Textile Research Journal

Flexibility, waterproofness, and breathability are fundamental demands for functional clothing and personal protective equipment. To solve problems caused by the low tensile strength and contradictions between hydraulic pressure and breathability of polyvinylidene fluoride (PVDF) electrospun membrane, graphene was added to PVDF spinning fluid to prepare a graphene/PVDF composite nanofibrous membrane using N,N-dimethylacetamide as a solvent, and the morphological structure, waterproofness, breathability, and mechanical flexibility of the nanofibrous membranes were characterized. The results show that the graphene/PVDF composite nanofibrous membrane was synergistically improved in waterproofness and breathability, and that the moisture permeability of the PVDF membrane increased from 2915.92 g/(m² 24 h) of pristine PVDF nanofibrous membrane to 4415.37 g/(m² 24 h) of 0.15 wt% graphene/PVDF composite nanofibrous membrane. This indicates that the addition of graphene can significantly improve the moisture permeability of PVDF nanofiber membrane. This finding is accompanied with a decrease in the contact angle and hydraulic pressure, from 143.5° and 23.4 kPa, respectively, for the PVDF membrane to 125° and 13.7 kPa, respectively, for the 0.15 wt% graphene/PVDF composite nanofibrous membrane, and indicates that the waterproofing performance was reduced. Controlling the graphene concentration at 0.1125 wt% was beneficial to improving both waterproofness and breathability. The average fiber diameter of the graphene/PVDF composite nanofibrous membrane with different graphene concentrations changed from 317 nm for the PVDF membrane to 355 nm (0.05 wt%) or 1268 nm (0.15 wt%) for the graphene/PVDF composite nanofibrous membrane. The incorporation of graphene in PVDF can satisfactorily change the flexibility of the PVDF nanofibrous membrane: the fracture strength and elongation of the PVDF membrane are 6.7 MPa and 8.3%, respectively, while the fracture strength and elongation of the graphene/PVDF composite nanofibrous membrane are, respectively, 13.2–19.8 MPa and 9.1%–17.9%. This work provides guidance for the preparation of a novel, smart, flexible, waterproof, breathable, wearable PVDF-based nanofiber membrane.

Poly(butylene succinate) (PBS) is a biodegradable material widely used in the field of plastic parts and films. However, its weak molecular entanglement limits its development in several fields, e.g., fibers. To address this issue, in this study, the relationship between macromolecular entanglement and branching structures on the crystallization and rheological properties of PBS were investigated. Both strategies strongly affect the performance of PBS by influencing its crystallinity and crystallization rate, spherulite growth and morphology. The decrease of spherulite growth is about 15 μm s⁻¹ at 85°C of the modified long-chain branched PBS. The structure also has a major influence on the rheological properties of the polymer: the network structure of the branched modified polyester makes PBS exhibit higher melt elasticity, which expands the processing window of PBS. Based on previous research, PBS multifilament were prepared by melt spinning. The existence of branched structure can significantly improve the performance of PBS fibers, the strength of PBS-POY fiber can reach 1.62 cN dtex⁻¹, but it shows greater thermal stress value. This provides a new route for the preparation of degradable PBS fibers.

The rise of e-commerce has brought women’s shirt images to the forefront, showcasing complex styles and diverse features. This presents a challenge for consumers in finding their preferred styles. Deep learning, known for its speed and superior retrieval performance, offers a solution by learning image features through multi-layer neural networks and extracting high-level semantic information, making it highly effective for recognizing and classifying clothing styles. The ResNet model excels in extracting detailed pixel information, but has limitations: (1) Jump connections in ResNet, which add input directly to the output in the residual block, can cause feature distortion, especially when input and output sizes do not match; (2) Despite many network layers, the effective receptive field of ResNet is smaller than theoretically expected. To address these limitations, this paper introduces the Transformer model into ResNet, proposing a new method for recognizing and classifying women’s shirt styles based on local detail feature extraction. The Transformer’s attention mechanism enhances the model’s ability to focus on important features and suppress less relevant ones, improving the accuracy of local detail feature extraction. This study examines six typical women’s shirt style features, applying the improved ResNet to their recognition and classification, resulting in a highly accurate and reliable model. This theoretical and practical advancement enhances the recognition of detailed features in women’s shirts, significantly contributing to the development of intelligent clothing design.

In this study, a machine vision system was developed to measure the angle of surface fibers on braided preforms produced via the radial braiding method in real time. The system includes an image acquisition device that captures the preform images and a controller unit that manages this device and performs image analysis. A calibration method using a laser sensor and a calibration cylinder was also developed, enabling precise angle measurements across preforms of varying diameter. An image analysis algorithm was proposed that automatically measures the angle using edge detection and autocorrelation techniques. The experimental results confirmed the accuracy of the system in measuring braiding angles on various preforms.

Down fabrics are the most natural form of thermal insulation due to their notable breathability. However, the primary issue with down jackets is their limited waterproofness. This study focuses on the down water-repellent process to improve the water-repellent qualities. The first stage discusses the single-quality optimization of process parameters using the Taguchi method. The down water-repellent process parameters for the experiment include the detergent, water-repellent agent, bridging agent and glacial acetic acid. The corresponding down fabric quality characteristics are water-repellent time, elasticity and oxygen number. The second stage was multi-quality optimization using Taguchi-based grey relational analysis. A grey relational grade is determined from the grey analysis. The optimized process parameters simultaneously led to a higher water-repellent, higher elasticity and lower oxygen number then verified through a confirmation experiment. According to experimental verification, the optimal process parameter combinations are: detergent concentration is 0.08% of bath ratio, weight ratio of water-repellent agent to down fabric is 5%, weight ratio of bridging agent to down fabric is 3% and glacial acetic acid concentration is 0.03% of the bath ratio. The corresponding optimal quality characteristics were: water repellency time of 120 min, elasticity of 18.4 cm/30 g and oxygen number of 0.8 mg/100 g. These were compared with the International Down and Feather testing laboratory standards used by the industry, which are water-repellent time of 60 min, elasticity of 14.5 cm/30 g and oxygen number of 10 mg/100 g. This study’s optimized water-repellent process’s qualities are much better than the industrial spec requirements for down fabrics.

Different environmental light affects the effect of textile surface color rendering, thus affecting the accuracy of machine vision-based textile quality inspection. In order to solve this problem, we propose an illumination correction model based on the improved Aquila Optimizer (AO) for the kernel extreme learning machine (KELM). First, a simulated annealing algorithm is used, and its better solution is used as the initial population of the AO, which increases the global search capability and improves the stability of the algorithm. Second, the KELM model is introduced and the AO optimization algorithm is used to obtain the optimal penalty parameter C and kernel parameter γ of the KELM, thus constituting the SA-AO-KELM model. Finally, some textile images are selected to test the effect of the constructed light correction model using their chromaticity features, and the effect of light in the image is eliminated according to the model output. In order to verify the effectiveness of the designed illumination correction method, this paper adopts the chromatic aberration detection for the comparative analysis of the images before and after correction to evaluate the effect of illumination correction on improving the performance of textile color quality detection. The experimental results show that the method proposed in this paper can effectively improve the textile image presentation. The average color difference between the corrected image and the standard image is calculated to be 2.9991 using CIEDE2000, which reduces the color difference by about 24.43% compared with the traditional kernel extreme learning machine.

The present study delves into the cognitive aspects of fabric hand evaluation, particularly under varying scenarios of real touch and visual representations (image, video). Conducted within a cognitive psychology framework, the study used a two-step rating-based double-faced applicability (DFA) test across a panel of 30 experts who evaluated 101 textile samples using 20 semantic differential descriptors. The findings indicate scenario-based discrepancies, especially pronounced within the thermal-wet sensation properties, underscoring the difference between real touch and visual representations on sensory ratings. By implementing the DFA test, the research reveals biases inherent in fabric hand evaluations and suggests modifications to enhance validity. This work not only contributes to textile quality assessment methods by recognizing scenario effects but also advances the understanding of the cognitive processing behind textile sensory perception.

Improved abrasion resistance and shear performance of alumina fiber yarns is crucial to the weaving process. Herein, a series of novel SiO₂@Al₂O₃ single-layer and double-layer covered yarns were prepared by winding 48 tex quartz yarn on the outer layer of 120–600 tex alumina yarn with wrapping twist of 50–500 twists/m. Wear resistance, shear force, and tensile fracture properties of the covered yarn were tested and analyzed. Wrapping twist ranges from 50 to 400 twists/m, with increased wrapping twist improving the SiO₂@Al₂O₃ covered yarn's breaking strength, shear force, and wear resistance. As wrapping twist ranges from 400 to 500 twists/m, uneven winding and wear of quartz yarn weaken the breaking strength, wear resistance, and shear properties of SiO₂@Al₂O₃ covered yarn. SiO₂@Al₂O₃ double-layer covered yarn exhibits superior shear force, wear resistance, and tensile breaking strength compared with its single-layer counterpart. In comparison with the initial alumina yarn, wear resistance of SiO₂@Al₂O₃ covered yarn improves by 50%, whereas the maximum shear strength and tensile breaking strength increase by 125% and 15%, respectively. High-strength, wear-resistant SiO₂@Al₂O₃ covered yarn is suitable for alumina fabrics and braid structures, and is expected to be widely used in aerospace and the high-temperature insulation industry.

Curling is a common phenomenon in knitted fabrics, which can affect production efficiency and quality, and edge curling is greatly affected by dyeing and finishing processes. This article proposes a pixel-analysis-based testing method to quantify edge curling problems and reveal the relationship between curling and dyeing parameters. This study investigated three different colors (white, blue, and black) on the same fabric sample. For each color, three finishing methods were used: no softener, adding softener, and high-temperature setting after adding softener. The results showed that the rolled edges of the fabric were severe, and even without the addition of softener, the rolled edges of all three colors of fabrics were improved significantly after dyeing. The rolled edges of white fabrics are the lightest, whereas the curled edges of blue and black fabrics gradually increase. Softener effectively improves the problem of edge curling by reducing the friction coefficient between yarns, decreasing internal stress in fabrics, and lowering the bending stiffness of yarns. The mechanical effects of high-temperature setting and pressure flatten the fuzz, which can reduce the friction coefficient and bending stiffness of the yarn, and also help improve edge curling.

Agro-industrial waste corncob was used to develop a new thin corncob/hemp composite fabric (CHCF) material to improve the sound absorption performance of fiber materials. CHCF materials were prepared from agro-industrial waste corncob pellets and natural hemp fiber by spreading corncob pellets in hemp fiber webs, layering, and high-temperature treatment. The sound absorption performances of the CHCF material, corncob material, and hemp fiber nonwoven fabric were compared, and the effects of the size, content, and gradient mass of corncob pellets, and the gradient thickness of the hemp fiber web on sound absorption by CHCF materials were also analyzed. The results showed that the CHCF material with a smaller thickness exhibited better sound absorption than corncob material and hemp fiber nonwoven fabric, and the smaller the corncob pellet size, the better the sound absorption. The content of corncob pellets had a significant impact on the sound absorption of CHCF. The gradient structures were conducive to further improving the sound absorption of CHCF materials.

To foster a circular economy, it is crucial to increase the recycling of solid waste significantly, particularly post-consumer textile waste, and identify viable applications for the reclaimed materials. This study specifically examined the integration of mechanically recycled post-consumer polyester and cotton fibers containing fractions into carded and needle-punched non-woven structures. The research investigated the carding processability of four mechanically recycled fibers, examining their performance when combined with virgin polyester fibers in varying ratios. The fiber fractions were characterized in terms of mean fiber length, short fiber content, fiber uniformity, diameter, fiber bundle strength, and elongation. The processability challenges encountered during the carding of the mechanically recycled textile fractions were mainly associated with high proportions of synthetic recycled fibers, which resulted in increased fiber piling on the metallic parts of the carding device. Furthermore, the effect of different blend ratios of mechanically recycled polyester and cotton fibers with virgin polyester fiber on the properties of carded and needle-punched structures, including mass per unit area, thickness, tensile strength, elongation, air permeability, morphology, and porosity was studied. The tensile properties and air permeability of non-wovens obtained from 50/50 blends of recycled polyester cotton and virgin polyester were comparable to those produced from 100% virgin polyester fibers. The results of this study demonstrate that carding and needle punching provide a robust route for processing polyester and cotton containing mechanically recycled end-of-life textile-based fractions without the use of chemical additives.

The present study aimed to employ a synergistic approach integrating experiments and molecular dynamics simulation to investigate the swelling of PET films caused by SC-CO₂ and sorption of CO₂ within the PET films. The rate of volume change (ΔV/V) of PET films increased with temperature elevation (50–140°C) and pressure elevation (10–27.5 MPa) which were often used in dyeing process. The sorption concentration decreased with rising temperature at the lower pressure, while at high pressure, it increased concomitantly with temperature elevation. The values of glass transition temperature (T_g) predicted by ΔV/V were 75.5, 69.7, and 60°C , and fitted by fractional free volume (FFV) were 75.8, 67.0, and 56.7°C , respectively. In addition, it was found that the swellability of PET was dependent upon the interaction between CO₂ and the carboxyl group of PET chains. Eventually, the model based on backpropagation neural network optimized by Sparrow search algorithm (SSA-BPNN) exhibited robust predictive capabilities, yielding results consistent with experimental data for training set and testing set. Therefore, it can be concluded that the SSA-BPNN model is verified to be an efficient quantitative tool to predict the ΔV/V caused by SC-CO₂ and sorption concentration within PET films in this work.

In many industries, workers are facing the inconvenience caused by icing on fabrics. Fabric is a porous material, and researchers generally hold that porous material is not suitable for being used as an anti-icing material. Therefore, research on the anti-icing properties of fabrics have not attracted researchers' attention, thus relevant research results are few. The influence of sample surface wettability on snow melting has been rarely studied in depth. In this paper, a test device for testing the melting rate of ice on the surface of a sample was developed. Based on this experiment, a simulation model for simulating the melting process of the ice on the aramid IIIA fabric samples was established based on the melting promoting anti-icing theory. This work also indicates that the use of computational models in the study of ice melting processes is successful. Results show that the relative error between the simulated value and the experimental value was 1.63%. These computational models provide details on the phase transition and temperature changes of ice during the melting process, which are difficult to observe in experiments. Meanwhile, the simulated phase transition of ice is consistent with actual observations. The results indicate that the simulation model established in this article can be extended to study the melting process of ice on aramid IIIA fabric samples, and can be used to guide the study of the anti-icing performance of aramid IIIA fabrics.

Fabric hand, or the tactile properties of fabric, is a vital consideration in textile quality assessment. Traditional evaluation methods, including sensory tests and instrumental assessments, reflect either human perception or objective data, but each has its own limitations. This study introduces Textile Attribute Integration and Learning (TAIL), a deep learning framework that combines numerical data, images, and videos of fabric to evaluate fabric hand comprehensively. TAIL enhances hand scoring prediction, classification, and decision-making by leveraging multimodal data fusion, capturing both human expertise and AI-driven features. Addressing the need for remote fabric evaluation in international trade and virtual environments, TAIL outperforms traditional methods, providing a robust tool for the textile industry. Experimental results confirm its superior accuracy and reliability in fabric quality assessment and decision-making.

Spacer fabrics are 3D technical textiles. Their compressibility can be adjusted during production, e.g. by changing the machine parameters or using different yarn materials. These steps require extensive knowledge of the production process and are difficult to change. This research takes an innovative approach to influence the compression behavior individually. It introduces the concept of creating locally sectioned spacer fabrics through sewing. The compression behavior of these fabrics, with seam distances of 43 mm, 24 mm, and 18 mm, was thoroughly tested and evaluated. A two-step numerical model was developed to accurately predict the compression behavior of these spacer fabrics with seams. The model’s validity was confirmed through experimental compression tests. The results show that spacer fabrics with a larger number of seams absorb compressive forces less. The numerical model agrees well with the experimental results and proves its applicability. Using the numerical model seam patterns for larger spacer fabrics can be tested and individual modified spacer fabrics can be produced.

The fiber arrangement in a sliver has a significant effect on sliver quality, especially on its evenness. In this work, a novel method was developed to simulate fiber arrangement based on the variation fineness of each sliver subsegment relative to the mean sliver fineness, which was acquired from the tested sliver unevenness curve by image processing. The modeling results of viscose indicated that the non-random fiber arrangement in the sliver could be appropriately and graphically expressed by the variation in the fineness of each sliver subsection relative to the mean sliver fineness. The results further indicated that the simulated fiber arrangements in the sliver were in line with actual ones. Moreover, the relative errors between the irregularities calculated from the fiber arrangement in the sliver and those measured were less than 8%, whereas the relative errors between the simulated values from the random fiber arrangement and those tested were approximately 95%. The obtained simulation results demonstrated that this new method accurately simulated the non-random fiber arrangement in the sliver and provided an accurate prediction of sliver irregularity in the further post-spinning processes.

Microplastic fibers (with a diameter of less than 5 mm) generated by synthetic textiles during the washing machine laundering process are considered to be one of the primary sources of pollution in both water and land environments. This study aimed to investigate the impact of novel combinations of washing parameters on the release of microplastic fibers. A wall-mounted drum washing machine was chosen as the subject of research in order to comprehensively examine the influence of washing conditions on the release of microplastic fibers during the washing process. The results showed that the length of shedding microplastic fibers were between 300 and 400 µm. The diameter of the shedding microplastic fibers was 13 ± 1 µm. High amounts of water and high load can lead to an increase of fiber microplastic release. Abrasion between fabrics influenced the release of microplastic fibers according to the movement of the fabric, which could affect fiber directional migration conditions. The findings on microplastic fibers release will help in the development of better front-loading washers.

Machine learning (ML) and deep learning (DL) are transforming the textile industry by integrating advanced technologies into various processes. Textiles, once seen as passive materials, are now essential components of complex systems due to automation and innovative materials. This review focuses on articles that utilized AI, ML, or DL in textile research and industry. The review presents bibliometric analysis of AI methods in textiles. Later, the review is structured into sections that examine the effect of ML and DL across the textile sector. We outline key ML and DL methods applied in textiles, discussing their main uses and potential applications. This overview aims to clarify the working principles behind these methods, which are explored in greater detail. The methods analyzed range from basic linear regression to ensemble techniques such as XGBoost. DL techniques include convolutional neural networks for image analysis and long short-term memory networks for time-series analysis. In addition, a bibliometric review identifies trends and gaps in the literature, highlighting areas for future research. We also provide a detailed examination of how these methods are implemented in textiles.

Three-dimensional (3D) braiding has excellent mechanical properties due to its unique spatial organization, in which simulation modeling of the braiding process is a key technology to study the complex spatial structure of 3D braided fabrics. The paper first summarizes the research methods of 3D braiding simulation modeling technology in recent years; second, it focuses on the analysis of numerical method modeling based on geometric dynamics, geometric modeling based on the braiding process, and modeling based on the finite-element method; then, based on the existing technology and application status quo, it sums up the advantages and shortcomings of the current 3D braiding process simulation modeling technology; and, finally, it describes the challenges and opportunities of the 3D braiding simulation modeling technology. The future development direction of the three modeling methods and the new idea of mutual coupling of the three methods are proposed and future development prospects are described. The paper provides technical support for the simulation modeling of the braiding process in 3D braiding technology.

This paper provides a comprehensive scoping review of external breast prostheses, an essential component of post-operative care for breast cancer patients, the most common cancer globally. Mastectomy, a frequent treatment, often leads to physical and psychological issues. External breast prostheses help improve body image and psychosocial well-being after surgery. We included studies on the views, evaluations, opinions, and experiences of women who had had a mastectomy and medical personnel regarding external breast prostheses, as well as the assessment, design, and simulation of these prostheses. A scoping review was conducted using Web of Science Core Collection, PubMed, Medline, Scopus, and Google Scholar. We reviewed relevant English research articles published in peer-reviewed journals up to December 2023, focusing on “breast cancer,” “mastectomy,” “post-mastectomy recovery,” “breast prosthesis satisfaction/dissatisfaction,” and “external breast prosthesis/prostheses.” Thirty-four studies were analyzed, covering various prosthesis types, user feedback, effects on daily activities, and new technologies and designs. This review reveals the prosthesis’ significantly positive effect on women’s lives after mastectomy, but also such persistent issues as weight, price, and body movement compatibility. Moreover, the paper suggests the potential for advances to improve comfort, aesthetics, and affordability.