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Neural network-based UV adjustment of the photopolymer surface for modification of coating properties printed in flexography

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Abstract

Processes of coating deposition often rely on printing techniques, with flexography being the most common one because of its ability to adjust the medium for the coating transfer (printing plate) to the specific type of coating and substrate by using photopolymer materials with different properties. Qualitative requirements for many types of coatings, especially in the printing industry, include uniformity, achieving desired thickness, definition of the edges of printed coating and optical density of colored coatings. This research was focused on the modification of the mechanical and surface properties of the common styrene–diene-based photopolymer materials in order to optimize the properties of the deposited coating—flexographic ink—by applying the UV post-treatment of the photopolymer. After the analyses of modified photopolymers, neural networks were built with the aim of finetuning of the photopolymer’s surface properties by the UV post-treatment. The results of the research enabled the analysis of the influence of changes that occur in the modified photopolymer material’s mechanical and surface properties on the coating thickness, optical density and printed element edge definition. Once the neural network was built, it enabled fast adjustment of the UV post-treatment of the photopolymer with the aim of optimizing the properties of the specific coating.

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Acknowledgments

The authors would like to thank Welsh Centre for Printing and Coating in Swansea for providing WYKO NT-2000 for analysis of surface roughness of photopolymer material.

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Authors and Affiliations

  1. Faculty of Graphic Arts, University of Zagreb, Getaldićeva 2, 10000, Zagreb, Croatia

    Sanja Mahović Poljaček & Tamara Tomašegović

  2. Faculty of Chemical Engineering and Technology, University of Zagreb, Trg Marka Marulića 19, 10000, Zagreb, Croatia

    Mirela Leskovac

  3. Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000, Zagreb, Croatia

    Suzana Jakovljević

Authors
  1. Sanja Mahović Poljaček
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  2. Tamara Tomašegović
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  4. Suzana Jakovljević
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Correspondence to Tamara Tomašegović.

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Poljaček, S.M., Tomašegović, T., Leskovac, M. et al. Neural network-based UV adjustment of the photopolymer surface for modification of coating properties printed in flexography. J Coat Technol Res 17, 271–284 (2020). https://doi.org/10.1007/s11998-019-00270-x

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  • DOI: https://doi.org/10.1007/s11998-019-00270-x

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