Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

Hung, O. N.; Song, Liu; Chan, C. K.; Kan, Chi Wai; Yuen, C.W.M.

doi:10.1007/s12221-011-1069-1

Cited by 44 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantage of ANNs is the ability of representing complex relationships directly from the data being modeled, while their representation (modeling) is always nonlinear [12][13][14] . Many researches were done in the textile industry to predict the properties of yarns, woven and nonwoven fabrics and many other characteristics of textile materials 10,[13][14][15][16][17][18][19][20][21][22][23][24][25] . Among these, just few studies are devoted to melt spinning and drawing of synthetic fibers.…”

Section: Introductionmentioning

confidence: 99%

Predicting the physical properties of drawn Nylon-6 fibers using an artificial-neural-network model

Rahbar¹,

Vadood²

2015

Mater. Tehnol.

View full text Add to dashboard Cite

Low-oriented nylon-6 fibers were drawn in a multistage drawing process, during which the number of drawing steps and the temperature of each step were changed. The physical properties of these fibers were measured and compared with the values predicted by a multiple-linear-regression model. Moreover, six input variables and four output variables were used in an artificial neural network (ANN) to establish the logical relationships between the inputs and outputs. Attempts were also made to determine the effective parameters for each physical property and explain the observed trends. The results showed that the models based on the ANN performed well and provided stable responses in predicting combined interactions between independent variables. Keywords: drawing process, artificial neural network, modeling, physical properties Malo orientirana vlakna najlon 6 so bila vle~ena z ve~stopenjskim postopkom, pri~emer se je pri vsakem vleku spreminjala stopnja vle~enja in temperatura. Izmerjene vrednosti teh vlaken so bile primerjane z vrednostmi, napovedanimi z modelom multivariantne linearne regresije. Poleg tega je bilo v umetni nevronski mre`i (ANN) uporabljenih {est vhodnih spremenljivk in {tiri izhodne, da bi ugotovili logi~ne odvisnosti med vhodnimi in izhodnimi spremenljivkami. Posku{alo se je ugotoviti u~inkovite parametere za vsako fizikalno lastnost in razlo`iti opa`ene tendence. Rezultati so pokazali, da so modeli na osnovi ANN dobri in ponujajo stabilne odgovore pri predvidevanju kombiniranih interakcij neodvisnih spremenljivk. Klju~ne besede: postopek vle~enja, umetna nevronska mre`a, modeliranje, fizikalne lastnosti

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting the physical properties of drawn Nylon-6 fibers using an artificial-neural-network model

Rahbar¹,

Vadood²

2015

Mater. Tehnol.

View full text Add to dashboard Cite

show abstract

“…This technique is very accurate and can work fast with good repeatability and reproducibility (Hung et al 2011;Kan et al 2010). …”

Section: Miscellaneous Usesmentioning

confidence: 99%

The use of laser in garment manufacturing: an overview

Nayak

Padhye

2016

Fash Text

View full text Add to dashboard Cite

Laser is being used in apparel industry from nineteenth century for various garment manufacturing applications. There are several advantages of using laser over the conventional processes in cutting, engraving, embossing, denim fading and other applications. In addition, product damage potential is reduced, no/less consumables are needed and no problem of toxic by-product disposal as found in some processes. Today's laser equipment is a result of continuous research and development of earlier products, which has undergone several changes. The initial laser systems were cumbersome, hard to run and difficult to maintain. However, the modern laser systems are simpler in operation and maintenance. Furthermore, the earlier systems were involved with more safety issues and needed the gasses to be constantly replenished. The garment manufactures around the globe should take the advantage of laser application in the post multi-fibre agreement regime to make their products more competitive. This review focuses on the technology of laser including various classifications. In addition it includes the applications of laser in garment manufacturing, their potential hazards and health related concerns.

show abstract

“…The method of least square is typically used to estimate the regression coefficients in a multiple linear regression model. Here, the linear regression model is applied in aforementioned experiment data [19,20]. The results are shown in Table 11.…”

Section: Linear Regression (Lr) Modelmentioning

confidence: 99%

Prediction of Color Properties of Cellulase-Treated 100% Cotton Denim Fabric

Kan

Wong

Song

et al. 2013

Journal of Textiles

Self Cite

View full text Add to dashboard Cite

Artificial neural network (ANN) model was used for predicting colour properties of 100% cotton denim fabrics, including colour yield (in terms of K/S value) and CIE L*, a*, b*,C*, andh° values, under the influence of cellulase treatment with various combinations of cellulase processing parameters. Variables examined in the ANN model included treatment temperature, treatment time, pH, mechanical agitation, and fabric yarn twist level. The ANN model was compared with a linear regression model where the ANN model produced superior results in the prediction of colour properties of cellulase-treated 100% cotton denim fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that cellulase treatment processing parameters played an important role in affecting the colour properties of the treated 100% denim cotton fabrics.

show abstract

Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

Cited by 44 publications

References 15 publications

Predicting the physical properties of drawn Nylon-6 fibers using an artificial-neural-network model

Predicting the physical properties of drawn Nylon-6 fibers using an artificial-neural-network model

The use of laser in garment manufacturing: an overview

Prediction of Color Properties of Cellulase-Treated 100% Cotton Denim Fabric

Contact Info

Product

Resources

About