Modeling the air permeability of pile loop knit fabrics using fuzzy logic and artificial neural network

HAROĞLU D.

Journal of the Textile Institute, vol.114, no.2, pp.265-272, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 114 Issue: 2
  • Publication Date: 2023
  • Doi Number: 10.1080/00405000.2022.2028361
  • Journal Name: Journal of the Textile Institute
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC
  • Page Numbers: pp.265-272
  • Keywords: Pile loop knit fabric, air permeability, fuzzy logic, artificial neural network, CLASSIFICATION, PREDICTION
  • Kayseri University Affiliated: No

Abstract

© 2022 The Textile Institute.Pile loop knit fabrics have attracted attention in biomedical applications particularly due to their unique porous three-dimensional structures. Since there is a close relationship between pore characteristics and air permeability of a textile structure, the control of air permeability property during production would improve production planning when designing new knitted fabrics. This study deals with the development of an Artificial Neural Network (ANN) model, and a Fuzzy Logic (FL) model for predicting the air permeability of pile loop knit fabrics. For this aim, pile loop knit structures with different areal densities were produced by using textured polyethylene terephthalate (PET) yarns from four different filament fineness. Multiple linear regression (MLR), FL, and ANN model analyses were done. The root mean square error of the MLR, FL, and ANN were found to be 14.934, 12.41, and 2.418, respectively. Thus, the ANN model provided superior performance over the MLR and FL model in predicting air permeability.