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Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries
Increasing the efficiency of the quality control phase in industrial production lines through automation is a rapidly growing trend. In non-destructive testing, active thermography techniques are known for their suitability to allow rapid non-contact and full-field inspections. The robotic manipulat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412682/ https://www.ncbi.nlm.nih.gov/pubmed/36016028 http://dx.doi.org/10.3390/s22166267 |
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author | Mineo, Carmelo Montinaro, Nicola Fustaino, Mario Pantano, Antonio Cerniglia, Donatella |
author_facet | Mineo, Carmelo Montinaro, Nicola Fustaino, Mario Pantano, Antonio Cerniglia, Donatella |
author_sort | Mineo, Carmelo |
collection | PubMed |
description | Increasing the efficiency of the quality control phase in industrial production lines through automation is a rapidly growing trend. In non-destructive testing, active thermography techniques are known for their suitability to allow rapid non-contact and full-field inspections. The robotic manipulation of the thermographic instrumentation enables the possibility of performing inspections of large components with complex geometries by collecting multiple thermographic images from optimal positions. The robotisation of the thermographic inspection is highly desirable to improve assessment speed and repeatability without compromising inspection accuracy. Although integrating a robotic setup for thermographic data capture is not challenging, the application of robotic thermography has not grown significantly to date due to the absence of a suitable approach for merging multiple thermographic images into a single presentation. Indeed, such an approach must guarantee accurate alignment and consistent pixel blending, which is crucial to facilitate defect detection and sizing. In this work, an innovative inspection platform was conceptualised and implemented, consisting of a pulsed thermography setup, a six-axis robotic manipulator and an algorithm for image alignment, correction and blending. The performance of the inspection platform is tested on a convex-shaped specimen with artificial defects, which highlights the potential of the new combined approach. This work bridges a technology gap, making thermographic inspections more deployable in industrial environments. The proposed fine image alignment approach can find applicability beyond thermographic non-destructive testing. |
format | Online Article Text |
id | pubmed-9412682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94126822022-08-27 Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries Mineo, Carmelo Montinaro, Nicola Fustaino, Mario Pantano, Antonio Cerniglia, Donatella Sensors (Basel) Article Increasing the efficiency of the quality control phase in industrial production lines through automation is a rapidly growing trend. In non-destructive testing, active thermography techniques are known for their suitability to allow rapid non-contact and full-field inspections. The robotic manipulation of the thermographic instrumentation enables the possibility of performing inspections of large components with complex geometries by collecting multiple thermographic images from optimal positions. The robotisation of the thermographic inspection is highly desirable to improve assessment speed and repeatability without compromising inspection accuracy. Although integrating a robotic setup for thermographic data capture is not challenging, the application of robotic thermography has not grown significantly to date due to the absence of a suitable approach for merging multiple thermographic images into a single presentation. Indeed, such an approach must guarantee accurate alignment and consistent pixel blending, which is crucial to facilitate defect detection and sizing. In this work, an innovative inspection platform was conceptualised and implemented, consisting of a pulsed thermography setup, a six-axis robotic manipulator and an algorithm for image alignment, correction and blending. The performance of the inspection platform is tested on a convex-shaped specimen with artificial defects, which highlights the potential of the new combined approach. This work bridges a technology gap, making thermographic inspections more deployable in industrial environments. The proposed fine image alignment approach can find applicability beyond thermographic non-destructive testing. MDPI 2022-08-20 /pmc/articles/PMC9412682/ /pubmed/36016028 http://dx.doi.org/10.3390/s22166267 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Mineo, Carmelo Montinaro, Nicola Fustaino, Mario Pantano, Antonio Cerniglia, Donatella Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries |
title | Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries |
title_full | Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries |
title_fullStr | Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries |
title_full_unstemmed | Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries |
title_short | Fine Alignment of Thermographic Images for Robotic Inspection of Parts with Complex Geometries |
title_sort | fine alignment of thermographic images for robotic inspection of parts with complex geometries |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412682/ https://www.ncbi.nlm.nih.gov/pubmed/36016028 http://dx.doi.org/10.3390/s22166267 |
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