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DC Field | Value | Language |
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dc.contributor.author | Amini, A | - |
dc.contributor.author | Gan, TH | - |
dc.date.accessioned | 2023-08-06T15:57:09Z | - |
dc.date.available | 2023-08-06T15:57:09Z | - |
dc.date.issued | 2023-01-13 | - |
dc.identifier | ORCID iDs: Amin Amini https://orcid.org/0000-0001-7081-2440; Tat Hean Gan https://orcid.org/0000-0002-5598-8453. | - |
dc.identifier | 1084 | - |
dc.identifier.citation | Amini, A. and Gan, T.H.. (2023) 'A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates', Applied Sciences (Switzerland), 2023, 13 (2), 1084, pp. 1 - 8. doi: 10.3390/app13021084. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26903 | - |
dc.description | Data Availability Statement Restrictions apply to the availability of these data. Data was obtained from VTT Technical Research Centre of Finland Ltd. and are available at https://www.vttresearch.com/en with the permission of VTT Technical Research Centre of Finland Ltd. | en_US |
dc.description.abstract | Copyright © 2023 by the authors. The demand for flexible large-area optoelectronic devices has been growing significantly during recent years. Roll-to-roll (R2R) printing facilitates the cost-efficient industrial production of different optoelectronic devices. Nonetheless, the performance of these devices is highly dependent on the printing quality and number of defects of R2R printed conductors. The image processing technique is an efficient nondestructive testing (NDT) methodology used to detect such defects. In this study, a computer vision-based assessment tool was utilized to visualize R2R printed silver conductors’ defects on flexible plastic substrates. A multistage defect detection technique was proposed to detect and classify both printing-induced defects and imperfections as well as the misalignment of the printed conductors with respect to the reference design. The method proved to be a very reliable approach that can be used independently or in conjunction with electrical testing methods for quality assurance purposes during the production of R2R prints. | en_US |
dc.description.sponsorship | This project received funding from the European Union’s HORIZON 2020 research and innovation program under Grant Agreement no. 820789. The work presented in this paper is part of the collaborative research project Innovative manufacturing processes and in-line monitoring techniques for the OLED and thin film and organic photovoltaic industries (CIGS and OPV) (OLEDSOLAR) funded by the European Collaborative Project Horizon 2020 and courtesy of support from VTT Technical Research Centre of Finland Ltd., TWI Limited, IRIS Technology Solutions, SL and Brunel University London. | en_US |
dc.format.extent | 1 - 8 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | automated defects recognition | en_US |
dc.subject | roll-to-roll | en_US |
dc.subject | printing | en_US |
dc.subject | organic photovoltaic | en_US |
dc.subject | thin film | en_US |
dc.subject | nondestructive testing | en_US |
dc.subject | image processing | en_US |
dc.subject | computer vision | en_US |
dc.title | A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/app13021084 | - |
dc.relation.isPartOf | Applied Sciences (Switzerland) | - |
pubs.issue | 2 | - |
pubs.publication-status | Published | - |
pubs.volume | 13 | - |
dc.identifier.eissn | 2076-3417 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Brunel Innovation Centre |
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FullText.pdf | Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 4.82 MB | Adobe PDF | View/Open |
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