Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25353
Title: Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric
Authors: Hung, ON
Song, LJ
Chan, CK 
Kan, CW 
Yuen, CWM
Keywords: 100 % cotton fabric
Artificial neural network (ANN)
CIE L, a, and b values
Colour properties
K/S value
Laser engraving process
Issue Date: 2011
Publisher: Springer
Source: Fibers and polymers, 2011, v. 12, no. 8, p. 1069-1076 How to cite?
Journal: Fibers and polymers 
Abstract: In this paper, artificial neural network (ANN) model was used for predicting colour properties of 100 % cotton fabrics, including colour yield (in terms of K/S value) and CIE L, a, and b values, under the influence of laser engraving process with various combination of laser processing parameters. Variables examined in the ANN model included fibre composition, fabric density (warp and weft direction), mass of fabric, fabric thickness and linear density of yarn (warp and weft direction). The ANN model was compared with a linear regression model where the ANN model produced superior results in prediction of colour properties of laser engraved 100 % cotton fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that laser processing parameters played an important role in affecting the colour properties of the treated 100 % cotton fabrics.
URI: http://hdl.handle.net/10397/25353
ISSN: 1229-9197
EISSN: 1875-0052
DOI: 10.1007/s12221-011-1069-1
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

13
Last Week
0
Last month
0
Citations as of Jul 23, 2017

WEB OF SCIENCETM
Citations

12
Last Week
0
Last month
Citations as of Jul 15, 2017

Page view(s)

29
Last Week
1
Last month
Checked on Jul 9, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.