Search Results (10)

Compression stockings have long been manufactured in a single color without patterns, but enhancing their aesthetic appeal through knitted designs can improve user compliance. This study explores the potential of punch lace knitted structures to create patterns in compression textiles by seamless knitting technology while maintaining sufficient pressure. The effects of yarn material, number of yarns used, and knitted patterns on pressure and thermal comfort will be studied. The fabric pressure was evaluated using pressure sensors with a leg mannequin, while the thermal properties were measured according to the textile standard. This study found that the pressure and thermal conductivity of fabric are significantly influenced by the number of yarn and yarn materials, but not the knitted pattern. Cupro/cotton/polyurethane yarn (A) exhibits the strongest positive impact on pressure, increasing by 2.03 mmHg with the addition of one end of yarn A while polyamide/lycra yarn (C) exhibits a higher thermal conductivity than yarn A. For air permeability, the number of yarn and knitted patterns significantly affects the ventilation resistance. Pattern B with an additional needle in a float stitch shows 0.023 kPa·s/m lower resistance than pattern A. The findings from this study can be widely used in health, medical, and sports applications. Full article

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with “one-line knit to shape” capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry. Full article

In order to withstand high-temperature environments, ultra-high molecular weight polyethylene (UHMWPE) fibers with cooling properties are being increasingly used in personal thermal management textiles during the summer. However, there is relatively little research on its combination with knitting. In this paper, we combine UHMWPE fiber and knitting structure to investigate the impact of varying UHMWPE fiber content and different knitting structures on the heat and humidity comfort as well as the cooling properties of fabrics. For this purpose, five kinds of different proportions of UHMWPE and polyamide yarn preparation, as well as five kinds of knitted tissue structures based on woven tissue were designed to weave 25 knitted fabrics. The air permeability, moisture permeability, moisture absorption and humidity conduction, thermal property, and contact cool feeling property of the fabrics were tested. Then, orthogonal analysis and correlation analysis were used to statistically evaluate the properties of the fabrics statistically. The results show that as the UHMWPE content increases, the air permeability, heat conductivity, and contact cool feeling property of the fabrics improve. The moisture permeability, moisture absorption and humidity conductivity of fabrics containing UHMWPE are superior to those containing only polyamide. The air permeability, moisture permeability, and thermal conductivity of the fabrics formed by the tuck plating organization are superior to those of the flat needle plating and float wire plating organization. The fabric formed by 2 separate 2 float wire organization has the best moisture absorption, humidity conduction, contact cool feeling property. Full article

The aim of this article was to compare the shrinkage and air permeability properties of woolen fabrics and fabrics with regenerated wool woven with different weaves for establishing the suitability of regenerated wool for blankets. Two series of products with yarns of different raw materials were woven. One group of fabrics was woven with regenerated woolen yarn in the weft and woolen yarn in the warp. The other group of fabrics was woven only from 100% woolen yarns. The shrinkage in the directions of the warp and the weft and the air permeability of the fabrics with regenerated wool and 100% woolen fabrics with different weaves were investigated. The shrinkage in the directions of the warp and the weft in the fabrics with regenerated wool in the weft and 100% woolen fabrics depended on the float length in the weave. When the length of the weave increased, the shrinkage also increased. The air permeability value changed depending on the number of intersections and the float length. The fabrics with regenerated wool in the direction of the weft had higher air permeability. The Two-way analysis of variance (ANOVA) results showed that the weave influenced the shrinkage in the directions of the weft and warp, but the raw material had no influence on the shrinkage. The weave did not influence the air permeability, in contrast to the raw material. The shrinkage in the directions of the warp and weft and the air permeability did not depend on the interrelationships of the weave group and the raw material of the fabric. Full article

A simple and flexible method for theoretical calculation of the main structural parameters of various weft-knitted fancy and combined patterns is presented in this article. It is especially important for patterns containing different elements, such as loops, floats of different lengths, tucks, and tuck stitches. Measurement of an actual average length of the loop in these fabrics is complicated because it is necessary to disassemble precisely one pattern repeat to measure the yarn length and divide it by the number of elements in this pattern repeat. For large and complex pattern repeats, this is difficult and usually gives a high number of errors. It is very important to have lengths of structural elements as it helps to predict the main physical properties of knitted fabrics and their mechanical behaviour, which is especially important for protective textiles. The main idea of the proposed method, based on Čiukas geometrical model, is to calculate lengths of various structural elements or even their parts separately, taking into account the number of needle bars and their formation principle, which gives great flexibility to such modelling. The proposed theoretical formulas can be used for various patterned weft-knitted structures containing not only loops but tucks, floats of different lengths, or additional yarns; they give very few errors in empirical calculations and are easy to use. Full article

The subject of the paper focuses on the effect of weave architecture on microbial barrier properties of woven fabrics or more precisely on identifying crucial elements of weave architecture that dominantly influence bacteria penetration in dry condition. For that purpose, 12 samples of cotton fabrics were woven and examined. In their structure, all samples had the same yarns (36 tex) in warp and weft, same densities of warp (24 yarns/cm), two weft densities (24 and 20 yarns/cm) and six different basic weave structures. Microbial barrier permeability was determined according to a previously developed test method in cooperation with University Hospital Center Zagreb. Bacterial endospores of apathogenic species of the genus Bacillus: Geobacillus stearothermophilus and Bacillus atrophaeus were used. The effect of weave pattern on microbial barrier properties was significant. Weave patterns, decisively determined the number of influencing pores and its sizes in woven fabrics, as well as the yarn floating which jointly almost perfectly correlated with bacteria penetration through the woven fabric. Multiple linear regression of pore numbers and floating threads produced equations which correspond in 99% to the measuring results for densities 24/24 and 24/20, and more than 98% considering both densities of the set. Among compared weave patterns, satin weave had significantly lower permeability of microorganisms (six–seven times) than basket weave (the highest), for both densities. Full article

This paper reports the study of the effects of cramming motion implemented during weaving and finer weft yarns used on dyed-yarn mixed woven fabrics produced by using raw white warps and multicolored-wefts. The cramming motion was used to increase the dyed-weft yarns cover factor of fabric, and thus, to reduce the negative effect of white warp floats at the fabric face on the color attributes of fabric. The surface structure of fabric was characterized by using several key geometrical parameters that determined the resultant fabric color attributes. The effects of fabric structure and density, weft yarn count, and the introduction of black yarn on the fabric face layer on the fabric surface geometrical parameters, physical properties, as well as color attributes were investigated under the implementation of cramming motion on the fabric. The color attributes of fabrics using cramming motion and finer yarns were also compared to the fabrics without cramming motion. The experimental results indicate that the weft yarn density and cover factor of fabric face layer are increased by applying cramming motion and finer yarns for fabricating the blue-red and/or black mixed fabrics. Consequently, the fabric lightness can be further reduced for achieving a better color effect on colorful and figured woven fabrics mainly using dyed-wefts for color mixing. Full article

Auxetic plied yarns are specially constructed with two types of single yarns of different sizes and moduli. This paper investigates how to use these types of yarns to produce woven fabrics with auxetic effects. Four-ply auxetic yarns were first incorporated into a series of woven fabrics with different design parameters to study their auxetic behavior and percent open area during extension. Effects of auxetic plied yarn arrangement, single component yarn properties, weft yarn type, and weave structure were then evaluated. Additional double helical yarn (DHY) and 6-ply auxetic yarn woven fabrics were also made for comparison. The results show that the alternative arrangement of S- and Z-twisted 4-ply auxetic yarns in a woven fabric can generate a higher negative Poisson’s ratio (NPR) of the fabric. While the higher single stiff yarn modulus of auxetic yarn can result in greater NPR behavior, finer soft auxetic yarn does not necessarily generate such an effect. Weft yarns with low modulus and short float over the 4-ply auxetic yarns in fabric structure are favorable for producing high NPR behavior. The weft cover factor greatly affects the variation of the percent open area of the 4-ply auxetic yarn fabrics during extension. When different kinds of helical auxetic yarns (HAYs) are made into fabrics, the fabric made of DHY does not have the highest NPR effect but it has the highest percent open area, which increases with increasing tensile strain. Full article

This paper reports the development of colored-yarn mixed woven fabrics by using raw white warps and multicolored-wefts, as well as a study of the influential factors on the color attributes of the resultant fabrics. Weft yarns in six colors, together with the white warp yarns, were used to create a series of fabric colors. Two types of new weft-backed structures were designed to assign the desired wefts for color mixing, as well as to reduce the white warp floats on the surface and thus, the lightness of the fabric. The effects of the proportion of yarn color components, weft density, and the introduction of black weft floats on the color attributes of fabrics, were investigated. The results show that through varying the proportion of mixing yarn color components, via fabric structure, a series of mixed red-blue and green-yellow colors for fabrics are created, respectively. Colored yarn mixed fabric presents a lowered lightness after a middle regulating layer is introduced into the structure. Compared to fabrics with a lower density, higher density fabrics possess lower lightness, higher redness and blueness in the blue-red fabrics, and higher greenness and yellowness in the yellow-green fabric. The lightness of fabric lowers after adding black yarn. Full article

Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation. Full article