Additive manufacturing (AM) technology is not only used to make 3D objects but also for rapid prototyping. In industry and laboratories, quality controls for these objects are necessary though difficult to implement compared to classical methods of fabrication because the layer-by-layer printing allows for very complex object manufacturing that is unachievable with standard tools. Furthermore, AM can induce unknown or unexpected defects. Consequently, we demonstrate terahertz (THz) imaging as an innovative method for 2D inspection of polymer materials. Moreover, THz tomography may be considered as an alternative to x-ray tomography and cheaper 3D imaging for routine control. This paper proposes an experimental study of 3D polymer objects obtained by additive manufacturing techniques. This approach allows us to characterize defects and to control dimensions by volumetric measurements on 3D data reconstructed by tomography.

International audience—In this paper, we present an advanced image processing sequence to perform nondestructive inspection from 3-D terahertz (THz) images. We develop all the steps starting from a 3-D tomographic reconstruction of a sample from radiographs acquired with a monochromatic millimeter-wave imaging system to an automated segmentation, extracting the different volumes of interest (VOI) composing the sample. This leads to 3-D visualiza-tion and dimensional measurements. This inspection is completed by a skeletonization and caliber analysis providing an accurate assessment of the structure, geometry, and morphology of the acquired object. Overall sequence is implemented onto an unique software and validated through different sample analysis

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Detection of explosives has always been an important fact for homeland security. Terahertz spectroscopy and imaging is an emerging, promising candidate in this field. We use time domain THz spectroscopy and we demonstrate that a methodology based on chemometrics is successful for discrimination of explosives and prediction of their concentration in complex samples made by mixture of products.

International audienceDetection 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

Terahertz phase retrieval is a promising technique able to assess the complex diffracted wave properties through an iterative processing algorithm. In this letter, we demonstrate the implementation of this technique in reflection geometry with a continuous wave acquisition system working at 0.287 THz. To ensure a high signal-to-noise ratio in the measured dataset, we proposed a double parallel recording scheme with one detector and two lock-in amplification operating with the complimentary sensitivity setting. This provided a higher numerical aperture than conventional rasterscanning focal plane imaging. A specialized digital interferometric post-processing procedure was applied to obtain a surface heights map from the reconstructed phase distribution in the object's irradiated area.

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