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Seeking a paper for digital printing with maximum gamut volume: a lesson from artificial intelligence

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Abstract

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.

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The raw data required to reproduce these findings are available to download from https://data.mendeley.com/submissions/evise/create?submission_id=S0300-9440(18)31304-&token=be39d4fd-4017-4c9f-9d95-8e06591581d6.

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Authors and Affiliations

  1. Department of Printing Science and Technology, Institute for Color Science and Technology, P. O. Box 16765-654, Tehran, Iran

    Maryam Ataeefard

  2. School of Chemical, Petroleum, and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran

    Seyyed Mohamad Sadati Tilebon

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  1. Maryam Ataeefard
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  2. Seyyed Mohamad Sadati Tilebon
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Correspondence to Maryam Ataeefard.

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Ataeefard, M., Tilebon, S.M.S. Seeking a paper for digital printing with maximum gamut volume: a lesson from artificial intelligence. J Coat Technol Res 19, 285–293 (2022). https://doi.org/10.1007/s11998-020-00393-6

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  • DOI: https://doi.org/10.1007/s11998-020-00393-6

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