| ABSTRACT: | NEAR-FIELD MICROWAVE IMAGING OF SUBSURFACE INCLUSIONS IN LAMINATED
COMPOSITE STRUCTURES
M. AbouKhousa1 and N. Qaddoumi2
1 Concordia University, Electrical and Computer Engineering, Montreal, Quebec 2 American University Of
Sharjah, Sharjah, United Arab Emirates
Laminated composite materials are becoming one of the most prominent engineering materials in a wide
range of applications. For most of the critical applications, these materials are engineered to have specific
attributes. Accidental variations in these attributes, practically, are not tolerable. Catastrophic failures may
occur to the parts manufactured form theses composites if subsurface inclusions are introduced within their
layers. Hence, there is an increasing demand for a nondestructive testing (NDT) technique that is capable of
evaluating the integrity of the composite structures and consequently preventing deployment failures. To this
end, it is required that the NDT technique provides quantitative measures about the type, location, and
orientation of the subsurface inclusions. Such information could be extracted from 2-D images captured for
theses inclusions.
Near-Field microwave imaging systems with open-ended rectangular waveguides as imaging probes have
shown promising results in detecting subsurface defects in such opaque media. The quality of the
experimental images captured with these systems has demonstrated the potential of the technique for
material NDT purposes. In most cases, these systems utilize an antenna to illuminate the composite with
electromagnetic (EM) waves and monitor the reflected waves. The EM waves penetrate deep into the
dielectric material where they interact with its interior and reflect back to the antenna. The properties of the
reflected wave will convey the needed information about the composite at hand. In this paper, the impact of
the theoretical image formation on optimizing the imaging systems will be highlighted. It will be shown that
the sensitivity and resolution of the waveguide sensor could be optimized to capture images of high fidelity
even for concurrent inclusions, and consequently this will provide independent detection for each one.
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