Complex Surface Texture Research Articles

LiFSO-Net: A lightweight feature screening optimization network for complex-scale flat metal defect detection

Defect recognition of flat metals is paramount for ensuring quality control during the production process. However, the diverse origins of metal surface damage, ranging from mechanical impacts to chemical corrosion, and the resulting varied morphology and scale of surface defects, particularly numerous microdefects and elongated defects with high aspect ratios, complicate defect recognition. Existing methods fail to select the most beneficial features during extraction and commonly lose critical feature information during gradient sampling. To overcome these challenges, we propose a lightweight network to optimize feature screening for defect recognition. First, we propose a deformable context–guided block that employs deformable convolution to dynamically adapt the perception of the spatial context, providing precise guidance of relevant semantic information in complex surface textures. Second, we develop a content-aware feature compression block that implements adaptive weighting of features, which significantly reduces information loss during the downsampling stage. Finally, we introduce an intra-scale feature interaction transformer block, which optimizes high-order semantic features to enhance the accuracy and reliability of defect detection. Experimental validation on the NEU-DET, APS-DET, and GC10-DET datasets demonstrated significant improvements in the detection accuracy and parameter efficiency, confirming the proposed method's robust generalizability.

Adherent Peanut Image Segmentation Based on Multi-Modal Fusion.

Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%.

Investigating in-vitro degradation, fatigue behavior, and fracture toughness of electrical discharge-processed Mg alloys for biodegradable implant applications

Biodegradable magnesium (Mg) alloys hold great potential for revolutionizing the field of biomedical engineering by offering temporary support during tissue healing and degrading without leaving permanent residues. However, their clinical applications have been limited due to their relatively high degradation rate. This study focuses on evaluating the in-vitro degradation, fatigue resistance, and fracture toughness properties of Mg alloys under cyclic loading conditions, mimicking real-life scenarios. Wire Electrical Discharge Machining (WEDM) was used to prepare spark-processed Mg samples with complex surface texture, and fine-polished Mg samples were used for comparison. The structural characterization, electrochemical corrosion behavior, degradation assessment, and mechanical integrity of the samples were comprehensively analysed. The results show that the Electrical Discharge processed (EDed) Mg sample exhibited uniformly distributed overlapped craters on the surface, which led to a lower charge transfer resistance and higher corrosion potential compared to the Pristine Mg sample. The rough surface topography and alkaline pH microenvironment of the EDed Mg sample facilitated rapid apatite mineralization, but the resulting Ca-deficient apatite compromised its structural stability. Both EDed and Pristine Mg samples exhibited a significant reduction in fatigue life and lower fracture toughness with prolonged immersion. These findings provide valuable insights into the performance of Mg alloys and their potential applications in biodegradable implants, guiding the design of robust implant materials for enhanced patient outcomes.

A Novel Particle Size Detection System Based on RGB-Laser Fusion Segmentation With Feature Dual-Recalibration for Blast Furnace Materials

Particle size detection (PSD) is used to obtain the particle size distribution of materials in the blast furnace charging process, which is significant for optimizing the gas flow distribution and ensuring stable production. However, due to the complex surface texture of the materials and the uneven illumination of the production environment, existing methods have difficulty obtaining the particle size distribution efficiently. This paper proposes an end-to-end PSD system based on image segmentation to obtain the particle size distribution online with high accuracy. First, to further enhance the expression of edge features and reduce the interference of complex textures, an RGB-laser particle segmentation network (RLPNet) is developed to obtain high-precision segmentation images by camera-LiDAR sensor fusion. Moreover, to improve the fusion of RGB and laser features, a feature dual-recalibration (FDR) module was designed and embedded in RLPNet, consisting of independent recalibration and joint recalibration with T-convolution. Finally, to reduce the error caused by missing edge particle pixels, an edge-recognition-based particle size calculation strategy (ERP) is presented. Experimental results demonstrate that the proposed method performs well on the constructed dataset and in industrial applications. With the segmentation accuracy of RLPNet reaching 64.19 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , the similarity between the particle size distribution predicted with ERP and the actual distribution reaches 79.19 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

The effect of surface geometry on the aerodynamic behaviour of a football

Several studies have investigated the effect of surface features on the aerodynamic behaviour of a sphere or a football. Research on footballs typically compare real footballs with highly complex seam geometry and surface texture, making it difficult to identify which features influence the aerodynamic behaviour. This study commissioned many 3D printed footballs with regularly increasing seam length and surface texture to undertake a designed experiment to study these changes in a controlled fashion. Each ball was tested in a wind tunnel in non-spinning cases, or spinning about a vertical axis, at a range of speeds and key aerodynamic parameters were extracted from the data. Several methods were employed to characterise the roughness of each ball, and these roughness metrics were statistically tested for correlations with selected aerodynamic parameters. Using these relationships provides design guidance to football manufacturers to understand how modifying their surface geometry would influence the ball’s aerodynamic behaviour. In general, increasing the volume of roughness of the ball, measured as the change in volume of the ball introduced through seams or texture compared to a smooth sphere, decreased the critical Reynolds number. Balls with larger texture elements, particularly those with protrusive texture, had a much lower critical Reynolds number than other balls with the same absolute volumes of roughness. The post-critical drag coefficient did not significantly correlate with any of the roughness features of the balls. In a spinning case, the balls with high roughness generated a higher side force, this relationship plateaued at a certain level of roughness. The reverse Magnus behaviour changed significantly with the surface roughness; as the overall roughness volume of the ball increased, the Reynolds number at which the reverse Magnus changed to a conventional Magnus effect decreased. The large protrusive texture elements were effective at preventing a reverse Magnus effect from occurring at all in the tested Reynolds number range.

Cryogenic ultrasonic vibration assisted turning to texturize AZ31 magnesium alloy surfaces

Magnesium alloys are increasingly used in the biomedical field thanks to their biocompatibility. However, poor corrosion resistance greatly limits their applications. Coating the devices is known to represent the most efficient way to increase the corrosion performances of magnesium alloys, but it requires specific surface preparation of the metal substrate. In the present paper, a hybrid machining technology, namely cryogenic–ultrasonic-assisted turning, is applied to generate magnesium alloy texturized surfaces with enhanced surface integrity characteristics. Results showed that the application of liquid nitrogen led to a harder and more complex surface texture than in the case of ultrasonic-assisted turning under dry conditions.

Reprogrammable Mechanical Metamaterials with Heterogeneous Assembly of Soft Shell‐Based Voxels

Mechanical metamaterials are well studied to enable functions that are difficult to achieve in natural materials, in which reprogramming of specific underlying structures targeting different mechanical responses is particularly challenging. Herein, a heterogeneous voxel assembly‐based 2D mechanical metamaterial is purposed, aiming at a reprogrammable design of the mechanical properties and shape‐morphing simultaneously. In particular, each voxel is structured with a bistable soft shell and soft beams, which can be well controlled to achieve various desirable mechanical properties by a parameterized design method. The engineered metamaterial has a periodic arrangement of the voxel structures that can be transformed into an arbitrary aperiodic architecture with low geometrical frustration in the metamaterial plane by manipulating voxels, in which reprogrammable stiffness and Poisson's ratios, as well as deformation sequences and complex surface textures, can be achieved. In addition, the voxelated assembly has two modes allowing for fine or extensive tuning of the stiffness and the optional generation of 1D or 2D textures. Under this design paradigm, the programmability of multiple mechanical responses can support the development of more intelligent mechanical metamaterials with great potentials to soft robotics and active deformable devices.

Visual inspection method of steel pipe surface cracks based on dry magnetic particle feature enhancement

ABSTRACT It’s challenging to use the machine vision to inspect the hot-rolled steel pipes with complex surface texture, uneven colour greyscale, and micro-cracks. On the basis of the principle of magnetic particle detection, a visual inspection method of steel pipe surface cracks based on the dry magnetic particle feature enhancement is proposed, in which the dry red magnetic particles are applied to the steel pipe’s surface to form magnetic particle indication, and complementary colour images are captured in red and green light sources, and image difference is used to eliminate background noise and enhance magnetic particle indication features in view of the fact that the red light source improves and the green light source reduces the magnetic particle indication greyscale. Afterwards, the principle of selection of complementary colours and the detection principle are presented and analysed. Then, the magnetic particle images acquisition and the processing operations of the magnetic particle indication feature enhancement and crack segmentation are presented in detail. Finally, the feasibility of the method proposed in this paper is verified by experiments, and image segmentation metrics demonstrate that it has a good detection effect on the hot-rolled steel pipes with complex surface texture, uneven colour greyscale, and fuzzy cracks.

Saliency detection for surface defects of ceramic tile

Considering the lack of techniques for automatically detecting the surface defects of ceramic tiles, owing to the numerous types and complex surface textures of ceramic tiles, a salient region detection (SRD) algorithm is proposed. After the superpixel segmentation of the surface image of ceramic tiles, colour and structure saliency maps were constructed by taking the differences between the defects and backgrounds, in terms of colour and structure, as the salient values. The final saliency map for the surface defects was then obtained through the fusion of these two maps. The salient regions in the map were determined using the connected domain method and were then marked using minimum circumscribed rectangles for detecting the surface defects. The feasibility and effectiveness of the proposed SRD algorithm was verified through comparative experiments. The experimental results show that the detection accuracy of the algorithm for unknown types of ceramic tiles is 92.60%, which was 5.40% higher than that of the ResNet-50 algorithm, which was the highest for known types of ceramic tiles.

An Online Detection Method for Coal Dry Screening Based on Image Processing and Fractal Analysis

In coal dry screening, online detection for screening efficiency is a significant challenge. Notwithstanding, the method of image processing is strenuous to implement in this field due to the complex surface texture of shattered coal. This method identifies the fractal phenomenon before and after coal screening is discovered for the indirect detection of screening efficiency. For better fractal dimension distribution, an image denoising and filter method for wiping off the coal image surface texture is applied. Additionally, an enhanced Kirsch edge-detection algorithm is employed to obtain coal particle edges. Furthermore, the relation between fractal dimension and screening efficiency is presented by using the box-counting method. In this research, we skilfully transform the tough problem of image detection for particle size distribution into the calculation of the fractal dimension of the coal-edge image, and closely associate the fractal dimension with screening efficiency. With this method, it will be easier to predict the screening efficiency in real-time.

Research on controllable deep learning of multi-channel image coding technology in Ferrographic Image fault classification

Utilizing computer technology to realize ferrographic analysis and intelligent fault diagnosis technology is fundamental research to ensure the normal operation of precision, complex and severe marine mechanical equipment. This work is focused on the issues that affect the feature extraction and accuracy of the computer model, such as fuzzy edge and complex surface texture of wear particle image in ferrographic image. This research creatively establishes a new multi-channel image encoder, which artificially encodes the information of the original ferrographic image. The new encoding image includes the original channel information, artificial edge enhancement information, and artificial surface enhancement information, to improve the edge and surface features of the image. The encoder can not only improve the visible edge and surface characteristics of the picture but also form the MCECNN model after connecting to the convolutional neural network. The model can successfully improve the accuracy, convergence speed, generalization, and controllability of the model, to solve the problem of online intelligent recognition in the field of ferrography to the greatest extent.

Marker-free fatigue crack detection and localization by integrating the optical flow and information entropy

Fatigue cracks caused by repetitive loads are one of the major threats to the structural integrity of civil infrastructure. Human inspection is the most common method for detecting fatigue cracks, but it is time-consuming, labor-intensive, and unreliable. In this paper, we propose a new vision-based fatigue crack detection and localization method that can detect the fatigue crack with marker-free and high precision using a consumer-grade digital camera. A motion tracking technology called optical flow algorithm is applied to the video for tracking the surface motion of the monitored structure under repetitive load. Then, a crack detection and localization algorithm based on optical flow information entropy are developed to search differential features at different video frames caused by the crack opening and closing. The proposed method’s precision is first validated by doing two experiments and then comparing its precision and efficiency to the existing crack detection methods, including image processing and digital image correlation. The results show that, when compared to the existing vision-based methods, the proposed method can accurately and efficiently identify the fatigue crack even when the crack is surrounded by other crack-like edges, covered by complex surface textures, or invisible to human eyes. In addition, based on the proposed methods, a practical application for calculating the stress intensity factor is given to track crack development.

Infrared Nanosecond Laser Texturing of Cu-Doped Bioresorbable Calcium Phosphate Glasses

The surface modification of bioactive glasses significantly impacts their performance for in-vivo biomedical applications. An affordable nanosecond pulsed laser surface-modification technique would provide great flexibility in applications such as cell scaffolding and fouling/anti-fouling engineered surfaces. This study reports on an infrared nanosecond laser modification technique we developed and applied to a Cu-doped bioresorbable calcium phosphate glass. With this technique, clean micro-protrusion features could be produced. By tuning the laser parameters such as the laser scan speed and average power, the width and height of the formed protrusions could be controlled. Finally, optimal laser parameters were defined to obtain complex surface textures without significant damage or thermal-stress-induced cracks. These results could provide effective aid for the affordable, fast, and selective surface texturing of metal-doped bioglasses, opening new possibilities in their application in the biological field.

Adaptive stochastic morphology simulation and mesh generation of high-quality 3D particulate composite microstructures with complex surface texture

Particulate composite materials have a broad range of potential applications in engineering and other disciplines. Accurate modeling of their microstructures and fast generation of the finite element meshes play a vital role in investigating many micromechanical phenomena and improving understanding of the underlying failure mechanisms. Due to the exceedingly intricate multiscale internal structures that they possess, the modeling and meshing of their microstructures still remain difficult in general. In this work, we present a computational framework and methodology for the representation, simulation, and mesh generation of 3D stochastic microstructures of particulate composites. Towards this goal, we propose a multi-level multiscale scheme that allows for capturing the multiscale structures of particulate composite materials at both the coarse and fine scales. A briging scale approach based on heat kernel smoothing is also presented to seamlessly link the coarse and fine scales. In addition to the microstructural modeling of particulate composite materials, we also develop an adaptive curvature-based surface and volume mesh generation algorithm for particulate composite microstructures with complex surface texture. Following the implementation of the morphology and mesh generation algorithm, a series of numerical examples are presented to demonstrate the capability and potential of the proposed method.

CT-based method to measure metal powder characteristics and to study their influence on the quality of additively manufactured parts

Laser powder bed fusion (LPBF) is increasingly used to produce metal industrial components for high value-added sectors, such as aerospace, automotive and biomedical. However, mechanical and structural properties of LPBF parts are often hindered by the large quality variability, poor geometrical and dimensional characteristics, complex surface texture and low density. The quality of the feedstock material is an important aspect to be taken into account, as it significantly influences such possible issues. In particular, metal powder used in LPBF should have shape and size distribution designed to facilitate good flowability, spreading and packing behaviour, so that the final fabricated parts have acceptable density, surface finish and mechanical properties. This work focuses on the accuracy of simultaneous measurement of powder size and shape from three-dimensional reconstructions obtained by X-ray computed tomography (CT). Results of CT measurements are compared with results from other methods based on laser diffraction and scanning electron microscopy. Different materials and powder morphologies were investigated. In addition, the CT-measured powder characteristics can be used to improve other CT analyses of LPBF parts.

Evaluation of X-ray computed tomography for surface texture measurements using a prototype additively manufactured reference standard

Additively manufactured (AM) components have complex surface textures compared with conventionally machined surfaces. There is an increasing demand from AM industry for reference standards to calibrate instruments for AM surface inspection; this is particularly important in application areas such as aerospace, where the failure of a mission-critical part can have dire safety consequences. Many AM surfaces have re-entrant features and internal surfaces and, therefore, are difficult or impossible to be accessed by conventional measurement techniques. X-ray computed tomography (XCT) is an emerging technology that is capable of measuring AM surfaces. However, the metrology framework of the technology is still under development. Recently, we have explored the calibration of XCT for surface texture evaluation using a reference standard with a unique design. The prototype surface texture reference standard has surfaces with varying roughness levels and has been designed for compatibility between profile and areal topography measuring instruments and tomography instruments. The measurement traceability of surface texture using XCT has been established for profile measurements via a calibrated contact measurement technique. This paper has further investigated the impact of sampling strategy in calibration. This preliminary study is to investigate the ability of XCT to evaluate the AM surface texture in different build orientations, where the surfaces may have limited evaluation length.

Classification of Magnetic Tile Surface Defects Based on Efficientnet Network with Attention

The magnetic tile image has the characteristics of uneven illumination, complex surface texture, and low contrast. Aiming at the problem that the traditional defect detection algorithm is difficult to accurately identify the defects, and the deep learning algorithm is difficult to balance the classification accuracy and the size of the speed model, a defect classification algorithm based on attention-based EfficientNet is proposed. The algorithm first enhances the network’s spatial and location information for image features by integrating the Convolutional Block Attention Module, and improves the network’s ability to identify defects. Then, on this basis, Criss-Cross Attention is added to the network, so that the network can better the context information of the horizontal and vertical cross of image features, so that each pixel can finally capture the full image dependency of all pixels. Experimental results show that the algorithm has higher classification accuracy than EfficientNet-B0, reached 99.11%, and has a better balance between accuracy, speed and model size than other classification models.

DENSE 3D OBJECT RECONSTRUCTION USING STRUCTURED-LIGHT SCANNER AND DEEP LEARNING

Abstract. Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation. While more than 20 companies develop commercially available structured light scanners, structured light technology accuracy has limitations for fast systems. Model surface discrepancies often present if the texture of the object has severe changes in brightness or reflective properties of its texture. The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture. These errors result in ridge-like structures on the surface of the reconstructed 3D model. This paper is focused on the development of a deep neural network LineMatchGAN for error reduction in 3D models produced by a structured light scanner. We use the pix2pix model as a starting point for our research. The aim of our LineMatchGAN is a refinement of the rough optical flow A and generation of an error-free optical flow B̂. We collected a dataset (which we term ZebraScan) consisting of 500 samples to train our LineMatchGAN model. Each sample includes image sequences (Sl, Sr), ground-truth optical flow B and a ground-truth 3D model. We evaluate our LineMatchGAN on a test split of our ZebraScan dataset that includes 50 samples. The evaluation proves that our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels.

Diet reduces the effect of exogenous grit on tooth microwear

Exogenous grit adherent to the surface of food items and food fracture properties have each been considered important factors contributing to pattern and degree of tooth wear in mammals. However, the interactions between these two factors in generating distinctive microwear textures have remained understudied. Here the authors revisit in-vitro results from simulated chewing to explore how adherent grit and physical properties of foods act together to create dental microwear textures on occlusal enamel surfaces. Results suggest that the effect of exogenous grit on microwear texture is dependent on the material properties foods to which they adhere. Grit in the absence of food causes more complex microwear surface textures than foods covered with similar levels and types of grit (for a given number of chews and angle of approach between opposing teeth). Different foods covered in grit also yield different complexity values. Grit-laden, pliant meat, for example, results in a less complex texture than does resistant, grit-laden raw carrot. This work implied that tooth wear assessment can benefit from considering grit load and food material properties together.

Ultrasonic flexible bulging process of spherical caps array as surface texturing using aluminum alloy 5052 ultra-thin sheet

Abstract Ultrasonic flexible bulging process based Ethylene-vinyl acetate (EVA) soft medium is presented for fabricating the surface texturing with 3 dimensional shape using ultra-thin metallic sheet of 50 μm in thickness. Advantages of ultrasonic vibration and flexible forming are both utilized to guarantee the forming quality of big area surface texturing, i.e. spherical caps. Effects of ultrasonic vibration, such as amplitude and duration time, were investigated, and compared between flexible and rigid punch. Experimental results show that the ultrasonic flexible bulging can improve the surface quality and increase the height of bulged cap and length of intersection edge. Analysis and discussion of ultrasonic flexible bulging mechanism are carried out from the viewpoint of stress states of ultra-thin sheet in bulging process by considering the acoustic softening effects and abilities of EVA soft medium in uniformly transmitting pressure. The results reveal that the proposed process is suitable to manufacture the complex surface texture of ultra-thin metallic sheet with high quality.