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Physics induced interpretable sparse unrolling network for precise terahertz thickness measurement in composites
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DOI：10.1016/j.aei.2026.105127.png)
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Abstract
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Terahertz non-destructive testing (THz NDT) has emerged significant potentials in the thickness measurement of composite structures. Generally, the THz thickness measurement depends on the accuracy of time-of-flight (TOF) extraction from measured THz signals. However, since THz wave is susceptible to the effects of attenuation, dispersion, and multiple reflections, the accurate TOF extraction is often compromised. The present methods include the physics-driven signal processing methods and the data-driven methods. The physics-driven methods rely on the sufficient prior knowledge of THz wave propagation and the manual selection of hyperparameters. Data-driven methods often suffer from the “black box” limitation and lack of physical interpretability. In this work, a physics induced interpretable sparse unrolling network for precise THz thickness measurement of composite structure is proposed. The network aims to solve the sparse inverse problem in THz thickness measurement by unrolling the iterative process of iterative shrinkage-thresholding algorithm (ISTA) into an end-to-end network with physical constraints. The network contains three key modules, including multiple multi-scale convolutional sparse coding (MS-CSC) unrolling layers, effective squeeze-and-excitation (eSE) attention and channel fusion module, and a dynamic sparse-weighted focal loss (DSWF-Loss). MS-CSC unrolling layers are used to perform the iterations, corresponding to the iterative process of ISTA. For each MS-CSC unrolling layer, a physics inspired relaxed dictionary is introduced to improve the interpretability and prediction ability of network. In addition, DSWF-Loss is specially designed to improve the feature capture ability of network for the non-zero elements in sparse TOF. Finally, a series of numerical simulations and comparison experiments are conducted to verify the effectiveness and advantages of the proposed network on three different composite structures. Overall, this work provides a novel insight for the accurate THz thickness measurement of composite structures, which will promote the application of physics-inspired interpretable network in THz NDT.
Keywords:
Composites, terahertz non-destructive testing
Thickness measurement
Sparse unrolling network
Physical interpretability
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