Table of Content

10 December 2024, Volume 32 Issue 12

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Fabrication and functional study of graphene/polyacrylonitrile skin core structural fibers

CUI Ruiqi, SHANG Yuanyuan, LI Juanjuan, ZHANG Hao, SHI Baohui, FANG Kuanjun

2024, 32(12):  1-9. 

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Graphene, as a new type of two-dimensional carbon nanomaterial, has emerged as a prominent research focus in recent years due to its unique composition and excellent conductivity, thermal conductivity, and mechanical properties. The graphene fiber, a novel carbon-based fiber crafted from graphene layers, inherits the advantages of graphene, such as lightweight and flexibility. In many practical applications, balancing the multiple properties of graphene fibers can be challenging, and it is necessary to choose a suitable spinning method to regulate the chemical composition and structure of graphene fibers and polymers, so as to broaden the application field of graphene-based fibers. 
Polyacrylonitrile is a synthetic fiber with good elasticity and weather resistance. It can be used in industrial production through wet spinning and can be combined with graphene incorporation to obtain high-performance composite fibers. Microfluidic spinning technology is a new type of spinning technology developed on the basis of microfluidic chip technology, which is combined with wet spinning technology to build a microfluidic wet spinning system. Microfluidic chip technology enables precise control over the microstructure of spinning solutions and is characterized by a high degree of miniaturization, integration, and cost-effectiveness. This article utilizes microfluidic wet spinning technology to prepare a novel type of conductive fiber with a core-sheath structure. By precisely adjusting the spinning fluid inside the microfluidic chip spinning channel, the spinning fluid exhibits laminar flow characteristics in the microchannel. Through a simple process, the directional control of graphene in the fiber outer layer is achieved, fully utilizing the high conductivity and high specific surface area of the outer layer material, as well as the mechanical properties of the core layer polyacrylonitrile fiber. As the flow rate of the composite fiber layer increases, the arrangement of graphene in the fiber layer gradually becomes standardized under shear stress and compression in the microfluidic chip channel, forming a continuous conductive path.
The article studied the morphology, mechanical properties, and electrical properties of skin core fibers. In SEM images and stress-strain curves, compared with pure polyacrylonitrile fibers, the diameter and strength of composite fibers showed a trend of first increasing and then decreasing with the increase of skin solution. The resistance of core-sheath composite fibers decreased and the conductivity increased. When the skin flow rate reached a certain value, due to the aggregation of graphene in microchannels, the conductivity of the composite fibers decreased, and their mechanical properties also declined. It could be found that by applying different voltages at both ends of the composite fiber to test the electrical heating performance of the composite fiber, the highest temperature reached by the fiber and the heating rate per second vary under different voltages. The higher the voltage applied at both ends of the fiber, the greater the maximum temperature reached and the heating rate also increases. The test shows that the core-sheath fibers had good thermal stability and electrical cycling, and the voltage at both ends of the composite fiber could be quickly heated to the expected temperature.
Functional heating conductive fibers are poised for long-term development in the textile industry, driven by intelligent production processes and technologies. The study of microfluidic wet spinning of graphene and polyacrylonitrile in this article can provide some reference for the development and application of conductive fibers.


Core-shell structured PEDOT:PSS/SA@MXene composite fibers with microwave absorption performance

XU Wenyu, WANG Huiya, ZHU Yaofeng

2024, 32(12):  10-28. 

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The booming development of the 5G era has facilitated the rapid growth of electronic information technology, providing efficiency and convenience, but inevitably giving rise to electromagnetic pollution, which poses irreversible harms to human health and the environment. To address this issue, microwave absorption materials (MAMs) have been developed and utilized. In civilian applications, MAMs are commonly used as patches in mobile devices and computers to prevent interference and minimize the electromagnetic radiation leakage. In military contexts, stealth technology enhances the survivability and defensive capabilities of weapons, providing a strategic advantage in modern warfare. Thus, MAMs play a crucial role in both military stealth operations and civilian protection. Traditional MAMs, such as ferrites, conductive carbon black, and magnetic metals, are typically incorporated into polymer matrices as powder fillers. However, they suffer from various drawbacks, including poor mechanical properties, inability to function as load-bearing components, high density hindering integration, and limited flexibility to meet the demands of modern electronics. One-dimensional fiber materials offer promising alternatives due to their lightweight, flexibility, and design versatility. However, most microwave absorbing fibers are produced by using methods such as chemical plating, coating, and impregnation. Addressing these challenges, this paper focuses on integrating modern textile techniques to produce flexible composite fibers with superior mechanical properties and exceptional absorption capabilities.
The PEDOT:PSS/sodium alginate@MXene (PA@M) composite fibers with core-shell structure were successfully fabricated by coaxial wet spinning process, to realize the integration of strong and efficient wave-absorbing functions. This paper mainly explored the effects of Ti3C2Tx MXene content on the morphology, mechanical properties, electrical conductivity and electromagnetic characteristics of the PA@M composite fibers. The results showed that because of the interactions between the MXene layers in the core and the PA components in the shell, the PA@M composite fibers exhibited remarkable mechanical properties, with a single-fiber breaking strength reaching (63.13±2.56) MPa and a corresponding elongation at break of (23.28±1.67)%, which can be originated from the interactions between the Ti3C2Tx MXene core layers and the PA shell components. Furthermore, the conductivity of PA@M composite fibers was increased from 0.71 S/m to 3.42 S/m due to the efficient electron transfer between MXene nanolayers. Meanwhile, the component modulation and microstructure design could effectively regulate the electromagnetic properties of the PA@M composite fibers, so that PA@M-1.0 composite fibers achieved the minimum reflection loss of –63.39 dB and effective absorption bandwidth of 3.20 GHz. This research presents a novel and efficient approach for the design and development of microwave absorption fibers.


In-situ monitoring of VARTM curing process based on self-sensing C-GNS

LIU Shuai, ZHONG Zhihao, WANG Xinyu, DAI Hongbo

2024, 32(12):  20-28. 

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 Advanced composite materials are widely used in navigation, shipbuilding, automobile and civil industries because of their high specific modulus, strength, corrosion resistance and impact resistance. However, due to the limitations of manufacturing and detection technology, the superior performance of composite materials can not be fully displayed. Common liquid composite molding processes such as resin transfer molding (RTM) and vacuum assisted resin transfer molding (VARTM) encounter difficulties in the application of processing large-scale composite materials. For example, due to the lack of real-time monitoring methods, it is impossible to accurately know whether the resin in the curing process is fully cured and whether there is residual stress, which ultimately results in difficulty in ensuring product quality. In order to guarantee the quality of composite material products and the repeatability of production, it is necessary to conduct in-situ real-time monitoring of the curing process of composite materials.
In this study, the ultra-thin, dense and uniform MWCNT conductive induction coating was constructed on the surface of high-porosity glass fiber nonwovens by ultrasonic atomized spraying technology, and the in-situ and real-time monitoring ability of the resin curing process in VARTM molding process was systematically analyzed. The results indicated that the C-GNS showed good response sensitivity (up to 16.77 Pa-1) in the detection process of vacuum calendering process at room temperature. In addition, the C-GNS resistor responded instantly, and could directly map the whole curing process of thermoset epoxy resin under the combined action of temperature field and pressure field and the key points (starting point of curing, starting point of gelation, gel point and complete curing) of phase transition involved in the process. Specifically, during the whole curing process, the resistance gain factor of the sensor reached up to 80%, and the real-time resistivity changes were highly consistent with the resin curing kinetic behavior measured by differential scanning calorimetry, rheology and dielectric curing. 
This study provides an effective curing monitoring method (C-GNS) for VARTM processing and manufacturing, and further expands the multi-functional applications of self-sensing nanocarbon composites.


Self-sensing PET-CNT nonwoven interleaf for the integrated interlaminar toughening and structural monitoring of glass fiber reinforced composites

ZHONG Zhihao, LIU Shuai, WANG Shouhao, DAI Hongbo

2024, 32(12):  29-37. 

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Glass fiber reinforced composites (GFRC) are a popular, low-cost, and lightweight structural material widely used in green energy fields, such as wind power generation, new energy vehicles, and battery shells. However, delamination damage is a common issue in GFRC structures during service. To improve the out-of-plane mechanical properties of laminated GFRC, various interlaminar materials have been extensively studied and applied. To prevent sudden delamination of GFRC during service, it is crucial to develop an in-situ, real-time, on-line non-destructive monitoring method to monitor the structural health of the system. This will help avoid catastrophic failure caused by sudden delamination. A PET-CNT self-sensing nonwoven composite interleaf was developed by using high-porosity PET nonwoven fabric, introducing functional intercalation into the interlayer relative to GFRC for modification. In addition, the one-step impregnation method produced a PET-CNT nonwoven interleaf with a multi-level network structure of entangling, loose and porous, allowing full impregnation with resin matrix. Upon solidification, a continuous and dense CNT-CNT seepage induction network was formed. The results demonstrated an 86% increase in initial fracture toughness (GIC,ini) and a 48% increase in propagation fracture toughness (GIC,prop) of the modified GFRC, effectively enhancing its mode I interlaminar fracture toughness (ILFT). Real-time acquisition of piezoresistive response and establishment of quantitative mapping relationship between resistance change and crack growth length revealed a 270% gain factor in resistance change rate during the experiment, demonstrating excellent in-situ monitoring sensitivity and accurate efficiency in monitoring the entire process of crack growth in DCB experiment.
In this study, a new PET-CNT nonwoven composite interleaf suitable for GFRC was prepared, and its integrated response behavior of interlayer toughening-structure monitoring was analyzed and verified, which proposed an effective and structural optimization method to improve the structural stability of GFRC and the overall robustness of GFRC throughout its life cycle. Additionally, it also provided a new scenario for expanding the industrial application of functional nonwoven materials.


Artificial accelerated iron mineralization and structural properties of textiles

JIA Rui, ZHENG Hailing, JIA Liling, FU Jiancong, PENG Zhiqin

2024, 32(12):  38-47. 

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Mineralization is a process of transformation from organic to inorganic states. Mineralization of textiles refers to the phenomenon where organic fibers are replaced by inorganic substances. This process is a comprehensive result of the dissolution of inorganic substances, migration of inorganic ions with water molecules, and interactions between inorganic ions and organic molecules. Investigating the mineralization of textiles contributes to understanding the lifestyle, production technologies, and social structures of ancient societies. It helps archaeological conservators in devising more effective methods and strategies for artifact preservation and allows researchers to further discuss  the interactions between fiber structures and mineral combination.
The scholarly study of archaeomineralized textiles can be traced back to the 18th century. Despite the long history, critical research findings remain scarce, primarily due to the limited availability of samples for study. To address this problem, simulated mineralization experiments were conducted in this paper. Silk fabrics from the Republican era were wrapped around rusted iron pieces and placed in a constant temperature and humidity chamber for mineralization. Samples were retrieved after five and six months and subjected to electron microscope observation, tensile strength testing, infrared spectroscopy analysis, Raman spectrum testing, X-ray diffraction analysis, and stable carbon isotope testing. By comparing these simulated mineralized textiles with Han Dynasty mineralized textiles unearthed in Shanxi province, this paper explored differences between simulated and archaeological mineralized textiles and investigated early phenomena and mechanisms of textile mineralization.
It was found that both raw and processed silk samples underwent self-mineralization and pre-mineralization after six months of mineralization. Mixed iron oxides such as goethite/magnetite/ lepidocrocite were contained in the samples, but organic components still constituted a significant proportion, indicating an early stage of mineralization. Iron mineralization served to protect the fibers, resulting in higher tensile strength compared to aged textiles. The stable carbon isotope ratios of mineralized textiles were lower than those of aged textiles. Simulated mineralized samples exhibited similarities to archaeological mineralized samples, demonstrating phenomena of self-mineralization and pre-mineralization. Protective layers formed by both mineralization methods in burial environments could inhibit microbial degradation and enhance fiber durability, thereby preserving organic components in the fibers.
Due to the short mineralization period, this study only addresses the early stages of textile mineralization. Significant organic components remained within the samples, and the uneven mineralization led to some degree of randomness in the experimental results. Nonetheless, these findings contribute to understanding the mechanisms of textile iron mineralization and offer insights into the protection and early stages of textile mineralization.


Automatic generation of blue calico's single pattern based on  Stable Diffusion

RAN Erfei, JIA Xiaojun, WANG Zixiang, XIE Hao, XU Congyuan

2024, 32(12):  48-59. 

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 Blue calico is a traditional craft printing and dyeing product in China with a long history. It is famous for its distinctive pattern design style and broken lines. However, the lack of an algorithm for the automatic generation of blue calico's single pattern has hindered innovative research on blue calico's patterns. For this reason, an end-to-end automatic generation method of the single blue calico's single pattern was proposed to realize the automatic generation of blue calico's single pattern. 
Our method is based on a diffusion model, which has been very popular recently. It has achieved great success in the field of image generation, and its main architecture consists of VAE (variational autoencoder), CLIP (contrastive language-image pre-training), and Unet. However, due to the high cost of fine-tuning the entire diffusion model, we choose to use improved DyLoRA (dynamic low-rank adaptation) technology to fine-tune the diffusion model. DyLoRA posits that changes in the parameter matrix during model training cannot achieve full rank. Therefore, the parameter matrix that needs to be updated is transformed into two small matrices multiplied so as to reduce the number of updated parameters. However, this parameter decomposition method has no effect on improving rank, so we improved this technique and proposed a new parameter decomposition method. Through this technology, we can fine-tune the diffusion model at an affordable cost to produce blue calico's single pattern. At the same time, in order to control the generation of blue calico, we also introduced the Controlnet network to control the overall layout of the generated single pattern.
There is no objective measurement standard in such experiments, so we used the generated image for visual comparison. In the experiment, to demonstrate the superiority of the proposed algorithm, we compared our algorithm with a model based on the CycleGan algorithm and original DyLoRA. The experimental results show that our proposed algorithm can effectively generate better blue calico single pattern than the other two methods, even though its input is only simple text. In the example, it can be seen that the generated blue calico single pattern conforms to the characteristics of broken lines and connected meanings, and is rich in artistic conception. At the same time, we used the ControlNet network to control the overall structure of the generated single pattern. 
As a part of national intangible cultural heritage, blue calico has important value and significance in digital inheritance and innovation. This article proposed a method for fine-tuning the diffusion model Stable Diffusion to generate the blue calico's single pattern. This method fully utilized the rich semantic information from the pre-trained Stable Diffusion 1.5 model. Based on this large pre-trained model, the improved DyLoRA fine-tuning method was used to enable the model to learn the style of blue calico's single pattern, and Controlnet was used to limit the structure of the generated content. Finally, we achieved the effect of outputting blue calico's single pattern by inputting appropriate prompt words, and hundreds of sample images were generated according to this method. Next, research will be conducted on the automatic generation of more types and complex blue calico's single pattern.


A fabric material recognition method based on spatially partitioned attention

NAN Keliang, JIN Yanxia, WANG Songsong, WANG Ting, ZHANG Xiaozhu, ZHANG Zhuangwei

2024, 32(12):  58-67. 

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To achieve high-precision identification of fabric materials, reduce identification time, and improve production efficiency, it is of great significance to develop a system capable of accurately distinguishing between various fabric types. In this paper, we proposed a fabric material recognition network that incorporates spatial segmentation attention. We utilized the pre-trained DenseNet121 network for experimental dataset selection and combined depthwise separable convolution (DSC) with the spatial partitioned attention module (SPAM) to create a network structure that fulfills the demands of swift recognition and high precision.
To obtain the best performance of the network, the dataset was preprocessed by color weakening, data augmentation and region division. We collected a series of fabric images with temporal sequence information from videos showing fabrics being blown by the wind. The RGB values of critical regions were weighted and recalibrated, and random perturbations, flips, and translations were applied to the images, enhancing clarity in critical regions while suppressing irrelevant ones. The Euclidean distance was used to calculate the displacement amount around the same pixel time of the fabric image, and the image region was divided into wrinkled area and flat areas. We obtained 6,000 grayscale images of 224x224 pixels, with 1,000 fabric images per class across six categories. We constructed the proposed mixed depthwise separable convolutional neural network (MDW-CNN) using Python. Firstly, the fabric video was segmented to obtain the fabric image for data preprocessing. Then, the improved convolutional neural network was used for feature extraction, and the ordinary convolution was replaced by the DSC, which enhanced the ability of the network to extract features and reduced the network parameters and calculation. Secondly, SPAM was introduced after each convolutional layer to enhance the saliency features, prevent the loss of too much information of the feature map, and improve the accuracy of the network. Finally, fabric material recognition was achieved through the global average pooling layer and the softmax layer.
The 224 px×224 px fabric image was used to complete the experiment on the Intel processor, and the CNN+LSTM, Timesformer, two-stream network, ViViT, YOLOv5, YOLOv8 and the network proposed in this paper were compared. The results show that the proposed MDW-CNN can maintain good recognition accuracy while ensuring a low number of parameters. The network proposed in this study shows strong performance in fabric material recognition, achieving a recognition accuracy of 93.9%. Regarding network parameters, the proposed method reduced them by 3.3%, 48.5%, 56.7%, 29.3%, 26.1%, and 12.7% when compared with CNN+LSTM, Timesformer, the two-stream network, ViViT, YOLOv5, and YOLOv8, respectively.
In this study, the improved convolutional network method has been applied to the task of fabric recognition. Experimental results indicate that the improved network offers faster detection speeds, significantly reduces the number of network parameters, achieves a recognition accuracy of 93.9%, and has a detection time of 83.14 ms for a single image. Thus, it achieves real-time performance while maintaining high recognition accuracy. 


Modeling and application of sizing yarns quality evaluation based on equal sizing rate technology

LU Haojie, JIN Enqi, LI Manli , ZHOU Jiu

2024, 32(12):  68-75. 

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The current dilemma in evaluating sizing quality lies in the different definitions of sizing yarn quality indicators, the lack of correlation research among indicators, and the limitations of existing evaluation methods. This paper tried to use equal sizing rate technology to detect and control the sizing rate of sizing yarn online. The goal is to achieve a scientific evaluation method that comprehensively reflects the quality of sizing yarns, and to conduct horizontal comparisons between different types of sizing agents.
To achieve this goal, a modified Maxwell Garnett (MG) equivalent dielectric constant model was established for the yarn structure, and a capacitive detection system was used to detect the sizing rate of the sizing yarn online. Three types of sizing agents including starch, poly vinyl alcohol (PVA), and modified sophora bean powder were selected to size cotton yarns on a sizing machine. A capacitive sizing rate detection system was set up to ensure every sizing sample have equal sizing rates, by adjusting the sizing process. The entropy weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model was introduced into the comprehensive performance evaluation of sizing yarns by sorting the degree of closeness. By normalizing and standardizing the initial matrix X, a standard matrix of yarn performance was obtained. The initial matrix consists of six sets of samples, with each set containing eight performance indicators.
Results of the experiments shows that the relative error of sizing rates between the capacitive detection system method and the desizing method is less than 10%, indicating that the capacitive sizing rate detection system can meet the needs of practical applications. Secondly, under the same conditions of solid content slurry and sizing process, the sizing rate of fine yarns is higher than that of a roving yarns. The information entropy for the hairiness reduction rate is minimum, indicating that this performance indicator is the most important in the overall quality evaluation of sizing yarns. The information entropy for the enhancement rate is -0.86, indicating that it is relatively less important. In the experiment, when the same sizing agent is used, the comprehensive quality of the sizing yarn improves with the increase in yarn fineness, while the sizing rate has a relatively small impact on it. When different types of sizing agents with the same sizing process are used, it is found that the quality of sizing yarn with PVA sizing agents is better than that of sophora bean powder and starch. However, the modified sophora bean powder sizing agent is a green one that can be used as a substitute for PVA and starch. This comprehensive evaluation method based on equal sizing rate technology and entropy weight TOPSIS theory provides a new approach for comprehensive quality evaluation and the development of sizing agents.


Indigo denim wet rubbing fastness enhancement process and mechanism

SONG Jiaweia, YANG Shuweia, YU Zhicheng, HUANG Min

2024, 32(12):  76-82. 

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As one of the most important and popular fabrics in the textile industry, denim is considered to be the most widely used fabric in the fashion industry; it is popular because of its high strength, distinct texture, moisture absorption and breathability, and beauty and comfort. In recent years, with the development of the textile industry and the improvement of textile technology, the competitiveness of denim apparel has been rising. It is predicted that the market value of denim fabrics worldwide will reach RMB 620 billion in 2025, which is a very broad market prospect.
Indigo dyeing has disadvantages such as high initial dyeing rate and poor dye penetration, which leads to very poor wet rubbing fastness of indigo dyed denim fabrics, generally only level 1. This affects the quality of denim fabrics and seriously restricts the development of the industry. Therefore, the low wet rubbing fastness and poor quality of indigo denim have become a common problem in the industry, so it is of great significance to improve the quality of denim and the wet rubbing fastness of indigo denim.
In this paper, we aimed to enhance the permeability of indigo dyes and improve the wet rubbing fastness of denim by strengthening the pretreatment process and regulating the state of indigo leuco. Based on this, we further explored the mechanisms and processes related to the influence of different fixing agent types and their molecular weights on enhancing the wet rubbing fastness. The experimental results show that the wet rubbing fastness of indigo-dyed denim can be enhanced to grade 3 by selecting polyurethane color fixing agent B with relatively large molecular weight and optimizing the pretreatment and dyeing process parameters. The optimized pretreatment process requires refining agent A of 10 g/L and caustic soda of 20 g/L at 95 ℃ for 30 min; the dyeing process requires an insurance powder of 18 g/L, urea sulfur dioxide of 1 g/L, caustic soda of 10 g/L, penetrant WET of 6 g/L; color fixing process requires color fixing agent B of 60 g/L at 160 ℃ for a baking time of 120 s.


A size measurement method for suit collar design based on a SAM model

PENG Zhouyan, MA Ling, SU Huimin, PAN Yiting, ZOU Fengyuan

2024, 32(12):  83-89. 

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As the center of the overall visual effect of a suit, the suit collar is the most crucial factor that affects the overall visual impression. In creating clothing patterns based on design sketches, the judgment of important detailed dimensions like the suit collar often relies on the experience of pattern makers and their repeated trials and errors. Therefore, using computer vision to assist in determining the detailed dimensions of the suit collar is of great significance. However, current research on clothing measurement mainly focuses on measuring the outer contour dimensions, with unsatisfactory results for measuring the internal contour dimensions.
Low contrast in the internal contour lines of clothing makes it difficult to extract feature points and complete contours and further results in the difficulty of measuring internal dimensions from images. To address this issue, a method based on the SAM model for segmenting components to extract internal dimensions was proposed. Firstly, the SAM model was used to segment the suit collar components to improve the edge accuracy. Next, the DP algorithm was used to approximate the suit collar components into polygons, and the LSD algorithm and geometric constraints were employed to correct the errors generated during the polygon approximation process. Finally, a structural model of the suit collar was established by incorporating pixel ratios to extract relevant data.
Three main conclusions are drawn. First, by segmenting the suit collar components with the SAM model, the masks of lapel, reverse collar and collar stand were obtained. Second, by using the LSD algorithm and geometric constraints to adjust the suit collar component polygon result, a suit collar structural parameter model through linear and parabolic functions was constructed. Third, the main design dimensions of the suit collar were measured, and the results showed that compared with the measurement software, the absolute error of dimensions ranged from 0.02 cm to 0.74 cm, and the absolute error of angles ranged from 0.02 to 0.27 . Compared with the physical measurement, the absolute error of dimensions ranged from 0.05 cm to 1 cm, and the absolute error of angles ranged from 0.06to 0.68. The measurement error met the standards for clothing measurement.
This method can provide pattern makers with reference dimensions for the suit collar, enhancing the efficiency of transitioning from design to sample. The dimensions obtained in this study are plan view sizes and not the actual dimensions of the clothing sample. In future studies, the mapping relationship between the two will be further explored to make corrections and adjustments.


Visualization analysis of research trends in Yi costumes based on CiteSpace

XIANG Fengpin, WANG Yahan, LIU Anding

2024, 32(12):  90-100. 

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In terms of clothing, the Yi costume is different from that of the Han nationality and other ethnic minorities. With unique national style, it enjoys the reputation of "wearing culture and embroidering history". In order to understand the current research status of Yi costumes in China, this paper employed quantitative analysis and qualitative analysis based on CiteSpace visualization software to analyze 508 valid papers in China National Knowledge Infrastructure (CNKI) database. Then research maps were made to explore the research hotspots and research trends of the Yi costume in the past 36 years.
The study of Yi costumes in China has been developing for 36 years. In the past, the research on the costume shape, cultural connotation, costume aesthetics and innovative application of different Yi branches has achieved fruitful results. Many scholars choose to explore the Yi costume culture from different perspectives such as culturology, anthropology, art, and history. Although the academic research on the Yi costume is relatively rich, there is a clear regional imbalance and a lack of landmark literature.
At present, the research on Yi costumes in China has formed a state of obvious professional division, institutional division and regional division. Most authors and institutions with a significant number of publications come from the field of ethnology and belong to the southwest region. The proportion of high-quality academic achievements published in CSSCI and Peking University core journals is extremely low, with varying quality levels of literature and a lack of influential articles. The research trend of Yi costumes is divided into three aspects: the cultural and historical development of costumes focusing on the category of "clothing"; the inheritance and development of costumes focusing on the category of “culture”; the innovative design and application practice of costumes focusing on the category of “behavior”. It embodies the history and current situation, the process of inheritance and design innovation of the research in the field of Yi costumes. The research hotspots are divided into three stages: from 1993 to 2008, it focused on the historical development and basic attributes of the Yi costume; from 2009 to 2017, the focus shifted from natural attributes to social attributes, exploring the evolution of Yi costumes of different branches from a gender perspective; from 2018 to 2023, the focus was on the social attributes of Yi costumes, emphasizing the inheritance, protection, innovative design and application of Yi costumes.
As for future research directions for Yi costumes, firstly, it is necessary to shift research objects to analyze costumes such as military uniforms, Nuo costumes (costumes worn during Nuo rituals), religious costumes, wedding costumes, funeral costumes, priest costumes, official costumes, and chieftain's costumes; secondly, it is necessary to shift the research regions from Sichuan province to Yunnan province, Guizhou province and other provinces, and from domestic regions to oversea regions; lastly; finally, it is necessary to combine with high technology for inheritance and promotion, and integrate resources for digital display.


Effects of service quality and sensory experience on the willingness to customized clothing online

ZHAO Ying, ZHOU Wei, GAO Yanghu, WEN Run,

2024, 32(12):  101-112. 

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To explore the mechanism by which online apparel customization service quality influences consumers' willingness to customize apparel, a theoretical model was constructed, with online apparel customization service quality and sensory experience as independent variables. With online clothing customization as the research backdrop, this study combines the literature review and research hypotheses to explore the effects of service quality and sensory experience on perceived value and willingness to customize apparel online. Based on the S-O-R model theory, service quality and sensory experience are regarded as external environmental stimuli (S), perceived value is regarded as an organism (O), and willingness to customize clothing online is a kind of behavioral response (R). Service quality (e.g., ease of use, fulfillment, safety, information and personalized service) and sensory experience (e.g., aesthetics and vividness) are informativeness variables, perceived value is the mediating variable and willingness to customize clothes online is the dependent variable.

The questionnaire consists of three parts: first, participants are instructed to experience a clothing personalization system (e.g., Spreadshirt or Cloud Clothing Customization) for 30 seconds, engaging in the shopping process except for the payment step. The second part is the measurement scale, including items on service quality, sensory experience, perceived value and willingness to customize apparel online. The third part of the questionnaire consists of basic personal information. In particular, the 5-point Likert scale is adopted in the second part of the questionnaires from "1 = strongly disagree" to "5 = strongly agree". A total of 310 valid samples were collected from individuals within online customization communities. Descriptive statistical analysis, factor analysis, and regression analysis were adopted to explore the influential factors on the willingness to customize apparel online and optimize the consumers' evaluation items. This study conducted a statistical analysis of the valid data sample, and the proportion of men and women was 44.5% and 55.5% respectively; the main participants were aged 18‒34 years old (95.6%). This study primarily targets the age group of 18‒29 years, who are more sensitive to new technologies and new things that are regarded as the potential customers. The findings show that online customization service quality, including ease of use, fulfillment, security, informativeness, and personalized service, as well as sensory experience, including aesthetics and vividness, have a positive impact on perceived value.; service quality (fulfillment, security and informativeness), sensory experience (aesthetics and  vividness), and perceived value have a positive impact on the willingness to customize clothing online; perceived value positively mediates between service quality (ease of use, fulfillment, security, informativeness and  personalized service) and willingness to customize clothing online, and fully mediates between sensory experience (aesthetics and vividness) and willingness to customize online. The findings of the study enrich the theory of the S-O-R model and explore the influential factors of service quality and sensory experiences on willingness to customize clothing online. It also provides reference for the development of clothing customization technology and the setup of evaluation items.  

Research progress on the structure, technology and performance of braided tubes

SHI Zixiang, HU Jiyong

2024, 32(12):  113-122. 

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Fibrous tubular materials refer to tubular materials produced through textile methods, which possess many advantages such as good fatigue resistance, high strength, and high modulus. They can be widely used in pipelines, pipe shells, bushings, ultra-high temperature insulation pipes, and structural support fields. Fibrous tubular materials generally include woven tubes, knitted tubes, non-woven tubes, and braided tubes. Among them, woven tubes are mainly evolved from double-layer woven fabrics, with low porosity, low elasticity and high mechanical strength; knitted tubes can be prepared by the two methods of weft knitting and warp knitting, with uniform pore structure, high porosity and high elasticity; non-woven tubes focus on the preparation of multi-layer tubes, with a unique fiber arrangement structure, but their pores are uneven, stability is poor and strength is low; the braided tubes can be shaped flexibly, with uniform pore structure, adjustable pores, and high axial strength.
Braiding, as an automated manufacturing technology with faster speed and higher efficiency, has been widely applied in various fields. The manufacturing process of braided tubes is relatively simple, and the structure and performance of braided tubes can be changed by changing parameters to meet the needs of different complex product designs. In recent years, research on braided tubes has become increasingly in-depth.
For braided tubes, the structure and performance can be changed by changing the braiding material and braiding parameters. Currently, high-performance materials such as medical materials, conductive materials, and high-temperature oxidation resistant materials have been used to prepare braided tubes suitable for different fields, demonstrating the unique advantages and application potential of braided tubes.
In terms of braiding parameters, there has been a series of progress in the study of braiding angles and mechanical properties of braiding tubes from theory to application. However, there is a lack of systematic research on the relationship between parameters such as number of braiding layers, number of braiding yarns, and core shaft specifications and the pore structure and performance of braiding tubes. In addition, scholars have studied the impact of new structures such as different pore structures, structures with different braiding angles, mixed yarn structures, and elastic structures on the performance of braided tubes. 
Therefore, it is necessary to systematically summarize the relationship between braiding parameters and the structural performance of braided tubes. Explaining the relationship between braiding parameters and the structural performance of braided tubes has practical guiding significance for the subsequent structural design, and can scientifically guide researchers in preparing ideal braided tubes.


Research progress on the application of radiation cooling technology in clothing

WANG Yiruonan, YAN Jianing, FAN Meixin, ZHOU Guangjie, DAI Hongqin,

2024, 32(12):  123-133. 

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With global warming and frequent abnormal high temperatures, radiation cooling technology, as an environmentally friendly cooling technology, has shown great potential in regulating thermal comfort. The human body in a specific environment can be regarded as an open system composed of human body, clothing and environment, and radiation plays an indispensable role in human heat dissipation. According to Kirchhoff's law of thermal radiation, radiation heat dissipation can be accelerated by regulating the optical properties of clothing materials and then regulating the heat radiation exchange between the human body and clothing. In the indoor environment, the selection and design of materials enable clothing fabrics to possess high mid-infrared transmission, allowing heat radiation emitted by the human body to dissipate into the environment almost unobstructed through the clothing to the environment. In the outdoor environment, where a single infrared transparent material cannot block the entry of external mid-infrared radiation, the emissivity of the outer surface of the clothing material is regulated to endow the fabric with high mid-infrared emissivity. This allows the fabric to absorb heat and convert it into radiant energy to dissipate heat into the environment.
The regulation of infrared radiation or solar radiation in the concept of radiation cooling technology is combined with personal thermal management technology, and a variety of radiation cooling textile materials are derived to regulate human thermal comfort. High transmission cooling materials are led by membrane composite materials, sub-band response cooling materials, cooling materials with cooling/thermal insulation dual function materials and cooling materials with unidirectional moisture conduction characteristics. In order to obtain mass production textile materials with high radiation cooling ability and ideal wearing comfort, researchers have prepared various fibrous materials with high radiation properties through phase separation, particle doping and other methods around the high transmission and high emission cooling mechanism, such as high-permeability fabrics, outdoor high-emission/high-reflection fabrics and all-weather selective emission/high-reflection fabrics.
The review found that the development of radiation-cooling materials still faces many challenges. At present, most of the developed membrane materials have problems such as poor air permeability, washable repeatability and wearability, so it is difficult for them to be applied in clothing development. The performance evaluation of radiation-cooling materials mainly includes three methods: spectral measurement, thermal measurement comparison and real person evaluation. Most studies are still in the laboratory stage, the testing of cooling characteristics has not been standardized and unified, and the overall application evaluation of clothing is lacking, so further research and improvement is needed. Currently, most of the radiation-cooled fiber material processes are still relatively complex, and it is difficult to achieve high industrialization and rapid preparation. In order to realize the commercialization goal of materials in the field of textile and garment, continuous optimization of material preparation processes should be conducted in the future in terms of improving the cooling efficiency of materials, enhancing wearability, simplifying preparation processes, optimizing overall clothing evaluation methods, and broadening the practical functions of materials.


Research progress on key technologies of clothing recommendation systems

LÜ Furong, SHI Yunlong, JING Xiaoning, ZENG Qianyi, ZHU Xuewei, LEI Haiyang

2024, 32(12):  134-144. 

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As the scale of e-commerce continues to expand, the number and variety of products are rapidly increasing, requiring customers to spend a considerable amount of time to find the products they need. This process of browsing through large amounts of irrelevant information and products undoubtedly leads consumers to be drown in an overload of information. To address this issue, recommendation systems have emerged. Recommendation systems are advanced business intelligence platforms built on massive data mining foundations, designed to provide e-commerce websites with personalized decision support and information services tailored to their customers. The emergence and development of the Internet have triggered a digital storm, gradually applying recommendation technology to various fields such as e-commerce, news delivery, social networking, and music entertainment. Clothing, as an important component of the fashion industry, benefits from the integration of the Internet and the fashion industry, bringing new possibilities for clothing design, production, and consumption. Clothing recommendation, as a significant research direction in the computer fashion field, has garnered widespread attention from fields like computer vision, multimedia, and information retrieval. Compared to traditional offline shopping, online purchase of clothing and accessories is more convenient. A typical recommendation system predicts user interest in a particular item based on given information about the product and the user, as well as interaction history, thereby providing personalized products or services to the user. Clothing recommendation can be seen as a specific application of recommendation systems in the field of e-commerce, but it possesses uniqueness in many aspects. People's demands for personalized clothing quality, styles, and matching are constantly growing, making digital transformation crucial for the clothing industry. Faced with massive clothing data, clothing recommendation systems play a crucial role as a key link, including personalized recommendations, enhancing user experience, and increasing revenue, bringing numerous practical benefits to both users and businesses, and simultaneously driving the industry towards intelligent and efficient development. This article combines the key aspects of clothing recommendation systems and summarizes the general process and related technologies for creating clothing recommendation systems, including data collection and preprocessing, feature engineering, and model construction. It provides a detailed overview of key technologies in both traditional recommendation techniques and deep learning applied in the field of clothing recommendations, analyzing the application and expansion of various algorithms. In terms of application, clothing recommendation systems are widely used in e-commerce platforms and clothing styling recommendation apps, offering users convenient shopping and styling suggestions. Finally, based on the application areas and development trends of clothing recommendation systems, it explores the pressing issues that clothing recommendation systems need to address and future innovative directions.