Automatic layout of irregular warp-knitted jacquard shoe uppers is subject to a fixed fabric width, prescribed wale-course orientation, and process-specific safety-distance constraints. This study proposes an adaptive optimization method that combines cell-based geometric preprocessing with two complementary layout strategies. For each shoe upper, the minimum bounding box and the contour projections along cell columns and cell rows are computed to efficiently determine feasible lateral and longitudinal offsets. A single-row sequential strategy provides flexible local adjustment, whereas a two-row grouping strategy maintains stable nesting between adjacent rows. The better feasible solution is selected automatically using a lexicographic evaluation criterion. Experiments on irregular and symmetric shoe-upper patterns achieved maximum normalized output densities of 20.55 and 22.88 shoe uppers per 1000 cell rows, respectively. For representative configurations containing 12 shoe uppers, automatic strategy selection reduced the total layout height by 3.95% and 5.56%, while the mean solving time ranged from 64 to 115 ms. The proposed method reduces manual placement in computer-aided design (CAD) systems and provides an efficient approach to production-oriented layout planning.
This experimental study focused on optimizing the machinability properties of Kevlar fiber–reinforced aluminum laminates (KRALL). For this, the gray relational approach and Taguchi approach were used to optimize the abrasive water jet machining (AWJM) factors with multi-response characteristics. Kevlar fiber and aluminum-based fiber metal laminates are known as KRALL. AWJM was used to machine the KRALL. The Taguchi L9 orthogonal array was used to establish the levels and factors. Water jet pressure, stand-off distance (SOD), and nozzle feed rate were considered as process factors in the Taguchi technique. The experimental results were analyzed by using the Taguchi method and gray relational approach. The gray relational approach and analysis of variance were used to analyze the most influential factors, such as roughness and kerf angle. The performance factors of roughness and kerf angle were found to be significantly influenced by SOD. According to the detailed experimental findings, the Taguchi approach and gray relational approach successfully optimized the machining factors of roughness and minimum kerf angle in the AWJM of KRALL.
Surface defect detection for synthetic fiber bobbins is crucial to intelligent textile manufacturing. Existing vision-based methods are limited by the lack of task-specific datasets, the inefficient adaptation of detectors to this application, and the difficulty of deployment on edge devices. To address these challenges, this study proposes a lightweight detection framework. First, an image acquisition system equipped with a bobbin pose-adjustment device is developed, and a new industry-oriented dataset, FiberBobbin-40K, containing 40,497 high-quality images, is established to fill the gap in fiber bobbin defect detection. Subsequently, a computationally efficient detector adaptation strategy is proposed. Finally, a compression framework integrating a redesigned detection head, layer pruning, and channel pruning is developed to enable efficient inference on edge devices. Experimental results show that, compared with the conventional YOLOv8, the optimized model reduces the number of parameters by approximately 71%, while incurring only 2.03% and 2.49% decreases in mAP and F1 score, respectively. In addition, it achieves 94.73% accuracy and 26.37 FPS on a CPU. The optimized model has only 2.47 million parameters and a model size of 4.86 MB, which is substantially smaller than Faster R-CNN and YOLOv7 while maintaining competitive detection performance.
As mobile and wearable devices evolve, the challenge of supplying adequate power within compact devices has become more critical than ever. Conventional solutions typically rely either on enhancing battery energy density or on harvesting supplementary electrical energy through work done in another way, such as the motion; however, these approaches often introduce considerable cost and design complexity. In this work, we investigate whether a triboelectric generator constructed entirely from common, low-cost textile materials can serve as a practical and significant alternative for powering in portable electronics. The fabricated textile-based generators were subjected to a series of performance and durability assessments, including washability and delamination-strength testing. Our results demonstrated that built up generators can produce sufficient energy in order to potentially power an electronics circuit.
This study investigates the effects of washing on the structural characteristics, bending, and compression properties of double jersey knitted fabrics made from 10 × 4 tex organic cotton yarn in 1×1 rib, half Milano rib, and Milano rib structures. Structural characteristics (number of wales per centimeter, number of courses per centimeter, fabric stitch density, stitch length, mass per unit area, and thickness) were measured before and after washing. Mechanical properties were assessed through bending properties (drape coefficient, stiffness, and bending modulus), and compression properties (compressibility, thickness loss, and compressive resilience). The results indicate that both the knit structure and washing significantly influence the mechanical properties of the organic cotton fabrics. Washing induced dimensional shrinkage, which altered structural characteristics and led to reductions in most measured properties, with exceptions observed in the stiffness and bending modulus of half Milano rib fabrics in the wale direction and thickness loss in Milano rib fabrics. Overall, washing enhanced bending behavior while reducing compression performance. Among the examined structures, 1×1 rib fabrics showed the highest stability, with the smallest changes in mechanical properties after washing. These findings highlight the critical role of knit structure in determining fabric performance after washing and provide practical guidance for designers in selecting structural parameters to optimize bending and compression properties.
Heat stress in high-temperature environments is a major concern for public health and animal welfare. However, cooling materials used in commercial companion animal apparel are often adopted from human apparel without independent scientific validation. This study compared the contact cooling performance of fabrics used in canine and human apparel based on the same physical performance index, Q-max, to examine the validity and limitations of applying this human-centered index to canine cooling garment design. Q-max was measured in accordance with JIS L 1927 (ΔT = 10 °C) for 24 commercially available cooling fabrics each for canine and human apparel under identical testing conditions. The relationships of fabric thickness, weight, air permeability, and material composition with Q-max were also statistically analyzed. Although the two groups showed similar mean Q-max values, they differed in distribution range and variability. Fabrics for canine apparel exhibited greater variance and a broader maximum range of Q-max values, and substantial variation was observed even within identical material compositions. Some canine apparel fabrics showed Q-max values equivalent to or higher than those of human apparel fabrics, confirming that contact cooling performance is not determined solely by end-use classification. Thickness, weight, and air permeability did not show consistent linear relationships with Q-max, suggesting that contact cooling performance is shaped by complex interactions between material composition and fabric structure rather than by a single factor. Because Q-max represents intrinsic contact heat transfer under direct-contact conditions, its application to canine apparel should be interpreted cautiously when fur alters the fabric–skin interface. These findings support a multi-property approach to the design and evaluation of canine cooling garments.
A three-dimensional garment pattern design system with an intuitive interface for three-dimensional body and garment models was developed. An open-source human modeling software was modified to generate 3D body models based on four basic body measurements: height, bust girth, waist girth, and hip girth. The generated body models were then used as input to construct three-dimensional garment models with ease allowance in a proprietary software environment developed in this study. The system enables users to draw cutting lines directly on 3D garment models and generates two-dimensional garment patterns through mesh cutting, reshaping, and flattening processes. In addition, a template-based design function was developed to save and replay the design process, enabling consistent pattern generation across body models with different sizes. The proposed workflow supports both compression garments and garments with ease allowance and was validated through virtual fitting simulations and physical garment construction. The results demonstrate the feasibility and practical applicability of the proposed mesh-based garment-to-pattern generation approach.
Fungal contamination on textile surfaces can contribute to disease transmission in agricultural and biomedical environments. Although copper nanoparticles (CuNPs) exhibit broad antimicrobial activity, CuNP-coated hydrophobic fabrics may show limited antifungal performance because ion-mediated activity is restricted under dry surface conditions. In this study, we report a hydrogel-assisted strategy to enhance the antifungal function of CuNP-coated polypropylene (PP) fabrics by creating a moisture-retaining interface around the nanoparticles. CuNPs with controlled particle sizes were synthesized by an ascorbic-acid-based reduction method and deposited onto PP nonwoven fabrics through a surface activation process. A thin hydrogel layer was then formed on the CuNP-coated fabric to provide a hydrated environment and continuous diffusion pathway. Compared with PP-CuNP fabrics without hydrogel, hydrogel-coated PP-CuNP fabrics exhibited markedly improved antifungal activity against Botryotinia fuckeliana and Cladosporium cladosporioides, while maintaining antibacterial activity against Escherichia coli. These results demonstrate that the local moisture environment is a critical factor governing the antifungal performance of CuNP-coated textiles. This work provides a structure-environment-function strategy for designing antifungal textile surfaces for agricultural and biomedical applications.
This study investigates the auxetic behavior of three-dimensional (3D) folded weft-knitted fabrics produced from aramid yarns. Although folded auxetic geometries have been previously reported, a systematic understanding of the influence of rib configuration and loop density on auxetic performance remains limited. In this work, a series of foldable 3D auxetic structures were engineered based on rib configurations of 6 & times;6, 8 & times;8, and 10 & times;10, with variations in loop density on the face and reverse sides (slack, medium, and tight) to examine their influence on NPR behavior. The effects of rib width and fabric density on the negative Poisson's ratio (NPR) were experimentally evaluated. The results revealed that both the zigzag rib geometry and the differential loop densities significantly affected the auxetic performance. In particular, an increase in rib structure size combined with a reduction in stitch density resulted in a pronounced enhancement of the NPR effect. The correlation analysis confirms that auxetic behavior in weft-knitted fabrics is primarily governed by structural looseness and geometric configuration. Thickness emerges as the most influential parameter, showing the strongest correlation with negative Poisson's ratio. The study provides a comprehensive structure property relationship, offering practical guidelines for the design of high-performance auxetic textiles.
This study presents a systematic framework that optimizes text-to-image generation prompts through Large Language Model (LLM) personas in fashion design applications. While generative models like Stable Diffusion show significant creative potential, prompt engineering remains challenging for domain experts lacking technical expertise. We developed a systematic five-stage methodology to optimize text-to-image prompts. First, we generate prompts using different AI personas with varying expertise. Then we create images, evaluate their quality, identify weaknesses, and optimize the prompts accordingly. Our optimized prompts demonstrated significant improvements over persona-based approaches across multiple evaluation dimensions. The Multi-expert persona achieved the highest baseline performance (9.11/11 points), which our optimization process enhanced to 10.05 points—a statistically significant 10.3% improvement (p<0.01). Optimized prompts significantly outperformed all persona approaches in requirement implementation and showed superior performance in human preference assessments. The optimized prompts achieved maximum CLIP scores of 0.9043 and ImageReward scores of 1.7452, demonstrating peak performance advantages across all metrics. In head-to-head comparisons, optimized prompts secured first-place rankings in 50% of human preference evaluations, significantly exceeding the 20% random expectation. This framework bridges the gap between language models and image generation systems, enabling fashion professionals to achieve consistent, high-quality AI-generated designs without prompt engineering expertise, thereby accelerating creative workflows and reducing design iteration time.
Analysis of fabric deformation finds use in several engineering fields, including performance garments, biomedicine, and composite forming. In this work, we present a computational framework of analyzing the deformation of a jersey knit fabric under external loads using a novel multiscale model. The finite element model presented here does not assume orthotropicity and consists of a multiscale model, with the mesoscale comprising of an infinitely spanning truss structure representing loops of the knit fabric, and the macroscale comprising of shell elements. The tangent modulus of the shell elements at every iteration of the nonlinear solver was derived by simulating small principal deformations on the mesoscale truss structure. In turn the truss structure at every step was dictated by the deformation of the macroscale elements it is embedded within. The mechanical characteristics of the fabric depend on the properties of the truss structure elements, which were estimated through tensile tests. The model was compared to several test conditions and generally good agreement was found, with root mean square error between the simulations and tests ranging from 6.12% in the best fit to 21.81% in the poorest fit. This model is expected to yield accuracy similar to that in other multiscale models, over wider test conditions, and due to simplicity of the mesoscale elements, requiring less computational resources compared to multiscale, microscale and mesoscale models. Future work will aim to improve model accuracy by increasing definition in the truss constitutive equations.
To address the challenges of balancing detection accuracy and computational efficiency in silk fabric defect detection, this paper proposes a lightweight model GSS-YOLOv8, designed to reduce parameter complexity while enabling real-time detection capabilities. A three-stage optimization strategy is adopted to target key bottlenecks. Firstly, in the backbone network, GhostHGNetV2 replaces the original feature extractor to enhance the feature representation of multiscale fabric defects while reducing the number of parameters. Secondly, a Slim-Neck structure is introduced, where the C2f module is replaced with VoVGSCSP and standard convolutions are substituted with GSConv, effectively reducing computational costs without sacrificing accuracy. Finally, a Shared Detail-Enhanced Head (SDEH) is designed. By sharing the parameters of two detail-enhanced convolutions, this module enhances the ability to capture fine-grained defect features and reduces parameter redundancy. The ablation experiments further evaluate the individual contributions of the GhostHGNetV2 backbone, the Slim-Neck paradigm design, and the proposed SDEH module, verifying the effectiveness of each improvement component. Additionally, experiments conducted on both the self-built silk fabric dataset and the Tianchi Fabric Defect Dataset confirm the feasibility and strong generalization capability of the proposed GSS-YOLOv8 model. The experimental results illustrate that compared with YOLOv8n, the GSS-YOLOv8 improves precision by 5.1 percentage points to 85.9% and mean average precision (mAP@0.5) by 2.1 percentage points to 86.5% with 80.0% recall, while reducing parameters by 51.3% to 1.46M, GFLOPs by 45.7% to 4.4G, and model size to only 3.7 MB, which fully meets the real-time detection requirements for silk fabric defects in industrial settings.
Particulate contamination deteriorates the appearance and hygienic performance of textile products. Although anti-soiling treatments are commonly applied during textile manufacturing, simple post-treatment approaches for controlling particulate adhesion after manufacturing remain insufficiently understood. This study investigated the effects of representative textile post-treatment agents—fabric softeners, antistatic agents, and water repellents—on the anti-soiling performance of cotton fabrics against particulate soil. Changes in friction characteristics, water repellency, and electrostatic behavior were evaluated using the Kawabata Evaluation System, spray tests, and electrostatic half-life measurements. Anti-soiling performance was assessed using the weight of soil adhesion (WS) and the removal rate of powder (RR). The results showed that treatment conditions significantly affected WS but not RR. Water-repellent treatment tended to reduce soil adhesion, whereas fabric softener treatment, despite reducing surface friction, tended to increase soil adhesion. Combination treatments generally preserved the individual effects of each agent but did not produce synergistic anti-soiling effects. These findings suggest that the adhesion and removal processes of particulate soil are governed by different mechanisms and that improvements in individual surface properties do not necessarily correspond to enhanced anti-soiling performance. The results provide basic guidelines for evaluating particulate anti-soiling performance from the perspective of textile surface properties and demonstrate the potential of simple post-treatment approaches for influencing particulate soil behavior.
The strength of the seam is one of the most important factors in determining the garment’s durability, structural soundness, and useful life. Conventional experimental-based assessment techniques are robust, but ineffective in cases where different sewing parameters interact non-linearly. This work is focused on the formulation and validation of an Artificial Neural Network (ANN) model for predicting seam strength associated with superimposed and lapped seams under different sewing conditions. These seam classes are commonly used in woven apparel products such as shirts, trousers, denim garments, sportswear, and workwear, where seam durability and structural integrity are important performance requirements. 60 samples of seam were prepared from plain cotton fabrics. Five sewing thread linear densities (21-59 tex), six stitch densities (5-11 SPI), and two seam classes were systematically used in combination. The seam strength according to ASTM D 1683-04 was tested with a calibrated Universal Testing Machine. A feed-forward ANN with one hidden layer was implemented in MATLAB and trained using the Levenberg-Marquardt backpropagation algorithm. The performance of the model was assessed using mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient. For training, validation, testing, and independent datasets, the ANN exhibited very good prediction capability with correlation coefficients greater than 0.99. Independent verification exhibited an RMSE of 2.38 N and a MAPE of 6.96%, indicating improved prediction accuracy compared to earlier ANN-based seam strength models with an approximately 15-30% lower prediction error. The model was successful in capturing the non-linear relationships between stitch density, thread linear density, and seam geometry. The developed model may assist garment manufacturers and textile engineers in rapid sewing parameter selection, reduction of trial-and-error, minimization of material wastage, and optimization of seam quality during apparel product development.
The objective of this research is the development of a polyether-ether-ketone (PEEK) yarn and its further processing into a sewing thread. The melt spun fine-titer PEEK yarn with a titer of 50 dtex and 18 single filaments (50f18) has a strength of 72.3 cN/tex (939.9 MPa) and a Young’s Modulus of 8.4 N/tex (10.9 GPa). Thus, the mechanical properties significantly exceed the state of technology. The PEEK yarn is further processed into two different sewing threads (50x2 and 50x3) with 2 twists per cm in z-direction. Using both types of thread for knitting, embroidery, and sewing is evidence of textile processing. The mechanical properties of the high-performance sewing threads are comparable to commercial sewing threads made of engineering polymers like polyethylene terephthalate (PET). The developed PEEK sewing threads can be used for non-crimped fabrics in high-performance composites.
The application of natural dyes on natural fiber materials enables the realization of fully bio-based textile products. Usually natural dyes are applied in combination with mordanting agents to improve color strength and fastness properties. This current paper is dedicated to a comparison of the natural fibers wool and cotton with the regenerated cellulose fiber Lyocell. For natural dyeing, extracts from logwood or madder root are used as natural dyes. As mordanting agent, water soluble salts of Fe 2+ and Cu 2+ are evaluated in comparison to the less common agent titanium dioxalate, which is a water soluble chelate complex of Ti 4+ . Applications are done as pre- or meta-mordanting procedures. In this experimental set-up, 42 different sample combinations are realized. Depending on type of dye and mordant, deep color shapes are achieved in the range of red, black to blue. By application of titanium mordant color shades are realized which are not covered by dyeing procedures using the other both mordants. Compared to the dyeing without mordant agent, the lightfastness increases clearly with the metal mordant application. This statement is especially valid for logwood dyeing which exhibits low light fastness grades of 1 or 2 without metal mordant. Depending on mordant procedure, dye/mordant combination and fabric, a light fastness in the range of grade 1–5 can be realized. Best values are determined with dye/copper mordant combinations leading to a light fastness of grade 5, which is an excellent value for natural dyes. In comparison, the determined light fastness from titanium containing applications are minor. Compared to the dyeing without mordant agent, the rubbing fastness decreases clearly with the metal mordant application. However, the application with titanium mordant is advantageous in rubbing fastness and with the combination of titanium/madder, a wet rubbing fastness grade 4 can be reached, which is an excellent result for a natural dye application. The actual study presents a broad range of dyeing recipes for natural dyes. Especially reported is the natural dyeing of Lyocell fiber materials and titanium mordant agents, which in that combination unique and up to now less considered in literature. Finally, the actual study is a helpful tool and starting point for future developments of natural dyeing of regenerated fibers as Lyocell in combination with metal mordanting agents.
Garment pressure measurement is essential for evaluating comfort, fit quality, and physiological function in both everyday clothing and specialized garments. The AMI-3037 pneumatic sensor has served as the reference standard for nearly three decades, but its high channel cost, time-consuming per-channel calibration, and temperature-dependent drift have limited broader adoption. This study introduces a Hall effect-based pressure sensor utilizing an integrated dome–annular magneto-elastomer architecture designed to overcome these constraints. The deformable magnetic composite modulates magnetic flux under applied pressure, eliminating the rigid-magnet dependency of conventional Hall-based designs. Finite element simulations confirmed that the dome–annular structure produces a stable, predominantly axial magnetic field that varies linearly with deformation, enabling predictable single-axis Hall transduction. Mechanical characterization demonstrated minimal hysteresis, high repeatability, and strong sensitivity within the garment-relevant low-pressure range. Mannequin testing across three compression-garment sizes and four curved anatomical sites showed that the sensor achieves measurement accuracy statistically equivalent to the AMI-3037 reference. The sensor maintains a 4 mm thickness profile matching the AMI-3037 form factor while offering substantial advantages: low-cost fabrication, reduced susceptibility to temperature-related pneumatic drift, conformability to curved surfaces, and elimination of repeated pre-experimental calibration. These characteristics enable practical multi-site pressure assessment previously infeasible with pneumatic systems. The sensor provides a rigorously validated alternative to pneumatic standards and establishes a foundation for next-generation garment pressure measurement in research, clinical, and industrial applications.
This study is on mechanical performance and water absorption behaviour of hybrid epoxy composites reinforced with human hair and goat hair fiber. The composites were fabricated using a compression molding technique. Three formulations were developed by maintaining a constant epoxy content of 70 wt.% while varying the reinforcement ratios of human hair (25, 22, and 20 wt.%) and goat hair (5, 8, and 10 wt.%). Mechanical properties were evaluated through tensile strength and microhardness tests, while water absorption tests were conducted to assess moisture resistance. The results show that the composite containing 20 wt.% human hair and 10 wt.% goat hair exhibited the highest tensile strength (13.42 MPa) and hardness (29.4 HV), along with the lowest water absorption (∼2.19%). SEM observations revealed improved fiber–matrix adhesion, uniform fiber dispersion, and reduced void content in the optimized composite. Statistical analysis (ANOVA) confirmed significant differences among the compositions (p<0.001), while regression analysis demonstrated a strong positive correlation (R 2 >0.90) between goat hair content and mechanical properties. Furthermore, all samples exhibited low moisture uptake (<3% after 24 h), indicating good dimensional stability. The findings suggest that hybridization of human and goat hair fibers provides a sustainable approach for developing lightweight composite materials with enhanced mechanical performance and moisture resistance.
Protective workwear calls for improved energy absorption that auxetic structures can answer. However, generating static charges through frictional contact remained a significant hurdle as textiles had high electrical resistance. This study produced conductive auxetic yarn (CAY) using core spun-based technology with commercially available staple fibers. It evaluated the impact of blends and Elastane (Lycra) count on the strength, auxeticity, electrical resistance, and air permeability characteristics of auxetic yarn and fabrics. Results demonstrated that CAY with 280D Lycra developed enhancement of yarn properties in comparison to 120D Lyra. For instance, yarn sample 2 better tensile strength (17.8 N), elongation (7.4%), electrical resistance (65 Ω) and auxeticity (PR of -3.97) than sample 1 yarn. Elevating the content of staple steel to 40% gave rise to a maximum of 42% less electrical resistance in yarn from sample 1 (80 Ω) to sample 3 (47 Ω). Like yarn, fabrics knitted with 280D Lycra containing CAY developed 13% better auxeticity as demonstrated by Poisson’s ratio of sample 6 (-2.02) and sample 5 (-1.746). However, coarser Lycra and higher staple steel content dropped air permeability. The highest air permeability of fabric sample 5 (521 mm/s) was lowered in sample 6 (441 mm/s) and sample 7 (388 mm/s) owing to coarser Lycra and higher content of steel fibers respectively. Electrical resistance of auxetic fabrics reduced utilizing 40% staple steel content yarns. The effect was more prominent in coarser Lycra, as sample 2 (1500 Ω) and sample 4 (800 Ω) drops by 46% in comparison to 30% reduction between sample 1 (970 Ω) and sample 3 (670 Ω). This verified the core spun-based CAYs and their resultant auxetic fabrics inherited substantial auxeticity with an electrically conductive nature for improved potential protective clothing.
Surging consumer demand for personalization is straining conventional mass customization paradigms, which have reached a functional bottleneck. Constrained by predefined modularity, conventional mass customization approaches can neither achieve the real-time, data-driven responsiveness of hyper-personalization nor deliver the creative openness inherent in co-design experiences. To address these limitations at a systemic level, we propose and validate a novel AI-driven Mass Personalized Customization (AMPC) paradigm that integrates generative intelligence and manufacturing adaptability within a unified, closed-loop framework. The AMPC paradigm features a four-layer architecture with a central Data & Technology Hub connecting the Semantic Intent, Design Generation, Physical Realization, and Closed-Loop Service subsystems. To validate this paradigm, we present a systematic case study in the apparel industry, detailing the complete workflow from user intent recognition, through AI-driven creative generation and virtual try-on, to smart manufacturing and feedback analysis. The analysis shows that the AMPC framework overcomes traditional limitations by enabling greater user design freedom and establishing a data-driven feedback loop for ongoing optimization, thereby offering a new model for the digital transformation of the apparel value chain.
