Modeling color change after spinning process using feedforward neural networks

Thevenet, L.; Dupont, Daniel; Jolly-Desodt, Anne-Marie

doi:10.1002/col.10114

Cited by 14 publications

(11 citation statements)

References 19 publications

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“…One of the most important textile characteristics is undoubtedly color (Thevenet et al, 2002). Color quality control is one very important step in any textiles, however excellent the fabric material itself is, if it lacks good color, then it may still result in dull sale (Kuo et al, 2007).…”

Section: Color Coordinates Conversion Color Separation and Categorizmentioning

confidence: 99%

“…Nevertheless, they can be divided into two groups. The first group concerns dyeing and printing stages, and is mainly governed by chemical rules, because the color attributes, which are added to the textile structure, are chemically fixed to the product (Thevenet et al, 2002). Expected depth of shade, color, color fastnesses and surface characteristics etc.…”

Section: Color Coordinates Conversion Color Separation and Categorizmentioning

confidence: 99%

“…The aim of the model is to predict the color obtained when fibers, with different colors, are blended. When the blend is homogenous, the color obtained can be predicted very well by theoretical and empirical models (Thevenet et al, 2002). Thevenet et al, 2002 described a model based on neural networks to predict color alteration after spinning process (roving to yarn).…”

Section: Color Coordinates Conversion Color Separation and Categorizmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Network Prosperities in Textile Applications

Tehran¹,

Maleki²

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Section: Color Coordinates Conversion Color Separation and Categorizmentioning

confidence: 99%

Section: Color Coordinates Conversion Color Separation and Categorizmentioning

confidence: 99%

Section: Color Coordinates Conversion Color Separation and Categorizmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Network Prosperities in Textile Applications

Tehran¹,

Maleki²

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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“…Although color measurement is possible in the laboratory with the help of specialized equipment like the spectrophotometers, few capabilities exist for the prediction of the color changes or the final color appearance, because the problem is multivariable. A model for the prediction of color change after the spinning process has been developed (Thevenet et al, 2003). The prediction of the color and the color solidity of a jigger dyed cellulose based fabric has been achieved by using cascade ANNs.…”

Section: Colormentioning

confidence: 99%

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

Vassiliadis¹,

Rangoussi²,

Çay³

et al. 2010

Woven Fabric Engineering

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“…Within the field of neural networks, Thevenet et al used neural networks to model colour changes that take place after the spinning process [8], and Furferi and Governi used a neural network to correct the spectral output from a StearnsNoechel model [9]. However, the general use of neural networks to model transformations between colour spaces is more than twenty years old.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to using neural networks to predict the colour of fibre blends

Hemingray

Westland

2016

Coloration Technology

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This work is concerned with the colour prediction of viscose fibre blends, comparing two conventional prediction models (the Stearns–Noechel model and the Friele model) and two neural network models. A total of 333 blended samples were prepared from eight primary colours, including two‐, three‐, and four‐colour mixtures. The performance of the prediction models was evaluated using 60 of the 333 blended samples. The other 273 samples were used to train the neural networks. It was found that the performance of both neural networks exceeded the performance of both conventional prediction models. When the neural networks were trained using the 273 training samples, the average CIELAB colour differences (between measured and predicted colour of blends) for the 60 samples in the test set were close to 1.0 for the neural network models. When the number of training samples was reduced to only 100, the performance of the neural networks degraded, but they still gave lower colour differences between measured and predicted colour than the conventional models. The first neural network was a conventional network similar to that which has been used by several other researchers; the second neural network was a novel application of a standard neural network where, rather than using a single network, a set of small neural networks was used, each of which predicted reflectance at a single wavelength. The single‐wavelength neural network was shown to be more robust than the conventional neural network when the number of training examples was small.

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Modeling color change after spinning process using feedforward neural networks

Cited by 14 publications

References 19 publications

Artificial Neural Network Prosperities in Textile Applications

Artificial Neural Network Prosperities in Textile Applications

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

A novel approach to using neural networks to predict the colour of fibre blends

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