Color formulation by fiber blending using the Stearns–Noechel model

Philips-Invernizzi, Bernadette; Dupont, Daniel; Jolly-Desodt, Anne-Marie; Cazé, C.

doi:10.1002/col.10028

Cited by 36 publications

(46 citation statements)

References 9 publications

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“…The value of the empirical constant has been evaluated for a range of different materials in the literature. 11,12 Nevertheless, even when M is accurately determined, the S-N model provides reliable results only for blends composed of a maximum of five to six differently colored fibers of the same material, thus limiting the approach to a lower number of cases since most companies mix together up to 15 to 20 differently colored fibers, often made of different materials such as wool, nylon, polyester, etc. 13,14 Therefore, although the above-mentioned theoretical approaches provide excellent results for predicting the color of turbid media, they may lead to unsuitable results in forecasting the reflectance factors of blends obtained by mixing precolored fibers.…”

Section: S-n Proposes the Following Empirical Additive Formula For Thmentioning

confidence: 99%

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Furferi

Governi

Volpe

2016

J. Electron. Imaging

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Abstract. Color matching of fabric blends is a key issue for the textile industry, mainly due to the rising need to create high-quality products for the fashion market. The process of mixing together differently colored fibers to match a desired color is usually performed by using some historical recipes, skillfully managed by company colorists. More often than desired, the first attempt in creating a blend is not satisfactory, thus requiring the experts to spend efforts in changing the recipe with a trial-and-error process. To confront this issue, a number of computer-based methods have been proposed in the last decades, roughly classified into theoretical and artificial neural network (ANN)-based approaches. Inspired by the above literature, the present paper provides a method for accurate estimation of spectrophotometric response of a textile blend composed of differently colored fibers made of different materials. In particular, the performance of the Kubelka-Munk (K-M) theory is enhanced by introducing an artificial intelligence approach to determine a more consistent value of the nonlinear function relationship between the blend and its components. Therefore, a hybrid K-M+ANN-based method capable of modeling the color mixing mechanism is devised to predict the reflectance values of a blend.

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Section: S-n Proposes the Following Empirical Additive Formula For Thmentioning

confidence: 99%

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Furferi

Governi

Volpe

2016

J. Electron. Imaging

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“…When the blend is homogenous, the color obtained can be predicted very well by theoretical and empirical models. 1,2 But the other stages of the group are not studied, because color appearance is produced by two components. The first obeys to optics laws, because it is concerned with diffusion, transmission, and absorption aspects.…”

Section: Introductionmentioning

confidence: 99%

Modeling color change after spinning process using feedforward neural networks

Thevenet

Dupont

Jolly-Desodt

2002

Color Research & Application

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“…It is possible that the constraint of using only three components in the blends could have been a factor in the relatively large errors obtained. Philips-Invernizzi et al also explored the Stearn-Noechel model [4]. They used thirteen coloured fibres as primaries and attempted to predict the colour of 234 two-component blends.…”

Section: Introductionmentioning

confidence: 99%

“…It was noted that this was not a practical approach, but it does hint at the possibility of improved predictions if b could be shown to vary systematically with, for example, the CIELAB coordinates of the blend so that simple models that predict b based on the colour of the blend could be derived. Philips-Invernizzi et al also considered an approach where b was optimised for each wavelength and this produced a systematic dependent of b on wavelength [4], thus b = (0.12λ + 42.75)/1000…”

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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Color formulation by fiber blending using the Stearns–Noechel model

Abstract: Blending colored fibers consists of combing fibers together many times in order to

Cited by 36 publications

References 9 publications

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Modeling color change after spinning process using feedforward neural networks

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

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