Article Dans Une Revue Sensors Année : 2023

Shock Properties Characterization of Dielectric Materials using Millimeter-wave Interferometry and Convolutional Neural Networks

Résumé

In this paper, a neural network approach is applied for solving an electromagnetic inverse problem involving solid dielectric materials subjected to shock impacts and interrogated by a millimeter wave interferometer. Under mechanical impact, a shock wave is generated in the material and modifies the refractive index. It has been recently demonstrated that the shock wavefront velocity and the particle velocity as well as the modified index in a shocked material can be remotely derived from measuring two characteristic Doppler frequencies in the waveform delivered by a millimeter-wave interferometer. We show here that a more accurate estimation of the shock wavefront and particle velocities can be obtained from training an appropriate convolutional neural network, especially in the important case of short-duration waveforms of few micro-seconds.

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hal-04902840 , version 1 (21-01-2025)

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Jérémi Mapas, Alexandre Lefrançois, Hervé Aubert, Sacha Comte, Yohan Barbarin, et al.. Shock Properties Characterization of Dielectric Materials using Millimeter-wave Interferometry and Convolutional Neural Networks. Sensors, 2023, 23 (10), pp.4835. ⟨10.3390/s23104835⟩. ⟨hal-04902840⟩
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