Artificial neural networks for colour prediction in leather dyeing on the basis of a tristimulus system / Malathy Jawahar in COLORATION TECHNOLOGY, Vol. 131, N° 1 (02/2015)  

[article]  inCOLORATION TECHNOLOGY > Vol. 131, N° 1 (02/2015) . - p. 48-57 Titre : Artificial neural networks for colour prediction in leather dyeing on the basis of a tristimulus system Type de document : texte imprimé Auteurs : Malathy Jawahar, Auteur ; Chandra Babu Narasimhan Kannan, Auteur ; Mehta Kondamudi Manobhai, Auteur Année de publication : 2015 Article en page(s) : p. 48-57 Langues : Anglais (eng) Catégories : Couleur Couleur -- Analyse Echantillonnage Essais (technologie) Prévision, Théorie de la Qualité -- Contrôle Réseaux neuronaux (informatique) Teinture -- Fibres textiles Index. décimale : 667.3 Teinture et impression des tissus Résumé : Computer-assisted colour prediction and quality control have become increasingly important to the dyeing process in many consumer goods manufacturing industries, including textile and leather. The most challenging aspect concerns dye recipe prediction for the production of the required shade on a given substrate. Computer recipe prediction based on the conventional and widely used Kubelka–Munk model often fails under a variety of conditions. In the present investigation, an attempt has been made to develop an artificial neural network model to predict colour in terms of tristimulus values (X, Y, Z) given the concentration of dyes. An artificial neural network model was trained with 300 pairs of known input vectors, i.e. dye concentrations, and output vectors, i.e. colour parameters, using a backpropagation algorithm. The artificial neural network topology consists of three neurons in the input layer to represent the concentration of dyes, three neurons in the output layer to represent the tristimulus values X, Y, and Z, and five neurons in the hidden layer with a log-sigmoid transfer function. The artificial neural network results showed a good level of colour prediction during the training and testing phase. The results also indicate that the artificial neural network has the potential to give better predictive performance than the conventional Kubelka–Munk model. Note de contenu : - INTRODUCTION : Conventional colour prediction model (the Kubelka-Munk model) - Neural network and colour prediction - EXPERIMENTAL : Materials - Leather sample preparation - Colour analysis - Database generation for the Kubelka-Munk model - Artificial neural network design - RESULTS AND DISCUSSION : Conventional colour prediction using the Kubelka-Munk model - ANN design optimisation - Artificial neural network training - Validation of the Kubelka-Munk and artificial neural network models DOI : 10.1111/cote.12123 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12123 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=22983 [article]

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