Abstract-Terahertz waves for contactless control and imaging in aeronautics industry

 

A. Chopard, J. Bou, Sleiman, Q. Cassara, J.P. Guillet, M. Pan, J.B. Perraud, A. Susset, P. Mounaix, 

https://www.sciencedirect.com/science/article/abs/pii/S0963869521000724

The usability of terahertz systems for specific inspection tasks and imaging in the aeronautics industry is assessed. Especially, we demonstrate the suitability of Frequency-modulated continuous-wave (FMCW) radars for health monitoring and see-through imaging. Additionally, terahertz time-domain data processing is performed for multi-layered paint structure characterization. FMCW radar principles are introduced. Available systems are described along with their benefits and limitations. Defect detection capabilities and progresses towards airplane covering see-through imaging are illustrated through FMCW experimental results on real samples. The suitability of FMCW radars as an advanced contactless non-destructive testing (NDT) tool for the aeronautics industry and maintenance services is demonstrated. A second application, based on terahertz time domain techniques, targets the characterisation of multi-layered painting structures through the assessment of dielectric properties and individual thickness of each layer deposited. Beside the review of extraction methods, the introduced new algorithm allows to derive a parametric transfer function, thus denoting the main contributions which give rise to the recorded terahertz electromagnetic field. Such a development pushes further the understanding and characterization of stratified structures by means of terahertz radiations and represents an indispensable tool to efficiently localise any deviation to the nominal painting stack in terms of thickness or dielectric properties.

Abstract-THz Tomography and image processing: a new tool for polymer and ceramic additive manufacturing quality control

15th Asia Pacific Conference for Non-Destructive Testing 

J.B. Perraud, A.F Obaton, B. Recur, H. Balacey, F. Darracq, J.P. Guillet, P.Mounaix,

http://www.ndt.net/events/APCNDT2017/app/content/Paper/129_Mounaix_Rev2.pdf

Additive manufacturing (AM) is an essential tool to make 3D objects having very complex shapes and geometries, unachievable with standard manufacturing approaches. Meanwhile, quality controls of such objects become challenging for both industrials and applications in laboratories due to both their complexity and the materials they are made of. Consequently, we demonstrate that terahertz (THz) imaging and THz tomography can be considered as efficient methods for such object inspection in routine applications. Thus, this paper proposes an experimental study of 3D polymer objects obtained by AM techniques. This approach allows us to characterize defects and to control dimensions by volumetric measurements on 3D data reconstructed by tomography. 

Abstract-Discrimination and identification of RDX/PETN explosives by chemometrics applied to terahertz time-domain spectral imaging

J. Bou-Sleiman, J.-B. Perraud, J.-P. Guillet, P. Mounaix

IMS, CNRS, Bordeaux Univ. (France)

B. Bousquet

CELIA, CNRS, Bordeaux Univ. (France)

N. Palka

Military Univ. of Technology (Poland)

Proc. SPIE 9651, Millimetre Wave and Terahertz Sensors and Technology VIII, 965109 (October 21, 2015); doi:10.1117/12.2197442

http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2466618

Detection of explosives has always been a priority for homeland security. Jointly, terahertz spectroscopy and imaging are emerging and promising candidates as contactless and safe systems. In this work, we treated data resulting from hyperspectral imaging obtained by THz-time domain spectroscopy, with chemometric tools. We found efficient identification and sorting of targeted explosives in the case of pure and mixture samples. In this aim, we applied to images Principal Component Analysis (PCA) to discriminate between RDX, PETN and mixtures of the two materials, using the absorbance as the key-parameter. Then we applied Partial Least Squares-Discriminant Analysis (PLS-DA) to each pixel of the hyperspectral images to sort the explosives into different classes. The results clearly show successful identification and categorization of the explosives under study.
 © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.