A 2-D Radon Transformation for Enhancing the Detection and Imaging of Embedded Defects in Layered Composite Structures Using Millimeter-Wave System

This paper aims to tackle the challenge of detecting the presence of embedded defects in layered composite structures using imaging techniques including a 2D Radon transformation. The proposed technique aims at enhancing the detection and imaging of embedded defects in layered composite structures t...

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Veröffentlicht in:IEEE sensors journal 2020-07, Vol.20 (14), p.7750-7760
Hauptverfasser: Vidhya, Natarajan, Ong, Ling Chuen, Siyal, M. Y., Karim, Muhammad Faeyz
Format: Artikel
Sprache:eng
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Zusammenfassung:This paper aims to tackle the challenge of detecting the presence of embedded defects in layered composite structures using imaging techniques including a 2D Radon transformation. The proposed technique aims at enhancing the detection and imaging of embedded defects in layered composite structures that is captured using a millimeterwave (MMW) radar system. A 94 GHz MMW system is used in the near field to detect defects such as disbond and delamination embedded in complex glass fiber composite structures. In order to simplify the detection in glass fibers, measurement data attained using a MMW radar are plotted as images using MATLAB. A stable detection and identification of these defects from the collected images is proposed using a robust two-stage algorithm. The Weiner filter-based deconvolution technique is used for the reflectivity deconstruction in the image and a two-dimensional Radon transformation called back-projection is used to as an additional filtering step enhance the detection of the embedded defects. The proposed method allows defects that are embedded 24mm below the surface of the scanning probe to be detected and identified in terms of location. Our results show potential in the proposed method to be employed in accurately imaging defects in composites that are used in the aerospace and civil infrastructure industries.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.2981757