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

材料科学 复合材料 弯曲 抗弯强度 复合数 三点弯曲试验 主成分分析 纤维 声发射 玻璃纤维 变形(气象学) 无损检测 计算机科学 人工智能 医学 放射科
作者
Lianhua Ma,Kun Zhang,Zhi‐bo Pan,Wei Zhou,Jia Liu
出处
期刊:Journal of Composite Materials [SAGE]
卷期号:56 (7): 1019-1037 被引量:6
标识
DOI:10.1177/00219983211066389
摘要

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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