| Titre : |
A computer learns colours : Colouristic based on artificial intelligence |
| Type de document : |
texte imprimé |
| Auteurs : |
Kevin Cremanns, Auteur ; Christian Schmitz, Auteur ; Lasse Wagner, Auteur |
| Année de publication : |
2020 |
| Article en page(s) : |
p. 34-39 |
| Note générale : |
Bibliogr. |
| Langues : |
Anglais (eng) |
| Catégories : |
Colorimétrie
Couleur
Intelligence artificielle
Pigments
Procédés de fabrication
|
| Index. décimale : |
667.9 Revêtements et enduits |
| Résumé : |
The use of artificial intelligence is currently finding its way into various industries and specialist disciplines. With the help of machine learning (ML), the experimental design, formulation, and testing of chemicals can made significantly more efficient in both time and resource efficiency. On possible application is to create a desired color code using different pigment pastes while producing the smallest possible number of samples. With a smaller number of tests, the new method presented leads to a more accurate prediction when compared to a classic experimental design plan. |
| Note de contenu : |
- From pigment to colour and back again
- Less is more
- Show your colour
- A picture says more than three number
- Fig. 1 : Manufacturing process of a colour sample
- Fig. 2 : Schedule of samples controlled by machine learning
- Fig. 3 : Depiction of the 10 starting samples and the 6x3 adaptations
- Fig. 4 : The prognosis quality of the three replacement models over the number of samples
- Fig. 5 : Representation of the predictions of the replacement models and the 95% confidence interval compared to the measurement
- Fig. 6 : Sensitivities of the pigments based on the respective CIELAB color codes axes
- Fig. 7 : Representation of the color space based on the three models when trained using the 28 experiments and the pigment pastes
- Fig. 8 : Depiction of the sample photos used as input information for training a "multi-output" model
- Table 1 : Results of the validation of the trained replacement models |
| En ligne : |
https://drive.google.com/file/d/14778DYZmdK4988vYbQP_zL-nYDyzjCu_/view?usp=drive [...] |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=34338 |
in EUROPEAN COATINGS JOURNAL (ECJ) > N° 7-8 (07-08/2020) . - p. 34-39
Aller sur edunet
Accueil
Ajouter le résultat dans votre panier Affiner la recherche
