Prediction and categorization of fabric drapability for 3D garment virtualization

Purpose: This study aims to create a classification system enabling users of 3D virtualization software to intuitively perceive the drapability of fabrics. Design/methodology/approach: 1,001 fabrics were used, and thickness, bending property, and tensile strength were identified as main mechanical properties influencing drapability; they have been set as independent variables in the model established to predict drape coefficient. Findings: A system to classify fabrics into eight groups by drapability was suggested by a cluster analysis, and a multinomial logistic regression analysis was used to set a model that allows users to predict which group a fabric belongs to from its mechanical properties. Originality/value: This paper provided basic materials for the construction of a virtual clothing simulation system, which is believed to contribute to cost and time savings in decision-making by reducing the number of trials and errors required by the conventional approach.