Estimation Of Screen Density According To Different Screening Methods With Artificial Neural Network Method In Flexo Printing System

Year 2018, , 575 - 580, 01.09.2018

Mustafa Batuhan Kurt Yelda Karatepe Mumcu , Lütfi Özdemir

https://doi.org/10.2339/politeknik.386932

Cited By: 3

Abstract

Choice of dot shape is the most important factors that
affect the printing quality in the flexographic printing system. The aim of the
operations performed by the machine operator during the printing process
(densitometric measurements, ink settings, etc.) is to achieve the same quality
from the first printing to last printing. This study attempts to estimate
screen density values obtained from the same polymer structure (DFR), 175 Lpi
screening and 10 different screen structures using the Artificial Neural
Networks method (ANN). Data necessary for calculations were obtained from real
values as a result of experimental studies. The correlation coefficient of the
data obtained from the model created with ANN for screen density values was
found to be 98,902% and this value was found to be consistent with scientific
values. According to the results, the neural network model used in flexographic
printing systems of different screening methods predictable effect on the
printing result.

Keywords

Packaging, flexo printing, screening methods, flexo plate, artificial neural networks

References

• [1] Kurt, M.B., “Determination of The Under Press Substances and The Pressing Surface Height of The Plate Used in Flexo Printing System”, PhD Thesis, Istanbul, TURKEY, (2012).
• [2] Crouch, J.P., “Flexography Primer”, Graphic Arts Technical Foundation Press,Pittsburgh, PA, (1998).
• [3] Sonmez S., “Development of Printability of Bio-Composite Materials Using Luffa cylindrica Fiber”, BioREsources 12(1): 760 –773, (2017).
• [4] Laurent GL., “Prediction of the substrate printing in flexography by using a new established Printing Coefficient”, PhD thesis, Royal Institute of Technology, Stockholm, Sweden, (2002).
• [5] http://esraprint.ir/wp-content/uploads/2016/06/expert_guide_screening_tech.pdf, (2016).
• [6] http://www.dupont.com/content/dam/assets/products-and-services/printing-package-printing/PG/assets/NA/PDS-NA0031-EN-Cyrel-DFR-Data-Sheet-i.pdf, (2016).
• [7] Olsson R., Yang L., Stam, J., and Magnus L., “Effects on ink setting in flexographic printing: coating polarity and dot gain” ,Nordic Pulp & Paper Research Journal, 21(5): 569–574, (2006).
• [8] Ural, E., "The Applied Observation Of The Relationship Between Printing Pressure And The Amount Of Ink Printed And Solid Tone Density In Offset Printing On Coated And Uncoated Papers” İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 9(17): 61–71, (2010).
• [9] Youssef K.T., “The Impact of FM-AM Hybrid Screening and Am Screening on Flexographic Printing Quality”, International Design Journal, 5(4): 1471–1476, (2015).
• [10] TAPPI T402, “Standard conditioning and testing atmospheres for paper, board, pulp handsheets, and related products”, TAPPI Press, Atlanta, GA, USA, (2013).
• [11] Farhana K., and Aishwarya P., “Artificial Neural Network: Framework for Fault Tolerance and Future.”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 648– 651, (2016).
• [12] Ghaedi M., Ansari A., Assefi Nejad P., Ghaedi A., Vafaei A., and Habibi M.H., “Artificial neural network and Bees Algorithm for removal of Eosin B using Cobalt Oxide Nanoparticle-activated carbon: Isotherm and Kinetics study”, Environ. Prog. Sustainable Energy, 34: 155–168, (2015).
• [13] Ham F.M., and Kostanic I., “Principles of Neurocomputing for Science and Engineering“, McGraw-Hill Higher Education, (2001).
• [14] Ozel Y. , Guney I., and Arca E. “Neural Network Solution to the Cogeneration System by Using Coal”, 12th WSEAS International Conference on CIRCUITS, Heraklion, Greece, 279–283, (2008).
• [15] Lenzi G.G., Evangelista R.F., Duarte E.R., Colpini L.M.S., Fornari A.C., Menechini Neto R., and Jorge L.M.M. & Santos O.A.A., “Photocatalytic degradation of textile reactive dye using artificial neural network modeling approach”, Desalination and Water Treatment, 57: 14132–14144, (2016).
• [16] Bates I., Zjakic I., and Budimir I., “Assessment of the print quality parameters’ impact on the high-quality flexographic print visual experience”, The Imaging Science Journal, 63(2): 103–110,(2015).
• [17] Cengiz C., and Kose E., “Modelling of color perception of different eye colors using artificial neural networks”, Neural Computing and Applications, 23(7): 2323–2332, (2013).