Journal of Fiber Bioengineering and Informatics
Journal of Fiber Bioengineering and Informatics (JFBI) is a peer-reviewed international journal to promote multidisciplinary research and collaborations across different fields in fiber science, engineering and technology. This quarterly published journal is fully committed to help authors to generate high impact research outputs in terms of academic citations, industrial applications and contributions to the society. It aims to create an international forum for exchanging novel ideas of research.
Robust Real-Time Garment Fitting from 3D Point Clouds with Physics-Guided Uncertainty and Reliability Monitoring
Real-time garment fitting from 3D point clouds is a fundamental capability for telepresence, virtual try-on, and augmented reality. Yet, it remains difficult due to high-dimensional non-rigid deformation, frequent self-occlusion, and the need to maintain physically plausible cloth behavior under noisy measurements. Existing studies have improved geometric registration, learning-based reconstruction, or cloth simulation in isolation, but still lack a unified real-time framework that combines dense vertex-level tracking, uncertainty-aware sequential estimation, and physics-guided regularization for robust garment fitting under noisy and incomplete 3D observations. To address this gap, we propose Dense-Decoupled Adaptive Kalman Filtering (DD-AKF), an integrative sequential estimation framework that tracks dense garment vertices in real time while explicitly propagating uncertainty. To make high-dimensional filtering tractable, DD-AKF employs a block-diagonal (decoupled) covariance approximation that reduces per-frame complexity to linear time in the number of vertices while retaining per-vertex uncertainty estimates. Physical plausibility is incorporated by introducing differentiable stretch, bending, and collision energies as soft constraints in a Gaussian-approximate (sequential MAP) update, enabling an adaptive trade-off between sensor fidelity and physics under degraded observations. We also compute an online reliability score based on innovation (residual) statistics to monitor tracking quality and trigger robustification in the presence of occlusion or sensor corruption. Experiments on FAUST, CLOTH3D, and real capture sequences show reduced temporal flicker and penetration compared with representative optimization-based, stochastic filtering, and learning-based baselines, while maintaining competitive geometric accuracy at interactive rates (30+FPS). These results support reliability-aware deployment in practical virtual try-on and AR garment-fitting pipelines using commodity 3D sensors.
Copyright (c) 2026 Journal of Fiber Bioengineering and Informatics
Morphological Classification of Female Abdominal-Hip and Crotch Regions for Menstrual Pants Design
Existing menstrual pants often suffer from poor fit, which can cause side leakage. While research on female lower-body is crucial for structural design, studies on abdominal and crotch morphology remain insufficient. This study therefore aims to analyze and classify the morphology of women’s abdominal-hip and crotch regions. Anthropometric data were collected from 143 female subjects aged 18-50 years wearing size L menstrual pants. Factor analysis identified four key factors: circumference, crotch length, width, and crotch depth. Cluster analysis classified abdominal-hip morphologies into three types (flat abdomen-flat hip, thick abdomen-flat hip, slender abdomen-prominent hip) based on abdominal-hip difference and hip protrusion degree; and crotch morphologies into three types (H-shaped medium crotch, A-shaped short crotch, V-shaped long crotch) based on the crotch width-length index. The bottom crotch points were categorized into anterior type (51.75%) and posterior type (48.25%). These findings provide an anthropometric basis for the structural design of differentiated menstrual pants.
A Machine Learning-Based Thermal Comfort Prediction Model for Older Adults
Indoor thermal comfort plays a critical role in safeguarding the health and quality of life of older adults (aged 60 and above), whose thermoregulatory capacity is diminished compared with younger populations due to reduced basal metabolic rate, impaired vasomotor responses, and elevated thermal sensation thresholds. While the Predicted Mean Vote (PMV) model has long served as a benchmark for thermal comfort assessment, its parameters are calibrated predominantly on young, healthy subjects and thus produce systematic prediction biases for older adults. Data-driven machine learning approaches have emerged as promising alternatives; however, existing studies that advocate deep learning architectures for thermal comfort prediction are predominantly based on small-sample datasets (N<500), leaving their generalizability under large-scale data conditions unverified. This study systematically compares a Convolutional-Recurrent Neural Network (CRNN, comprising one-dimensional convolution and Gated Recurrent Unit layers) with three established machine learning algorithms—K-Nearest Neighbors (KNN), Gradient Boosting Decision Trees (GBDT), and Random Forest (RF)—using a dedicated dataset of 5 820 older adult samples constructed from the ASHRAE Global Thermal Comfort Database II. Model performance was evaluated across both regression (continuous thermal sensation prediction) and classification (discrete seven-level thermal sensation determination) tasks. Results demonstrate that RF outperformed all models: in regression, RF achieved a mean absolute error (MAE) of 0.597 3 and root mean square error (RMSE) of 0.842 7, approximately 13.1% and 15.1% lower than CRNN (MAE = 0.687 6, RMSE = 0.992 2), respectively. In classification, RF attained 78.0% accuracy and a weighted F1 score of 0.539, compared with 68.7% and 0.513 for CRNN. The CRNN exhibited pronounced overfitting and majority-class bias, attributable to an architectural incompatibility between its sequential modeling design and the non-temporal tabular nature of thermal comfort data. These findings provide evidence-based guidance for selecting models to construct individualized thermal comfort prediction systems for older adults and inform the development of intelligent age-friendly textile products.
Temporal Dynamics of Academic Research and Public Attention Toward Generative AI in Fashion Design: A Bibliometric and Baidu Index Study
Generative AI has moved quickly into fashion design since 2022, yet how this diffusion plays out across academic and public spheres has received little empirical attention. We analyzed 855 Web of Science publications (2014 to 2025) alongside Baidu Index search data from the Chinese market. The bibliometric record falls into three phases: algorithmic exploration, application development, and scenario empowerment. A qualitative comparison of the two time series shows that the lag between growth in public search interest and growth in academic output appears to have narrowed from roughly 18 to 24 months before 2020 to about 6 to 12 months after 2022, suggesting that the two domains are becoming more closely coupled. Formal causal testing is needed to confirm the direction of influence.
A Case Study on the Physical and Psychological Health Design Requirements of Postoperative Female Breast Cancer Patients
While existing rehabilitation products for breast cancer survivors primarily focus on compression therapy, they often neglect the integrated physical and psychological needs of patients, leaving a critical gap in holistic home-based care. Therefore, this study integrates Quality Function Deployment (QFD) and the Theory of Innovative Problem Solving (TRIZ) to systematically address this gap. Through a questionnaire survey and expert evaluation, patient requirements were gathered, prioritized, and translated into design specifications using QFD, while TRIZ was employed to resolve technical contradictions among them. Based on these findings, an exploratory rehabilitation apparel system is proposed that integrates an adaptive structure, functional materials, embedded hardware for physiological monitoring, and an interactive platform. Potential validation approaches are outlined to assess its safety, functionality, and practical implications in future research.
A YOLOv8-Based Clothing Detection Framework Incorporating Semantic Uncertainty Modeling
Garment style recognition is critical for intelligent retail but suffers from semantic uncertainties caused by high intra-class similarity and non-rigid deformations. Existing detection and attention-based methods often apply uniform attention across all feature levels. This approach limits fine-grained discrimination in complex scenarios. To address this gap, this study proposes a hierarchical attention-enhanced YOLOv8 framework to optimize recognition precision. By integrating a Convolutional Block Attention Module (CBAM) at the mid-level and an Efficient Channel Attention (ECA) module at the high-level, the model effectively strengthens structural perception and suppresses semantic channel dispersion. Experimental results show that the proposed method achieves 84.99% mAP@0.5, an 11.82% improvement over the baseline, while maintaining real-time performance at 126 FPS. This framework improves fine-grained garment recognition and provides a practical solution for intelligent retail applications in complex scenarios.
Energy Efficiency Enhancement in Cotton Pneumatic Transport Systems Using Parallel Pipelines
The efficiency of pneumatic conveying systems plays a decisive role in the energy performance of cotton-processing plants, where airflow is the dominant electrical power consumption. Despite widespread industrial use, conventional single-duct systems still suffer from high pressure losses and energy inefficiency due to excessive air velocity and wall friction. Previous studies primarily focused on granular or powdered materials, while the specific aerodynamic behaviour of low-density fibrous cotton remains insufficiently investigated. This knowledge gap limits the optimisation of energy-saving designs for cotton pneumatic transport. This research develops a mathematical and experimental model for a resource-efficient pneumatic conveying system employing parallel pipelines to enhance energy efficiency and transport stability. The model integrates Bernoulli’s principle, the Darcy–Weisbach equation, and empirical correlations for friction and particle-air interaction. Comparative simulations and experiments were performed for single- and dual-pipeline configurations operating at air velocities of 15-20 m/s. The results show that the parallel configuration reduces pressure drop by 23-26% and total power consumption by 20-25% compared to the conventional 355 mm single-duct system, while maintaining stable fibre transport and minimising clogging. The findings validate the theoretical framework and provide practical guidelines for retrofitting existing cotton pneumatic systems to achieve substantial energy savings. This study fills a key research gap by demonstrating the aerodynamic advantages of parallel airflow distribution for fibrous materials. It contributes to sustainable industrial modernisation by offering a scalable, energy-efficient pneumatic transport design suitable for cotton-processing facilities worldwide.
A Lightweight Diffusion Framework for Cultural Pattern Generation Using LoRA
Atlas patterns, a representative element of Uyghur intangible textile heritage from Xinjiang, are renowned for their vibrant colors and intricate symbolic structures. However, existing generative AI approaches often struggle to replicate their cultural semantics or require significant computational resources. This study addresses these limitations by proposing a low-resource, high-fidelity generation method based on Low-Rank Adaptation (LoRA) applied to the Stable Diffusion (SD) model. A categorized dataset of 103 Atlas pattern images was constructed and annotated using Peirce’s semiotic framework, including Iconographic, Directional, and Cultural Semiotic categories. LoRA modules were used to fine-tune the U-Net component of SD, substantially reducing training complexity and memory usage. Experimental results demonstrate that under optimal parameters (epoch = 100, batch size = 2, U-Net learning rate = $8 × 10^{−4}),$ the model achieves superior performance in MSE, PSNR, and SSIM compared to the base model. The generated patterns effectively reflect the geometric symmetry and cultural motifs of traditional Atlas textiles. This research highlights the practical potential of LoRA-adapted diffusion models in preserving and innovating cultural heritage, especially in the fields of digital fashion, pattern design, and virtual cultural applications.
Mapping Global Research on Sustainable Textile Materials: A Comparative Bibliometric Study of China and International Literature
At present, the world is facing a critical stage of sustainable development transformation. In particular, the introduction of the EU Sustainable Products Regulation (ESPR) marks a fundamental shift in the regulatory paradigm from end-of-pipe governance to ecological design. However, sustainable materials still face challenges in terms of cost, performance, strength, comfort, and large-scale production. At present, there is a lack of cross-country comparative bibliometric analysis on the emerging sustainability trends in textile materials research. Based on this, this study uses CiteSpace (v6.4.RI) to conduct a visual analysis of this field based on Chinese and English literature from 2010 to 2025 in CNKI and Web of Science databases using bibliometric methods. The key analysis indicators include annual publication volume, national and institutional cooperation networks, high-frequency keywords, keyword clustering, evolution path and emerging terms to compare the research status at home and abroad. This study aims to systematically map and compare the domestic and international research trends of sustainable textile materials to determine the evolving themes and future directions. The results show that the number of related papers has continued to grow in the past 14 years, and China is in a leading position in terms of the number of published papers. International cooperation is characterized by multi-center and decentralization. The current research mainly focuses on three directions: material innovation, performance optimization and environmental governance mechanism. It is expected that future research will focus more on bio-based textile technology (especially bionic materials) and intelligent response materials that can be applied on a large scale, such as thermochromic fibers and self-healing composites.
Natural Mordants in Focus: A Comparative Study of Alum and Calcium Hydroxide in Cotton Dyeing with Senna Siamea Heartwood
This study evaluates the dyeing performance of cotton fabric with a natural dye extracted from the heartwood of Senna siamea, focusing on the effects of anodic electrochemical pretreatment combined with varying concentrations of two mordants: potassium aluminium sulfate (alum) and calcium hydroxide. Sodium chloride (10 g/L) was maintained as the auxiliary in all dye baths, while mordant concentrations ranged from 0 to 20 g/L for both alum and calcium hydroxide. Colourimetric analysis (CIE $Lab$*) revealed that alum treatment generally lightened the fabric and decreased colour saturation, with $∆E$ values reaching 13.11 at 20 g/L, indicating a shift toward washed-out shades. In contrast, calcium hydroxide enhanced fabric brightness and significantly increased yellow chroma, producing warmer and more vibrant hues, particularly at 10 g/L, which was visually the lightest and showed the greatest colour difference ($∆E$ = 14.32). Washing fastness (ISO 105-C10) remained consistently good (ratings 3-4/5) across all mordant types and concentrations. Rubbing fastness (ISO 105-X12) improved with increasing mordant concentration, especially for alum at 15-20 g/L and calcium hydroxide at 10 g/L, with wet rubbing ratings improving from 2/3 to 3/4. These results highlight the distinct influences of alum and calcium hydroxide on colour development and durability, offering practical insights for optimising natural dyeing with Senna siamea extract. The study promotes accessible, eco-friendly dyeing techniques suitable for rural artisans, enhancing traditional textile quality and supporting sustainable production.
Design Guidelines for Accessible Dining Apparel for the Visually Impaired: Toward Inclusive and Sustainable Fashion
This study is to address the lack of targeted research on accessible clothing for the visually impaired. In response to this gap, we conducted a mixed-methods study involving in-depth interviews, questionnaire surveys, real-world experiments with visually impaired participants to examine their specific clothing needs during meals, and Market investigation. Based on the analysis of over 70 sample cases, the main challenges were identified as color recognition, stain detection, garment cleaning, functional design, and personalization. Meanwhile, we identify core design requirements for accessible bibs, including stain-resistant materials, ergonomic structures, and aesthetic considerations tailored to users preferences. This paper contributes empirical insights and practical guidelines to support inclusive design in sustainable fashion, offering a theoretical foundation for future development of accessible clothing for the visually impaired.
Comparison of Support and Clothing Pressure Distribution in Japanese and Australian Maternity Support Garments
During the middle and late stages of pregnancy, the foetus grows rapidly, and the movements of the expectant mother are restricted. Therefore, a method that can support a sagging abdomen to support movement and distribute the weight of the lower abdomen throughout the torso to reduce the burden on the lower back needs to be investigated. This study aimed to develop a maternity support garment (MSG) that would provide comfortable support for rapid abdominal changes during the later stages of pregnancy. Nine types of MSGs used in Japan and Australia were analysed in terms of their clothing pressure distribution with a pregnant mannequin. In addition, pressure sense was indexed by the sensory evaluation of Japanese nongravid women in their 20s and 30s, and clothing pressure values obtained via simulation were analysed. The clothing pressure distribution of Japanese MSG is either almost perfect or looser, and when used alone, the MSGs keep the abdomen warm, except when the attached belt is used. Conversely, the Australian MSG is used with three types of tightness recommended by maternity hospitals; however, when double-layered with soft and hard types, the soft type concentrates pressure on the abdomen, and even the single-layer hard type MSG was observed to have higher pressure than the tight-fit sensation. Although the Australian MSGs provide support rather than insulation, the pressure that they exert on the gravid abdomen can restrict the blood flow to the foetus. Therefore, a narrow belt was combined with a single MSG and measured. The overlapping angle of the narrow belt generated excessive clothing pressure on the back from the heavy abdomen, making $15^◦$ the most suitable. The Australian soft-type single-layer MSG provided a perfect fit of clothing pressure but proved inadequate for supporting the heavy abdomen. Therefore, combining a narrow belt yielded good results. It was found that the medium type of MSG was preferred either as a single or double layer according to the pregnant woman’s preference, rather than combined with the belt. These findings are expected to advance understanding of the effects of existing MSGs on the pregnant abdomen, thus propelling further development of MSGs.
Optimising Natural Dyeing of Cotton with Butea Monosperma Through Anodic Pretreatment and Eco-Friendly Auxiliaries
This study investigates the combined effect of anodic pretreatment and varying auxiliary formulations on the dyeing performance of cotton fabric using a natural dye extracted from Butea monosperma flowers. While past studies have examined the role of mordants and pH modifiers in natural dyeing, limited attention has been given to how auxiliary systems interact with electrochemically modified fabrics to influence colour yield and fastness performance. This research addresses that gap by evaluating how anodically pretreated cotton responds to common dyeing auxiliaries—sodium chloride (NaCl), potassium aluminium sulfate (alum), sodium carbonate $({\rm Na_2 CO_3}),$ and calcium oxide (CaO)—in terms of shade development, chromatic richness, and durability. Using the CIE Lab colour system, significant colour variations were observed across all treated specimens (2–7) compared to the salt-only control (specimen 1). $∆{\rm E}$ values ranged from 12.49 to 19.95, with the highest chromatic shift seen in the ${\rm NaCl}$ + alum sample, which produced a vivid orange hue with enhanced redness and yellowness. Combinations of alum and alkalis yielded deeper, more saturated shades, while alkali-only formulations produced earthier tones with moderate lightness. These outcomes highlight the synergistic effect of anodic surface activation and auxiliary chemistry on improving dye-fibre interactions. Fastness tests, conducted according to ISO 105-C10 and ISO 105-X12 standards, revealed moderate washing durability (colour change ratings of 3 to 4) and good rubbing fastness (ratings up to 4). Specimens treated with mordants and alkalis generally exhibited improved fixation and reduced staining, whereas anodic treatment enhanced surface adhesion of the dye. This study demonstrates a promising, low-impact approach to improving natural dye uptake and fastness on cotton fabric by integrating anodic pretreatment with accessible auxiliary systems. The findings support the development of sustainable, decentralised dyeing processes using locally available plant waste, with particular relevance for rural craft and textile communities seeking eco-friendly alternatives to synthetic dyes.
Copyright (c) 2025 Journal of Fiber Bioengineering and Informatics
Evaluating the Impact of Blended Learning on Self-Directed Learning and Critical Thinking in Taiwanese Fashion Design Education
Blended learning has become increasingly important in Taiwanese higher education, particularly in creative disciplines such as fashion design. With the rapid advancement of digital tools and the growing emphasis on technology in education, blended learning provides fashion design students with greater flexibility to engage with both theory and practice. Despite its growing adoption, research into its impact on fashion design education remains limited. This study seeks to address this gap by examining how blended learning influences academic achievement, self-directed learning (SDL), and critical thinking within Taiwanese fashion design education. Specifically, it investigates how blended learning enhances creative studio practice and fashion-specific learning outcomes. The findings demonstrate significant improvements in SDL and critical thinking, which are key to developing creativity and problem-solving skills in fashion design students. The study underscores the potential of blended learning to bridge the gap between theory and practice in fashion education, offering valuable insights for its optimisation in similar contexts.
Sun-Protective Clothing Design for Wheelchair Users: Integrating Function and Accessibility
Research on sun-protective clothing for wheelchair users is of great significance in advancing the development of accessible clothing. Currently, sun-protective clothing available on the market is mainly designed for the general public. Traditional sun-protective clothing has shortcomings in multiple aspects such as garment length and bottom hem circumference, failing to meet the daily wearing needs of wheelchair users. Based on this, this study aims to design sun-protective clothing that caters to the needs of wheelchair users. The study employed questionnaire surveys and interviews, identifying seven key design points, and proposed a systematic design method based on three functional areas: sun protection, breathability, and safety protection. In the sun protection functional area, the sun protection area is determined by integrating the characteristics of the fabric and the wearing scenario. In the breathable functional area, the breathable area is determined and suitable materials are screened. In the safety protection functional area, reflective materials are integrated into the clothing structure line. Based on this, this study designs barrier-free settings for sun protection clothing. Three testers were invited to conduct wear testing and evaluation. The results showed that the comfort and functionality of the sun-protective clothing have been significantly improved, providing new design ideas for accessible clothing and creating possibilities for wheelchair users to have more suitable sun-protective clothing in the summer.
An Intelligent Framework for Multi-Scenario Fabric Selection in Cycling Apparel: A User-Expert Dual-Driven Approach
Traditional evaluation of cycling trouser fabrics employs fixed-weight methods, failing to address the diverse performance demands of dynamic cycling scenarios. To overcome this limitation, this study developed and validated an intelligent scoring system using a “user-expert” dual-driven dynamic weight optimisation strategy. A dynamic weighting scheme was first constructed by integrating the Analytic Hierarchy Process (AHP) with user survey data. To mitigate data scarcity, a Generative Adversarial Network (GAN) was utilised to augment the dataset. Subsequently, a random forest regression model, integrated with a fuzzy logic engine, was employed to dynamically adjust performance weights (abrasion resistance, moisture permeability, air permeability) based on scenario inputs. The model achieved a coefficient of determination ($R^2$) of 0.924 on the training set and an average $R^2$ of 0.893 on the test set. Further validation demonstrated a prediction error of $\le 5.2\%$ for new fabrics and maintained $R^2$ values above 0.85 under hyperparameter sensitivity analysis, indicating strong generalisation and robustness. This research provides a validated, data-driven framework for fabric selection in customised clothing and introduces a novel paradigm for the performance-based design of advanced technical textiles.
Advances of Functional Inks for Printed E-Textiles
Electronic textiles (E-textiles) have emerged as promising platforms for applications in healthcare, sports training, and human-machine interaction, owing to their flexibility, permeability and integration capability. Printing technologies such as inkjet, screen, stencil, gravure, and flexographic printing offer scalable and cost-effective routes for fabricating conductive pathways and embedding functional components onto textiles. Despite rapid progress, the development of high-performance printed E-textiles is hindered by challenges in ink formulation, printability, mechanical compatibility, and especially ink-textile adhesion. Current research lacks a systematic understanding of how ink properties influence long-term durability and device functionality under real-world use. This review aims to address these issues by discussing recent advances in functional inks for printed E-textiles, with a focus on ink composition, printing techniques, and adhesion strategies. Key insights into the relationship between material properties and textile performance are highlighted. Finally, future directions are proposed to guide the development of durable, stretchable, and reliable printed E-textiles.
Extraction of Natural Dye from Phellodendron Amurense and Its Photofading
Phellodendron amurense was used as natural dye source. Orthogonal experiment was carried out to select\r optimal extract process of phellodendron amurense dye. The orthogonal analysis results showed extract\r temperature was the most important factor. Stability investigation showed phellodendron amurense\r extract was stable to weak acid and alkaloid environment. Common metal ion had little effects on\r absorbance of phellodendron amurense extraction. Wool fabric and silk fabric had been dyed with\r phellodendron amurense aqueous solution. Solarization experiment showed high concentration dyeing\r sped up photofading rate and the dyed silk fabric was more sensible to the solarization. Vitamin C acid\r treatment slowed down photofading rate.
Natural Dye;Phellodendron Amurense;Extraction;Photofading
Copyright (c) 2025 COPYRIGHT: © Global Science Press
Biomimicry of Fibrous Materials: The Thermal Conductivity and morphology of Man-Made and Natural Fibrous Materials
With the aim to gain an appreciation of how nature insulates animal bodies with furs, in this exploratory\r study we investigated the TC and morphology of natural and synthetic fibrous materials. Specifically, we\r present our experimental observations on coyote, wolverine, and synthetic furs, as well as Polyacrylonitrile\r (PAN) nanofibers and knitted fabric. The TC was measured at a temperature range of \u221210 \u25e6C to 65 \u25e6C.\r The natural furs consist of fibers with different diameters and cuticular scale patterns. They form coreshell\r fibers with complex cellular structures in the core. The natural furs have lower TC compared to\r that of the synthetic fibrous structures. The coyote furs have the lowest TC value of \u223c 0.046 W/mK\r at \u221210 \u25e6C. While the TC of all of the samples decreased with temperature, the TC of the natural furs\r decreased at a higher rate and showed a different behavior with temperature, compared to the synthetic\r fibrous materials.
Thermal Conductivity;Natural Fur;HotDisc Method;Heat Transfer;Fibrous Materials
Development & Characterization of Alginate/Graphene Oxide Fibers with Improved Electrical Conductivity
Graphene has demonstrated extraordinary electrical, optical, thermal and mechanical properties. In last\r decade, a lot of research has been done to improve fabrication and solution processing of graphene to\r graphene oxide and reduced graphene oxide. Graphene oxide contains more number of oxygen containing\r functional groups which increase interlayer distance and make its dispersion easy in aqueous solutions.\r These advances in graphene have further improved its properties including tensile strength, elastic\r modulus, low resistance, carrier mobility and stability against higher temperatures and chemicals.\r Alginate is obtained from brown seaweeds and has potential applications in treatment of wounds and cell\r differentiation due to its non-toxicity, biodegradability and biocompatibility. It is a hydrophilic natural\r polysaccharide and provides moist and ideal environment for wound healing and cell growth. However,\r Alginate fibers still also display some unsatisfactory properties, such as low mechanical strength and\r electrical conductivity. Combining the intriguing properties of graphene and alginate, we develop smart\r composite fibers with suitable conductivity and mechanical strength which can be processed to nonwoven\r wound dressings and can be used as biosensors for medical applications. Sodium alginate/Graphene oxide\r fibers were fabricated by using wet spinning setup. The developed sodium alginate/graphene oxide fibers\r were further thermally and chemically reduced to improve conductivity. Their structure and properties\r were characterized by conductivity measurements, fiber strength testing, absorption behaviors, FTIR\r (Fourier transform infrared spectroscopy) and SEM (scanning electron microscope). The addition of\r graphene oxide improves the strength of sodium alginate/graphene oxide fibers due to high compatibility\r and even distribution of graphene oxide fillers in alginate matrix. Thermal and chemical reduction\r methods increase the conductivity of sodium alginate/graphene oxide fibers due to removal of oxygen\r containing groups. Chemical reduction method seems to have greater effect in improving the conductive\r properties of sodium alginate/graphene oxide fibers. These fibers also have good ability to absorb fluid\r and forms hydrogel to keep appropriate moist environment for wound healing which make them ideal\r material to develop smart wound dressings.
Alginate;Graphene;Conductivity;Fibers;Wet Spinning
Fillers Compounding and Process Optimization of Polyamide Wet-coating
In view of the many problems in the coating of the trademark, such as the poor stability of the filler,\r the poor adhesive resistance and ink absorption of the fabric, the high pH value and insufficient printing\r stability, etc. this paper focuses on the study of two kinds of common inorganic fillers, wollastonite and\r kaolin, on the structure of the polyamide 6 coating film and the properties of the wet coating trademark\r fabric. The mixture of these two kinds of filler is applied to the coating slurry, and the ratio and dosage\r are optimized. On this basis, combined with production practice, the wet coating process conditions\r were optimized. The results showed that the mass ratio of wollastonite and kaolin is 1:1 (the mass of\r the filler is 22% of anhydrous methanol), the compatibility of coating slurry is higher than that of the\r coating slurry prepared from single wollastonite, it is not easy to settle, but also the dispersion of coating\r slurry is better than that of only kaolin, and the microporous structure on the surface of coating film\r is more uniform. Moreover, the coated fabrics presented higher ink absorption performance. When the\r coating temperature is 35 \u25e6C, the viscosity of the coating is 2\u223c3 Pa\u00b7s, the baking temperature is 160 \u25e6C,\r and the baking time is 80 s. The color fastness to the soaping color of the coated fabric reaches to grade\r 4-5, the printing effect reaches to 3-4, the whiteness and the fabric style are better.
Filler;Rheologic Properties;Mechanical Property;Polyamide Wet-coating;Process Optimization
Evaluation and Comparison of Acoustic Performance and Thermal Conductivity of Spacer Fabrics
The utilization of 3-Dimensional (3D) porous textile materials by the civil and mechanical engineers for\r improved thermo-acoustic environment has widened the research scope. Unconventional three-dimensional\r textile material which grabs the attention of the researchers for multi-functional applications is spacer\r fabrics. Since spacer fabrics have superior thermal and acoustic characteristics compared to conventional\r woven/knitted structures or nonwovens due to their wonderful 3D porous nature. It has two outer layer\r connected with the help of monofilament or multifilament spacer yarn which kept the fabric bulkier\r with low density and highly breathable. Due to porous nature, interconnected pores, bulkier and 3D\r structure, the spacer fabrics have ability to attenuate more sound energy than the conventional materials.\r This research paper presents an experimental investigation on the sound absorption behaviour and\r thermal properties of warp knitted spacer fabrics. The Sound absorption coefficient (SAC) and thermal\r conductivity (K) were measured using two microphone impedance tube and Alambeta. This study deeply\r discusses the influence of material parameters and characteristics on acoustic properties of 3D spacer\r knitted fabrics. The results show that the fabric surface property, porosity, flow resistivity and tortuosity\r have significant effects on the sound absorption as well as thermal conductivity. With the obtained\r results, this work derives regression equations and correlation between noise absorption and thermal\r properties of spacer fabrics.
Noise Reduction Coefficient (NRC);3D Spacer Fabrics;Flow Resistivity;Thermal Conductivity
Garment Pleats Modeling and Force Analysis Based on Different Yarn Directions
Pleating is a common modeling technique in the manufacturing of garments. As requirements for the style of pleats are gradually increasing, it is necessary to consider the pleats modeling and force conditions In this paper, draping experiments were carried out with the warp yarn on horizontal, vertical, and a 45\u25e6\u00a0angle (bias) for three types, After analyzing the modeling garment pleats, force conditions and comparing the angle of the resultant force, it can be concluded that the modeling and the force of the pleats varies with the direction of the yarn. The species of pleats are different and their modeling is forced differently. The results demonstrate that the modeling of draped pleats and sunray pleats-upper-length are mainly affected by gravity, the bias fabric was desirable in terms of roundness, symmetry and softness. For sunray pleats-whole-length style, the neckline, modeling, quantity and size of pleats, the stability of hem, and the effect of force should be rationally utilized to achieve the optimal styling. The modeling of mummy pleats was greatly affected by the stretch, the bias stripes were the optimal choice.
Pleat;Warp Direction;Draping;Modeling;Force Analysis
Influence of Fabric Properties on Clothing Thermal Comfort under Different Environmental Temperatures
Temperature greatly in fluences the choice of clothing type. It is difficult to study the effect of temperature on the performance of a garment and the used fabric, as changes in temperature varies signi\fcantly within a year, a season or even a day. This research paper studied the effect of fabric properties on human thermal comfort under different environmental temperatures. The main methodology entails using a CAD software to simulate clothing thermal comfort value of a garment made of 6 fabric types, then conducting analysis on the simulation results. The results indicate that fabrics perform better within a range of temperature. The further they stray from the range, the smaller the impact of the fabrics on human comfort. The results from the methodology also demonstrates that activity types and temperature greatly affect the comfort value.
Textile CAD technology;Mathematical modeling;Mathematical analysis;Big data model
Application of Visual Illusion Patterns on Modern Women's Knitwear Design
The jersey knitwear market occupies most of the knitting market due to its relatively simple manufacturing process and high public acceptance. Pattern design composed of some simple lines and geometric patterns usually applied to jersey knitwear, but it can not take advantage of knitting fabrics as much as possible. Therefore, in order to combine the merit of knitwear, the research process is as follows: firstly, learning about women's preferences for knitwear in their 30s and 50s through questionnaire distribution and in-store inquiries. Secondly, designing and producing urban women's knitwear collections with optical illusion patterns. The yarn used in this design experiment is mostly 100% 80s Australian wool, and the equipment is STOLL M1 plus--a computerized at knitting machine, all of which are provided and assisted by the FSF factory, Suzhou, China. It is \fnally determined that the jacquard pattern put into production is double-sided birdeye jacquard and air layer jacquard. In terms of the \fnal collection effect, the design ideas of \"Fake Two Pieces\" and \"Simulated Pleated Skirt\" are reflected in the collection named \"chengSHi\". The combination of knitwear and optical illusion patterns dramatically simpli\fes the production process and improves production efficiency owing to knitwear's one-piece weaving. Moreover, it also provides new inspiration for knitwear design.
Optical Illusion Pattern;Knitwear Design;Pattern Design;Visual Effects;Flat Knitting
Relationship between Ease and Thermal Insulation of Men's Pyjamas Made of a Knitted Fabric with New Cotton Non-Twisted Hollow Yarn
We examined the relationship between ease of pyjamas and body on the thermal insulation of men's pyjamas made from a knitted fabric of a new cotton-based non-twisted hollow yarn to obtain design guidelines for higher heat retention. We made six sets of men's pyjamas that have different ease for the circumferences of chest, hip, and sleeve. We measured the heating power required for different parts of a thermal manikin to remain at a constant temperature when the manikin wore the pyjamas. We also measured the thermal insulation when the cuffs of the upper garment and the hems of the lower garment were closed with belts. To evaluate the effect of ease of wearing state on thermal insulation quantitatively, the differences of circumference and area of the cross-section between thermal manikin and a pyjama worn on the manikin were calculated from those 3D scanned data. We investigated the relationship between the heating power and the differences. As a result, the heat retention increased as the differences increased although there are variations depending on the location. There was almost no effect of convection from the cuffs and hems in the experimental environment without wind.
Knitted Fabric;Non-twisted Hollow Yarn;Pyjamas;Thermal Properties
Representation and Application of Jacket Knowledge Based on Domain Ontology
This paper takes jacket as an example, constructs a clothing knowledge model based on domain ontology, and explores ways to represent and reuse clothing knowledge in the context of the Internet economy. In this paper, jacket knowledge is sorted out based on the dimension of life week of clothing products, and the jacket knowledge framework is subdivided under the guidance of the modular design concept, laying a foundation for further application of the framework. The research shows that combining ontology modeling technology and knowledge representation, jacket domain ontology can be constructed to realize the classification of jacket design knowledge. This paper also further discusses the application prospect of jacket knowledge framework, which can play a guiding role in the customization of the jacket and modular design of the jacket, improve the reuse rate of jacket knowledge, and provide a feasible application mode for garment knowledge management.
Jacket;Product Life Cycle;Knowledge Classification;Knowledge Framework
Fabrication and Characterization of Electrospun Nanofibrous Composite Membrane for Air Filtration
One kind of composite membrane by combining electrospun nanofibrous membrane with fiberglass mesh was developed for air filtration. The fiber diameter, morphology of membranes, pressure drop and filtration efficiency of composite membrane were evaluated. The results revealed that the pressure drop and filtration efficiency of composite membrane decreased with the increase of winding speed (the higher winding speed is, the less of nanofibers are). The pressure drop of all samples at an airflow speed of 5 m/min was less than 25 Pa, which was much lower than the values from other reported work; and the highest filtration efficiency of sample in this work was more than 83% for particulate matters with 1.88 \u0016m.
Electrospinning;Nanofiber;Light Transmittance;Pressure Drop;Filtration Efficiency.
Si Cheng, Yuan-Xiang Xiao, Lian-Ying Zhao, Lv-Tao Zhu, Ren-Long Li, Guo-Cheng Zhu
Structural Design and Fabrication of Silk/Polyester-Based Bifurcated Stent-Graft
The present paper describes the structural design and fabrication of silk fibroin (SF)/polyester (PET)- based bifurcated stent-graft (BSG) using orthogonal experimental design (OED) and range analysis (RA). An orthogonal design comprising of three factors was used, including basic weave, warp x weft density and warp x weft materials, each factor contains three different levels. As a result, nine kinds of BSGs with different weaves, densities and materials were prepared using a modified rigid rapier weaving loom. Water permeability and wall thickness were evaluated according to standard protocols (ISO 7198: 2016). Furthermore, weaving process was optimized and RA was used to detect how performance was affected by factors. The results showed that the thickness of almost all samples is near or less than 0.1 mm, which is required for BSG used in endovascular graft exclusion. Whereas, the water permeability is with a large variation compared to thickness, because BSGs made of pure SF possess significant lower water permeability than that made of pure PET or SF-PET mixed. The water permeability of sample g is only 5.19 ml/(cm2\u00a0x min), which can prevent blood leakage after transplantation according to the standard. In conclusion, the SF-based BSG has better performance in terms of water permeability, which is more suitable as BSG used in endovascular exclusion.
Bifurcated Stent-graft;Silk;Woven;Wall Thickness;Water Permeability;Orthogonal Experimental Design.
Ze-Kun Liu, Gang Li, Yi Li, Yu-Ling Li, Xiao-Qin Wang
Grafting of Poly (Cysteine Methacrylate) Brush from Polysulfone Membrane via Surface-Initiated ATRP and Their Anti-Protein Fouling Property
In this paper, we first grafted the poly cysteine methacrylate (pCysMA) brush on polysulfone membrane by surface-initiated atomic-transfer radical polymerization and studied for their antifouling properties. The surface topological structure, chemical composition, and wettability of the as-prepared surface are characterized by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), fourier transform infrared spectra (ATR-FTIR), and water contact angle (WCA) measurements. The hydrophilicity and anti-biofouling activities of the polymer brush surface were evaluated by protein adsorption test. The results displayed that the pCysMA shows better hydrophilicity and effectively resisted the adsorption of bovine serum albumin (BSA) protein.
Polysulfone;Cysteine;ATRP;protein adsorption.
Ling Yang, Si-tian Ma, Huan Xia, Yi-ming Bu. Jing-jing Huang, Shao-jin Gu