Prediction and categorization of fabric drapability for 3D garment virtualization

Jimin Kim, Yun Jeong Kim, Myounghee Shim, Youngmin Jun, Changsang Yun

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Pages (from-to)523-535
Number of pages13
JournalInternational Journal of Clothing Science and Technology
Issue number4
StatePublished - 15 Jul 2020

Bibliographical note

Publisher Copyright:
© 2020, Emerald Publishing Limited.


  • 3D virtualization
  • Classification system
  • Cluster analysis
  • Drapability
  • Mechanical properties


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