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 R2 of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (mwarp, mweft), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (cwarp, cweft, cbias), and force required for 1% tension in the warp direction (fwarp). 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.
|Translated title of the contribution||Prediction of Fabric Drape Using Artificial Neural Networks|
|Number of pages||8|
|Journal||Journal of the Korean Society of Clothing and Textiles|
|State||Published - 2021|
Bibliographical notePublisher Copyright:
© 2021, The Korean Society of Clothing and Textiles. All rights reserved.
- Artificial neural network
- Fabric drape
- Multiple linear regression
- Number of hidden neurons
- Physical property