Prediction of CIELab data and wash fastness of nylon 6,6 using artificial neural network and linear regression model

Balci O., Ogulata S. N., ŞAHİN C., Ogullata R. T.

FIBERS AND POLYMERS, vol.9, no.2, pp.217-224, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 2
  • Publication Date: 2008
  • Doi Number: 10.1007/s12221-008-0035-z
  • Journal Name: FIBERS AND POLYMERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.217-224
  • Keywords: CIELab, wash fastness, artificial neural network, Levenberg-Marquardt algorithm, linear regression model, ACID-BASED AFTERTREATMENT, METAL-FREE, PART 3, DYES, FABRICS, CLASSIFICATION, ELONGATION, FIBERS, YARNS
  • Çukurova University Affiliated: Yes

Abstract

We tried to predict the CIELab data and wash fastness values of scoured nylon 6.6 knitted fabric dyed with 1:2 metal-complex acid dyes and aftertreated using three different methods named as syntan, syntan/cation and full backtan by artificial neural network (ANN) with Levenberg-Marquardt algorithm and regression models. Afterward, the predicting performance of these models was tested and compared with each other using unseen data sets. We were able to achieve to predict the all colorimetric data satisfactorily such as L*, a*, b*, C, h degrees and wash fastness performance using both models. The statistical findings indicated that the regression models provide more accurate prediction for all colour data with an average error of 1% contrast to previous study. In terms of prediction of fastness, artificial neural network is a bit more useful than regression models for prediction of staining value on the nylon part of adjacent multifiber.