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Research Article |

A Novel Optimized Neural Network Model for Ink Selection in Printed Electronics

Author(s): Alagusundari Narayanan* and Dr. Sivakumari Subramania Pillai

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 4

Publisher : FOREX Publication

Published : 02 December 2023

e-ISSN : 2347-470X

Page(s) : 1103-1109

DOI: https://doi.org/10.37391/ijeer.110430

Abstract

The field of Printed Electronics (PE) is experiencing significant growth in the industrial sector and generating considerable interest across various industries due to its ability to produce intricate components. The functionality of printed electronic products heavily relies on the utilization of conductive ink during the printing process, which plays a vital role in developing flexible electronic circuits and improving the communicative functionalities of objects. Selecting the right ink for printing is crucial to meet consumer requirements. However, the conventional approach to this process has been manual, labor-intensive, and time-consuming, relying on the expertise of designers. This paper presents an automated ink selection model for printed circuits. This novel method has been incorporated with Multilayer Perceptron Neural Network (MLPNN) and Particle Swarm Optimization (PSO), named PSO-MLPNN. A dataset containing material features is generated by gathering information from both literature and experimental observations. To ensure uniformity, the data undergoes preprocessing using the min-max method, which scales all features to a standardized range between 0 and 1. A four-layer MLPNN is constructed to choose the most suitable ink. The network is trained with the PSO algorithm. The bias and weight values of MLPNN are tuned using the PSO algorithm to attain high accuracy. The computed findings confirm that the ink selection is highly effective and more accurate when compared to both the standard MLPNN.

Keywords: Conductive ink, Ink selection, Multilayer perceptron, Printed Electronics, Particle swarm optimization.




Alagusundari Narayanan*, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamilnadu, India; Email: nalagusundari43@gmail.com

Dr. Sivakumari Subramania Pillai, Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamilnadu, India

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Alagusundari Narayanan and Dr. Sivakumari Subramania Pillai (2023), A Novel Optimized Neural Network Model for Ink Selection in Printed Electronics. IJEER 11(4), 1103-1109. DOI: 10.37391/ijeer.110430.