Infrared thermography nondestructive testing faces challenges due to the diffusive nature of heat, which causes increasing blurring with depth beneath the surface during the reconstruction of internal structures and affects the determination of thermal properties in multilayered materials. This article explores the contextual application of pulse compression and virtual wave methods in active thermography to address these challenges. Pulse compression enables extended, modulated heating of materials and compresses the resulting heat signatures in the time domain. The virtual wave method, on the other hand, transforms temperature signals into virtual propagating damped wave signals, facilitating the reconstruction of defects following the excitation. We analyzed thermal data before and after applying the virtual wave method, and our findings demonstrate enhanced signal-to-noise ratio and high linearity in defect characterization. Building on these insights, we propose a deep spatio-temporal fusion network trained to mimic the virtual wave transformation, thereby enabling efficient and physically guided tomographic reconstruction.

Thermography-Tomographic Imaging: Integration of Pulse Compression Thermography and Virtual Wave for Physics-Based Learning

Ricci, Marco;Laureti, Stefano;Zito, Rocco;
2026-01-01

Abstract

Infrared thermography nondestructive testing faces challenges due to the diffusive nature of heat, which causes increasing blurring with depth beneath the surface during the reconstruction of internal structures and affects the determination of thermal properties in multilayered materials. This article explores the contextual application of pulse compression and virtual wave methods in active thermography to address these challenges. Pulse compression enables extended, modulated heating of materials and compresses the resulting heat signatures in the time domain. The virtual wave method, on the other hand, transforms temperature signals into virtual propagating damped wave signals, facilitating the reconstruction of defects following the excitation. We analyzed thermal data before and after applying the virtual wave method, and our findings demonstrate enhanced signal-to-noise ratio and high linearity in defect characterization. Building on these insights, we propose a deep spatio-temporal fusion network trained to mimic the virtual wave transformation, thereby enabling efficient and physically guided tomographic reconstruction.
2026
Defect detection and characterization
multilayer material
nondestructive testing (NDT)
physics-based deep learning
pseudonoise (PN) pulse compression thermography
virtual wave
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/406204
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