@article{youn_knowles_mills_mathur_2024, title={Comparative study of physical and virtual fabric parameters: physical versus virtual drape test using commercial 3D garment software}, volume={2}, ISSN={["1754-2340"]}, url={https://doi.org/10.1080/00405000.2024.2314273}, DOI={10.1080/00405000.2024.2314273}, abstractNote={The adoption of three-dimensional (3D) fabric simulator technology is rising in the apparel and textile supply chains. However, a standard virtual fabric test method has not yet been developed. This paper aims to investigate fabric simulation parameters, including digitized physical properties and particle distance that influence drape simulation. To achieve this goal, the paper consists of three phases. The first phase focuses on developing a reliable virtual drape test setup compatible with the Cusick drape tester by adjusting different variables, such as the cylinder’s height and ring diameters. The second phase investigates the drape coefficient (DC) influencing parameters using the devised drape test method, specifically focusing on digitized physical properties obtained from standard testing equipment or a simplified fabric kit. The last phase investigates the effect of particle distances on virtualized fabric. By understanding the simulation parameters that affect the virtualized fabric, the study suggests approaches to minimize the gap and optimize the ability of simulator technology.}, journal={JOURNAL OF THE TEXTILE INSTITUTE}, author={Youn, Seonyoung and Knowles, Caitlin G. and Mills, Amanda C. and Mathur, Kavita}, year={2024}, month={Feb} }
 @article{youn_west_mathur_2024, title={Evaluation of a new artificial intelligence-based textile digitization using fabric drape}, volume={4}, ISSN={["1746-7748"]}, url={https://doi.org/10.1177/00405175241236881}, DOI={10.1177/00405175241236881}, abstractNote={ Three-dimensional (3D) textile-based garment prototyping, widely adopted in the apparel and textile industry, enhances cost efficiency, work productivity, and seamless communication via visual prototyping. Neural network-based 3D textile digitization has the potential to streamline manufacturing processes by negating the need for traditional physical property (PT) measurements. However, a research gap exists concerning the accuracy of the technology and its applicability to advanced functional apparel manufacturing. The primary research question is to investigate how variations in digitized physical properties obtained from PT measurements and artificial intelligence (AI)-based textile digitization impact the accuracy of a fabric’s mechanical representation. In this study, we aimed to evaluate AI-based textile digitization accuracy using a drape test method. The drape coefficient (DC) analysis revealed that the PT-based simulated DC exhibited a normalized mean absolute error (NMAE) ranging from 2% to 11%, while the AI-based simulated DC showed a range of 3–51%. Notably, for the samples, except those with very limp or very stiff fabric samples, the AI-based simulation exhibited a NMAE within 3–15%. }, journal={TEXTILE RESEARCH JOURNAL}, author={Youn, Seonyoung and West, Andre and Mathur, Kavita}, year={2024}, month={Apr} }
 @article{youn_knowles_ju_sennik_mathur_mills_jur_2023, title={Simulation-Based Contact Pressure Prediction Model to Optimize Health Monitoring Using E-Textile Integrated Garment}, volume={23}, ISSN={["1558-1748"]}, url={https://doi.org/10.1109/JSEN.2023.3293065}, DOI={10.1109/JSEN.2023.3293065}, abstractNote={Advancements in wearable technology have integrated textile sensors into garments for long-term electrocardiogram (ECG) monitoring. However, optimizing biosignal quality, motion artifacts, and wearer comfort in electronic textiles (E-textiles) remains challenging. While designing appropriate contact pressure (CP) is crucial, there is a lack of guidance on proper material selection and sizing for achieving the desired CP. This article presents a novel CP prediction model that utilizes three-dimensional garment simulation (3DGS) to optimize knit textiles for health monitoring. First, a stress test method is devised in the simulator to examine the reliability of simulated stress. Based on understanding the simulated stress mechanism, the CP model is developed using simulation parameters. The model is validated against experimental CP values, exhibiting high accuracy ( ${R}^{{2}}= {0.9}$ ). The effectiveness of the CP model is validated through the demonstration of a customized ECG armband incorporating screen-printed dry electrodes on knit fabrics. Analyzing ECG signals, CP, and applied strains validates the benefits of strategically selected materials and sizing. Specifically, the knit sample with 90% polyester and 10% spandex (S-10) for the 15%–20% range and the knit sample with 85% polyester and 18% spandex (S-18) for the 10%–15% strain range significantly enhance ECG quality, resulting in higher signal-to-noise ratios (SNR) of 33.45 (±1.72) and 34.57 (±0.84)−36.61(±1.81), respectively. These design parameters achieve the desired CP range of 1–1.5 kPa, optimizing the functionality and comfort of the ECG armband. The CP model sets a benchmark for the strategic manufacturing of health monitoring garments by integrating digital technology.}, number={16}, journal={IEEE SENSORS JOURNAL}, author={Youn, Seonyoung and Knowles, Caitlin G. and Ju, Beomjun and Sennik, Busra and Mathur, Kavita and Mills, Amanda C. and Jur, Jesse S.}, year={2023}, month={Aug}, pages={18316–18324} }
 @inproceedings{youn_mathur_knowles_ju_sennik_jur_2023, title={Simulation-based Prediction Model to Optimize Contact Pressure of Knitted Fabrics
            for Wearable Garments}, url={http://dx.doi.org/10.54941/ahfe1002944}, DOI={10.54941/ahfe1002944}, abstractNote={This paper proposes a simulation-based contact pressure (CP) prediction model for prototyping electronic textile (e-textile) wearable devices for health monitoring. This study uses a CLO 3D garment simulator, and knit fabrics are investigated in different weights and polyurethane contents. The first phase presents a comparative analysis of simulated and experimental stress. Based on the understanding of simulated stress, the CP model is developed by modifying Laplace’s law and using the simulated stress. The CP model is validated using a pressure sensor to compare the actual contact pressure. The developed CP model helps garment designers and engineers select the appropriate material and product size to achieve the target pressure required for ECG health monitoring in their decision-making.}, booktitle={Human Interaction and Emerging Technologies (IHIET-AI 2023): Artificial
          Intelligence and Future Applications}, publisher={AHFE International}, author={Youn, Seonyoung and Mathur, Kavita and Knowles, Caitlin and Ju, Beomjun and Sennik, Busra and Jur, Jesse}, year={2023} }
 @inbook{dewey_youn_budhathoki‐uprety_mathur_2022, title={Ancient Weaving and Dyeing Techniques}, url={http://dx.doi.org/10.1002/9781119983903.ch10}, DOI={10.1002/9781119983903.ch10}, booktitle={Handbook of Museum Textiles}, publisher={Wiley}, author={Dewey, Hannah and Youn, Seonyoung and Budhathoki‐Uprety, Januka and Mathur, Kavita}, year={2022}, month={Dec}, pages={193–207} }
 @article{3d 가상화를 위한 드레이프성 간이 측정법 개발_2021, url={https://doi.org/10.5850/JKSCT.2021.45.5.881}, DOI={10.5850/JKSCT.2021.45.5.881}, abstractNote={This study proposes a simple drape measurement method for the 3D virtualization of garments. The pro-posed method uses angles or disks of different diameters to evaluate the drape properties easily. We divided 710 fabrics into ten groups based on the drape coefficient, of which 49.6% had drape coefficients of 30 or less. The drape properties were measured to classify the groups into smaller clusters using the angle formed when the center of the fabric was fixed. Accordingly, three clusters were formed for 60° and 100° angles. A method was devised using ten disks of different diameters to classify the remaining two clusters, except the cluster con-taining only the D10 group (D1-D5 and D5-D9). Three criteria‒grade match, a sum of deviation, and standardization of deviation‒were used for the classifications. The discriminative ability between groups was high for D1-D5 with disks with 24.0 and 25.5 cm diameters. Furthermore, a disk with a diameter of 16.5 cm was effective for D5-D9. The three-dimensional drape shapes were unique for the ten groups, which can be utilized as fundamental data for 3D virtualization.}, journal={Journal of the Korean Society of Clothing and Textiles}, year={2021}, month={Oct} }
 @article{kim_youn_choi_kim_shim_yun_2021, title={Indexing surface smoothness and fiber softness by sound frequency analysis for textile clustering and classification}, volume={91}, url={http://dx.doi.org/10.1177/0040517520935211}, DOI={10.1177/0040517520935211}, abstractNote={Cutting-edge technology is being used in the fashion industry for three-dimensional (3D) virtual fitting programs to meet the demand for clothing manufacturing as well as textile simulating. For expanding the textile choices of the program users, this research looks at the indexation of tactile sensations, the texture of fabrics, which has been subjectively evaluated by the human hand. Firstly, this study objectively measured and indexed the surface smoothness and fiber softness of 749 fabrics through a tissue softness analyzer that mimics human hands. Secondly, after statistical analyses of the drape coefficient, each bending distance and Young's modulus for the initial tensile strength in the warp–weft directions, the thickness, and the weight of the fabrics, it was found that drape (Pearson coefficient = 0.532) and bending properties are the key factors in the fabric surface smoothness (TS750), while the fiber softness (TS7) showed a weak correlation with thickness (Pearson coefficient = 0.364), followed by the log value of the Young's modulus in the weft direction. Thirdly, we classified nine clusters for TS750 based on the 11 regression variables with significant Pearson coefficients, and characterized each cluster in order of surface smoothness (TS750) after Duncan post-hoc tests and analyses of variance (all statistically significant, p < 0.01) with microscopic surface images of one sample for each cluster. For precise TS750 classification, we finally trained the 267 samples with the same 11 variables, resulting in 93.3% prediction through an artificial neural network with multiple hidden layers. This prediction with Fisher discriminants for the clusters will enable the 3D virtual program users to predict further clustering of newly added fabrics.}, number={1-2}, journal={Textile Research Journal}, publisher={SAGE Publications}, author={Kim, Hye Jin and Youn, Seonyoung and Choi, Jeein and Kim, Hyeonji and Shim, Myounghee and Yun, Changsang}, year={2021}, month={Jan}, pages={200–218} }
 @article{인공신경망을 이용한 드레이프성 예측_2021, url={https://doi.org/10.5850/JKSCT.2021.45.6.978}, DOI={10.5850/JKSCT.2021.45.6.978}, abstractNote={This study aims to propose a prediction model for the drape coefficient using artificial neural networks and to analyze the nonlinear relationship between the drape properties and physical properties of fabrics. The stu-dy validates the significance of each factor affecting the fabric drape through multiple linear regression analysis with a sample size of 573. The analysis constructs a model with an adjusted R 2 of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m warp , m weft ), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c warp , c weft , c bias ), and force required for 1% tension in the warp direction (f warp ). Finally, an artificial neural network was created using seven selected factors. The performance was examined by increasing the number of hidden neurons, and the most suitable number of hidden neurons was found to be 8. The mean squared error was .052}, journal={Journal of the Korean Society of Clothing and Textiles}, year={2021}, month={Dec} }
 @article{youn_park_2019, title={Development of breathable Janus superhydrophobic polyester fabrics using alkaline hydrolysis and blade coating}, volume={89}, url={http://dx.doi.org/10.1177/0040517518760750}, DOI={10.1177/0040517518760750}, abstractNote={ Alkaline hydrolysis is a common finishing method that is used to give polyester (polyethylene terephthalate, PET) a more natural touch and improved luster via chemical or physical changes in the fibers. However, its potential as a tool for surface modification in the development of single-sided superhydrophobic materials has not been studied yet. In this research, Janus superhydrophobic PET fabrics with asymmetric wetting properties (one side of the PET surface was rendered superhydrophobic while the other side was simply hydrophobic) were fabricated in two steps. Fine roughness was first achieved on the surface of PET fabrics by alkaline hydrolysis. Subsequently, optimized foam-coating emulsions were applied on only one surface of the alkaline-hydrolyzed PET. Alkaline treatment time, solution temperature, and viscosity of the foam-coating emulsions were varied to find optimal conditions in terms of structural changes, mechanical properties, superhydrophobicity, and absorption ability. The specimen treated with an aqueous solution of 8% sodium hydroxide at 70℃ for 60 min and coated with the mixture of the fluoro-emulsion and thickener in the volume ratio of 40:2 was determined to be the optimal conditions for the Janus superhydrophobic property. This sample showed a contact angle of 162.8° and a shedding angle of 5.6° on one side, whereas it completely permitted the percolation of water drops on the other side within 109 s. The mechanical properties of the developed Janus PET under the optimal conditions did not decrease significantly; its weight and tensile strength were found to have decreased by 3.3% and 19.2%, respectively. Furthermore, the single-sided superhydrophobic specimen demonstrated higher moisture transmissibility than the double-sided coated PET under the same alkaline treatment conditions. The method developed herein eliminates the requirement for an additional process to deliver nanoscale surface roughness and has the potential to produce waterproof–breathable PET fabrics for outdoor clothing. }, number={6}, journal={Textile Research Journal}, publisher={SAGE Publications}, author={Youn, Seonyoung and Park, Chung Hee}, year={2019}, month={Mar}, pages={959–974} }