Modeling, prediction, and analysis of alkyd enamel coating properties via neural computing / Javier E. Vitela in JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH, Vol. 3, N° 2 (04/2006)  

[article]  inJOURNAL OF COATINGS TECHNOLOGY AND RESEARCH > Vol. 3, N° 2 (04/2006) . - p. 141-149 Titre : Modeling, prediction, and analysis of alkyd enamel coating properties via neural computing Type de document : texte imprimé Auteurs : Javier E. Vitela, Auteur ; Eduardo Nahmad-Achar, Auteur Année de publication : 2006 Article en page(s) : p. 141-149 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Brillance (optique) Formulation (Génie chimique) Polyalkydes Réseaux neuronaux (informatique) Revêtement émail:Peinture-émail Revêtements:Peinture Séchage Index. décimale : 667.9 Revêtements et enduits Résumé : The use of artificial neural networks (ANNs) in the modeling and prediction ofalkyd enamel coating properties, as well as in the sensitivity analysis that can be performed between the properties and the different paint components, are described. A feedforward neural network with sigmoidal activation functions was used with a conjugate gradient algorithm to recognize the complex input-output relation between the paint properties and the formula components. We restricted the study to only two properties of alkyd enamel paints: gloss and drying time. A database of five different families of alkyd enamel paints, containing the different components of the formulations as well as process information, was used in this study. The results obtained show, within the expected uncertainty tolerance, that predictive power of more than 90% for these two properties can be achieved. A sensitivity analysis was also performed using ANNs, yielding the relative importance of the different components of the formulation on the properties of the enamel coatings, which agrees with experience for gloss but gives mixed results for the drying time (apparently due to the high uncertainties in the measurement of this property). DOI : 10.1007/s11998-006-0017-2 En ligne : https://link.springer.com/content/pdf/10.1007%2Fs11998-006-0017-2.pdf Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=3705 [article]

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