µm. In contrast, the absence of flow or excessive turbulence did not generate defects such as unfilled zones or non-uniform thicknesses. The shells obtained were validated as functional tools in SEDM, demonstrating their viability for the generation of textures with high geometric fidelity. This approach optimizes the manufacturing of textured electrodes and opens new opportunities for their application in advanced industrial processes, providing a more efficient and sustainable alternative to conventional methods. Full article

Novel view synthesis and 3D reconstruction have been extensively studied. Three-dimensional Gaussian Splatting (3DGS) has gained popularity due to its rapid training and real-time rendering capabilities. However, RGB imaging is highly dependent on ideal illumination conditions. In low-light situations such as at night or in the presence of occlusions, RGB images often suffer from blurred contours or even complete failure in imaging, which severely restricts the application of 3DGS in such scenarios. Thermal imaging technology, on the other hand, serves as an effective complement. Thermal images are solely influenced by heat sources and are immune to illumination conditions. This unique property enables them to clearly identify the contour information of objects in low-light environments. Nevertheless, thermal images exhibit significant limitations in presenting texture details due to their sensitivity to temperature variations rather than surface texture features. To capitalize on the strengths of both, we propose thermal geometrically accurate Gaussian Splatting (TGA-GS), a novel Gaussian Splatting model. TGA-GS is designed to leverage RGB and thermal information to generate high-quality meshes in low-light conditions. Meanwhile, given low-resolution thermal images and low-light RGB images as inputs, our method can generate high-resolution thermal and RGB images from novel viewpoints. Moreover, we also provide a real thermal imaging dataset captured with a handheld thermal infrared camera. This not only enriches the information content of the images but also provides a more reliable data basis for subsequent computer vision tasks in low-light scenarios. Full article

Cobalt–chromium (CoCr) alloys are frequently used to produce customized dental applications such as crowns, bridges, or prostheses. These medical products have anatomical forms, and can be effectively manufactured using the laser-based powder bed fusion (PBF-LB/M) technique. A major disadvantage of this approach is the extended time required to refine the resultant surface. The purpose of this research is to reduce the surface roughness of PBF-LB/M/CoCr dental crowns by adopting drag finishing (DF) technology. To evaluate the impact of this automatic post-processing, surface roughness measurements and geometrical investigations were undertaken. The microstructure was characterized using scanning electron microscopy (SEM), and the chemical composition was verified by energy-dispersive X-ray spectroscopy (EDAX). On outside surfaces, the DF post-processing decreased the initial surface roughness by 70–90%. The dental crown’s surface roughness value after DF post-processing was comparable to that of the basic form (cylinder). The lowest roughness was obtained with DF3 post-processing (Ra~0.60 μm). The inner surfaces were limitedly finished. The 3D surface texture showed that the DF method reduced the height of peaks, uniformizing the surfaces. CMM work compared the deviations between the virtual model and the printed samples before and after DF post-processing. This analysis revealed that dimensional deviations were reduced on the outside crown walls, ranging from +0.01 to +0.05 mm. The laser parameters and the heat treatment applied increased the hardness of CoCr crowns to 520 HV, but the proper DF conditions identified reduced the surface roughness and improved the accuracy. Full article

An accurate 3D pose estimation of spherical objects remains challenging in industrial inspections and robotics due to their geometric symmetries and limited feature discriminability. This study proposes a texture-optimized simulation framework to enhance pose prediction accuracy through optimizing the surface texture features of the design samples. A hierarchical texture design strategy was developed, incorporating complexity gradients (low to high) and color contrast principles, and implemented via VTK-based 3D modeling with automated Euler angle annotations. The framework generated 2297 synthetic images across six texture variants, which were used to train a MobileNet model. The validation tests demonstrated that the high-complexity color textures achieved superior performance, reducing the mean absolute pose error by 64.8% compared to the low-complexity designs. While color improved the validation accuracy universally, the test set analyses revealed its dual role: complex textures leveraged chromatic contrast for robustness, whereas simple textures suffered color-induced noise (a 35.5% error increase). These findings establish texture complexity and color complementarity as critical design criteria for synthetic datasets, offering a scalable solution for vision-based pose estimation. Physical experiments confirmed the practical feasibility, yielding 2.7–3.3° mean errors. This work bridges the simulation-to-reality gaps in symmetric object localization, with implications for robotic manipulation and industrial metrology, while highlighting the need for material-aware texture adaptations in future research. Full article

The widespread use of journal bearings in rotating machinery has intensified the need to optimize their operational performance. A key determinant of bearing efficiency lies in the lubricant film thickness between the rotor and bearing surface. Recent studies demonstrate that strategically engineered surface textures can effectively modulate film thickness and enhance both static and dynamic characteristics of bearing. This investigation specifically examines how cubic, cylindrical, and semi-ellipsoidal texture geometries at varying depths influence the performance of noncircular two-lobe bearings. Through finite element analysis, the study evaluates critical performance parameters including load capacity, rotor attitude angle, critical mass threshold, and whirl frequency ratio to establish texture–depth relationships with system stability and operational efficiency. The analysis reveals that texturing the maximum pressure zone in lobe 2 significantly enhances bearing performance, with semi-ellipsoidal, cylindrical and cubic geometries, respectively. Also, the results demonstrate that texture geometry and depth significantly influence two-lobe bearing performance. Optimal enhancements in load capacity, whirl frequency reduction, and critical rotor mass occur at texture depths below the bearings clearance space width, with performance peaking before gradually declining as depth increases further. Notably, texture application in low-pressure or cavitation regions generally yields diminished or even counterproductive effects. The findings demonstrate that cubic textures provide optimal bearing performance across all depth ranges, with cylindrical and semi-ellipsoidal geometries ranking second and third, respectively, in comparative analysis. Full article

Among the biggest challenges in modern manufacturing techniques is deliberately shaping the surface layer of a part to suit the conditions in which it will be used. The degree of difficulty increases with the increase in the functional requirements of the items to be manufactured and with the complexity of the technology developed. Hybrid machining processes allow functional surfaces to be shaped by combining different machining operations into a single operation. The values of the amplitude and length parameters of the resulting surface geometrical texture are largely determined by the technological parameters of the combined machining operations. However, it is the tool guidance and kinematic–geometric conditions during the hybrid machining process that are responsible for the surface texture. This paper describes the results of an investigation into the influence of the milling tool guidance strategy during shaping milling and tooling guidance during burnishing of workpieces made of 42CrMo4 steel tempered to a hardness of 35 ± 2 HRC—a material commonly used in the construction of machine parts. It was shown that running the burnishing with two crossing passes oriented obliquely to the marks left by the cutter was the most favourable of the burnishing strategies tested. Full article