Terahertz (THz) nondestructive testing (NDT) technology has been increasingly applied to the internal defect detection of composite materials. However, the THz image is affected by background noise and power limitation, leading to poor THz image quality. The recognition rate based on traditional machine vision algorithms is not high. The above methods are usually unable to determine surface defects in a timely and accurate manner. In this paper, we propose a method to detect the internal defects of composite materials by using terahertz images based on a faster region-convolutional neural networks (faster R-CNNs) algorithm. Terahertz images showing internal defects in composite materials are first acquired by a terahertz time-domain spectroscopy system. Then the terahertz images are filtered, the blurred images are removed, and the remaining images are enhanced with data and annotated with image defects to create a dataset consistent with the internal defects of the material. On the basis of the above work, an improved faster R-CNN algorithm is proposed in this paper. The network can detect various defects in THz images by changing the backbone network, optimising the training parameters, and improving the prior box algorithm to improve the detection accuracy and efficiency of the network. By taking the commonly used composite sandwich structure as a representative, a sample with typical defects is designed, and the image data are obtained through the test. Comparing the proposed method with other existing network methods, the former proves to have the advantages of a short training time and high detection accuracy. The results show that the mean average precision (mAP) without data enhancement reached 95.50%, and the mAP with data enhancement reached 98.35% and exceeded the error rate of human eye detection (5%). Compared with the original faster R-CNN algorithm of 84.39% and 85.12%, the improvement is 11.11% and 10.23%, respectively, which demonstrates superb feature extraction capability and reduces the occurrence of network errors and omissions.

Based on nonequilibrium molecular dynamics (NEMD) and nonequilibrium Green’s function simulations, the interfacial thermal conductance (ITC) of graphene/h-BN in-plane heterostructures with near-interface defects (monovacancy defects, 585 and f5f7 double-vacancy defects) is studied. Compared to pristine graphene/h-BN, all near-interface defects reduce the ITC of graphene/h-BN. However, differences in defective structures and the wrinkles induced by the defects cause significant discrepancies in heat transfer for defective graphene/h-BN. The stronger phonon scattering and phonon localization caused by the wider cross-section in defects and the larger wrinkles result in the double-vacancy defects having stronger energy hindrance effects than the monovacancy defects. In addition, the approximate cross-sections and wrinkles induced by the 585 and f5f7 double-vacancy defects provide approximate heat hindrance capability. The phonon transmission and vibrational density of states (VDOS) further confirm the above results. The double-vacancy defects in the near-interface region have lower low-frequency phonon transmission and VDOS values than the monovacancy defects, while the 585 and f5f7 double-vacancy defects have similar low-frequency phonon transmission and VDOS values at the near-interface region. This study provides physical insight into the thermal transport mechanisms in graphene/h-BN in-plane heterostructures with near-interface defects and provides design guidelines for related devices.

Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures.