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

10 Scopus citations

Abstract

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
Volume32
Issue number4
DOIs
StatePublished - 15 Jul 2020

Bibliographical note

Funding Information:
This work was supported by the Ewha Womans University Research Grant of 2018 and by the Korea Textile Trade Association. We also appreciated the cooperation of CLO Virtual Fashion LLC.

Publisher Copyright:
© 2020, Emerald Publishing Limited.

Keywords

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

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