R-UNet Deep Learning-Based Damage Detection of CFRP With Electrical Impedance Tomography

Carbon fiber reinforced polymer (CFRP) with excellent properties is widely used in many fields. During the production process and service life of CFRP-related products, CFRP may suffer some damages, which are difficult to be detected. Due to the electrical conductivity of CFRP, electrical impedance tomography (EIT) can be used to detect the damage in CFRP. However, the inverse problem of EIT is nonlinear and ill-conditioned, and the reconstructed images based on EIT have low accuracy. In order to solve these problems, a U-Net combined with a shallow residual network method (R-UNet) network with four modules (preliminary imaging module, backbone feature extraction module, enhanced feature extraction module, and prediction module) is proposed in this article. First, a shallow residual network is introduced to reconstruct the image. Then, the initial image is input into U-Net for backbone feature extraction and enhanced feature extraction. Finally, the image with fewer artifacts and clear edges is output. The network is trained with 12 600 simulated data. Four algorithms were compared with R-UNet. The simulation results showed that the R-UNet algorithm performed better than other algorithms in imaging quality. Correlation coefficient (CC) and structural similarity index (SSIM) were used as the indicators to evaluate the simulation results. The average CC and SSIM of R-UNet reached 0.93 and 0.95, respectively. In addition, an EIT experimental platform was established to verify R-UNet. The comparison of infrared thermal imaging results and experimental results of EIT indicated that R-UNet realized reliable detection results of CFRP damage.

Automated summary

This article proposes a U-Net combined with a shallow residual network method for the detection of damage in CFRP. The method is validated through the comparison of simulated data and experimental results.