Textile Research Journal

Due to the brittleness of glass fibers, cut-resistant fabrics knitted from glass fibers tend to develop broken fiber ends on the surface during use. This can cause itching, redness, or even allergic reactions on the wearer’s skin, severely affecting wearing comfort. Glass fiber bundles (GF) and glass/steel fiber bundles (GS) were impregnated with ultraviolet-cured polyurethane acrylate (UV PUA) resins at varying hydroxyethyl methacrylate (HEMA) concentrations, resulting in glass fiber/UV PUA composite threads (GUCT) and glass/stainless steel fiber bundles/UV PUA composite (GSCT). The impact of different concentrations of HEMA on the composite threads was examined in the study. Its main objectives were to evaluate the mechanical properties of the PUA resin, the tensile strength of the composite threads, and the washability, wear resistance, and cut resistance of the knitted composite fabrics. The research results show that the infiltration of PUA resin improved the tensile strength of GF and GS (increased by 64.35% and 64.84%, respectively), thus reducing the likelihood of glass fibers breaking due to excessive tensile stress. The cutting load of GF fabric increased by 24.51%, whereas GS fabric decreased by 18.25%. In addition, the wear resistance of both composite thread fabrics decreased due to changes in fiber bundle thickness caused by PUA resin impregnation, which affected the outer yarn density. The washability of the fabric indicates that this process significantly reduced the number of broken ends in glass fibers, improving the comfort of GF and GS fabrics.

In the research, the dye uptake of PET fibers treated with Disperse Yellow 23 dye in supercritical CO₂ was systematically explored across a range of parameters including dyeing time (10–90 min), dyeing temperature (70–130°C), and dyeing pressure (15–30 MPa). The models based on back propagation neural network (BPNN), genetic algorithm-BPNN (GA-BPNN), particle swarm optimization-BPNN (PSO-BPNN), and Sparrow search algorithm-BPNN (SSA-BPNN) were trained with input layer encompassing dyeing time, dyeing temperature, and dyeing pressure, and the output layer composed of dye uptake of Disperse Yellow 23 dye. The results show that the SSA-BPNN model demonstrated higher prediction accuracy, as evidenced by metrics including mean absolute error, mean squared error, root-mean-squared error, mean absolute percentage error, and correlation coefficient (R²), with values of 0.823, 1.139, 1.067, 3.565%, and 0.994 for the training set and 0.641, 0.626, 0.792, 3.558%, and 0.995 for the testing set.

3D braiding technology is a crucial approach for the low-cost and rapid production of composite materials. However, the length of the fiber catenary changes during the braiding process. To keep the yarn tension stable, it is necessary to retract and release the yarn. Defects in the preform caused by unstable tension during this process occur frequently in 3D braiding. A 3D spherical braiding machine is proposed in this paper to solve this problem. A general model of guide slider trajectory in the track is proposed to describe the spatial motion of the carrier. The nonlinear mapping relationship between carrier motion and fiber catenary length is established. The track models of spherical and cylindrical braiding machines are established, respectively. The spatial position of the carrier outlet serves as the boundary condition to analyze the dynamic change principle of fiber catenary. Finally, experiments are conducted using a 3D spherical braiding machine and a cylindrical braiding machine of YunLu Composite and KUKA robot. The model is reliable and the error is mainly caused by the offset of the fell point due to the interaction force between the yarns and the clearance in machines. The fluctuating speed of the catenary can be reduced by 43.2% and the storage capacity of the carrier is reduced by 51.8%. The proposed spherical braiding machine has great application potential for higher precision and lower wear braiding of important structural components.

This paper presents a parallel web crawling system designed to collect publicly available customer shopping data from a retailer's website, aiming to understand customer purchase behaviors and to formulate customized engagement strategies. The system architecture includes a parallel crawling framework with scalable leaf units that facilitate the distributed crawling process, expediting the complete downloading of the retailer’s product webpages. The collected data encompasses customers’ purchase data, including product name, price, and date, alongside demographic information such as gender, age group, and location. Our dataset comprises 836,369 shopping records, representing 27,160 items purchased online by 455,088 customers over a decade-long period. This dataset can be transformed into recency–frequency–monetary (RFM) metrics, suitable for k-means clustering analysis to segment customers into distinct clusters: Lost, Potential, Hibernating & Valuable, Extant & Active, and Loyal & Lucrative. These clusters provide valuable customer profiles to assist the retailer to meet the unique needs and preferences of each cluster. Demographic analysis of the clusters reveals that female customers constitute nearly 90% of the Potential, Extant & Active, and Loyal & Lucrative groups, underscoring their significant role as active and valuable consumers on the website. Furthermore, customers aged 50 and above account for over 62% of the demographic across all clusters, highlighting the retailer's appeal among older shoppers. A geographical breakdown shows that California, Texas, New York, and Florida are the states with the highest customer concentration in every cluster.

The viscose components of waste wool/polyester/viscose fabric were dissolved using the phosphoric acid/polyphosphate system. Following filtration, the wool underwent dissolution through the urea/sodium bisulfite/sodium dodecyl sulfate (SDS) system, and subsequent keratin extraction ensued. The impact of urea, sodium bisulfite, and SDS concentration, as well as reaction time, on the solubility and extraction rate of keratin during the wool extraction process was examined. The obtained keratin was subjected to performance testing. The remaining polyester and viscose cellulose were not used as primary research subjects for the related experiments. Aged wool samples were used as test samples and treated with keratin/ethylene glycol diglycidyl ether (EGDE) and keratin/glutaraldehyde, respectively. The optimal conditions for wool dissolution were determined as 300 g/L urea, 40 g/L sodium bisulfite, and 20 g/L SDS for 6 hours, resulting in wool dissolution and keratin extraction rates of 89.53% and 49.32%, respectively. After testing and calculation, the stress–strain at break of the aged wool sample was 6.67 MPa and 5.25%, while the stress–strain of the keratin/EGDE and keratin/glutaraldehyde-reinforced samples was 22.67 MPa and 21.5%, and 24.41 MPa and 25.25%, which is about two times higher compared with the aged sample, and the glutaraldehyde proved to be more effective as a reinforcing additive. The stress–strain was increased by about two times in comparison with the aged sample. These findings offer new ideas and research methods for conserving and consolidating organic matter artifacts in “keratin/EGDE” and “keratin/glutaraldehyde” artifact samples.

Polylactic acid (PLA) fiber, renowned for its renewable and biodegradable nature, is increasingly recognized as a promising alternative to conventional polyethylene terephthalate (PET) polyester fiber in the textile industry. However, the poor dyeability of PLA has limited its widespread industrial application. This study aims to enhance the dyeability of PLA fabrics by modifying the cross-sectional shape of PLA filaments and optimizing the dyeing conditions for disperse dyes. PLA filaments with circular, cruciform, and trilobal cross-sections (designated as PLAO, PLA+, and PLAY, respectively) were fabricated and then knitted into fabrics. Dyeing experiments using three different disperse dyes consistently demonstrate that PLA+ fabrics exhibit the best dyeing performance, followed by PLAY fabrics, with PLAO fabrics showing the lowest effectiveness. These results highlight the significant effect of filament cross-sectional shape on dyeing performance. Further macroscopic and microscopic analyses indicate that profiled PLA+ and PLAY filaments possess higher non-roundness factors and specific surface areas, lower glass transition temperatures, and reduced crystallinity, collectively enhancing their dyeability. In addition, lower fabric luster contributes to achieving deeper color. The study also identifies the optimal dyeing conditions for PLA fabrics with the selected disperse dyes: pH 5.0, dye concentration of 4% owf, dyeing temperature of 110°C, and dyeing time of 30 minutes. Under these optimized conditions, PLA+ and PLAY fabrics exhibit significantly enhanced K/S values, while dyeing levelness and color fastness across all PLA fabrics remain within acceptable ranges.

Uncontrolled wound bleeding and bacterial infection are the most common problems in wound healing/skin regeneration, and failure to provide effective care will obviously increase the risk of other diseases and reduce the quality of wound healing. The ideal strategy is to prepare a simple dressing with rapid hemostasis and durable antimicrobial activity as an alternative to conventional hemostatic gauze to facilitate satisfactory wound healing. In this study, Ag-doped TiO₂ nanoparticles modified with unsaturated carbon–carbon double bonds (Ag@M-TiO₂) were synthesized and grafted onto the surface of cotton fabrics by chemical finishing. Compared with the common cotton fabrics, the Ag@M-TiO₂ modified cotton fabrics (C-Ag@TiO₂) had a higher blood affinity and were able to absorb spilled blood and achieve rapid hemostasis by aggregating erythrocytes, stimulating blood platelets and activating hemostasis-related pathways. The mouse liver internal hemorrhage experiment confirmed that the wound could achieve rapid hemostasis within 30 s. In addition, the C-Ag@TiO₂ dressings exhibited broad spectrum antimicrobial properties (>99%) and could promote wound healing by preventing bacterial infection (the wound closure ratio was almost 100% after 14 days). The study demonstrated the important role of the multifunctional C-Ag@TiO₂ dressings in healing infected wound and controlling internal bleeding. It provides a potential strategy for wound healing and skin regeneration to replace traditional cotton gauze.

In order to solve the problems of noise, blurring, and loss of detail in fabric defect images due to camera location and factory environments, a fabric defect image denoising method based on improved generative adversarial network (GAN) is proposed. A mathematical model is developed to simulate the noisy images of fabric blemishes in a real factory environment. GAN, as a deep learning method widely used in the field of computer vision, generates high-quality data samples through the competition between generators and discriminators. In this study, the traditional GAN is improved by introducing multiple loss terms and adopting a new adaptive weighting strategy in order to make the denoised image closer to the original clear image while better preserving the structural and detailed features of the image. In addition, the network structure is improved by introducing a multiscale discriminator, a convolutional block attention module (CBAM) and a residual network to capture feature information at different spatial scales more effectively and to promote the propagation of low-level features. The experimental results show that the method can effectively remove fabric defective image noise, and its subjective effect and objective indexes are better than similar algorithms. The research results are of great value to quality inspectors, related researchers, and image denoising technology professionals in the textile industry. These results help to improve the efficiency and accuracy of fabric defect detection and image denoising processing, thus promoting the development of related technologies.

Thermally activated shape memory polymers are of increasing interest nowadays. Their incorporation in textile-based preforms reinforced composites is being focused on, owing to some functional applications of medical textiles, e.g., posture support of joints after fracture surgery where gradual healing of fractures requires the joints to be moved slightly, which is not possible with conventional supports. Hence, this study was aimed at the development and characterization of thermally activated shape memory composites having woven glass fiber reinforcements for such applications. Three different stackings or laminates were developed (single-, double-, and triple-layered composites), solely using glass fiber, whereas resin material induced shape memory function. The glass transition temperature of the engineered specimens was determined, and the shape memory function was evaluated at three different temperatures, namely, below, at, and above the glass transition temperature (45°C, 65°C, and 85°C). Increasing the characterization temperature decreased the shape recovery time by 75%, owing to softening and better polymer chain mobility at higher temperatures. The shape recovery velocity also increased with increasing temperature. Increasing the number of glass fabric layers compromised the shape memory function; however, the interlaminar shear strength was improved.

In this paper, a new analytical model is proposed that employs experimental data as input to predict the in-plane shear modulus of plain-woven fabric composites. This model boasts high efficiency and rapid computation. Initially, the composite yarn is considered as an idealized curved beam, with its undulation path described by an arc curve and its cross-section simplified into a lenticular shape. Subsequently, a force analysis of the yarn segment is conducted, and the results of the analytical model are derived using the minimum potential energy principle and Castigliano’s second theorem. The input parameters for the analytical model are obtained through thermal ablation experiments and microscopic observations, with T300/Cycom970 plain-woven fabric composites selected as the experimental subject. In addition, tensile tests on ±45° laminates are conducted to validate the effectiveness of the analytical model. The results demonstrate a minimal deviation of −4.15% between the experimental outcomes and the predictions of the analytical model, thereby verifying the model’s validity and the accuracy of the experimental parameters. Leveraging the validated model, the study investigates the influence of crimp ratio and fiber volume fraction on the in-plane shear modulus. Furthermore, to enhance the applicability of the model, a simplified improvement scheme that incorporates temperature and humidity considerations is proposed. This model not only effectively addresses the mechanical property analysis challenges of plain-woven fabric composites, but also provides valuable references for parameter estimation, model construction and experimental design of other woven composite materials.

In the pretreatment process of warping, it is extremely tedious work to take out a large number of package yarn ends from the warping frame and pull them to the warping machine; this task is currently completed manually. To automate this process, in this work, a composite robot equipped with a red-green-blue-depth (RGB-D) camera is adopted to automatically identify, locate, and grasp the package yarn ends on the warping frame. This work builds on previous research, where the yarn end was made to protrude from the package surface through the application of negative pressure. With the aim of addressing the challenge of determining depth accurately, owing to the weak features of single yarn ends, a novel approach is proposed that combines point cloud and image processing to estimate the precise spatial pose of the robot when grasping yarn ends. First, point cloud processing enables precise identification and spatial localization of the circular contour on the package end. Subsequently, image processing, with the introduction of a reinforcement operator for weak features, facilitates extraction of the yarn end and accurate determination of its relative position with the center of the circular contour on the package end. Finally, an automated grasping path for the composite robot is planned, based on geometric information about the spatial circle on the package end and the relative positioning between it and the yarn end. The accuracy of identifying and localizing yarn ends detached from the packages, as well as the effectiveness of the automatic robot gripping strategy, are validated through simulation and experimentation.

To enhance the quality of cotton webs during the high-speed combing process, an analysis of the formation mechanism of fiber bundle hooks was conducted. Predictive models for the length and mass of fiber bundle hooks were developed based on parameters of the combing process and cotton lap. The length of hooks was measured by ImageJ software, while the mass of hooks was determined by the cutting-weighing method. The accuracy of the predictive models was evaluated by comparing theoretical values with actual measured values. Additionally, the impact of speed on the length and mass of fiber bundle hooks, as well as the coefficient of variation (CV) of cotton webs and combed slivers was investigated. The results indicate that the theoretical values predicted by the models exhibit good consistency with the actual measured values. For long staple cotton fiber bundles, the maximum error between theoretical and actual hook lengths is 7.0%, while for medium staple cotton, it is 8.5%. Similarly, the maximum errors between theoretical and actual hook mass of long staple cotton and medium staple cotton fiber bundles are 7.1% and 7.9%, respectively. In addition, the increase in speed and the decrease in the flexural rigidity of cotton lap result in a gradual increase in the length and mass of the hooks, accompanied by a rapid reduction in fiber bundle length, which subsequently leads to an increase in the CV of cotton webs and combed slivers. The predictive models provide a theoretical foundation for optimizing the nipper mechanism of the comber.

The emergence of dual-feed-opening (DFO) rotor spinning has garnered notable attention in the area of innovative spinning techniques. In this research, we delved into the effects of the angle between two fiber-transfer channels (FTCs) on airflow dynamics, yarn structure, and other properties within a DFO rotor spinning system. Computational fluid dynamics simulation revealed that a 110° angle created an asymmetric airflow, marked by pronounced vortices and localized high-pressure zones, which diminished yarn tenacity to ≈11.6 cN/tex and increased unevenness to over 16%. In contrast, samples 18BW and 18WB demonstrated superior performance in hairiness index and imperfections. Digital image processing analysis indicated that, at a 110° FTC angle, fibers from feed roller 1 were more likely to accumulate on the yarn surface. Conversely, symmetrical FTCs produced a consistent airflow, promoting a uniform fiber arrangement, regardless of the feeding method. The ratio of the area occupied by two different colored fibers in the yarns was close to 1. This investigation holds significant importance in the advance of rotor-spun yarn development.

Sustainability in textile processing can be induced by the use of green nanotechnology and biomaterials. In this study, silver nanoparticles (AgNPs) have been synthesized by the bio-reduction of silver nitrate using eucalyptus (Eucalyptus globulus) leaves extract. Biosynthesized AgNPs were characterized by using particle size analyzer, ultraviolet (UV) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD) analysis. AgNPs were employed for natural dyeing of cotton fabric with pomegranate rind (Punica granatum) extract and chitosan as a binder. The effects of AgNPs concentration (100–300 mg L^–1), chitosan concentration (0.5–1.5%), and pomegranate rind extract (5–15%) on color strength (K/S) and UV protection (UPF) values were investigated. These variables were optimized using response surface methodology. The quadratic models were developed for further sustainable product development. AgNPs of 279 mg L⁻¹ concentration, 1.3% chitosan, and 14.8% pomegranate dye yielded maximum K/S (8.11) and UPF (223.5) values. This research work can be extended for other bio-reducing agents.

Needleless electrospinning technology is considered an effective way to produce nanofibrous materials on a large scale. The electric field strength and its distribution during the electrospinning process play an essential role in controlling the diameter and quality of the nanofibrous membrane. To achieve the mass production of nanofibers, this study proposed the design concept of a new type of needleless electrospinning spinneret with a beveled tip and narrow slit shape. The finite element analysis software of Comsol Multiphysics 6.0 was used to simulate the electric field contour of the new type of needleless electrospinning spinneret with a beveled tip and narrow slit shape. In performing the simulation of electrospinning with this spinneret, the values of the electric field strength on the surface of the spinneret and the variation of its electric field strength distribution curve were obtained. The optimal beveled narrow-slit spinneret structure was determined by varying the beveled tip angle, inner ring material, applied voltage, and receiving distance. The results showed that the highest value of electric field strength at the tip of the spinneret could be obtained when the inner ring was made of PTFE and the beveled narrow-slit spinneret was at 120°. Finally, the polyurethane nanofiber membrane was successfully fabricated using this new type of electrospinning spinneret. The nanofiber membrane with a mass fraction of 17% had a good micro-morphology. The diameter distribution was very uniform, and the average diameter was 624 nm. The production was up to 9.6 g/h, which was 96 times higher than that of single-needle electrostatic spinning. The feasibility of mass production of nanofiber membranes using this novel spinneret was verified.

Textile laundering is an energy-consuming and environmentally challenging process. This paper reviews the reasons for the necessity of textile laundering and its relation to odor, and alternative strategies to reduce garment washing frequency. The paper also discusses at length the various sources of odors and types of odor management techniques. It explores the current odor evaluation standards and processes, and further debates the challenges in odor management technologies in textiles.