AccScience Publishing / IJAMD / Online First / DOI: 10.36922/IJAMD025430040
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ORIGINAL RESEARCH ARTICLE

Computer vision and deep learning-based prediction for inkjet-printed electrodes

Gareth Quinn1* ,  Achu Titus1,2,3* ,  Anesu Nyabadza1,2,3 ,  Éanna McCarthy1,2,3 ,  Sithara Sreenilayam1,2,3 ,  Dermot Brabazon1,2,3
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1 School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland
2 I-Form Advanced Manufacturing Centre Research, Dublin City University, Dublin, Ireland
3 DCU Institute for Advanced Processing Technology, Dublin City University, Dublin, Ireland
IJAMD, 025430040 https://doi.org/10.36922/IJAMD025430040
Received: 22 October 2025 | Revised: 28 November 2025 | Accepted: 8 December 2025 | Published online: 17 December 2025
(This article belongs to the Special Issue Applications of Deep Learning in Advanced Materials Processing)
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

With the development of inkjet-printed electrodes, artificial intelligence-based quality control is essential for classifying inkjet-printed electrodes in a quality control environment. The quality of printed structures can be significantly affected by defects such as cracks, smudging, and misaligned deposits, which can degrade electrical performance and overall device reliability. Traditional quality control methods, including manual inspection and electrical testing, are time-consuming, subjective, and invasive, and they are unsuitable for high-throughput manufacturing environments. This work explores the application of computer vision and deep learning, specifically Convolutional Neural Networks (CNNs) and Feedforward Neural Networks, to automate defect detection and quality classification of inkjet-printed electrodes. To demonstrate the accessibility of deep learning techniques, Neural Architecture Search was implemented, showing the importance of automated model design in achieving high performance without extensive manual tuning or the need for expertise. The CNN models proved to be the most suitable approach for this image classification task, achieving a testing accuracy of 90.9% and a precision of 88.9% for a dataset of 2,406 electrode images containing both high-quality (1,020) and low-quality (1,386) prints.

Graphical abstract
Keywords
Inkjet printing
Electrodes
Defect detection
Deep learning
Computer vision
Convolutional Neural Networks
Feedforward neural networks
Neural architecture search
Funding
This publication arises from research supported by Research Ireland under Grant Number 21/RC/10295_P2 and is co-funded by the European Regional Development Fund. This work is supported by I-Form.
Conflict of interest
Dermot Brabazon is one of the Associate Editors of the journal but was not involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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