Search results

Reports how the theorem of the texture “tuned” mask was modified to solve some problems encountered in the automatic faults (including filling bars, oil stains, weft‐lacking and holes) detection and recognition of the plain woven fabrics. These problems are the faults of variable shapes and sizes, those of variable structure and the grey‐level differences in the faults of oil stains. The index of the “tuned” mask in the texture “tuned” mask theorem was modified to converge the variability of the faults, and to elongate the distances between each fault’s average texture energy so that the texture energy in normal texture and in faults can be confined to different fixed ranges. The results show that the optimum texture “tuned” mask found from the modified theorem of the texture “tuned” mask can be used satisfactorily to identify different faults due to structure, shapes and size variation. However, in the case of undertoned oil stains and lower density filling bars, this method may sometimes cause misidentification.

This study aims to investigate the effects of irregular groove textures on the friction and wear performance of sliding contact surfaces. These textures possess multiple depths and asymmetrical features. To optimize the irregular groove texture structure of the sliding contact surface, an adaptive genetic algorithm was used for research and optimization purposes.

Using adaptive genetic algorithm as an optimization tool, numerical simulations were conducted on surface textures by establishing a dimensionless form of the Reynolds equation and setting appropriate boundary conditions. An adaptive genetic algorithm program in MATLAB was established. Genetic iterative methods were used to calculate the optimal texture structure. Genetic individuals were selected through fitness comparison. The depth of the groove texture is gradually adjusted through genetic crossover, mutation, and mutation operations. The optimal groove structure was ultimately obtained by comparing the bearing capacity and pressure of different generations of micro-convex bodies.

After about 100 generations of iteration, the distribution of grooved textures became relatively stable, and after about 320 generations, the depth and distribution of groove textures reached their optimal structure. At this stage, irregular texture structures can support more loads by forming oil films. Compared with regular textures, the friction coefficient of irregular textures decreased by nearly 47.01%, while the carrying capacity of lubricating oil films increased by 54.57%. The research results show that irregular texture structures have better lubrication characteristics and can effectively improve the friction performance of component surfaces.

Surface textures can enhance the friction and lubrication performance of metal surfaces, improving the mechanical performance and lifespan of components. However, surface texture processing is challenging, as it often requires multiple experimental comparisons to determine the optimal texture structure, resulting in high trial-and-error costs. By using an adaptive genetic algorithm as an optimization tool, the optimal surface groove structure can be obtained through simulation and modeling, effectively saving costs in the process.

This paper aims to study the lubrication and sealing performance on the textured piston pair under the cross action of the shape and structure parameters. This paper further carries out the optimization design of low energy consumption hydraulic impact piston pair.

Based on the characteristics of the ring gap seal piston pair, the flow field analysis model of the whole film gap is established for its periodic treatment. The friction power loss of the piston pair is defined as the evaluation index of the lubrication performance and the leakage power loss as the evaluation index of the sealing performance. The orthogonal test design and CFD software were used to analyze the lubrication and sealing performance of the textured piston pair.

The cross action of shape and structure factors has a great influence of the lubrication and sealing performance on the textured piston pair. Clearance and shape parameters have great influence on it, while seal length and depth diameter ratio have little influence. The sealing performance of conical textured piston pair is good, while the lubrication performance of square textured piston pair is good. The primary and secondary order of influence of shape and structure on energy consumption on piston pair is B (seal clearance) > C (texture shape) > D (area ratio) > A (seal length) > E (depth diameter ratio).

Breaking the defect of local optimization design on traditional piston pair structure, then find the matching relationship of structural parameters on textured piston pair. Further improve the lubrication and sealing performance of the piston pair, and provide reference for the global optimization design of the low energy consumption hydraulic impact piston pair.

The purpose of this paper is to study the pumping efficiency of oil seals with different surface textures at different speeds, and the influence of the rotation direction of triangular texture on the sealing performance was further analyzed.

Based on the theory of elastohydrodynamic lubrication and the pumping mechanism of rotary shaft seals, establishing a numerical model of mixed lubrication in oil seal sealing area. The model is coupled with the lip surface texture parameters and the two-dimensional average Reynolds equation considering the surface roughness.

The results show that the application of lip surface texture technology has obvious influence on the oil film thickness, friction torque and pumping rate of oil seal. The triangular texture has the most significant effect on the increase of pump suction rate. When the rotation direction of triangular texture is 315 degrees, the pumping rate of oil seal is the largest compared with the other seven directions.

The model has a comprehensive theoretical guidance for the design of new oil seal products, which provides a certain basis for the application of surface texture technology in the field of sealing in the future.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2020-0198/

Visual quality control on raw textile fabrics is a vital process in weaving factories to ensure their exterior quality (visual defects or imperfection) satisfying customer requirements. Commonly, this critical process is manually conducted by human inspectors, which can hardly provide a fast and reliable inspection results due to fatigue and subjective errors. To meet modern production needs, it is highly demanded to develop an automated defect inspection system by replacing human eyes with computer vision.

As a structural texture, fabric textures can be effectively represented by a linearly summation of basic elements (dictionary). To create a robust representation of a fabric texture in an unsupervised manner, a smooth constraint is imposed on dictionary learning model. Such representation is robust to defects when using it to recover a defective image. Thus an abnormal map (likelihood of defective regions) can be computed by measuring similarity between recovered version and itself. Finally, the total variation (TV) based model is built to segment defects on the abnormal map.

Different from traditional dictionary learning method, a smooth constraint is introduced in dictionary learning that not only able to create a robust representation for fabric textures but also avoid the selection of dictionary size. In addition, a TV based model is designed according to defects' characteristics. The experimental results demonstrate that (1) the dictionary with smooth constraint can generate a more robust representation of fabric textures compared to traditional dictionary; (2) the TV based model can achieve a robust and good segmentation result.

The major originality of the proposed method are: (1) Dictionary size can be set as a constant instead of selecting it empirically; (2) The total variation based model is built, which can enhance less salient defects, improving segmentation performance significantly.

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval is proposed based on a combination of texture and colour information. The main purpose of this paper is to propose a new content based image retrieval approach using combination of color and texture information in spatial and transform domains jointly.

Various methods are provided for image retrieval, which try to extract the image contents based on texture, colour and shape. The proposed image retrieval method extracts global and local texture and colour information in two spatial and frequency domains. In this way, image is filtered by Gaussian filter, then co-occurrence matrices are made in different directions and the statistical features are extracted. The purpose of this phase is to extract noise-resistant local textures. Then the quantised histogram is produced to extract global colour information in the spatial domain. Also, Gabor filter banks are used to extract local texture features in the frequency domain. After concatenating the extracted features and using the normalised Euclidean criterion, retrieval is performed.

The performance of the proposed method is evaluated based on the precision, recall and run time measures on the Simplicity database. It is compared with many efficient methods of this field. The comparison results showed that the proposed method provides higher precision than many existing methods.

The comparison results showed that the proposed method provides higher precision than many existing methods. Rotation invariant, scale invariant and low sensitivity to noise are some advantages of the proposed method. The run time of the proposed method is within the usual time frame of algorithms in this domain, which indicates that the proposed method can be used online.

This paper aims to investigate the feasibility of adding macro-textures to triangle meshes for additive manufacturing (AM) focusing on possible time and quality issues in both software processing and part fabrication.

A demonstrative software tool was developed to apply user-selected textures to existing meshes. The computational procedure is a three-dimensional extension of the solid texturing method used in computer graphics. The tool was tested for speed and quality of results, considering also the pre- and post-processing operations required. Some textured meshes were printed by different processes to test build speed and quality.

The tool can handle models with realistic complexity in acceptable computation times. Parts are built without difficulties or extra-costs achieving a good aesthetic yield of the texture.

The tool cannot reproduce sample patterns but requires the development of a generation algorithm for different type of textures. Mesh processing operations may take a long time when very fine textures are added to large parts.

Direct texturing can help obtain parts with aesthetic or functional textures without the need for surface post-treatments, which can be especially difficult and expensive for plastic parts.

The proposed method improves the uniformity and consistency of textures compared to existing approaches, and can support future systematic studies on the detail resolution of AM processes.

The purpose of this paper is to develop a new objective evaluation method of fabric pilling using data-driven visual attention model.

First, the multi-scale filtering images are formed by Gaussian pyramid decomposition. Second, center-surround differences algorithm is used between multi-scale filtering images to build saliency map. On this basis, the pilling information is segmented from saliency map by the segmentation threshold. Finally, the pilling is objectively evaluated by extracting pilling feature. Experimental result shows that compared with the traditional detection methods, the proposed objective evaluation method has strong anti-interference ability, and correct classification rate (CCR) is 96 percent.

Fabric pilling saliency can be effectively improved by data-driven visual attention model, which will lead to stronger anti-interference ability and higher correct classification rate.

To void uneven illumination, noise, and texture interference, the proposed method can enhance the saliency of small targets in saliency map using a bottom-up visual attention model. Through the threshold segmentation according to pilling feature, the pilling information is effectively from the fabric texture. Pilling feature about pilling area density is extracted to pilling grade evaluation.

This is indeed the age of revolution, when timeless attitudes are changing and new ways of living being born. To most it is a bewildering complex, with uneasy forbodirtgs of the outcome. Improvement and change, there must always be—although change is not necessarily progress—but with unrest in the schools, universities and industry, one naturally questions if this is the right time for such sweeping reorganization as now seems certain to take place in local government and in the structure of the national health service. These services have so far escaped the destructive influences working havoc in other spheres. Area health boards to administer all branches of the national health service, including those which the National Health Service Act, 1946 allowed local health authorities to retain, were recommended by the Porritt Committee a number of years ago, when it reviewed the working of the service.

Since the term was first coined in 1977, fractals seem to have pervaded every branch of science. Attempts to explain what fractals are and what they are being used for. Are they a fad or are they really useful? Considers factors including quantitative measurement, image compression and computer graphics. Concludes that the future will see an increase in the use of fractal graphics.