Artificial neural network approach for predicting colour properties of laser-treated denim fabrics

O. N. Hung, C. K. Chan, Chi Wai Kan, C. W M Yuen, L. J. Song

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


In this study, artificial neural network (ANN) and linear regression (LR) approaches are proposed for predicting colour properties of laser-treated denim fabrics. Denim fabrics were treated under different combinations of laser processing parameters, including pixel time (μs), resolution (dot per inch) and grayscale (lightness percentage) as inputs. Colour properties, including colour yield (K/S sum value), CIE L*, a* and b* values and yellowness index were predicted as outputs in these approaches. Later, the prediction performances of two approaches were compared and the statistical findings revealed that ANN approach was able to provide more accurate prediction than LR approach, especially for L value. Moreover, among the three input variables, grayscale (lightness percentage) was found to be the most important factor affecting colour properties of laser-treated denim fabrics.
Original languageEnglish
Pages (from-to)1330-1336
Number of pages7
JournalFibers and Polymers
Issue number6
Publication statusPublished - 1 Jan 2014


  • Artificial neural network (ANN)
  • Colour properties
  • Grayscale
  • Laser engraving process
  • Linear regression (LR)

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)
  • Polymers and Plastics

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