Engineering the drapability of textile fabrics

The drape attributes of fabrics, number of folds, depth of folds and evenness of folds were measured together with the drape coefficient. The relationship between these measurements and the subjective evaluation of the fabric drape was modelled for each end-use on a neural network using back propagation, which can correctly predict the grades of 90 per cent of the samples. The relationship between the drape attributes and fabric bending, shear and weight was also modelled using neural networks. It was found that using the natural logarithm of the material property divided first by the weight of the fabric produced the most predictive model. Together, these models provide a powerful predictive tool to determine both the drape attributes and the drape grade from the mechanical properties of a fabric. The accuracy of the prediction of this system was found to be 83 per cent overall. Combining this with a novel feedback system, the drape grade or drape attributes of a fabric can be modified to fit the customer requirements and then the changes to the material properties required to achieve them can be determined.