pISSN : 1229-9197 / eISSN : 1875-0052
Fibers and Polymers, the journal of the Korean Fiber Society, provides you with state-of-the-art
research in fibers and polymer science and technology related to developments in the textile
industry. Bridging the gap between fiber science and polymer science, the journal’s topics
include fiber structure and property, dyeing and finishing, textile processing, and apparel science.
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Latest Publication (Vol. 26, No. 10, Oct. 2025)
FEA Modelling of Thick CFRP, GFRP, and BFRP Composite Laminates Under Charpy Impact
Dongdong Chen Maozhou Meng Tim Searle Shoune Xiao
This study explores the impact responses of thick laminated composites, including carbon fibre reinforced plastics (CFRP), glass fibre reinforced plastics (GFRP), and basalt fibre reinforced plastics (BFRP). Three layups were prepared using the resin infusion method: unidirectional (UD), cross-ply (CP), and angle-ply (AP). These were tested using the Charpy impact test taking the ASTM-E23 as reference. A two-scale finite-element (FE) model was developed to bridge the computational relationship between micro-scale characteristics (mechanical properties of fibre and matrix, fibre volume fraction, and layup) and macro-scale impact resistance. Results showed that the impact strength of composite laminates decreased in the order of UD, CP, and AP, while GFRP and BFRP laminates exhibited an approximately 42.2–78.3% and 90.7–187.7% increase in impact strength compared to CFRP. Reasons can be owed to the stiffness mismatch between adjacent composite plies, which contributed to the tensile and compressive energy absorption mechanisms in CP layups. Different materials and layups demonstrated distinct failure mechanisms, attributable to the better ductility of glass and basalt fibres. The conclusions of this study aim to deepen the understanding of damage and energy absorption mechanisms in thick composite laminates, thereby providing practical guidelines for structural design.
Behavior Analysis of Macro-fiber Composite (MFC) Under Curvature and Comparison with Modal Analysis Using Finite Element Model
Jae-Ha Kim Joo-Yong Kim
This study explores the dynamic behavior of macro-fiber composite (MFC) under varying voltage and curvature conditions, aiming to optimize its maximum displacement. We identified a direct relationship between applied voltage and displacement, with higher voltages leading to increased displacements. Concurrently, the natural frequency decreased as effective stiffness and electromechanical coupling changed. To measure maximum displacement, a novel sweep method was introduced and validated, demonstrating a low error rate and offering a reliable alternative to traditional techniques, particularly for curved or irregular structures. Further, the study revealed that curvature significantly impacts both the natural frequency and maximum displacement of MFCs. A critical curvature point was identified, where displacement behavior shifted, providing essential insights for optimizing MFC design and application. These findings contribute to a deeper understanding of MFC dynamics and open new avenues for applying the sweep method to other piezoelectric materials and complex geometries. This research sets the stage for future studies aimed at refining the sweep method for more precise and efficient use in advanced engineering applications.
Deep Learning Approach for Predicting the Physical Properties of Air-Jet Textured Yarn with PET/PTT Bicomponent Fiber
Hyeongmin Moon Md Morshedur Rahman Seunga Choi Sarang Oh Chang Kyu Park Joonseok Koh
Accurate prediction of air-jet textured yarn (ATY) properties is essential for product development and quality control in textile manufacturing. This study proposes a deep learning-based regression model to predict the properties of ATY produced with PET/PTT bicomponent fiber as the core yarn. The model’s performance was compared with traditional statistical regression methods. The dataset included key process parameters—denier, overfeed, air pressure, and processing speed—and their corresponding physical properties: tenacity, initial modulus, length instability, and loop density gap. Hyperparameter tuning, regularization, and K-fold cross-validation were employed to enhance model performance and reduce overfitting. Mean absolute error convergence analysis was also used to determine the optimal number of training epochs. Results showed that the multilayer perceptron deep learning model consistently outperformed statistical regression models, achieving R2 values above 0.7 for initial modulus, length instability, and loop density gap. Prediction for tenacity showed limited improvement due to weak feature-property correlations. Importantly, the deep learning model improved predictive accuracy for the loop density gap, a property with minimal linear correlation to process variables, though with higher variance—indicating that additional data could improve stability. These findings demonstrate the potential of deep learning as a powerful tool for predicting complex material properties in textile processes, particularly when dealing with nonlinear relationships among multiple process factors.
Preparation, Analysis, and Characterization of Miura-ori Structure Woven Fabric
Yuan Tian Zhaoqun Du Qiaoli Xu Xiao Liu
To produce textiles with geometric patterns and stable structure, the classic Miura-ori structure in the origami folding structure was selected. Through material selection and fabric structural design, the Miura-ori structure fabric was developed using a combination of elastic and inelastic weft yarns on automated jacquard looms. Miura-ori structure woven fabric Miura-45 and the regional fabrics Miura-M, Miura-V, and Miura-P forming the structure were successfully fabricated. Miura-45 exhibited well-defined creases and flat unfolded surfaces, with elastic weft shrinkage effectively forming the Miura-ori structure post-weaving. To further study the Miura-ori structure fabric and prove the stability of the structure, the mechanical properties and thermal–moisture performances of Miura-45 and fabrics in each regions were analyzed. The results showed that Miura-45 exhibited minimal tensile deformation ratio (0.2%), maximum stiffness in the warp direction (353 mN·cm on the front), and optimal compression resistance (compression ratio 16.1%), confirming structural stability. Furthermore, Miura-45 showed superior air permeability (69.4% improvement under the pressure difference of 200 Pa), moisture transmission (15.3% higher), and thermal resistance (0.105 m2·K/W) compared to Miura-P. Miura-ori woven textiles demonstrate remarkable structural integrity and scalability for industrial production. Their combination of geometric precision and enhanced performance suggests promising applications in technical textiles and smart material systems.
Illuminance-Invariant Defect Detection for Multiform Fabrics Using Multiscale Feature Fusion Model
Hao Wu Yuxuan Deng Jie Meng Shunjia Wei Liquan Jiang
Illuminance variations and the diversity of fabric defect types present significant challenges for defect detection in multiform fabrics. To address these issues, this paper proposes a novel multiscale feature fusion model for illuminance-invariant defect detection, incorporating feature extraction, feature selection, and feature fusion. First, to mitigate the substantial illuminance differences caused by variations in the material, texture, color, and knitting density under supplementary lighting, an adaptive local feature extractor is introduced. This extractor alleviates the limitations of traditional Detail Processing Modules under varying illuminance conditions. Second, to handle the challenges arising from fabric diversity in terms of material composition and knitting techniques, and the consequent variability in defect types (like size, shape), an elastic-parameterized feature selection module (EFSM) is proposed. Leveraging B-spline parameterization, the EFSM significantly reduces the parameter burden for feature selection. Finally, a multiscale information and attention-integrated defect classification module, called enhanced defect classification module, is developed to accurately fuse and classify diverse defect features. These enhancements eliminate the model’s dependency on illuminance conditions and significantly improve defect detection performance for multiform fabrics. The effectiveness of the proposed approach is validated in public and homemade datasets.
Investigating the Influence of Fabric’s Dynamic Elastic Recovery on the Body Free Movement: The Effect of Knit Structure and Loading Direction
Andisheh Motamedi Nazanin Ezazshahabi Fatemeh Mousazadegan Mohammad Amani Tehran
The dynamic elastic recovery (DER) of fabrics is considered as a factor to evaluate garment resistance against body movement and restraining the body’s movability that can affect the wearers’ performance during physical activities. In this study, the effect of the loading direction on the DER of weft knitted fabrics with different knit structures, including Rib 1 × 1, Rib 2 × 2, Interlock and Full Milano under three strain levels of 20, 30 and 40% is investigated. Fabric resistance against body movement was assessed by measuring the pressure exerted by the garment on the arm in various hand positions, while lifting weights. The obtained results presented that in all samples, course and wale directions have the highest and lowest tensile modulus, respectively, and the highest tensile modulus is associated with the Full Milano and Interlock; however, Rib 2 × 2 has the lowest value. In terms of DER, the rise of strain level leads to a reduction in DER. The lowest DER belongs to the Full Milano and Interlock; conversely, Rib 2 × 2 has the highest DER. Evaluation of the applied pressure on the arm reveals that the DER of the fabric and fabric resistance against body movement are related oppositely. Therefore, knit structures with higher DER, such as Rib 2 × 2, applied lower pressure on the arm. In addition, the increase in strain level and the quantity of lifting weight can enlarge the exerted pressure on the skin. Furthermore, hand position due to causing extra strain in the fabric, can increase the applied pressure.
Study on the Manufacturing Process of High-Pressure Fire Hose with Variable Diameter Design and Its Pressure Resistance Performance
Xiangli Hu Sarkodie Ebenezer Ameyaw Yantao Gao Zan Lu Wenfeng Hu
The current design of fire hose couplings is overly simplistic, which forces firefighters to carry multiple hoses of different specifications in emergency situations, thereby increasing the burden of firefighting operations. To address this issue, this study proposes the use of two-dimensional woven high-strength polyester fabric as the reinforcing layer and thermoplastic polyurethane tubing as the inner lining of the hose. Through a heat-press bonding process, high-pressure variable diameter hoses are manufactured, and hoses of different specifications are designed and fabricated. This study explores the composite process parameters of the involved materials and investigates their interfacial bonding performance and failure mechanisms under pressure. The results show that by rationally setting the heat-press bonding process parameters and adjusting the weaving structure of the hose, the interfacial bonding strength and pressure resistance of the fire hose can be effectively enhanced. Experimental tests also reveal the failure mechanisms of gradient-diameter hose materials.
Proposal for Simple Quantification Method for Textile-Derived Microplastics Through Comparison of TOC and Py–GC/MS Method
Mingyeong Shin Hyoyoung Lee Jae-Woo Kim Min Ho Jee
This study reports the applicability of total organic carbon (TOC) analysis as a simpler and more efficient alternative for microplastic quantification. Comparative analysis of TOC and pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS) methods was conducted on microplastics released from polyester 100% and polyester/cotton (60/40) blended fabrics. The results revealed a significant quantitative correlation between the two methods, with minor variations attributed to fiber composition differences. For example, in the case of the polyester 100%, TOC analysis showed microplastic content ranging from 70.4 to 82.6 ppm, with an average of 74.6 ppm, while Py–GC/MS analysis showed a range of 68.2 to 88.2 ppm, with an average of 75.7 ppm. In addition, TOC analysis demonstrated significant advantages in terms of time and cost efficiency, making it highly suitable for large-scale environmental monitoring and industrial applications. As a result, the findings of this study on the TOC analysis method are expected to provide practical insights for managing microplastics from textile products, environmental monitoring, and proposing policy alternatives.