It is necessary to investigate the flexural damage and evolution behavior of 3D printed continuous fiber-reinforced composites. In this paper, acoustic emission technology (AE) is used to monitor the bending damage of 3D printed Kevlar fiber (KF) and glass fiber (GF)-reinforced composites in real time, and gray relational analysis (GRA) and k-means are utilized for clustering analysis of AE signals. Subsequently, micro-computed tomography (micro-CT) is employed to identify the internal damage degree. For 3D printed specimens, matrix buckling and interface failure are the main failure modes, and the number of matrix and interface damage is more than that of fiber damage, and the surface of compression side (specimen's top side) is more serious than that of the back side. KF-reinforced composites have poor delamination resistance and more serious damage than that of GF. The average maximum stress of GF and KF-reinforced specimens is 56.37 and 42.15 MPa, and the average bending modulus is 1576.42 and 1395.36 MPa, respectively. The combination of complementary detection methods is beneficial to the nondestructive evaluation and health monitoring of 3D printed composite materials.

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Three-dimensional (3D) printing has been triumphantly applied for the manufacture of various composite components. In this work, acoustic emission (AE), X-ray micro-computed tomography (Micro-CT) are used in conjunction with digital image correlation (DIC) measurement to investigate the mechanical behaviors of 3D printed continuous fiber reinforced composites under three-point bending test. Meanwhile, several mechanical experiments are carried out to study the flexural properties of three kinds of composite specimens, among which the specimens with larger glass fiber content exhibit more superior mechanical properties. Furthermore, AE response characterizations and microscopic damage morphology are also examined. In consequence, the complementary nondestructive testing (NDT) technology combining AE, DIC, and Micro CT is successfully applied to evaluate the mechanical behaviors of 3D printed composites, and the flexural deformation and damage are comparatively investigated for different composite specimens. The cross-validation results of cluster analysis (k-means), K-Nearest Neighbor (KNN) and principal component analysis (PCA) show that AE parameters including frequency, amplitude, and RA value (rise time divided by peak amplitude) are closely associated with the damage process of different specimens. The results show that the PCA can confirm the selected K-means cluster analysis parameters (peak frequency and peak amplitude) and the dimensionality reduction effects of 20% glass fiber specimens have the best results, indicating that the proportion of the principal components extracted can represent the original parameters is 81%. It was also confirmed that the supervised learning KNN algorithm corresponding to different damage patterns can verify the unsupervised learning k-means cluster. Correspondingly, the strain fields characterized by DIC are reasonably matched with the AE signal responses. In addition, the critical damage and delamination mechanisms of the 3D printed continuous fiber reinforced composites are clearly revealed by Micro-CT characterization.

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