Search Results (16)

Zinc oxide-modified tourmaline-based negative ion polyester fiber (ZnO/TM/PET), as a new functional fiber with excellent negative ion emission characteristics, is of great significance to human health, and its industrial application needs to be expanded and promoted. In this paper, using zinc oxide, tourmaline, and polyethylene terephthalate as the main raw materials, ZnO/TM/PET negative ion functional fiber with 5% ZnO/TM composites was prepared. Then, it was blended with cotton fiber and interknitted with wool yarn and spandex yarn, from which we developed five kinds of negative ion polyester/cotton-blended yarn and four different kinds of knitted double-sided fabric using different equipment and process parameters. The micromorphology of the fiber samples, the basic properties of the blended yarns, and the wearability and functional properties of the knitted fabrics were tested. The results show that the ZnO/TM negative ion additive is randomly dispersed in the polymer matrix without visible conglobation and the fiber has a good appearance. The blending ratio has an important effect on the properties of functional polyester/cotton blended yarn. The higher the ratio of negative ion polyester fiber in the blended yarn, the better the mechanical index of the blended yarn, the higher the negative ion emission, and the lower the hairiness index. The performances of fabric are influenced by the comprehensive action of fiber raw material type, yarn ratio, fabric tightness, and structure. The mechanical properties of the fabric knitted from negative ion polyester/cotton-blended yarn are lower than those made from negative ion polyester filament yarn. In the case of the same fabric structure, the negative ion emission performance, far-infrared emission performance, and antibacterial property of the fabric with a higher ratio of negative ion functional fiber is better than the lower ratio. With the same yarn composition, the negative ion emission performance and air permeability of the fabric with a loose structure are better than that of the fabric with a tight structure, but the moisture permeability, far-infrared emission properties, and antibacterial properties show little difference. Full article

The spinning industry makes a major contribution to environmental pollution due to the excessive use of natural assets and the generation of remarkable amounts of waste during manufacturing processes. Now, the spinning industries are concentrating on sustainable activities due to environmental issues. While textile recycling efforts have been widely explored, the utilization of soft waste (process waste) in yarn production remains underexplored. This study addresses this gap by investigating a sustainable approach incorporating soft waste into producing sustainable yarn using the ring-spinning technique. The research explores the properties of yarns manufactured from a blend of virgin cotton and soft waste, and 100% virgin cotton yarn is produced for comparison. The results indicate that incorporating soft waste leads to an increase in CVm% (13 vs. 11), hairiness (6.9 vs. 5.1), and IPI (165 vs. 125) compared to virgin cotton yarn. However, the elongation percentage (7.1% vs. 8%) and tensile strength (12.6 cN/tex vs. 16.2 cN/tex) showed a reduction, highlighting potential trade-offs in mechanical properties. The statistical analysis applies one-way ANOVA to evaluate the significance of variations in yarn characteristics made from the mixture of soft waste + virgin cotton and only virgin cotton. The manufactured yarns were examined in a modern weaving machine as weft yarn for fabric (denim) manufacturing and found to be perfect for normal operation. The article focuses on reducing negative impacts on the fabric (denim) manufacturing environment by incorporating soft waste to produce sustainable yarn. This research provides important insights into the production of sustainable yarns, focusing on environmental concerns. Full article

In textile manufacturing, ensuring high-quality yarn is crucial, as it directly influences the overall quality of the end product. However, imperfections like protruding and loop fibers, known as ‘hairiness’, can significantly impact yarn quality, leading to defects in the final fabrics. Controlling yarn quality in the spinning process is essential, but current commercial equipment is expensive and limited to analyzing only a few parameters. The advent of artificial intelligence (AI) offers a promising solution to this challenge. By utilizing deep learning algorithms, a model can detect various yarn irregularities, including thick places, thin places, and neps, while characterizing hairiness by distinguishing between loop and protruding fibers in digital yarn images. This paper proposes a novel approach using deep learning, specifically, an enhanced algorithm based on YOLOv5s6, to characterize different types of yarn hairiness. Key performance indicators include precision, recall, F1-score, mAP0.5:0.95, and mAP0.5. The experimental results show significant improvements, with the proposed algorithm increasing model mAP0.5 by 5% to 6% and mAP0.5:0.95 by 11% to 12% compared to the standard YOLOv5s6 model. A 10k-fold cross-validation method is applied, providing an accurate estimate of the performance on unseen data and facilitating unbiased comparisons with other approaches. Full article

With an expanding global clothing and textile industry that shows no signs of slowing, concerns over its environmental impacts follow. Fibre fragments (FFs)—short pieces of textiles that have separated from a textile construction—are a growing area of concern due to increasing evidence of their accumulation in the environment. Most of the existing research on this topic focuses on the role of consumer behaviour rather than the textiles themselves. A systematic literature review is used here to explore the key textile parameters that influence FF release. A search of articles published between 2011 and June 2024 was conducted following the PRISMA guidelines. Three databases (Scopus, Web of Science, and EBSCO) were used, and articles were screened to ensure that a minimum of one textile parameter was manipulated in the study. A total of 52 articles were selected and where appropriate, comparisons between samples used and key findings were made. The textile parameters that were found to reduce FF release include fibres of a longer length and higher tenacity, as well as filament yarns with low hairiness and higher twists. At the fabric level, tight fabric structures and high abrasion resistance show lower FF shedding. Mechanical finishes that reduce the number of protruding fibre ends or chemical finishes that increase abrasion resistance also prove to be beneficial. Lastly, sewing and cutting methods that enclose or seal the textile edge can reduce FF release. While optimal parameters have been identified, they are not applicable to all textile end-uses. Rather, these factors can serve as a guide during future production and be applied where possible to limit FF release. Full article

Biobased nylon (PA56) not only has the same physical properties as nylon (PA6/PA66) but its production method is also more environmentally friendly. PA56 fabric has the advantages of moisture absorption, perspiration, high-temperature resistance, and flexibility, which have been widely studied by scientific researchers. Wool has the advantages of beauty, environmental protection, and anti-wrinkle. However, pure wool fabrics have low strength and are easy to shrink when washed, which has always been a problem. Hence, this work adopted the ring spinning method to prepare wool/PA56 blended yarn with wool content of 0, 10, 30, 50, 70, and 100 wt%. Thus, to examine the effects of different blending ratios and twists on yarn performance, PA56 was blended with wool. The results showed that findings indicate that yarn performance is influenced by both yarn twist and blending ratio. The yarn thickens and takes on more linear density as the blending ratio and yarn twist increase. As the wool ratio increases, the yarn’s breaking stress and breaking strain decrease. It is obvious that the strength and elongation at break of pure PA56 yarn are 2.09 cN/Dtex and 33.92%, respectively. When the wool content was 100 wt%, the strength and elongation at break of the blended yarn were 0.66 cN/Dtex and 21.15%, respectively. With the amount of wool blending, the yarn hairiness index’s H-value initially rises and subsequently falls. The percentage of blended wool reaches 50% at 2.14; less blending might exacerbate the yarn’s stem, resulting in neps and unevenness features. The quality of the yarn improves as the blending percentage rises. The yarn has the advantages of resource saving, biodegradability, and environmental friendliness and has a broad application prospect in the automotive interior field. Full article

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton’s principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems. Full article

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some textile yarn characteristic parameters. The irregularity of the textile thread influences its physical properties/characteristics and there may be a possibility of a break in the textile thread during the fabric manufacturing process. This can contribute to the occurrence of unwanted patterns in fabrics that deteriorate their quality. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. The main findings/results of the study are the yarn analysis method as well as the developed algorithm, which allows the analysis of defects in a more precise way. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation, and standard hairiness deviation, as well as performing spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology. Full article

Control of tension distribution in the spinning triangle region that can facilitate fiber motion and transfer is highly desirable for high quality yarn production. Here, the key mechanisms and a mechanical model of gradient regulation of fiber tension and motion with rotary heterogeneous contact surfaces were theoretically analyzed. The linear velocity gradient, effected on a fiber strand using rotary heterogeneous contact surfaces, could balance and stabilize the structure and stress distribution of spinning triangle area, which could capture exposed fiber to reduce hairiness formation and enhance the internal and external fiber transfer to strengthen the fiber utilization rate. Then, varied yarns spun without and with the rotary grooved and rotary heterogeneous contact surfaces were tested to compare the property improvement for verifying above-mentioned theory. The hairiness, irregularity, and tensity of the yarns spun with rotary heterogeneous contact surfaces spun yarns were significantly improved compared to other spun yarns, which effectively corresponded well to the theoretical analysis. Based on this spinning method, this effective, low energy-consuming, easy spinning apparatus can be used with varied fiber materials for high-quality yarn production. Full article

Comfort is an important quality criterion, especially for sportswear. It influences well-being, performance and efficiency. The necessary dissipation of heat and air flow, at high metabolic rates, must be designed and planned in advance. The influence of structure, density, mass and thickness of fabric were considered as well as yarn material composition, yarn linear density, yarn evenness and yarn hairiness. The influence of the mentioned parameters on thermal properties and air permeability was calculated. From the correlation analysis, it can be concluded that yarn’s linear density, yarn short fibers hairiness, and mass per unit area of knitted fabric has the greatest impact on heat resistance. The yarn linear density, the yarn hairiness of the longer protruding fibers, and the thickness of the knitted fabric have the greatest impact on air permeability. A statistically significant model of multiple linear regression equations was offered to predict the thermal comfort of knitted fabric. Full article

The process and efficiency of sizing aramid yarns before the weaving process was studied. The sizing was carried out under different conditions, with and without the pre-wetting of the threads before the actual sizing process. Two groups of yarns were tested. The first group consisted of five yarn samples that were blended with 95% meta-aramid and 5% para-aramid in counts of 20 × 2, 17 × 2, 14 × 2 and 12.5 × 2 tex. The second group of yarns consisted of three yarn samples that were blended with 93% meta-aramid, 5% para-aramid and 2% carbon in counts of 20, 20 × 2 and 17 × 2 tex. The inlet moisture of the yarn before sizing was 40% (with pre-wetting) and 4% (without pre-wetting), and the outlet moisture after drying was 4%. In order to carry out such tests to reproduce them, the sizing was carried out on a laboratory-sizing machine with the possibility of adapting to industrial conditions. According to the obtained results related to the properties of yarn before and after sizing, it can be concluded that sizing of aramid yarns is justified. When sizing the yarn without pre-wetting, the mechanical properties improved, especially breaking force, strength and abrasion resistance. Irregularity and hairiness were also reduced, especially when sizing with pre-wetting. Yarn hairiness or the frequency of protruding fibres also decreased with sizing in almost all samples and sizing conditions. The second group of yarns with a carbon fibre content mostly showed better mechanical properties before sizing, which continued after sizing. In general, the aramid yarn sized with pre-wetting showed certain deformations caused by stretching in the wet state and thus reduced the size pick-up, which caused less breaking forces and strength. Sizing with pre-wetting resulted in a slightly better smoothness of the thread and its higher evenness. It can be concluded that the aramid yarn should be sized with a lower size percentage (up to 4.5%), i.e., without pre-wetting in order to minimise the deformation of the yarn during sizing and thus improve the mechanical properties in the weaving process. Full article

It is well known that objectionable fibres emerge from the surface of the yarn due to the centrifugal force of the spinning device. Furthermore, the hair removal process is based on the same physical principles. However, the fibres that are >30 µm (PcF) are the fibres that appear in the hairiness of the yarn and are eliminated by dehairing. It has always been presumed that the PcF was linearly correlated with the diameter of the fibre (MFD) in llamas, but not so in alpaca fibres. Nevertheless, there is evidence that this relationship is curvilinear and behaves the same way in both species. The objectives of this study are to explore the relationship between MFD and PcF in both llamas and alpacas, to explore the existence of a breaking point (BP) in this curvilinear relationship, and to determine the frequency of fleeces that do not require dehairing because the PcF ≤ 3.2%. In addition, the existence of a positive bias of coarse fibre content on the hairy surface (CFs) of the yarn to coarse fibre content within the yarn fibres (CFy) was determined, which may explain the effect of the dehairing on the prickle factor of SAC fibres. The relationship of PcF on MFD behaves the same way in alpacas and llamas. It conforms to a power distribution and presents a BP of 23 µm, with PcF being constant before the BP and increasing significantly after it. Most animals (≤91% of alpacas and ≤87% of llamas) are above the threshold (≤3.2%), requiring dehairing to correct it. By means of a shaving technique on the surface of the fabric sample, it was established that the objectionable CFs content is 8.15% higher than the objectionable CFy content. In the evoked-coarse fibre in the dehaired samples, a CFs-CFy difference below 5.9% (p > 0.05) is not significantly detected by panellists. The surface MFD is more than 2.7 µm coarser than the yarn MFD. Full article

Textile yarns are the fundamental building blocks in the fabric industry. The measurement of the diameter of the yarn textile and fibers is crucial in textile engineering as the diameter size and distribution can affect the yarn’s properties, and image processing can provide automatic techniques for faster and more accurate determination of the diameters. In this paper, facile and new methods to measure the yarn’s diameter and its individual fibers diameter based on image processing algorithms that can be applied to microscopic digital images. Image preprocessing such as binarization and morphological operations on the yarn image were used to measure the diameter automatically and accurately compared to the manual measuring using ImageJ software. In addition to the image preprocessing, the circular Hough transform was used to measure the diameter of the individual fibers in a yarn’s cross-section and count the number of fibers. The algorithms were built and deployed in a MATLAB (R2020b, The MathWorks, Inc., Natick, Massachusetts, United States) environment. The proposed methods showed a reliable, fast, and accurate measurement compared to other different image measuring softwares, such as ImageJ. Full article

The aim of this research was to determine the optimum twist equation for ring-spun yarns. The yarn twist can be calculated by different equations. With the research, we tried to find the appropriate equation to determine the yarn twist, which is determined by the values of yarn strength and hairiness. In the research, yarns from long staple combed cotton rovings and of different fineness (10 tex, 11.8 tex, 20 tex and 29.4 tex) were analyzed. The yarn twist was calculated using the equations of Koechlin and Laetsch. The analyzed yarns were produced in the spinning mill on the laboratory ring spinning machine Spinntester. In the second part of the investigation, yarn strength and hairiness were analyzed as a function of yarn twist. The results showed that Laetsch’s equation is suitable for determining the twist for yarns with a fineness of 10 tex, 11.8 tex, 20 tex and 29.4 tex, since, in this case, the calculated number of yarn threads is higher and thus the strength and elongation at break are also higher. The yarn hairiness is higher in analyzed samples for yarns with the twist calculated according to the Koechlin’s equation. Full article

The use of synthesized natural starches for the sizing process in fabric production is primarily an environmental contribution. Synthesized corn starch is environmentally friendly and productive, showing good results in cotton yarn sizing. Acrylamide (AA) and 2-hydroxyethyl methacrylate (HEMA) were applied for the grafting process of corn starch, and the initiators azobisisobutyronitrile (AIBN), potassium persulfate (KPS), and benzoyl peroxide (BP) were chosen to form the grafted monomers more effectively. The application of synthesized corn starch has been confirmed, especially with the AIBIN initiator in the grafting process of HEMA onto starch. The FTIR analysis confirmed that new and efficient products for sizing cotton yarns based on natural raw material (corn) were developed. The research showed that the synthesized corn starch improved physical-mechanical yarn properties and abrasion resistance and reduced yarn surface hairiness. Ultrasonic desizing of yarn and the use of a lower size concentration led to better results than desizing by washing, and the Tegewa numbers confirmed that the desizing process was successful. Full article

The objective of this research was to verify the feasibility of the use of newly synthesized biopolymer materials for sizing cotton yarns based on the basic principles of chemical modification. Research included acid hydrolysis of potato starch up to controlled molar masses together with graft-polymerization and methacrylic acid onto hydrolyzed starch to improve hydrophilicity and solubility, to increase the capability of film forming, to increase adhesive potential and to avoid retrogradation phenomena. Research objectives were primarily focused on finding an appropriate, environmentally-friendly and productive sizing agent for cotton yarns via the analysis and systematization of a large number of synthesis methods in conjunction with the characterization and properties of graft-copolymers. The research results showed that potassium persulfate initiator was most efficient in grafting of methacrylic acid onto hydrolyzed starch, while azobisisobutyronitrile (AIBIN) initiator was most efficient in grafting of acrylic acid (AC). FTIR analysis confirmed that new and efficient products for sizing cotton yarns from synthetized potato starch were obtained. Research on rheological properties of copolymers shows a higher viscosity of grafted products indicating the good stability of potential starches. Ecological improvements have been established through high desizing degree as well as improvements in physical-mechanical properties of yarn, abrasion resistance and decrease in yarn surface hairiness were noticed. The use of new derivatives of potato starch, especially of hydrolyzed starch grafted with methacrylic acid (MAA), potassium persulfate (KPS) as initiator, was confirmed. Anova statistical analysis determined the influence of the entire sizing process on individual yarn parameters. Full article