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The microspinning technology has generally been used for cotton in the case of small scale spinning test methods (50 gram fibres). One type of silk fibre waste -pierced cocoonprepared previously as short silk fibre with cut length of 35 mm is blended with cotton fibre to obtain further data concerning two blending techniques in this microspinning, and to compare pure and blended yarns. The intimate (before carding and drawframe blending as well as the roll settings in the drawing system are being examined. The silk content was changed at 0/100, 25/75 and 50/50 ratio for a yarn count of 30 tex. The physical properties, the irregularity and the fibre arrangement as terms of the Index of Blending Irregularity and the Migration Indices of the blended yarns have been studied. In addition, the effects of the blending techniques as well as those of the silk content have been brought to the fore.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

The quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of this paper is to propose a relatively simple and effective technique to detect and assess the quality of produced textile fibers.

In order to achieve automatic visual inspection of fabric defects, first, images of the textile fabric are pre-processed by using Block-Matching and 3-D (BM3D) filtering. And then, features of textile fibers image are respectively extracted, including color, texture and frequency spectrum features. The color features are extracted by using hue–saturation–intensity model, which is more consistent with the human vision perception model; texture features are extracted by using scale-invariant feature transform scheme, which is a quite good method to detect and describe the local image features, and the obtained features are robust to local geometric distortion; frequency spectrum features of textiles are less sensitive to noise and intensity variations than spatial features. Finally, for evaluating the quality of the fabric in real time, two quantitatively metric parameters, peak signal-to-noise ratio and structural similarity, are used to objectively assess the quality of textile fabric image.

Compared to the quality between production and pre-processing of textile fiber images, the BM3D filtering method is a very efficient technology to improve the quality of textile fiber images. Compared to the different features of textile fibers, like color, texture and frequency spectrum, the proposed detection and assessment method based on textile fabric image feature can easily detect and assess the quality of textiles. Moreover, the objective metrics can further improve the intelligence and performance of detection and assessment schemes, and it is very simple to detect and assess the quality of textiles in the textile industry.

An intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

For cloths having direct contact with the skin, comfort properties are a priority than the physical and mechanical properties. Innerwear clothes should induce pleasant feelings because they have a direct influence on human psychological satisfaction, health and work efficiency. The purpose of this study is to investigate the impact of cotton fiber parameters on the sensorial comfort of woven fabrics.

Four types of cotton fiber with different fineness, mean length, uniformity index, short fiber content, strength and elongation were used to develop yarns used to weave fabric samples. Kawabata evaluation system (KES) was used to analyze the fabrics’ sensorial comfort.

Results showed that cotton fiber parameters have a significant effect on surface friction and roughness properties. Low stress tensile, tensile resilience and tensile strain properties were affected by fiber micronaire, mean length, uniformity index, short fiber content, fiber strength and elongation. However, fabric shear, bending and compression properties were least dependent on fiber parameters. The correlation of the dependent variable and the independent variable was also statistically analyzed and reported. From the results, it was shown that cotton fiber parameters play a significant role in woven fabrics’ sensorial comfort.

The cloths that are in contact with the skin can be developed using the results of these studies to feel pleasant. This will, in turn, have a direct effect on the customer's psychological satisfaction, health and work performance.

This paper aims to present a technical approach to evaluate the quality of textures obtained by an inkjet during binder jetting in 3D printing on a powder bed through contours detection to improve the quality of the surface printed according to the result of the assembly between the inkjet and a granular product.

The manufacturing process is based on the use of computer-aided design and a 3D printer via binder jetting. Image processing measures the edge deviation of a texture on the granular surface with the possibility of implementing a correction in an active assembly through a “design for manufacturing” (DFM) approach. Example application is presented through first tests.

This approach observes a shape alteration of the printed image on a 3D printed product, and the work used the image processing method to improve the model according to the DFM approach.

This paper introduces a solution for improving the texture quality on 3D printed products realized via binder jetting. The DFM approach proposes an active assembly by compensating the print errors in upstream of a product life cycle.