A Reliable and Intelligent Ink Selection System for Printed Electronics Using Artificial Neural Network

A Reliable and Intelligent Ink Selection System for Printed Electronics Using Artificial Neural Network

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-1
Year of Publication : 2024
Author : Alagusundari Narayanan, Sivakumari Subramania Pillai
DOI : 10.14445/22315381/IJETT-V72I1P119

How to Cite?

Alagusundari Narayanan, Sivakumari Subramania Pillai, "A Reliable and Intelligent Ink Selection System for Printed Electronics Using Artificial Neural Network," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 193-201 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P119

Abstract
Printed electronics is rapidly expanding in the industrial sector and attracting a lot of interest from a wide range of sectors due to its potential to fabricate components with intricate features. For the functionality of the products in printed electronics, the printing of conductive ink is crucial. Conductive inks are used to print flexible electronic circuits and make objects more communicative. Particularly based on consumer requirements, it is crucial to select ink for printing purposes. Ink selection has always relied on the experience of designers. Manual ink selection is a laborious and time-consuming process. Therefore, this paper intends to design an automatic ink selection system for printing applications using a novel Artificial Neural Network (ANN) framework. The literature and experimental data are used to construct the material feature dataset. The min-max approach is used for preprocessing data to align all characteristics within a common range of 0 to 1. Lastly, to choose the ink according to the input characteristics, a Multilayer Perceptron Neural Network (MLPNN) is created. The performance of the proposed system is analyzed by varying the number of hidden layers, hidden neurons, and training samples. The experimental results showed that the MLPNN appropriately selects ink for printing applications when it has optimal topology.

Keywords
Artificial Neural Network, Ink selection, Multilayer perceptron, Printed electronics, Screen printing.

References
[1] Ethan B. Secor et al., “Gravure Printing of Graphene For Large Area Flexible Electronics,” Advanced Materials, vol. 26, no. 26, pp. 4533-4538, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Colin Reese et al., “Organic Thin Film Transistors,” Materials Today, vol. 7, no. 9, pp. 20-27, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jia Shi et al., “Multi-Objective Optimization Design through Machine Learning Drop-on-Demand Bioprinting,” Engineering, vol. 5, no. 3, pp. 586-593, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] César Aliaga et al., “Influence of RFIS Tags on Recyclability of Plastic Packaging,” Waste Management, vol. 31, no. 6, pp. 1133-1138, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Yvan Bonnassieux et al., “The 2021 Flexible and Printed Electronics Roadmap,” Flexible and Printed Electronics, vol. 6, no. 2, pp. 1-49, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] H. Kipphan, Handbook of Print Media, Technologies and Production Methods, 1st ed., Springer Berlin Heidelberg, pp. 1-1207.
[Google Scholar] [Publisher Link]
[7] Xiling Yao, Seung Ki Moon, and Guijun Bi, “A Hybrid Machine Learning Approach for Additive Manufacturing Design Feature Recommendation,” Rapid Prototyping Journal, vol. 23, no. 6, pp. 983-997, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Fahmida Pervin Brishty, Ruth Urner, and Gerd Grau, “Machine Learning Based Data Driven Inkjet Printer Electronics: Jetting Prediction for Novel Inks,” Flexible and Printed Electronics, vol. 7, no. 1, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Xinbo Qi et al., “Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives,” Engineering, vol. 5, no. 4, pp. 721-729, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Alan Brunton, Can Ates Arikan, and Philipp Urban, “Pushing the Limits of 3D Color Printing: Error Diffusion with Translucent Materials,” ACM Transactions on Graphics, vol. 35, no. 1, pp. 1-13, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Kazuki Nagasawa et al., “Prediction of the Layered Ink Layout for 3D Printers Considering a Desired Skin Color and Line Spread Function,” Optical Review, vol. 28, pp. 449-461, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Navid Ansari et al., “Mixed Integer Ink Selection for Spectral Reproduction,” ACM Transactions on Graphics, vol. 39, no. 6, pp. 1-16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ebrahim Vahabli, and Sadegh Rahmati, “Application of an RBF Neural Network for FDM Parts’ Surface Roughness Prediction for Enhancing Surface Quality,” International Journal of Precision Engineering and Manufacturing, vol. 17, no. 12, pp. 1589-1603, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Dazhong Wu, and Changxue Xu, “Predictive Modelling of Droplet Formation Processes in Inkjet-based Bioprinting,” Journal of Manufacturing Science and Engineering, vol. 140, no. 10, pp. 1-9, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jida Huang et al., “Unsupervised Learning for the Droplet Evolution Prediction and Process Dynamics Understanding in Inkjet Printing,” Additive Manufacturing, vol. 35, pp. 1-14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Alexander Kamyshny, Joachim Steinke, and Shlomo Magdassi, “Metal-based Inkjet Inks for Printed Electronics,” The Open Applied Physics Journal, vol. 4, pp. 19-36, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Rama Venkata Krishna Rao et al., “Conductive Silver Inks and Its Applications in Printed and Flexible Electronics,” RSC Advances, vol. 5, pp.77760-77790, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Elina Jansson et al., “Suitability of Paper-Based Substrates for Printed Electronics,” Materials, vol. 15, no. 3, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Junpyo Kwon et al., “Conductive Ink with Circular Life Cycle for Printed Electronics,” Advanced Materials, vol. 34, no. 3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Pradeep Lall et al., “Deep Learning Neural Network Approach for Correlation between Print Parameters and Realized Electrical Performance and Geometry on Ink-Jet Platform,” 2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm), San Diego, CA, USA, pp. 1-9, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Tianjiao Wang et al., “In-Situ Droplet Inspection and Closed-Loop Control System using Machine Learning for Liquid Metal Jet Printing,” Journal of Manufacturing Systems, vol. 47, pp. 83-92, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ting Leng et al., “Screen-Printed Graphite Nanoplate Conductive Ink for Machine Learning Enabled Wireless RadiofrequencyIdentification Sensors,” ACS Applied Materials, vol. 2, no. 10, pp. 6197-6208, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Jian Qin et al., “Research and Application of Machine Learning for Additive Manufacturing,” Additive Manufacturing, vol. 52, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] S. Gopal Krishna Patro, and Kishore Kumar Sahu, “Normalization: A Preprocessing Stage,” ArXiv, pp.; 1-4, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ziqiang He et al., “Research on the Measurement Method of Printing Ink Content Based On Spectrum,” Optik, vol. 243, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Hassan Saba et al., “Electrohydrodynamic Jet Printing for Desired Print Diameter,” Materials Today: Proceedings, vol. 46, no. 4, pp. 1749-1754, 2021.
[CrossRef] [Google Scholar] [Publisher Link]