| Titre : |
Seeking a paper for digital printing with maximum gamut volume : a lesson from artificial intelligence |
| Type de document : |
texte imprimé |
| Auteurs : |
Maryam Ataeefard, Auteur ; Seyyed Mohamad Sadati Tilebon, Auteur |
| Année de publication : |
2022 |
| Article en page(s) : |
p. 285-293 |
| Note générale : |
Bibliogr. |
| Langues : |
Américain (ame) |
| Catégories : |
Algorithmes génétiques
Couleur
Impression au laser
Impression numérique
Papier
Réseaux neuronaux (informatique)
|
| Index. décimale : |
667.9 Revêtements et enduits |
| Résumé : |
The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness. |
| Note de contenu : |
- Printing trials
- Measurement of color gamut and paper properties
- Model development and optimization
- Table 1 : Scenarios considered as internal data for the constructed model
- Table 2 : Parameter values used in NSGA II optimization
- Table 3 : Reliability of MLP ANNs with different structures and one hidden layer |
| DOI : |
https://doi.org/10.1007/s11998-021-00393-6 |
| En ligne : |
https://link.springer.com/content/pdf/10.1007/s11998-020-00393-6.pdf |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=37158 |
in JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH > Vol. 19, N° 1 (01/2022) . - p. 285-293
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