Finite-element simulation of garment represents a fundamental technology for digital design and virtual fitting; however, conventional methods are hindered by challenges such as complex modeling, difficulties in parameter acquisition, and low computational efficiency. Artificial intelligence (AI) technologies, including deep learning, physics-informed neural networks, graph neural networks, and large language models, offer novel avenues to address these limitations. This paper reviews essential AI technologies for fabric parameter identification and surrogate model construction, discusses the intelligent computer-aided engineering mechanism driven by large language models, and establishes a closed-loop intelligent simulation framework. This framework facilitates a fully automated finite-element simulation workflow, significantly reducing the modeling threshold while improving efficiency and accuracy, thus offering a new, efficient, and cost-effective paradigm for the digital transformation of the apparel industry. Finally, the paper summarizes current challenges in data management, physical consistency, and multiscale coupling, and anticipates future developments, including AI agents and digital twin garment.
In this study, perovskite-type LaFeO 3 catalyst was successfully synthesized via the sol–gel method and applied to the catalytic degradation of reactive dye wastewater in the H 2 O 2 system. Experimental results revealed that, under optimized conditions (catalyst dosage = 0.3 g/L; H 2 O 2 concentration = 1 g/L; pH = 3; reaction temperature = 50°C; reaction time = 40 min), the degradation efficiency of Reactive Black WNN reached 99%. Mechanism analysis demonstrated that the catalyst could activate H 2 O 2 through the Fe 3+ /Fe 2+ redox cycle to produce hydroxyl radicals (·OH) and singlet oxygen ( 1 O 2 ). The synergistic effect of these two active species enabled the efficient degradation of dye molecules. In addition, LaFeO 3 exhibited excellent cycling stability; its degradation efficiency toward Reactive Black WNN remained 87% after five consecutive reuse cycles. The treated wastewater was recycled for cotton fabric dyeing. Compared with deionized water, the recycled wastewater showed no obvious differences in key dyeing performance indicators, including dye uptake, fixation rate, K / S , color difference (Δ E < 0.5), and color fastness to rubbing; this verified that the wastewater treated by this system possesses promising reusability for textile dyeing. Compared with the homogeneous Fenton system using ferrous sulfate, the LaFeO 3 catalytic system achieves comparable degradation efficiency, while featuring prominent advantages, such as no massive iron sludge production, low turbidity of effluent, recyclable catalyst, and convenient subsequent solid–liquid separation.
Currently, the risk of work-related musculoskeletal disorders (WMSDs) in the textile industry is on the rise annually. Effectively assessing the risk of WMSDs caused by the operational behaviors of spinning frame tenders has become a critical issue demanding urgent resolution in the textile sector. Taking the task of replacing roving bobbins by spinning frame tenders as an example, this paper proposes a risk assessment method for WMSDs among spinning frame tenders. Firstly, a motion capture system is employed to accurately collect real-time working condition data during the tenders' operations. Second, the tenders' movement characteristics are analyzed, and a rigid-body dynamics theoretical model for their roving replacement actions is established based on the Newton–Euler method. Subsequently, real-time operational data are used to drive the OpenSim simulation model to validate the accuracy of the theoretical model. Finally, a risk assessment method suitable for spinning frame tenders' operations is developed by integrating a WMSD assessment model. The results indicate that, under the same workload, as the roving bobbin weight gradually increases within the range of 1–3 kg, the WMSD risk level for tenders also rises. When the roving bobbin weight exceeds 2 kg, the risk values all reach level 4. Given that risk levels above 4 can adversely affect tenders' health, measures such as optimizing rest schedules or providing auxiliary operational devices are recommended to reduce the risk of occupational musculoskeletal disorders. This study offers a feasible approach for preventing occupational musculoskeletal disorders in the future textile industry.
Protective gloves are critical in commercial fishing, where workers face hazardous and variable environments. Unlike other high-risk industries, there are no formal standards or guidelines in place to ensure adequate hand protection for this workforce. This narrative review investigates literature on current glove use and research, injury risks, hazards, relevant standards, and available testing methods. While research on commercial fishing injuries is well established, evidence specific to hand protection is limited, and few standards reflect the environmental and task-based demands of fishing work. Gaps exist between available glove technology, laboratory testing methods, and real-world fishing conditions, leaving workers vulnerable to preventable injury. Findings indicate that commercial fishing remains significantly underrepresented in protective glove research. A coordinated, interdisciplinary effort is needed to develop testing protocols, inform standards, and guide product development tailored to the unique risks of commercial fishing to improve long-term safety and worker wellbeing.
Staple yarns are formed by twisting assemblies of discontinuous fibers and exhibit high axial strength and radial compliance, making them widely used in textile applications. However, the microscale nature and random distribution of staple fibers make yarn formation difficult to characterize at the fiber level. In this study, a fiber-scale modeling framework is proposed to simulate staple yarn formation and woven fabric construction. A generative adversarial network model is employed to generate crimped fiber configurations, followed by twisting and weaving simulations to reconstruct the fiber-scale structures of staple yarns and woven fabrics. The proposed framework reproduces fiber migration, twist propagation, and fiber entanglement during yarn formation, and is further extended to construct homogeneously blended and segmented blended yarns and their corresponding woven fabrics. Good agreement is achieved between the simulated and experimental structures in terms of fiber distribution, microstructural morphology, and fabric texture. This work provides an effective framework for the fiber-scale digital modeling of staple yarns and woven fabrics, facilitating structural design and performance prediction.
While standardized methods exist for evaluating microplastic fiber fragment (MPFF) release during textile washing, dry-state release during garment manufacturing and use remains poorly understood. In this study, MPFF release from raw and finished knitted fabrics, including dyed and hydrophilic-softened (S), and dyed, softened, and antibacterial-finished (SP) samples, was investigated using novel peeling, cutting, and Martindale abrasion tests. The results demonstrate that fabric finishing alters MPFF release behavior: it reduces MPFF release during peeling but increases release under abrasion. In addition, cutting generates substantial emissions, particularly for raw fabrics and in the course direction. These findings highlight the importance of considering multiple mechanical actions when assessing MPFF release and indicate that considerable emissions occur during both manufacturing and use. The proposed approaches contribute to the development of more comprehensive testing methods and support the development of improved mitigation strategies in the textile industry.
Towels are some of the most common pile fabric products, and although their tactile properties are extremely important, they exhibit variations in these properties globally, which makes their evaluation difficult. Therefore, this study aimed to develop a widely applicable and easy-to-use prediction method for estimating the mechanical stimulus on the skin induced by towel–skin contact. The mechanical properties of towels materials were quantified through an indentation test. In the test, the mechanical modulus and thickness of each towel sample were identified. In addition, the mechanical stimulus induced during towel–skin contact was evaluated using the finite-element method, in which the von Mises stress was used as the stimulus. The relationship between the towel properties and stress was found to be approximable by a quadratic function. Based on several validations, a stimulus can be accurately predicted using a quadratic function with coefficients as a function of thickness. Without computational analysis, the mechanical stimulus acting on the skin can be predicted using the property data of a given towel.
Global fiber demand is rising rapidly, yet less than 1% of discarded textiles are recycled into new materials. Here, cold-alkali swelling followed by acid reprecipitation was investigated as a pretreatment to improve cellulose accessibility and reactivity in textile-waste feedstocks. Two recycled cotton-based textiles were compared with cotton linters and dissolving wood pulp. Swelling was performed in 10 wt% NaOH at –20°C, followed by reprecipitation and washing. Cold-alkali treatment increased water retention value (WRV) for all samples, reaching 3.0 g g −1 for recycled pure-cotton textile, 2.5 g g −1 for the cotton–synthetic blend, and 1.6 g g −1 for both cotton linters and dissolving wood pulp. Initial acid-hydrolysis rates increased from 9.0 to 26.3 mg l −1 min −1 for cotton linters, 10.0 to 27.5 mg l −1 min −1 for dissolving wood pulp, 11.6 to 28.8 mg l −1 min −1 for recycled pure-cotton textile, and 12.9 to 22.4 mg l −1 min −1 for the cotton–synthetic blend. These increases occurred despite moderate intrinsic-viscosity losses of 7-26%, depending on feedstock. X-ray diffraction showed broader and less-resolved diffraction peaks after swelling and reprecipitation, with crystallinity index values decreasing from 90% to 63% for cotton linters, from 78% to 66% for dissolving wood pulp, from 89% to 61% for recycled pure-cotton textile, and from 85% to 50% for the cotton–synthetic blend. Overall, cold-alkali swelling and reprecipitation improved cellulose accessibility and reactivity in textile-derived feedstocks and offer a practical pretreatment route for upgrading low-value cotton-rich waste toward regenerated-cellulose and cellulose-derivative processes.
Worldwide, breast cancer is the primary cause of deaths in women. Because of the restrictions of current clinical imaging techniques, such as mammography, ultrasound scanning, and magnetic resonance scanning, as well as harmful radiation, which is expensive and a hindrance to patients, researchers have been motivated to investigate alternate methods, including the use of microwave components, to detect breast tumors in the early stages. This study is focused on the implementation of an antenna for the detection of small tumors using a novel material as a substrate at an operating frequency of 2.42 GHz. The material utilized is a textile fabric woven using a plain pattern of jute thread with cotton threads. The proposed antenna has overall dimensions of 50 mm × 62 mm × 0.71 mm 3 and demonstrates a peak gain of 3.56 dBi, with a radiation efficiency exceeding 70% throughout the operating band. Breast models with and without tumor cells with tumor radii of 2, 3, and 5 mm were designed using Computer Simulation Technology software. The designed antenna was placed on breast models with and without tumors and simulated. The return loss, gain, current density, E -field, and H -field distributions exhibited deviations when the antenna was placed on the breast model with a tumor. However, the specific absorption rate (<1 W/kg) did not exceed the standard limit. The antenna was fabricated and measured using a vector network analyzer. The measured results show that the return loss value (−22.3 dB) was close to the simulated value (−26.3 dB).
Objectives: To map and critically synthesize the current evidence on electronic textiles applied to sport, exercise, and rehabilitation, focusing on validation approaches, methodological quality, technological maturity, and translational barriers. Design: Scoping review. Methods: Four electronic databases were systematically searched from 2010 to 2025. Eligible studies investigated electronic textiles actively used during physical activity, exercise, or rehabilitation. The methodological quality was appraised using design-appropriate assessment tools, and technological maturity was evaluated based on development status and validation context. The data were synthesized descriptively across application domains, sensing modalities, and translational factors. Results: Twenty studies published between 2016 and 2023 were included, covering joint kinematics or movement analysis ( n = 11), plantar pressure ( n = 5), cardiorespiratory monitoring ( n = 2), sweat biochemistry ( n = 1), and usability-focused evaluation ( n = 1). Electronic textiles often showed promising technical accuracy under controlled conditions. However, most investigations relied on small convenience samples and laboratory-based protocols, with limited evaluation in field or sport-specific environments. Key methodological and translational limitations included insufficient statistical power, inconsistent reporting practices, a lack of control groups, and a systemic absence of data on durability, washability, thermophysiological comfort, and mechanical textile properties. Conclusions: Electronic textiles show potential for unobtrusive biomechanical and physiological monitoring, but their integration into sport and rehabilitation remains constrained by methodological and translational limitations. The field is characterized by a mismatch between rapid material innovation and limited methodological rigor. Future research requires ecologically valid designs, robust validation protocols, and greater attention to long-term reliability, comfort, textile mechanics, and user-centered implementation factors.
The batt is the first semiproduct in the yarn spinning process, and its longitudinal mass unevenness significantly affects the properties of the spun yarn. Therefore, establishing a theoretical model for batt unevenness is crucial for accurately predicting and controlling yarn quality. However, previous models only address cross-sectional unevenness and are not applicable to unevenness along the commonly used segment length (i.e. 1 m). Furthermore, these models are semiempirical and no longer valid because of the progress in both equipment and technology for yarn spinning. In this paper, we first derive an expression for batt unevenness caused by the random positional distribution of tufts—the constituent elements of the batt—that is valid for arbitrary segment lengths. Second, an improved expression is developed by incorporating tuft mass randomness via variance addition. Then, using MATLAB and the Monte Carlo method, the randomness of tuft number and mass was simulated to obtain the unevenness and mass variation of the batt. Finally, data from experiments with cotton, viscose, and polyester fibers for segment lengths ranging from 30 to 300 mm and from previous literature for a segment length of 1 m were used to verify the proposed expression and simulation method. Results show highly consistent trends between measured and predicted batt unevenness, with correlation coefficients exceeding 0.92 for cotton, exceeding 0.91 for viscose, and exceeding 0.94 for polyester, strongly confirming their effectiveness. This research provides a basis for predicting batt and yarn properties, and offers guidance for designing optimum process parameters in yarn production.
As a smart textile, humidity-responsive fabrics can automatically adjust their physical form and response characteristics, such as porosity and hygroscopicity, according to changes in environmental humidity, so as to significantly improve the wearing comfort of the human body. In recent years, the field has made significant progress in material innovation, structural design, and application expansion, but it still faces many challenges, such as insufficient sensitivity and accuracy in response performance, poor durability, difficulty in balancing comfort and functionality, and cost and sustainability issues. This paper systematically reviews the research progress of moisture-responsive fabrics, focusing on the implementation paths and effects of strategies including fiber material selection and modification, fabric structure innovation design, and finishing technology in different studies. By comparing and analyzing the advantages and disadvantages of existing schemes, this paper clarifies the core problems and development bottlenecks in this field, and looks forward to the possible future research directions, aiming to provide theoretical support for the industrialization of moisture-responsive fabrics.
Polyvinylidene fluoride (PVDF) is widely used in flexible sensors due to its excellent mechanical and piezoelectric properties. However, its performance is limited by low β -phase content and poor durability. This research optimizes PVDF-based piezoelectric films by incorporating binary and ternary copolymers, namely PVDF-trifluoroethylene and PVDF-trifluoroethylene-chlorotrifluoroethylene (PTC), using a tape casting and extrusion–calendaring process. A total of 18 blend compositions were evaluated, with a PVDF:PTC ratio of 1:1 identified as the optimal formulation, exhibiting the highest β -phase enhancement and improved ferroelectric response. Structural and electrical characterization was performed using Fourier transform infrared spectroscopy, X-ray diffraction, ferroelectric property testing, and impedance analyses, achieving values up to 250×10 −6 C N −1 . The optimized films demonstrated superior sensing performance, with high output voltage under dynamic finger tapping and strong linearity between the applied force and voltage response. Durability testing showed that the sensor retained >97% of its initial output after 8000 cycles and remained stable up to 11,100 loading cycles, indicating excellent mechanical robustness and long-term stability. Compared with pure PVDF, the optimized composite films exhibited significantly enhanced piezoelectric response, improved β -phase crystallinity, and better signal consistency. Finally, the films were successfully integrated into seamless wearable textiles for real-time motion sensing, demonstrating their applicability in smart sportswear. This study provides a scalable, effective approach to developing high-performance PVDF-based flexible sensors with enhanced efficiency, durability, and wearable compatibility.
This study addresses insufficient fit, localized pressure concentration, and unstable knee support in compression running tights during running. Using CLO3D virtual fitting technology, a coupled human–garment simulation model was constructed, and 130 continuous virtual pressure measurement points were established on the lower limbs, enabling full-field visualization and evaluation of pressure distribution beyond conventional localized pressure-point assessment. Integrating ergonomic principles and skin deformation characteristics, a structural optimization scheme combining a spiral-oriented compression structure with a knee support unit was proposed. Comparative analyses of pressure distribution, regional average pressure, and peak pressure were conducted under both static and multiple dynamic postures. The results indicate that the optimized structure guided pressure transmission along the longitudinal axis of the lower limb, reduced the average dynamic pressure in the hip, thigh, and calf regions, and enhanced knee support stability without substantially increasing peak pressure, thereby achieving a coordinated improvement in both fit and functional support. This study demonstrates the engineering feasibility of a virtual-simulation–driven structural optimization approach for the design of functional compression sportswear. The CLO3D-derived pressure results should be interpreted as relative optimization trends, and future research will further refine and validate the findings through physical pressure testing and continuous motion validation.
Three-dimensional (3D) braided composites are extensively utilized in aerospace laminated composites owing to their superior out-of-plane shear properties and interlaminar delamination resistance compared with two-dimensional (2D) textile laminated composites. However, existing laminate models remain incapable of elucidating the enhancement mechanisms underlying the superior out-of-plane shear properties of 3D braided architectures. Understanding the influence of 3D braided yarn architecture on out-of-plane failure mechanisms is critical for enabling rapid design and batch-efficient manufacturing of aerospace composite components. This study has established a refined mechanical model for 3D braided laminated composites by integrating Hashin3D failure criterion with cohesive traction-separation law. The model quantitatively decoupled the respective contributions of braided architecture and interlaminar interfaces to out-of-plane failure. Results reveal that 3D braided laminated composites exhibit mixed failure modes of buckling and delamination, whereas 2D textile laminated composites failed predominantly through interfacial delamination. This divergence stems from the reduced interlaminar interfaces in 3D braided laminated composites mitigated catastrophic delamination risks. Concurrently, the interlaminar interlacing yarn architecture suppresses interfacial cracks propagation thereby enhancing out-of-plane properties.
The development of advanced heating materials for active thermal management in aircraft cabin flooring and automotive seats demands exceptional safety, superior thermal comfort, and highly efficient electrothermal conversion. This work addresses these needs via tailored fiber placement of carbon fiber on a glass fiber substrate, creating electrothermal textiles featuring linear, sinusoidal, and hemispherical configurations for differentiated thermal management. A methyl vinyl silicone rubber-based flame-retardant/insulating coating incorporating fumed silica, aluminium hydroxide, and zinc borate was developed for fabric treatment. It confers good hydrophobicity, a self-extinguishing capability, and high thermal insulation. The coating exhibited exceptional abrasion resistance while maintaining full functionality. Electrothermal performance was effectively tuned through voltage and geometric design, achieving 62.75 degrees C with hemispherical patterning at 20 V. Successful integration with three practical substrates demonstrated rapid and uniform heating, confirming its potential for practical thermal management systems. This work provides a novel strategy for developing surface-active thermal management materials that combine efficient heating, high safety, and excellent comfort.
Predicting the color of blended spun products remains an arduous challenge, which hinders practical application, due to the effects of the material structure. To solve this issue, this paper proposes a new structural spectral color prediction method based on the single-constant Kubelka-Munk (KM-1) model, here named the multiscale spectral color prediction model. This method involves fiber-yarn and yarn-fabric reflectance structure and Lab space spectra transfer model, building upon our previously proposed color and non-color prediction method. Significantly, by introducing structural twisted yarns between fabrics knitted from parallel fibers, only five groups of different monochromatic materials and 14 color-blended fibers were needed to train this model. This greatly reduces the number of training samples and improves prediction accuracy. Experimentally, the average and maximum color difference of 87 blended yarn samples were only 0.65 CIEDE2000 units and 1.56 CIEDE2000 units, markedly lower than those of the KM-1 model (similar to 6.18, similar to 12.26), two-constant KM (KM-2) model (similar to 1.33, similar to 9.50), Friele model (similar to 1.62, similar to 3.06), and Stearns-Noechel (S-N) model (similar to 1.14, similar to 3.51). Moreover, the average and maximum color difference of 87 blended fabric samples were 0.54 and 1.25, smaller than those of the KM-1 model (similar to 6.01, similar to 12.27), KM-2 model (similar to 1.17, similar to 8.94), Friele model (similar to 1.29, similar to 2.31), and S-N model (similar to 1.02, similar to 4.09). The results indicate that the optimization model has good performance and can be used to predict the color of mixed color-matched spun products.
Fabric shape retention is a critical property that directly determines the functional performance and aesthetic quality of textile products; existing numerical simulation methods have limitations in simultaneously capturing the geometric deformation and internal stress distribution of fabrics during shape retention processes. This paper proposes a yarn-scale finite-element simulation model specifically designed for fabric shape retention detection, which covers three sequential stages of fabric shape change: the lifting and arching process, the compression process, and the release and recovery process. Three common fabric types were tested under different compression durations to verify the model’s applicability. To ensure the reliability of the proposed simulation model, an image-based verification method was developed. This method introduces crease image technology into yarn-scale simulation validation, representing an innovative application. Results confirm high simulation accuracy and mechanical reliability. Pearson correlation coefficients between simulated and experimental curvature values are 0.967 (cotton), 0.978 (polyester), and 0.969 (linen), with all fabrics showing satisfactory fitting precision ( P < 0.01). Stress analysis reveals distinct gradient distributions in the crease regions. Cotton exhibits the highest residual stress, reaching 37 MPa under 90-second compression. Residual stress increases nonlinearly with arching duration. The yarn-scale simulation model integrated with the image-based validation method enables internal stress evaluation without physical crease tests. This provides key theoretical support for fabric structure optimization and high-performance textile development.
Having in mind the goal of unobstructive, lightweight, and wearable electronics for monitoring human health, more precisely gait, textile-based force sensing resistors (FSRs) have emerged as a promising solution. These sensing elements can reliably detect plantar pressure and, through them, analyze gait, as well as continuously acquire large amounts of data, due to their flexibility, comfort, and adaptability compared with traditional rigid transducers. The review also sought to present in a tabular way quantitative outcomes, such as sensor accuracy, plantar pressure measurement, sensitivity, response time, and durability and to compare them in an objective manner. Through PubMed, IEEE Xplore, Scopus, Web of Science, and Cochrane Library, a comprehensive literature search was conducted. Inclusion criteria encompassed all original studies which pertain to textile-based FSR transducers incorporated into wearable devices for pressure detection and gait analysis, with reported quantitative outcomes. Reviews, patents and studies without experimental data were excluded from this research. Finally, risk of bias was evaluated as well, utilizing the Joanna Briggs Institute (JBI) checklist for analytical cross-sectional studies. After going through the literature and filtering studies which conform to the defined criteria, a total of 24 studies were included. As sensor accuracy is one of the most significant quantitative parameters of sensor usefulness, a meta-analysis of this parameter revealed a pooled mean of 92.28% (95% CI: 88.65%–95.91%), indicating high reliability across different applications. Textile-based FSR transducers alongside readout electronic smart systems integrated into wearable technologies demonstrate high accuracy, reliability, and sensitivity, supporting their potential for clinical and sports applications.
