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Carbon fiber reinforced plastics inevitably develop defects such as delamination, inclusions, and impacts during manufacturing and usage, which can adversely affect their performance. Ultrasonic phased array inspection is the most effective method for conducting nondestructive testing to ensure their quality. However, the diversity of defects within carbon fiber reinforced plastics makes it challenging for the current ultrasonic phased array inspection techniques to accurately identify these defects. Therefore, this paper presents a method for the ultrasonic phased array nondestructive testing and classification of various internal defects in carbon fiber reinforced plastics based on convolutional neural networks. We prepared an ultrasonic C-scan dataset containing multiple types of internal defects, analyzed the defect features in the ultrasonic C-scan images, and established an autoencoded classifier network to recognize manufacturing defects and impact defects of varying sizes. The experiments showed that the proposed method demonstrates superior defect feature extraction capabilities and can more accurately identify both impact and manufacturing defects. [ABSTRACT FROM AUTHOR]

• Based on the principle of superposition, the proposed method provides the displacements as the measured displacements. • The strain distributions are obtained from measured displacements without errors due to numerical differentiation. • The strains near the fiber-matrix interface are evaluated compensating for the insufficient resolution of microscope images. A DIC-FEM hybrid method is proposed to capture micro-scale deformation behavior near the fiber-matrix interface by combining displacement distributions measured using digital image correlation (DIC) with finite element method (FEM) analysis. Images of CFRP cross section with fine random pattern before and after deformation are taken with a laser microscope. The in-plane displacement components near the interface of fiber and matrix are obtained using digital image correlation and they are used as the input of the hybrid method. Not only the displacement distributions but the strain fields are determined using the proposed hybrid method, based on the superposition principle, so that the same distributions are obtained as those obtained by the measurement. The proposed DIC-FEM hybrid provides the strains near the interface between fiber and matrix, compensating for the insufficient resolution of microscope images. [ABSTRACT FROM AUTHOR]