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유동주 ( Dongjoo Yu ),윤주희 ( Juhee Yoon ),이상욱 ( Sang Wook Lee ),윤창상 ( Changsang Yun ) 한국의류학회 2021 한국의류학회지 Vol.45 No.2
The purpose of this study was to examine the effect of fabric movement on wrinkle recovery in a clothing care system and to propose an algorithm to improve wrinkle removal performance by adjusting fabric movements. With an increase in the reciprocating speed of the movement system, the number and amplitude of curves on the fabric also increased. This allowed the fabric to be applied to a larger tension, resulting in better wrinkle removal performance at higher speeds. However, even at high reciprocating speeds, wrinkles could not be removed effectively because of nodes at a few specific locations. Based on the results of fabric movement and wrinkle recovery, a complex movement algorithm was proposed with a mixture of various reciprocation speeds. It showed a 41%p (24%→65%) improvement of wrinkle recovery when compared with the conventional algorithm that showed simple fabric movement at 180 rpm. This was because the positions of nodes and antinodes changed continuously and the force by the reciprocating motion could be applied evenly to the fabric.
신보나 ( Bona Shin ),유동주 ( Dongjoo Yu ),이소민 ( Somin Lee ),윤선영 ( Seonyoung Youn ),심명희 ( Myounghee Shim ),윤창상 ( Changsang Yun ) 한국의류학회 2021 한국의류학회지 Vol.45 No.5
This study proposes a simple drape measurement method for the 3D virtualization of garments. The proposed 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 containing 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.
이소민 ( Somin Lee ),유동주 ( Dongjoo Yu ),신보나 ( Bona Shin ),윤선영 ( Seonyoung Youn ),심명희 ( Myounghee Shim ),윤창상 ( Changsang Yun ) 한국의류학회 2021 한국의류학회지 Vol.45 No.6
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 study 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<sup>2</sup> of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m<sub>warp</sub>, m<sub>weft</sub>), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c<sub>warp</sub>, c<sub>weft</sub>, c<sub>bias</sub>), and force required for 1% tension in the warp direction (f<sub>warp</sub>). 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, and the correlation coefficient was .863, confirming a satisfactory model. The developed artificial neural network model can be used for engineering and high-quality clothing design. It is expected to provide essential data for clothing appearance, such as the fabric drape.