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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.

Focuses on Korean garment manufacturing and the feasibility and benefits of introducing an expert system. Reviews previous literature on knowledge‐based system and describes in detail the stages followed in developing, constructing, testing, evaluating, integrating, implementing and maintaining the expert system. Concludes that an expert system could be used to solve various garment manufacturing problems and would contribute to garment quality improvement through a standardized apparel production process.

People involved in the cotton industry, from growers to spinners, have been concerned about the problem of stickiness encountered during cotton processing from fibres to yarn. Many methods have been successfully developed to identify and measure stickiness and also to reduce the effect of these contaminations. Yet due to the poor fundamental scientific knowledge regarding the mechanisms of sugar adhesion on fibres, the major part of these methods is empirically based. Today, gaining knowledge about stickiness seems impossible without the help of a fundamental study on the accurate composition of honeydew and on the mechanisms of honeydew adhesion. This paper introduces a new quantitative analysis method for honeydew adhesion behaviour measurement. Adhesion measurement has been carried out on both individual sugar and sugar mixture and it reveals a strong influence of moisture content on adhesion energy and a weak influence of the type of metallic surface on adhesion energy.

A lot of fibre and fabric structures or finishing parameters influence the functional properties of fabrics. In order to assess the thermal properties of conventional polyester and microfibre types of fabrics the plate/fabric/plate method for conductivity or cool/warm feeling was used. Fabrics made of microfibres show lower heat conductance and therefore higher thermal insulation properties. Microfibre fibres exhibit a warmer feeling than conventional polyester fabrics depending on pressure, which may be due to the difference in the fibre and fabric surface in contact with the human skin.

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.

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 purpose of this paper is to design and fabricate a three-dimensional (3D) bionic airflow sensing array made of two multi-electrode piezoelectric metal-core fibers (MPMFs), inspired by the structure of a cricket’s highly sensitive airflow receptor (consisting of two cerci).

A metal core was positioned at the center of an MPMF and surrounded by a hollow piezoceramic cylinder. Four thin metal films were spray-coated symmetrically on the surface of the fiber that could be used as two pairs of sensor electrodes.

In 3D space, four output signals of the two MPMFs arrays can form three “8”-shaped spheres. Similarly, the sensing signals for the same airflow are located on a spherical surface.

Two MPMF arrays are sufficient to detect the speed and direction of airflow in all three dimensions.

The purpose of this paper is to propose a theoretical model to predict the mechanical behaviour of needle punched heavy geotextiles in uniaxial tensile test.

The model was constructed using theory of layered composite materials and finite element method. The properties of a reference fabric were used as initial data in theoretical calculations and a commercially available finite element program was chosen to carry out stress analysis. A comparison is made between theoretical calculations and experimental data to evaluate the deformation mechanism of geotextile fabrics in uniaxial tensile test.

The results indicate that compatible data were predicted in terms of stress values and stress distribution of fabrics. The inconstant lateral contraction of nonwoven fabrics in tensile test is also successfully simulated by the model. However, in the case of elongations, the model could not predict the strains of heavy geotextiles accurately.

The study aims at predicting the mechanical behaviour of needle punched heavy geotextiles by using the structural and mechanical properties of a “reference fabric” instead of constituent fiber properties.

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.