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25. Feb 2022 | Production & Lab

Artificial intelligence for digital printing with maximum gamut volume

In a new study, artificial intelligence approaches are applied to predict the performance of a paper to achieve the maximum colour gamut.
Test results indicate that the reproducibility depends considerably on the features of the paper. 
Image source: fovito - stock.adobe.com (symbol image).

The colour gamut of imaging media is significant for the reproduction of colour images because its magnitude directly affects the degree to which colours change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry. The colour 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 colour gamut and replication quality.

In a new study, artificial intelligence approaches are applied to choose and predict the performance of a paper with appropriate properties to achieve the maximum colour gamut. A genetic algorithm-based computer code is developed to optimise the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic colour printing.

Reproducibility depends considerably on the features of the paper

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 colour 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.

The study has been published in Journal of Coatings Technology and Research, Volume 19, 2022.

European Coatings Journal

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