Abstract-Double-Sided Terahertz Imaging of Multilayered Glass Fiber-Reinforced Polymer

Przemyslaw Lopato


http://www.mdpi.com/2076-3417/7/7/661

Polymer matrix composites (PMC) play important roles in modern industry. Increasing the number of such structures in aerospace, construction, and automotive applications enforces continuous monitoring of their condition. Nondestructive inspection of layered composite materials is much more complicated process than evaluation of homogenous, (mostly metallic) structures. Several nondestructive methods are utilized in this case (ultrasonics, shearography, tap testing, acoustic emission, digital radiography, infrared imaging) but none of them gives full description of evaluated structures. Thus, further development of NDT techniques should be studied. A pulsed terahertz method seems to be a good candidate for layered PMC inspection. It is based on picosecond electromagnetic pulses interacting with the evaluated structure. Differences of dielectric parameters enables detection of a particular layer in a layered material. In the case of multilayered structures, only layers close to surface can be detected. The response of deeper ones is averaged because of multiple reflections. In this paper a novel inspection procedure with a data processing algorithm is introduced. It is based on a double-sided measurement, acquired signal deconvolution, and data combining. In order to verify the application of the algorithm stress-subjected glass fiber-reinforced polymer (GFRP) was evaluated. The obtained results enabled detection and detailed analysis of delaminations introduced by stress treatment and proved the applicability of the proposed algorithm.

Abstract-Depth resolution enhancement of terahertz deconvolution by autoregressive spectral extrapolation

Junliang Dong, Alexandre Locquet, and D. S. Citrin

https://www.osapublishing.org/ol/abstract.cfm?uri=ol-42-9-1828&origin=search

This Letter presents a method for enhancing the depth resolution of terahertz deconvolution based on autoregressive (AR) spectral extrapolation. The terahertz frequency components with a high signal-to-noise ratio (SNR) are modeled with an AR process, and the missing frequency components in the regions with low SNRs are extrapolated based on the AR model. In this way, the entire terahertz frequency spectrum of the impulse response function, corresponding to the material structure, is recovered. This method, which is verified numerically and experimentally, is able to provide a “quasi-ideal” impulse response function and, therefore, greatly enhances the depth resolution for characterizing optically thin layers in the terahertz regime.

© 2017 Optical Society of America

Abstract- Terahertz deconvolution

http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-20-25-27230

The ability to retrieve information from different layers within a stratified sample using terahertz pulsed reflection imaging and spectroscopy has traditionally been resolution limited by the pulse width available. In this paper, a deconvolution algorithm is presented which circumvents this resolution limit, enabling deep sub-wavelength and sub-pulse width depth resolution. The algorithm is explained through theoretical investigation, and demonstrated by reconstructing signals reflected from boundaries in stratified materials that cannot be resolved directly from the unprocessed time-domain reflection signal. Furthermore, the deconvolution technique has been used to recreate sub-surface images from a stratified sample: imaging the reverse side of a piece of paper.