Full scale promoted convolution neural network for intelligent terahertz 3D characterization of GFRP delamination

Abstract

Damage detection of composite materials is crucial to monitor the component condition over the life cycle for the maintenance management and possible replacement. Delamination damage, as a common damage form in glass-fiber reinforced polymer (GFRP) composites, may occur during the manufacturing and service process due to the mechanical and thermal loads. Terahertz (THz) NDT technique, as a novel characterization approach, can provide promising alternatives to fulfill the 3D characterization of delamination defects in multi-layer GFRP composites. During THz testing, in order to attain adequate discrimination in depth direction, complex signal processing and prior knowledge of the undamaged stratigraphy are usually required to suppress confounding effects from noise, overlap, and dispersion of THz signal, which hinders the realization of automatic defect detection. Therefore, here we propose an effective, reliable, and end-to-end 3D THz characterization system based on deep learning methods to fulfill the automatic localization and imaging of delamination defects in GFRP composites without any additional signal processing or prior knowledge. In the localization process, the full-scale promoted convolution neural network (FSP–CNN) is developed by integrating dual mechanisms of the full-scale feature learning and the promoted classifier. In the imaging process, the class encoding strategy is employed to obtain the 2D and 3D information of delamination defects based on the classification results. Finally, a series of experiments validate the effectiveness of the system for automatic localization and imaging of delamination defects in GFRP composites, which provides a novel and efficient paradigm for the intelligent and automatic THz 3D characterization of hidden delamination defects in composites.