Predicting the Pilling Propensity of Fabrics through Artificial Neural Network Modeling

Rafael Beltran, Lijing Wang, Xungai Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

53 Citations (Scopus)

Abstract

Fabric pilling is affected by many interacting factors. This study uses artificial neural networks to model the multi-linear relationships between fiber, yarn and fabric properties and their effect on the pilling propensity of pure wool knitted fabrics. This tool shall enable the user to gauge the expected pilling performance of a fabric from a number of given inputs. It will also provide a means of improving current products by offering alternative material specification and/or selection. In addition to having the capability to predict pilling performance, the model will allow for clarification of major fiber, yarn and fabric attributes affecting fabric pilling.

Original languageEnglish
Pages (from-to)557-561
Number of pages5
JournalTextile Research Journal
Volume75
Issue number7
DOIs
Publication statusPublished - Jul 2005
Externally publishedYes

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Polymers and Plastics

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