Non-destructive testing of composite fibre materials with hyperspectral imaging – evaluative studies in the EU H2020 FibreEUse project

Tools

Yan, Yijun and Ren, Jinchang and Zhao, Huan and Windmill, James F.C. and Ijomah, Winifred and Wit, Jesper De and Freeden, Justus Von (2022) Non-destructive testing of composite fibre materials with hyperspectral imaging – evaluative studies in the EU H2020 FibreEUse project. IEEE Transactions on Instrumentation and Measurement, 71. pp. 1-13. 6002213. ISSN 1557-9662 (https://doi.org/10.1109/TIM.2022.3155745)

[thumbnail of Yan-etal-IEEETIM-2022-Non-destructive-testing-of-composite-fibre-materials-with-hyperspectral-imaging-evaluative-studies]
Preview
 Text. Filename: Yan_etal_IEEETIM_2022_Non_destructive_testing_of_composite_fibre_materials_with_hyperspectral_imaging_evaluative_studies.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (1MB)| Preview

Abstract

Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture, and chemical composition. Therefore, HSI has been successfully applied in various applications, including remote sensing for security and defense, precision agriculture for vegetation and crop monitoring, food/drink, and pharmaceuticals quality control. However, for condition monitoring and damage detection in carbon fiber reinforced polymer (CFRP), the use of HSI is a relatively untouched area, as existing non-destructive testing (NDT) techniques focus mainly on delivering information about physical integrity of structures but not on material composition. To this end, HSI can provide a unique way to tackle this challenge. In this article, with the use of a near-infrared (NIR) HSI camera, applications of HSI for the non-destructive inspection of CFRP products are introduced, taking the European Union (EU) H2020 FibreEUse project as the background. Technical challenges and solutions on three case studies are presented in detail, including adhesive residues detection, surface damage detection, and cobot-based automated inspection. Experimental results have fully demonstrated the great potential of HSI and related vision techniques for NDT of CFRP, especially the potential to satisfy the industrial manufacturing environment.

ORCID iDs

Yan, Yijun, Ren, Jinchang, Zhao, Huan ORCID logoORCID: https://orcid.org/0000-0001-6689-0964, Windmill, James F.C. ORCID logoORCID: https://orcid.org/0000-0003-4878-349X, Ijomah, Winifred, Wit, Jesper De and Freeden, Justus Von;
CORE (COnnecting REpositories)