Experimental investigation on the mechanical behavior and damage of 3D printed composites under three-point bending

Abstract

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.

Effect of triaxial woven fabric on bending properties of unidirectional composites

“…Ren et al 26 investigated the phenomenon of surface delamination in the bending process of carbon fiber composites, recorded the damage by AE, identified the damage types by cluster analysis, and verified the damage by micro‐CT, which can realize health monitoring of the composite structure. Ma et al 27 examined the behavior of 3D printed continuous fiber reinforced composites during bending. They used digital image correlation (DIC), AE and CT, to obtain information on the bending properties, damage process and distribution, key damage and delamination mechanism.…”

Damage identification of fiber-reinforced composites during three-point bend tests based on acoustic emission and unsupervised learning methods

“…They used AE waveforms associated with different damage modes, i.e., matrix cracking, delamination, debonding, and fiber breakage. Recently, Ma et al [7] used AE and X-ray micro-computed tomography in conjunction with digital image correlation measurements to investigate the mechanical behavior of 3D printed continuous fiber-reinforced composites in three-point bending. They ascertained that parameters such as frequency, amplitude, and amplitude ratio were closely associated with the damage process of different specimens using the crossvalidation results of cluster analysis (k-means), K-Nearest Neighbor and principal component analysis.…”

Investigation of the effect of spirally stacked fiber reinforcement on the torsional behavior of a polymer composite rod by AE analysis

“…The study found that the torsional AE analyzed signals had good agreement with the FCM classi cations. Other than this, the k-means clustering method can also be used to precisely analyze the AE results by easily identifying the different cluster zones [21].…”