Terahertz transfer characterization for composite delamination under variable conditions based on deep adversarial domain adaptation

For the reliability assessment of composite materials, the data-driven terahertz nondestructive evaluation (THz NDE) technique has emerged attractive potentials for automatic and intelligent identification of composite damages. Most previous data-drive methods depend on the sufficient labeled damage data and work under the strict assumption that the training data (source domain) and the testing data (target domain) must conform to similar distributions. However, in the actual THz NDE scenario, due to different testing conditions such as environmental changes, equipment instability and system noise etc., the collected THz signals may exhibit different variations in both of the amplitude and phase, which will result in the domain shift between the source domain and target domain and thus highly degrade the robustness and generalization performance of data-driven methods. A large training dataset can alleviate this issue, however the collection and labeling of the dataset composed of all possible damage scenarios is cumbersome and inaccessible for the practical industrial applications. Herein, an intelligent THz 3D characterization system based on the deep adversarial domain adaptation (DADA) strategy is proposed to automatically locate and image the hidden delamination defects in the composite under different operation conditions. Specifically, the unsupervised CNN-DADA model is established to address the domain shift problem between different THz datasets by adversarial learning. A series of experiments verify the superior generalization performance of the CNN-DADA model on different THz datasets. Compared with the traditional deep learning models, our proposed method can fulfill the automatic localization and imaging of delamination defects with high accuracy and resolution even for the THz datasets with significant distribution discrepancies, which will further facilitate the deployment of the data-driven THz intelligent characterization in practical industrial scenarios.

Automated summary

This paper proposes an intelligent THz 3D characterization system based on the deep adversarial domain adaptation (DADA) strategy, which can automatically locate and image hidden delamination defects in composites under different operating conditions. Through adversarial learning, an unsupervised CNN-DADA model is established to address the domain shift problem between different THz datasets. Experiments demonstrate the superior generalization performance of the CNN-DADA model on different THz datasets, enabling high accuracy and resolution automatic localization and imaging of delamination defects even with significant distribution discrepancies, facilitating the deployment of data-driven THz intelligent characterization in practical industrial scenarios.