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To Predict the Waterfastness Rate of Foil Print Applying Artificial Neural Network

by Mahasweta Mandal, Swati Bandyopadhyay
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 35
Year of Publication: 2019
Authors: Mahasweta Mandal, Swati Bandyopadhyay
10.5120/ijca2019919184

Mahasweta Mandal, Swati Bandyopadhyay . To Predict the Waterfastness Rate of Foil Print Applying Artificial Neural Network. International Journal of Computer Applications. 178, 35 ( Jul 2019), 3-8. DOI=10.5120/ijca2019919184

@article{ 10.5120/ijca2019919184,
author = { Mahasweta Mandal, Swati Bandyopadhyay },
title = { To Predict the Waterfastness Rate of Foil Print Applying Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2019 },
volume = { 178 },
number = { 35 },
month = { Jul },
year = { 2019 },
issn = { 0975-8887 },
pages = { 3-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number35/30765-2019919184/ },
doi = { 10.5120/ijca2019919184 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:52:16.883463+05:30
%A Mahasweta Mandal
%A Swati Bandyopadhyay
%T To Predict the Waterfastness Rate of Foil Print Applying Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 35
%P 3-8
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of this present study to evaluate the print quality by the waterfastness properties of foil prints which are exposed to water or rain. Waterfastness is an important property for any kind of packaging products, specially for food and medicine packages for assessing their print stability. The fastness properties of prints can be described in terms of print durability and image stability. Moreover, the poor fastness properties of prints will affect the product sale adversely. Little work has been done to study the fastness properties of printed films and foils. This study has preferred to take the blister foils as samples which are printed by gravure process as the blister foil has extensive usage in food and medicine packaging. Water immersion method and water spray methods both are used to study the water fastness of magenta ink on foil and the results were found to be similar. The evaluation is carried out by the measured spectral curves and colorimetric values before and after exposure using the oceanographic spectroradiometer (DH2000BAL) device. A significant change in reflectance in the blue and red regions with time indicates the fading of magenta print with time. This article has proposed a newly approach based on artificial neural network (ANN) model to determine the waterfastness rate of foil prints with variation of time. A comparative analysis is also made between the ANN model and regression model. However, the artificial neural network (ANN) has given a bit more excellent prediction than regression. In the context of prediction of waterfastness, the artificial neural network (ANN) model has given optimal results with the MSE -1.2409and a correlation coefficient of 0.9990..

References
  1. Vikman K., “Fastness Properties of Ink Jet Prints on Coated Papers—Part 2: Effect of Coating Polymer System on Water Fastness”, Journal of Imaging Science and Technology, 47(1), pp 38-43, 2003.
  2. Vikman K. and Vuorinen T., "Water Fastness of Ink Jet Prints on Modified Conventional Coatings ", Journal of Imaging Science and Technology, Vol. 48(2), pp 138-147, 2004.
  3. Yoldas B.E., “Design of sol-gel coating media for ink-jet printing”. J. Sol-Gel. Sci.Tech. 13(1-3), pp147-152, 1998.
  4. Glittenberg D., and Voigt A., “Economic formulations for improved quality ink-jet papers”. Paper Tech. 42(9), pp24-29, 2001.
  5. Bugner D. E., “Handbook of imaging materials”, Marcel Dekker., New York, , pp603-628,2002.
  6. Fryberg, M., Hofmann, R., and Brugger, P. A. “Permanence of ink-jet prints: A multi-aspect affair”, IS&T’s NIP13: International Conference on Digital Printing Technologies. Seattle, WA. IS&T, USA, pp 595-599, 1997.
  7. Soleimani-gorgani A. and Pishvaei M.," Water Fast Ink Jet Print Using an Acrylic /Nano-Silver Ink ", Prog. Color Colorants Coat. pp479-83, 2011.
  8. Soleimani-Gorgani1 A.and Jalili M. “Evaluating the Effect of Reactive Dye Structure and Penetrant Type on the Fastness of Ink-Jet Prints”, Prog. Color Colorants Coat. 7, pp73-83, 2014.
  9. Stankovská M., Gigac J., Letko M., Opálená E.," The Effect of surface sizing on paper wettability and on properties of inkjet prints", Wood Research,59(1),pp67-76,2014.
  10. Gigac J., Stankovská M., Pažitný A.," Influence of the coating formulations and base papers on inkjet printabilit "Wood Research,61(6),pp915-926,2016.
  11. Gigac J., Stankovská M., Opálená E., Pažitný A.,” The effect of pigments andbinders on inkjet print quality”, Wood Research 61(2), pp215-226, 2016.
  12. Wexler A. , Latex complexes as stabilized colorant. Kodak Polychrome Graphics, USA. U.S. Pat. 6297296 B1, 2001.
  13. Kasahara K.,” A New Quick-Drying, High-WaterResistant Glossy Ink Jet Paper”, Recent Progress in Ink Jet Technologies II, Chapter 6, Ink and Media, 1999.
  14. Ahmed J., Kaur A., and Shivhare U.," Color Degradation Kinetics of Spinach, Mustard Leaves, and Mixed Puree ", Journal of Food Science 1089, .67(3), pp 1088-1091(2002).
  15. Balci et al.,” Prediction of CIELab Data and Wash Fastness of Nylon 6,6 Using Artificial Neural Network and Linear Regression Model”, Fibers and Polymers, 9(2),pp 217 -224,2008.
  16. Bas D., Dudak C. F., Hakkı Ismail B.," Modeling and optimization IV: Investigation of reaction kinetics and kinetic constants using a program in which artificial neural network (ANN) was integrated ", Journal of Food Engineering 79(4)1152–1158 (2007).
  17. Kov´acs B., T´oth J.," Estimating Reaction Rate Constants with Neural Networks" International Journal of Applied Mathematics and Computer Science ,4(1) , pp 1305-5313, 2007.
  18. Dutot A, Rude J, Aumont B.," Neural network method to estimate the aqueous rate constants for the OH reactions with organic compounds ", Atmospheric Environment 37(2),pp 269–276, 2003.
  19. Mostafa A., Mehdi N., Hassan A.," New Approach in Modeling of Metallocene-Catalyzed Olefin Polymerization Using Artificial Neural Networks", Macromol. Theory Simul., 18(3) ,pp195–200,2009.
  20. Fernandes N. A. and Lona. F. M, "Neural Network Application Polymerization Processes", Brazilian Journal Of Chemical Engineering, . 22(3) ,pp 401 – 418,2005.
  21. Kuroda C., Kim J., "Neural network modeling of temperature behavior in an exothermic polymerization process", Neurocomputing ,43(1-4),pp 77–89 ,2002.
  22. Christo C. F., Masri R. A., Nebot M. E. , “Utilising artificial neural network and repro-modeling in turbulent combustion”, Proceedings of the IEEE International Conference on Neural Networks, Perth, 1, December, 1995.
  23. Fathi M.; Mohebbi M;Razavi Mohammad Ali S., "Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit", Food and Bioprocess Technology, 4(8),pp1357-1366 , 2011.
  24. Tang Y. S. et al," Application of Artificial Neural Network to Predict Colour Change, Shrinkage and Texture of Osmotically Dehydrated Pumpkin", IOP Conference Series: Materials Science and Engineering 206 June, 2017.
  25. Goyal S et al., "Shelflife Prediction of Processed Cheese Using Artificial Intelligence ANN Technique", Croatian Journal of Food Technology, Biotechnology and Nutrition ,7 (3-4), pp184-187 (2012).
  26. Goyal S, Goyal G. K. “Development of intelligent computing expert system models for shelf life prediction of soft mouth melting milk cakes”. International Journal of Computer Applications, 25(9), 41-44, 2011.
  27. Mandal M. and Bandyopadhyay S. “To predict the lightfastness rate of foil prints To Predict The Lightfastness Rate of Foil Print Applying Artificial Neural Network” Communicated to Packag Technol Sci. June 2019.
  28. Basheer I.A. and Hajmeer M.," Artificial neural networks: fundamentals, computing, design, and application", Journal of Microbiological Methods, 43 ,pp3–31 ,2000.
  29. Haykin, S. Neural networks: a comprehensive foundation. NewJersey: Prentice Hall. (1994).
  30. Sharma G., Edul W.Wu, Dalal N," The CIEDE2000 Color-Difference Formula: Implementation Notes, Supplementary Test Data, and Mathematical Observations ", COLOR research and application, 30(1) ,pp 21-30 (2005).
  31. Marsland S.. Machine Learning: An Algorithm Perspective.New York: Chapman and Hall/CRC.(2009).
  32. Bealen M.H., Hagan M.T.,and Demuth H.B., Neural Network Toolbox, Revised for Version 7.0 (Release 2010b), 2010.
  33. Wilhelm H and McCormick-Goodhart M., "An overview of the permanence of inkjet prints with Traditional color prints", The Society of Imaging Science and Technology, 2000.
  34. Andree F et al. "Rhodamne Dyes Which Are Sparingly Soluble Or Insoluble N Water" USA. U.S. Pat. 3,708,499, 1973.
Index Terms

Computer Science
Information Sciences

Keywords

Waterfastness Spectral data ANN CIE Lab Gravure printing

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