Band 21, Heft 2

To solve the problem of defect detection in printed fabrics caused by abundant colors and varied patterns, a defect detection method based on RGB accumulative average method (RGBAAM) and image pyramid matching is proposed. First, the minimum period of the printed fabric is calculated by the RGBAAM. Second, a Gaussian pyramid is constructed for the template image and the detected image by using the minimum period as a template. Third, the similarity measurement method is used to match the template image and the detected image. Finally, the position of the printed fabric defect is marked in the image to be detected by using the Laplacian pyramid restoration. The experimental results show that the method can accurately segment the printed fabric periodic unit and locate the defect position. The calculation cost is low for the method proposed in this article.

Polyester is a popular class of material used in material engineering. With its 0.4% moisture regain, polyethylene terephthalate (PET) is classified as highly hydrophobic, which originates from its lack of polar groups on its backbone. This study used a parallel-plate nonthermal plasma dielectric barrier discharge system operating at medium pressure in dry air and nitrogen (N 2 ) to alter the surface properties of PET fabrics to increase their hydrophilic capabilities. Water contact angle, atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS) were utilized to analyze any effect from the plasma treatment. The wettability analysis revealed a reduction in the contact angle of more than 80% within 5 min for both discharges. Scanning electron microscopy analysis showed no microscopic damage to the fiber structure, guaranteeing that the fabrics’ structural integrity was preserved after treatment. AFM analysis showed an increase in the nanometer roughness, which was considered beneficial because it increased the total surface area, further increasing the hydrophilic capacity. XPS analysis revealed a sharp increase in the presence of polar functional groups, indicating that the induced surface changes are mostly chemical in nature. Comparing that of untreated fabrics to treated fabrics, a Increase in water absorption capacity was observed for air-treated and N 2 -treated fabrics, when these fabrics were used immediately after plasma exposure.

Rapid and precise methods (geometrical and statistical), which aim to predict the amount of sewing thread needed to sew a garment using different over-edge stitches of class 500 (501, 503, 504, 505, 512, 514, 515, and 516), have been provided. Using a geometrical method of different over-edge stitch shapes, sewing consumption value was determined to avoid the unused stocks for each stitch type. The prediction of the sewing thread consumption relative to each investigated over-edge stitch was proposed as a function of the studied input parameters, such as material thickness, stitch density, yarn diameter, and seam width (distance between the needle and the cutter and the distance between two needles). To improve the established models using a geometrical method, a statistical method based on multi-linear regression was studied. Geometrical and statistical results were discussed, and the coefficient R2 value was determined to evaluate the accuracy of the tested methods. By comparing the estimated thread consumption with the experimental ones, we concluded that the geometrical method is more accurate than the statistical method regarding the range of R 2 (from 97.00 to 98.78%), which encourages industrialists to use geometrical models to predict thread consumption. All studied parameters contributing to the sewing thread consumption behavior were investigated and analyzed in the experimental design of interest. It was concluded that the most important parameter affecting thread consumption is the stitch density. The material thickness and the seam width (B1) have a little impact on thread consumption values. However, the seam thread diameter has a neglected effect on thread consumption.

The study involved laminated nylon and viscose nonwovens, both perforated and non-perforated, with a view to using them for packaging of powders in mineral warmers. The nonwovens were examined in terms of morphology as well as tensile strength in dry and wet states. Thermal properties were determined by differential scanning calorimetry. Dynamic mechanical analysis was carried out in a broad range of temperatures. Surface wettability and water vapor permeability were assessed. The findings were analyzed to determine the utility of the studied materials as mineral warmer packaging materials in cold work or living environments.

This article focuses on developing a methodology which can be used to estimate the concentration of dyestuff released from textiles during domestic laundering, so that further studies involving decolorization of the wastewater from domestic washing machine can be conducted in an attempt to develop eco-friendly domestic washing processes. Due to the complexity of the problem, an approach was adopted so that, as an initial step, synthetic red and blue reactive dye solutions were prepared as representative wastewater solutions using Reactive Red 195 and Reactive Blue 19 dyestuffs for the estimation of dye concentration. This was followed by an experimental work consisting of washing tests involving the calculation of dye concentration in the wastewater obtained from domestic washing machine as well as tergotometer as a machine simulator. For this part of the work, dyed cotton plain jersey fabric samples were used to obtain wastewater solutions. All the dye solutions and the wastewater samples were measured with VIS spectrophotometer, and the maximum absorbance values were obtained at relevant wavelengths. Although the characteristics of absorbance spectra of synthetic and wastewater solutions were very different, the maximum absorbance values of both solutions overlapped at relevant wavelengths. The concentration of the dyestuff was calculated from the absorbance values measured at 540 and 592 nm for the red and blue, respectively. The statistical analysis of the data suggested that tergotometer can be used as a domestic washing machine simulator. Moreover, the regression analysis done for the dyestuff concentration under discussion revealed that the most significant factor was the washing step (main wash or rinsing) (89.5%) followed by color (red or blue) (3.4%) and washing device (washing machine or tergotometer) (1.5%).

Warp-knitted spacer fabrics are generally used for sportswear, functional clothing, protective clothing, and other applications. This article studied the heat and mass transfer properties of polyester warp-knitted spacer fabrics from low thickness (2 mm) to high thickness (20 mm), from low mass (247.34 g/m 2 ) to high mass (1,585.9 g/m 2 ), and surface structure in plain or mesh construction. Water vapor permeability, air permeability, water absorption, and thermal insulation property were conducted to evaluate the spacer fabrics. The results revealed that with increasing volume density the water vapor permeability of spacer fabrics decreased, but the water absorption ratio increased. The water vapor permeability of fabrics increased when thickness decreased and volume density increased. It was further found that spacer fabrics with mesh worn nearby the skin and plain structure worn far from the skin could facilitate water vapor and air transmission. The difference of 8.82% for water vapor permeability and 14.19% for air permeability were found between testing mesh side up and down for the spacers (2.56 and 3.37 mm), respectively. Thermal insulation ratio was highly and significantly correlated with heat transfer coefficient at −0.958 and with thickness at 0.917. Thermal insulation ratio is highly and significantly correlated with air permeability at 0.941.

This study was aimed to develop a quick detection method to test aldehydes and ketones in textiles in order to control the quality of automotive textiles in the development process from fabric production to end-use in vehicles. In this study, a pretreatment of samples was applied to simulate the actual environment of textiles used in vehicles. Collected volatiles were reacted with 2,4-dinitrophenylhydrazine and then eluted with acetonitrile tetrahydrofuran. The eluent was analyzed with high-performance liquid chromatography. Findings showed more than 90% volatiles could be detected in the established method; the lowest determination limit was 0.0297 mg/mL; and the lowest quantification limit was 0.0991 mg/mL, which meant sensitivity and capability of the method were high. Regression coefficients of linear models between volatile concentrations and chromatographic peak characteristics were >0.995, indicating that the method could effectively and efficiently determine the contents of volatiles in automotive textiles.

Miao embroidery of the southeast area of Guizhou province in China is a kind of precious intangible cultural heritage, as well as national costume handcrafts and textiles, with delicate patterns that require exquisite workmanship. There are various skills to make Miao embroidery; therefore, it is difficult to distinguish the categories of Miao embroidery if there is a lack of sufficient knowledge about it. Furthermore, the identification of Miao embroidery based on existing manual methods is relatively low and inefficient. Thus, in this work, a novel method is proposed to identify different categories of Miao embroidery by using deep convolutional neural networks (CNNs). Firstly, we established a Miao embroidery image database and manually assigned an accurate category label of Miao embroidery to each image. Then, a pre-trained deep CNN model is fine-tuned based on the established database to learning a more robust deep model to identify the types of Miao embroidery. To evaluate the performance of the proposed deep model for the application of Miao embroidery categories recognition, three traditional non-deep methods, that is, bag-of-words (BoW), Fisher vector (FV), and vector of locally aggregated descriptors (VLAD) are employed and compared in the experiment. The experimental results demonstrate that the proposed deep CNN model outperforms the compared three non-deep methods and achieved a recognition accuracy of 98.88%. To our best knowledge, this is the first one to apply CNNs on the application of Miao embroidery categories recognition. Moreover, the effectiveness of our proposed method illustrates that the CNN-based approach might be a promising strategy for the discrimination and identification of different other embroidery and national costume patterns.

By applying the simultaneous corona-temperature treatment, the effect of electret temperature on the structure and filtration properties of melt-blown nonwovens was investigated. Fiber diameter, pore size, thickness, areal weight, porosity, crystallinity, filtration efficiency, and pressure drop were evaluated. The results demonstrated that some changes occurred in the structure of electret fabrics after treatment under different temperatures. In the range of 20°C~105°C, the filtration efficiency of melt-blown nonwovens has a relationship with the change in crystallinity, and the pressure drop increased because of the change in areal weight and porosity. This work may provide a reference for further improving filtration efficiency of melt-blown nonwovens.

Natural fiber-reinforced composites are getting more attention from researchers and manufacturing companies to replace metals and synthetic materials that have dominated the manufacturing industries. In this study, the mechanical properties of unidirectional (UD) flax roving-reinforced composites and woven fabric-reinforced composites were investigated. Three different composites were prepared from flax rovings, which have the same linear density and epoxy resin matrix, with different reinforcement and composite preparation methods. The samples were subjected to experimental tests of flexural rigidity and tensile strength in a parallel and perpendicular direction to fiber orientation. The test results showed that flexural rigidity and tensile strength of flax fiber-reinforced composites are highly dependent on the direction of fiber orientation. The results also reveal that in a parallel direction to fiber orientation, UD composites have higher flexural rigidity and tensile strength than woven fabric-reinforced composite.

This article focuses on the development of the mask design knowledge base, which is expected to be applied in a personalized mask design system. To realize the proposed knowledge base, a perceptual descriptive space of the mask is first developed for the description of both functional and aesthetic perceptions of a mask. The mask ontology is also developed to form the mask element matrix. Mask design knowledge is expressed as the relationship between the perceptual descriptive space and the mask ontology, which is extracted by a group of experienced designers through a sensory evaluation procedure. This relationship is then simulated by fuzzy logic tools. The proposed knowledge base has been validated that it is reliable. The personalized mask design system can be further developed with the propose mask design knowledge base.

In this study, polypyrrole/silver (PPy/Ag) conductive polyester fabric was synthesized via an in-situ polymerization method under UV exposure, using silver nitrate (AgNO 3 ) as an oxidizing agent in the presence of sodium dodecyl benzene sulfonate (SDBS) and polyvinylpyrrolidone (PVP). The effect of the preparation processes on the properties of the conductive fabric was studied experimentally, and the optimal preparation process of the conductive fabric was obtained. X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FTIR) showed the chemical structural properties of the PPy/Ag conductive polyester fabric. X-ray diffraction (XRD) confirmed the presence of silver nanoparticles in the prepared material. Furthermore, subsequent test results proved that the PPy/Ag conductive polyester fabric prepared by UV irradiation had good electrical conductivity and antibacterial property. The sheet resistance of the prepared conductive fabric was 61.54 Ω • sq −1 .

Socks’ comfort has vast implications in our everyday living. This importance increased when we have undergone an effort of low or high activity. It causes the perspiration of our bodies at different rates. In this study, plain socks with different fiber composition were wetted to a saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in wet state at different moisture levels. Theoretical thermal resistance is predicted using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fiber) in different models. By this modification, these mathematical models can predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance has reason able correlation with experimental results in both dry (laboratory conditions moisture) and wet states.

Comfort properties of garments are influenced by fiber properties, fabric properties, and applied finishes. Denim garments are widely used apparels, and they are processed with different industrial finishing treatments. Finishing treatments and fabric weight have a great influence on the thermal comfort of denim fabrics. The aim of this paper was to evaluate the effects of finishing treatments (rigid, bleaching, resin, softener) on the thermophysiological comfort and moisture management properties of denim fabrics considering three weight categories (light, medium, heavy). The thermophysiological comfort (thermal resistance, thermal absorptivity, air permeability, water vapor permeability tests) and liquid moisture transport capabilities (transfer wicking and drying behavior of fabrics) were measured and analyzed statistically. As a result of the study, fabric weight and finishing had been found to be important parameters for the comfort properties of denim fabrics. As a conclusion, it could be stated in the light of the results that the fabric weight type (light, medium, heavy) had a significant impact on the fabric thermophysiological comfort properties.

Modification of cotton with 3-chloro-2-hydroxypropyltrimethylammonium chloride (CHPTAC) has been studied extensively and can be operated by means of exhaustion, cold pad-batch, and continuous and pad-steam methods. Most of the research addresses the cationization of cotton fabric after bleaching or mercerization, or during the mercerization process. In our studies, we performed a comparison of the cationization effects on raw, enzymatic, and alkali-scoured cotton knitted fabrics applying CHPTAC according to the exhaustion method. The charge density of the cotton surface was measured using a Muetek Particle Charge Detector and a “back titration” method with polyelectrolytes. These results were compared with the nitrogen content in the samples, K/S measurements of tested samples after dyeing with anionic dye (Acid Yellow 194), and other physicochemical parameters such as weight loss, whiteness, and wettability.