Prediction of drape profile of cotton woven fabrics using artificial neural network and multiple regression method

Fabric drape is one of the most important factors which affect the graceful appearance of the garment. The drape coefficient is the widely used parameter to describe fabric drape but it needs other parameters to explain the fabric behavior. In this study, we have investigated the relationship between the fabric drape parameters such as drape coefficient, drape distance ratio, fold depth index, amplitude and number of nodes and low stress mechanical properties. Drape parameters were tested on a specially developed instrument based on a digital image processing technique and the low stress mechanical properties were tested by the Kawabata evaluation system. Then the drape parameters were predicted by constructing models using multiple regressions method and feed-forward back-propagation neural network technique. Simple equations are derived using regressions method to predict the five shape parameters of drape profile from the low stress mechanical properties. It is observed that bending, shear and aerial density affect the drape parameters most whereas the tensile and compression have little effect on the drape parameters.