The main aim of the present study was to investigate the mechanical and thermophysiological comfort properties of a polylactic acid (PLA)-based single-jersey knitted fabric. To study the mechanical behavior of PLA fabric, its abrasion resistance and bursting strength were investigated. The PLA demonstrated assertive mechanical behavior against abrasion in a dry state and stress. To characterize the liquid transport, the moisture management tester was used to determine the overall moisture management capability (OMMC). Analyses of the thermal properties, water vapor permeability (WVP), and air permeability (AP) were also performed to investigate the thermophysiological comfort of the PLA fabrics. The PLA fabric exhibited lower thermal conductivity and thermal absorptivity, making it suitable for thermal insulation. Moreover, excellent water vapor permeability and air permeability were observed for the PLA knitted fabrics. The PLA single jersey fabric stands out for its impressive mechanical and comfort properties, making it a strong contender against polyester. The research findings also demonstrate that the PLA single jersey fabric is exceptionally breathable and comfortable, making it an excellent choice for a wide range of applications.
The industrialization of Chinese national sports dance, rooted in a rich cultural heritage, plays a crucial role in preserving and promoting traditional culture. This study explores the vital role of integrating modern artificial neural networks (ANNs) in safeguarding and promoting traditional Chinese folk sports dance, a rich cultural asset. The novelty of this work lies in its focus on optimizing the relationship between music and dance movements through a detailed ANN model, which uses underlying features rather than relying solely on high-level statistical characteristics. The model was trained using a genetic algorithm and validated through experimental data. Results show that using underlying features for music-movement correspondence led to accuracy rates ranging from 69 % to 77 % across various dance forms, such as “Danube Night” achieving 75 %, “Beautiful Countryside” 71 %, and “Deep Night” 77 %. In comparison, high-level statistical features produced significantly lower accuracy, ranging from 55 % to 71 %. Additionally, the Root Mean Square Error (RMSE) for the model was 0.15, while the Mean Absolute Error (MAE) was 0.10, underscoring the model’s precision. Evaluation by 40 participants further confirmed the quality of the synthesized dance performances, with high agreement between predicted and actual movements. The success of this model suggests that ANN-based systems can significantly contribute to the commercialization and wider dissemination of traditional dances. Future research should aim to improve the model by incorporating more complex neural network architectures and refining it to account for variations in individual performances and environmental conditions, ultimately enhancing the preservation and industrialization of cultural resources.
This study conceptualizes dog-walking harnesses as canine wearable clothing systems and examines how harness structure, motion condition, leash traction level and body condition influence clothing pressure distribution. Y-type, H-type and vest-type harnesses were modelled in CLO 3D and simulated on normal and obese canine avatars under walking and running conditions with three displacement-controlled leash traction levels: slack, mild and strong. Pressure values were extracted from four harness-body contact regions and analysed using pressure maps and analysis of variance. Harness structure, body condition, motion condition and leash traction level significantly affected mean harness pressure. Leash traction and motion condition were the main use-condition factors driving pressure amplification. Strap-centric structures produced localized pressure concentrations, whereas the vest-type structure distributed pressure across a broader contact area. Obese body conditions increased overall clothing pressure. The study extends clothing pressure analysis to canine wearable equipment and proposes a reproducible virtual fitting framework for pressure-based evaluation of pet apparel and walking harness design. Pressure values should be interpreted as relative simulation-based indicators and require validation through sensor-based wear trials.
This study concerns wool photochromic sensors incorporated into bio-epoxy resin composites strengthened by natural (flax, wool) and man-made (glass, carbon) fibres. The sensors were developed using a cost-effective, ecological screen-printing method on wool fabric. Upon exposure to UVA radiation, a reversible colour change from white to pink is visible, with the intensity increasing as the absorbed dose increases. The paper presents the color-changing characteristics of the sensors, performed using reflectance spectrophotometry. Additionally, the morphology of the composites was imaged using Scanning Electron Microscopy (SEM), and their structure was examined with Micro Computed Tomography (micro-CT). Finally, the potential applications of these materials for measuring the 2D/3D UV dose distribution and as UV exposure indicators of composite materials were discussed.
To investigate the failure issue of shoe upper materials, six types of warp-knitted jacquard spacer shoe materials with different proportions of thick, thin, and mesh structures were selected. Bursting experiments were conducted at three rates (100, 300, and 500 mm/min), and the effects of rate and structural parameters on bursting performance were systematically analyzed. At the same time, in order to achieve the rapid screening of the optimal upper material for functional areas, reduce and avoid the waste of raw materials caused by sampling and performance testing, a finite element model was established based on the optimal structure at 300 mm/min, and the feasibility of the model was further verified through simplified structure experiments by removing the spacer layer and the plain-cloth bottom layer. The results show that: the bursting performance of fabrics with different jacquard layer structures changes non-monotonically with the increase of the rate, and reaches a peak at 300 mm/min; the finite element model can effectively predict the fabric bursting process with an error less than 9 %; its jacquard layer structure model further indicates that this method can better simulate the force conditions of the coil structure during bursting, providing new ideas for the optimization of shoe material process design.
The weft insertion system is a critical functional unit in high-speed weaving equipment, and its driving capability, energy loss, and operational stability directly affect weft insertion quality and overall machine performance. To overcome the limitations of conventional mechanically contacted weft insertion methods under high-speed conditions, such as restricted speed improvement, severe wear, and high noise, this study investigates an electromagnetic induction weft insertion system for high-speed weaving and performs a multi-objective optimization of its key parameters. The proposed system integrates high-temperature superconducting magnetic levitation guidance with electromagnetic induction drive to enable non-contact, high-speed, and stable shuttle motion. Driving plate thickness and excitation frequency were selected as design variables, while traction force and eddy-current loss were taken as optimization objectives. A surrogate-assisted bi-objective optimization framework combining finite element modelling, response surface methodology, and NSGA-III was established. The results show that the high-traction region overlaps significantly with the high-loss region, indicating an inherent multi-objective trade-off in the parameter design of this type of weft insertion equipment. Further optimization yielded a well-distributed set of Pareto-optimal solutions. For representative solutions, the relative errors between the surrogate predictions and finite element re-evaluations were below 3 %, and experimental results further confirmed the predictive capability of the model for traction response. The proposed method provides a quantitative basis for parameter selection and performance matching of electromagnetic induction weft insertion systems in high-speed weaving equipment, and offers useful guidance for the engineering design and application of novel non-contact weft insertion devices.
Promoting low-carbon consumer behavior is essential for climate mitigation, especially in textile and apparel consumption where purchase, use, reuse, and disposal decisions jointly shape carbon and resource impacts. Existing intelligent intervention systems are often constrained by behavioral complexity, biased feedback, and the intention-action gap. To address these limitations, this study proposes Dynamic Causal Disentanglement with Reinforcement Learning (DCDRL), a deep reinforcement learning framework for dynamically identifying consumers’ intrinsic low-carbon motivation and separating it from extrinsic behavioral biases such as conformity and salience effects. By combining causal graph modeling, disentangled representation learning, conservative offline reinforcement learning, and dynamic negative sampling, DCDRL learns debias-aware intervention policies for sustainable textile/apparel and other low-carbon consumption scenarios. The corrected experiments separately evaluate Smart Energy (SE-1M) and Sustainable Choice-Apparel (SC-A) datasets and show that DCDRL outperforms sequential recommendation, deep learning, and offline reinforcement learning baselines under a consistent full-catalog evaluation protocol.
This paper presents a generative-type textile humidity sensor made entirely from commercially available electroconductive fabrics. The sensor consists of two woven fabric strips metallised with different metals (copper and nickel), ultrasonically welded to a polyester-woven fabric substrate. In the presence of moisture, the structure forms an electrochemical cell that generates a voltage dependent on relative humidity. Four sensor variants were investigated with respect to their static and dynamic properties. The results show a nonlinear relationship between generated voltage and relative humidity, with effective operation mainly above 50 % RH. Dynamic tests revealed response times of several seconds and recovery times of several minutes, governed by the drying behaviour of the textile substrate. The sensors also respond to artificial acid and alkaline sweat. Temperature tests indicated a positive temperature coefficient. The proposed fully textile sensor offers a simple, flexible solution for textile humidity sensing in smart garments.
Hemp (Cannabis sativa L.) is a fiber plant. The fibers are extracted from the stem of the plant. One of the fiber extraction methods is retting. However, the traditional water retting method cannot be controlled totally, leading to variations in the retting process and affecting the overall quality of the extracted fibers. Therefore, extracted fiber quality is inconsistent. In this study, a locally adapted microbiological retting method was developed via using autochthonous strains isolated from hemp retting water in the Black Sea region of Türkiye. At the beginning of the study, three high polygalacturonase-producing isolates (Pantoea sp., Bacillus sp., and Clostridium beijerinckii) were selected, and then, a dual-phase inoculation protocol (aerobic followed by anaerobic) was applied to hemp stalks. The developed retting was completed in 7 days. Fiber quality determined by SEM, FTIR, color and yellowness measurements, fiber yield, and spinning performance. The new process removed pectin effectively without breaking fiber integrity. Pantoea sp., and Clostridium beijerinckii were first used in hemp retting. Then, retted fibers with the new method were successfully blended with cotton and spun into Ne 12/1 yarn in an open-end spinning system. The new approach supplies a rapid retting model for high-quality hemp fiber production.
Natural fiber composites have gained increasing attention lately due to being biodegradable and environmental friendly. The use of natural materials in composites would help to prevent global warming by reducing greenhouse gas production. In this study, the synergetic effects of fiber hybridization and weaving types in the mechanical properties of the composites were examined in detail by systematically presenting a comparative analysis of 27 different mono and hybrid fabric configurations of combined weaving structures (plain, twill, and basket) and fiber combinations (jute, hemp, and flax) in both warp and weft directions. Composite samples were manufactured via the vacuum infusion method and characterized using tensile and three-point bending tests. The results showed that weave architecture directly influences the crimp ratio of fibers, thereby affecting load transfer efficiency within the matrix. The results pointed out composites with flax fibers displayed the highest tensile (56.88 MPa) and flexural strength (60.48 MPa), while twill woven composites reached highest average tensile and flexural strength values. Basket weaves provided superior isotropic stability. These results suggest a practical database for engineers for design applications, enabling the selection of optimal architecture-hybrid pairings.
To enhance the efficiency of solving mechanical properties of lattice materials, this paper constructs a rapid-solving model based on the CNN-GRU algorithm. Initially, methods for constructing the mathematical model of lattice units are investigated, and the equivalent Young’s modulus, shear modulus, and Poisson’s ratio are derived using the asymptotic homogenization theory. Subsequently, a voxel model based on the lattice structure is established, and a hybrid CNN-GRU model is developed to achieve rapid solving of key mechanical properties. Finally, the accuracy and efficiency of the model are verified. The results indicate that the CNN-GRU model significantly surpasses traditional finite element methods in computational efficiency, achieving a solution accuracy exceeding 92 %, thereby demonstrating the model’s effectiveness and adaptability. This research provides a novel approach for the design and optimization of lattice materials and contributes to the advancement of additive manufacturing technologies in material applications.
The aim of this study was the design, fabrication, and durability assessment of three-dimensional cut-resistant structures applied to textiles by a coating process using stencils fabricated by additive technology. The developed stencils were characterized by patterns of varied geometry and size, enabling the deposition of well-defined 3D structures on the substrate. The paper presents both the design aspect, i.e., stencil design and pattern orientation relative to the direction of test blade movement, and the technological aspect, i.e., the deposition of polymer structures and evaluation of coating durability. The resulting coated material is intended for protective applications, including cut-resistant PPE. We performed cut-resistance tests and conducted a 3D spatial evaluation of the structures deposited on the fabric using non-contact measurement methods to assess the effectiveness of the coating technique.
The growing demand for functionalized fiber-reinforced plastics has intensified research on the robust integration of actuating elements such as Shape Memory Alloy (SMA) wires to enable adaptive structural behavior. While SMA-based actuation is well established, mechanical performance is fundamentally limited by the SMA-textile interface, particularly in force transmission zones. This study addresses the lack of systematic design rules for 3D multilayer woven structures by investigating three key parameters: deflection (d), horizontal length (h) and vertical length (v), Utilizing a three-stage experimental design, d and v were identified as the dominant factors governing friction according to Capstan's Law. Quantitatively, while insufficient embedding reached only 33-57 N, optimized configurations achieved consistent forces of 100-110 N. A structural threshold at d >= 4, corresponding to a "Critical Length" (L c ) where binding strength exceeds the wire's tensile capacity (similar to 108 N), resulting in wire fracture rather than extraction. Furthermore, central vertical positioning within the 3D structure was found to be essential to maintain the necessary counter-pressure for stable friction. These findings provide validated quantitative design guidelines for SMA integration, ensuring that structural binding withstands maximum actuation forces. This work significantly enhances the functional reliability and durability of adaptive systems.
This study evaluates the impact of three-dimensional coating structures on the cut resistance of aramid knitted fabrics. Nine different coating geometries (elliptical, diamond, and stripes) were created using template matrices and tested for cut resistance according to EN ISO 13997. required to cut the fabric. The results showed that the geometric shape significantly influenced cut resistance, ranging from 7.8 N for elliptical patterns to 23.8 N for diamond patterns, which provided the highest level of protection. To support the experimental findings, an unsupervised machine learning approach was employed to provide an objective, data-driven validation of the differences in the coated layers. The application of a modified NbClust decision fusion approach explicitly linked the geometric parameters with the resulting cut resistance, confirming that the surface geometry fundamentally dictates the observed performance clusters. This quantitative analysis provided an objective validation of the grouping of coating structures, confirming that surface geometry is a key determinant influencing the cut resistance of protective textiles.
The spinning metering pump serves as a critical component that delivers the spinning solution with high precision and directly governs the efficiency of wet spinning industrialization. Nevertheless, manual adjustment of pump output often causes raw material loss and low operational efficiency. A geometric model of the metering pump was imported into the computational fluid dynamics (CFD) solver for flow field simulations and visualization of the internal solution dynamics to rapidly determine the optimal operating parameters. Transient flow analysis quantified the influence of fluid properties, flow field conditions, and structural dimensions on outlet flow rate, flow ripple, and volumetric efficiency. The simulations reveal that high viscosity fluid, fast rotational speed, low outlet pressure, and small radial clearance yield a volumetric efficiency of 97.99 % and markedly suppress flow ripple. This study provides an accessible yet powerful strategy for visualizing the performance of spinning metering pumps and guiding the design of high efficiency and low power spinning systems with strong theoretical and engineering value.
This study addresses homogenization in the Mamianqun market using an integrated FAHP-QFD-TRIZ methodology. The process quantifies consumer requirements, identifying “Chinese Style” and “Easy Wearability” as core demands. QFD translates these into technical parameters centered on pleating innovation. Key technical conflicts are resolved using the TRIZ contradiction matrix and separation principles. Empirical results confirm the method’s effectiveness: final designs outperform conventional models, scoring over 2.5 points higher in aesthetic innovation and craftsmanship while enhancing wearability without compromising cultural authenticity. This structured process provides a replicable model for revitalizing traditional apparel.
In order to clarify the brain perception mechanism of tactile discomfort caused by wet fabric on the skin, and compare the changes of brain response to dry and wet tactile stimulation of the skin, five kinds of knitted mid-tube compression socks were used as samples, fMRI experiments was performed on the brain under the condition of skin contact stimulation with dry clothing and wet clothing respectively. The results showed that the brain region where the maximum positive activation intensity located was shifted from the secondary somatosensory cortex to the primary somatosensory cortex as the stimulation clothing changed from dry to wet, and the contact stimulation from the wet clothing reduced the maximum positive activation intensity, the maximum number of activated voxels and the total number of activated voxels in the sensory cortex, which was related to the neural pathway of somatosensory information, the heat transfer mechanism, the moisture transfer mechanism and the friction mechanism of textiles. The finding not only contributed to understanding the brain cognitive mechanism in the wet tactile perception, but also was of great significance for evaluating tactile comfort of clothing.
With sustainable concept developing, the recycled nylon in the textile industry is becoming more and more widely used. Because the physical and chemical properties of virgin nylon and recycled nylon are very close to each other in terms of appearance and morphology, crystallinity, etc., traditional detection methods are unable to effectively distinguish between them. In this study, oligomers were collected from both virgin and recycled nylon samples through high-performance liquid chromatography (HPLC) technology. Then, the classification was achieved through feature extraction and pattern recognition of the chromatographic data based on a ResNet model, which can achieve a recognition accuracy of 90.6 %. At the same time, the Permutaion Importance method was applied to interpret and visualize the decision-making process of the ResNet. It was shown that the distribution and content of caprolactam peaks made the most significant contribution to the identification between virgin and recycled nylon and so they could be used to train the model. The development of a rapid identification system can serve the textile quality supervision and green certification, and has important practical value for regulating the recycled fiber market and promoting the development of circular economy in textile industry.
The progress of textile technology continually drives the development of the textile product market, making the clarification of its research trajectory a key priority in the field. This paper aims to map the research status, core themes, and future trends of textile technology during 2005–2025, and specifically to elucidate the profound reasons for the field’s recent research growth entering a plateau after a period of high output. The study adopts the bibliometric method, using 1,365 papers retrieved from the Web of Science database as a sample, and employs CiteSpace for a systematic analysis of keywords, authors, institutions, and disciplines. The results show that the field has formed a well-established research cluster centered on materials science and engineering, with representative institutions including Lodz University of Technology and Donghua University. Research topics are highly concentrated on nanotechnology and surface modification, dedicated to enhancing fabric functionality, such as strengthening mechanical properties and increasing antibacterial capacity. At the same time, emerging themes such as smart textiles and silver nanoparticles have concentratedly emerged since 2023, reflecting a transformation demand towards intelligence and functionalization. Crucially, evidence at the institutional and disciplinary levels indicates that cooperation and knowledge integration between cutting-edge knowledge streams such as artificial intelligence, biology, and ecology and the aforementioned mainstream materials science cluster are significantly insufficient. The structural divide between the mainstream and the cutting-edge is the fundamental reason for the field’s stagnation following a period of rapid growth. Future research emphasis should shift from incremental optimization within material science to breaking disciplinary boundaries and fostering deep interdisciplinary integration.
Tech pack is a comprehensive communication document that provides detailed instructions and specifications for apparel production. It serves as contracts between design sections, production sections, and clients, ensuring consistency, accuracy, and effective communication throughout the product development process. This study aims to identify and examine the specific components of tech packs. Through surveys, interviews, and analysis of existing tech packs, the study validates the importance of each component and its contribution to the overall effectiveness of tech packs. The methodology involves hypothesis generation, questionnaire development, data collection through surveys, and data analysis using statistical techniques. Confirmatory factor analysis (CFA) technique was used to prove the model statistically by using AMOS and SPSS software. Results were improved by several iterations, and they verified that the obtained components of tech packs are necessary to get the right information from customers. Finally, the study developed a conceptual model that describes the relations among all tech pack components and illustrates their interactions and benefits within the apparel sector in question. Furthermore, this study will contribute to the improvement and standardization of tech pack development, ultimately enhancing productivity and ensuring consistent and accurate apparel product development.
