Prediction of the reactive dye recipe in cotton fabric dyeing using the pad-dry-pad-steam process by particle swarm optimisation-least squares support vector machine with matching database / Chengbing Yu in COLORATION TECHNOLOGY, Vol. 138, N° 6 (12/2022)  

[article]  inCOLORATION TECHNOLOGY > Vol. 138, N° 6 (12/2022) . - p. 632-639 Titre : Prediction of the reactive dye recipe in cotton fabric dyeing using the pad-dry-pad-steam process by particle swarm optimisation-least squares support vector machine with matching database Type de document : texte imprimé Auteurs : Chengbing Yu, Auteur ; Wengang Cao, Auteur ; Qingxuan Wang, Auteur ; Zheng Zhe, Auteur ; Qihang Li, Auteur ; Jinyan Ning, Auteur Année de publication : 2022 Article en page(s) : p. 632-639 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Bases de données Colorants réactifs Colorimétrie Coton Le coton est une fibre végétale qui entoure les graines des cotonniers "véritables"(Gossypium sp.), un arbuste de la famille des Malvacées. Cette fibre est généralement transformée en fil qui est tissé pour fabriquer des tissus. Le coton est la plus importante des fibres naturelles produites dans le monde. Depuis le XIXe siècle, il constitue, grâce aux progrès de l'industrialisation et de l'agronomie, la première fibre textile du monde (près de la moitié de la consommation mondiale de fibres textiles). Informatique Teinture -- Fibres textiles Index. décimale : 667.3 Teinture et impression des tissus Résumé : Compared with manual matching, computer colour matching is faster and more reliable. Computer colour-matching results are determined by the method used; however, in practical application, the accuracy of colour matching still needs to be improved. In this paper, we first designed a series of colour-matching schemes with two reactive dyes, Levafix Blue and Levafix Amber, then dyed cotton fabric through the pad-dry-pad-steam process, and finally established the matching database. Furthermore, two colour-matching sub-models were developed by least squares support vector machine (LSSVM) to predict the dye recipe in cotton fabric dyeing, while particle swarm optimisation (PSO) was applied to optimise and tune the parameters of the LSSVM models. Herein, the model inputs are the colour parameters L*, a* and b* of the dyed samples and the model output is dye concentration. It is confirmed that the colour-matching models have excellent evaluation indexes and colour-matching effects. The coefficient of determination is more than 0.99, and the colour-matching precision is more than 98%. All the results prove that the colour-matching models by PSO-LSSVM can be accurately applied in practical dyeing to predict the reactive dye recipe, which is suitable for use on an industrial scale. Note de contenu : - EXPERIMENTAL : Materials - Dyeing using the PDPS process - Colour measurements - Building of the matching database - RESULTS AND DISCUSSION : Establishment of the colour-matching models - Validation of the colour-matching models - Indices assessment of the colour-matching models - Dyeing reliability assessment of the colour-matching models - Table 1 : The fitness value and output parameter group of the sub-models - Table 2 : The evaluation index of the colour-matching models - Table 3 : Colour parameters of the test samples and the predicted samples DOI : https://doi.org/10.1111/cote.12622 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12622 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=38377 [article]

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