pISSN : 1225-1089 / eISSN : 2288-6419
Textile Science and Engineering(Text. Sci. Eng.) is the journal of the Korean Fiber Society.
It was launched in 1964.
It is published bimonthly(February, April, June, August, October and December) in either Korean or English.
Total or a part of the articles in this journal are abstracted in Chemical Abstract Service, DOI/Crossref and Korea Citation Index.
Copyright by the Korean Fiber Society. All rights reserved. Reproduction in whole or in part in any form without permission in writing from the Korean Fiber Society is strictly prohibited.
It is published bimonthly(February, April, June, August, October and December) in either Korean or English.
Total or a part of the articles in this journal are abstracted in Chemical Abstract Service, DOI/Crossref and Korea Citation Index.
Copyright by the Korean Fiber Society. All rights reserved. Reproduction in whole or in part in any form without permission in writing from the Korean Fiber Society is strictly prohibited.
최근 발간 목록 (62권 6호, 12월 2025)
뼈대 구조와 군집 분석을 이용한 인체 마네킨의 최적 3D 모델링
설인환
Large-sized objects such as human manikins need a proper a priori mesh segmentation process for successful 3D printing. This paper applied well-known statistical mesh clustering functions, including k-means, k-medoids, DBSCAN, and pairwise distance. Especially, an inventive clustering metric, designated as point-to-bone distance, was proposed to take advantage of bone structure which can be acquired easily from free software such as Adobe Mixamo. Furthermore, the cut parts were oriented to the optimal directions, in which the minimal support structure was expected, using our previous work. Now any textile or apparel scientists can easily 3D-print their human manikins with arbitrary shapes and sizes with the help of python language.
뼈대 구조와 군집 분석을 이용한 사용자 정의 삼차원 인체 계측
설인환
Recent advancements in deep learning technology have automated human body measurement, particularly the generation of skeletal information. Among various free technologies for generating skeletal information, this study investigated a method to easily obtain diverse human body measurement data using Adobe Mixamo. For the experimental verification, SizeKorea 6th Edition data for 20s women and men with low and high mesh resolutions were used. Landmarks and cross-sectional curves were found based on bilinear interpolation of bone endpoints. Moreover, the system was designed to allow users to define landmarks easily via simple Python-based scripts. Girth sizes and feature vertical positions showed a maximal 10.7% of errors compared to the SizeKorea manual measurement. The errors are mainly from the noise in the clustering process. However, the proposed method had an advantage in that it can be applied to shapes with arbitrary poses.
고내열 방열 특성을 갖는 PI/MgO 복합 필름의 제조 및 특성
백지연 이상오 김경효 이재웅
Polyimide (PI), a high heat-resistant polymer, was reinforced with thermally conductive magnesium oxide (MgO) fillers to fabricate composite films with enhanced thermal stability and heat dissipation properties. Polyamic acid (PAA) solutions blended with varying MgO contents were thermally imidized, confirming complete conversion of PAA to PI in all cases. Morphological analysis indicated that increasing MgO content led to higher filler particle density on the film surface, with partial sedimentation observed due to density differences.
Thermal analysis showed that all composites exhibited glass transition temperatures above 380°C and decomposition temperatures above 500 °C. Thermal conductivity increased significantly with MgO addition, rising from 0.272W/m·K for pure PI to 0.423W/m·K at 10 wt%, with a steep increase observed beyond 8wt% due to the formation of effective heat transfer pathways. In contrast, mechanical properties declined with increasing MgO loading, as tensile strength decreased from 10.95MPa for pure PI to 4.20MPa at 10wt%, accompanied by reduced elongation at break. These results demonstrate the potential of PI/MgO composites as heat-resistant materials with tunable thermal conductivity, albeit with compromised mechanical performance at higher filler contents.
락을 활용한 박테리아 셀룰로스 섬유소재의 천연염색 특성 연구
안젤린셀시야 민준영 김혜림
This study investigates the coloration of bacterial cellulose (BC) using the animalderived natural dye lac with natural mordants such as pomegranate peel and grape seed. The effects of Al pre-treatment, dye concentration, dyeing solution pH, and dyeing temperature on the dyeability of BC were systematically examined. FT-IR analysis confirmed the formation of coordination complexes between lac and Al, demonstrating the presence of chemical bonding between them. FE-SEM images showed that some lac and Al were physically entrapped within the BC nanostructure. The application of bio-mordants was evaluated alongside metal mordants to assess color fastness properties. Samples treated with bio-mordants exhibited excellent fastness performance, with rubbing and dry-cleaning fastness rated between grades 4, 4–5, and 5. These findings demonstrate that the biomordant can be utilized as an environmentally sustainable and effective mordant.
전기방사를 이용한 이중층 구조 나노 섬유 골 유도 재생 차폐막의 제조와 특성
이경태 허소윤 홍영기
Guided bone regeneration (GBR) is a surgical technique that promotes bone formation by using a barrier membrane during implant placement or in cases of alveolar bone defects. The membrane prevents the infiltration of rapidly proliferating fibrous connective and epithelial tissues while providing a physical space for osteogenic cell growth. However, conventional membranes often exhibit hydrophobic properties, which limit cell adhesion and proliferation. To address this limitation, a hydrophilic nanofibrous barrier layer was fabricated by electrospinning a blend of biodegradable poly(ε-caprolactone) (PCL) and hydrophilic polyvinylpyrrolidone (PVP). Subsequently, to ensure sufficient mechanical integrity, a double-layered nanofibrous membrane was prepared by electrospinning a PCL/poly(glycolic acid) (PGA) solution. The membranes were characterized by attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR) and scanning electron microscopy (SEM). Surface hydrophilicity was evaluated via water contact angle (WCA) measurements, mechanical properties were analyzed using a universal testing machine (UTM), and cytocompatibility was assessed through a lactate dehydrogenase (LDH) assay based on extract testing. The PCL/PVP layer exhibited a tensile strength below 1 MPa, which increased to 8.62 MPa after forming the double-layered structure. All samples demonstrated over 70% cell viability in the LDH-based cytotoxicity test, confirming the potential of the developed membranes as biodegradable barrier materials for guided bone regeneration.
초음파와 니들펀칭 공정을 이용한 소수성 ITO 나노입자의 PET 직물 담지
최정욱 이소현 황혜인 장준혁 조승민 이웅규 김병효
Incorporation of long-chain capped nanoparticles on polyester textiles such as polyethylene terephthalate (PET) is challenging due to the low surface energy and chemical inertness of PET. We present a binder-free, ultrasonication method to immobilize indium tin oxide (ITO) nanoparticles on PET textiles while preserving the native fiber structure. Oleic acid-capped colloidal ITO nanoparticles were successfully incorporated to the PET textiles at 40 °C, which is confirmed by X-ray diffraction (XRD). The nanoparticle-incorporated PET textiles are shows high stability in washing condition even in the presence of detergent. The plasmonic property of ITO nanoparticles endows photothermal effect. Under a xenon solar simulator, ITO-loaded textiles exhibited high heating rates. This simple process enables durable, NIR-responsive PET textiles without plasma treatment or polymer binders.
바이오 폴리우레탄이 도입된 변성 비닐에스터 기반 유리섬유 SMC 복합재의 물성 연구
서대경 최순호 남윤성 배진석
In this study, a sheet molding compound (SMC) composite was developed using a glass fiber reinforcement and a bio-based polyurethane (Bio-PU) modified vinyl ester matrix. The Bio-PU was synthesized to incorporate urethane linkages into the vinyl ester side chains through the reaction between hydroxyl (-OH) groups in the vinyl ester and isocyanate (-NCO) groups in the polyurethane, thereby forming a urethane-grafted vinyl ester network. Bio-PU-modified vinyl ester resins containing 10, 30, and 50 wt.% Bio-PU were formulated and subsequently processed into SMC prepreg using TBPB as a thermal initiator. The mechanical, and morphological properties of the resulting composites were tested and compared with those of conventional vinyl ester-based glass fiber SMCs. The mechanical strengths of all composite specimens increased with higher fiber volume fractions. However, as the Bio-PU content increased, the interfacial adhesion between the glass fibers and the resin matrix weakened, resulting in reduced tensile and flexural strengths. In contrast, the impact strength exhibited an opposite trend, increasing progressively with higher Bio-PU content. Overall, the Bio-PU-modified vinyl ester resin demonstrates strong potential as a matrix for eco-friendly, high-performance SMC composites. The material system offers a well-balanced combination of mechanical performance, formability, and environmental benefits, making it suitable for structural composite and semi-structural applications.
AI 딥러닝 모델에 따른 원단 결점 분석에 대한 연구
최현석 이한건 이영훈
This study presents a comparative analysis of representative object detectionbased AI deep learning models(YOLOv4, YOLOX, RT-DETR) to enhance the accuracy and real-time performance of fabric defect detection, a critical step in textile manufacturing processes. Experimental results indicate that RT-DETR outperforms the other models in Accuracy, Recall, and F1-Score, with the exception of Precision. In particular, RT-DETR demonstrates a significantly higher Recall, suggesting its suitability for product categories where defect omission is unacceptable. This superiority appears to stem from its Transformer-based architecture, which effectively captures global features in textile images characterized by repetitive patterns and complex surface textures. However, RT-DETR exhibits a maximum processing speed of approximately 130 FPS, which is lower compared to the other models. While it is sufficient for general manufacturing processes, it may impose limitations in ultra-high-speed production environments. Overall, the findings confirm that the Transformer-based RT-DETR model represents the most suitable option for fabric defect analysis using AI deep learning techniques.