Development of a fabric classification system using drapability and tactile characteristics

Somin Lee, Yoojung Han, Changsang Yun

Research output: Contribution to journalArticlepeer-review


When producing clothing using virtual fitting technology or purchasing textile and clothing products online, it is challenging to make judgments or communicate information about sensory characteristics, such as drapability and tactile sensations, as there are no clear objective indicators for these factors. Therefore, the study aims to develop a classification system for the sensory properties of fabrics using drapability and tactile characteristics as quantitative indicators. The developed system was verified through subjective evaluations by an expert group, and it was found to be meaningful in reflecting classification levels in practice. The drapability and tactile sensation (softness; TS7) of the fabric were classified using fuzzy c-means cluster analysis, and the results were confirmed through a subjective evaluation by experts. The classification system was then used to predict the classification group, constituted by drapability and tactile characteristics, from mechanical properties using an artificial neural network. The network was trained on 534 fabric samples for drapability and tactile sensation (softness), and it correctly predicted 202 samples out of 243 validation data, with a forecasting accuracy of 83.5%. The developed classification system enables predictions and judgments about subjective characteristics like fabric drapability and tactile sensation based on the mechanical property values of various samples.

Original languageEnglish
Article number2
JournalFashion and Textiles
Issue number1
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).


  • Classification
  • Clustering
  • Drapability
  • Neural network
  • Panel evaluation
  • Softness


Dive into the research topics of 'Development of a fabric classification system using drapability and tactile characteristics'. Together they form a unique fingerprint.

Cite this