TY - JOUR
T1 - Spatiotemporal characterization of 3D fracture behavior of carbon-fiber-reinforced polymer composites
AU - Pei, Shenli
AU - Wang, Kaifeng
AU - Li, Yang
AU - Zeng, Danielle
AU - Su, Xuming
AU - Li, Jingjing
AU - Yang, Hui
AU - Xiao, Xianghui
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/11/1
Y1 - 2018/11/1
N2 - This study proposed a spatiotemporal algorithm to quantitatively characterize the in-situ 3D fracture behavior of carbon-fiber-reinforced polymer (CFRP) composites at microscale. In-situ micro X-ray computed tomography (µXCT) integrated with a tensile stage was applied to capture the 3D fracture evolution of the CFRP composites, where the initiation and propagation of fracture features (e.g., fiber tip-end crack and fiber/matrix debonding) were identified. After the reconstruction of the 3D material microstructure, the proposed spatiotemporal algorithm thereafter extracted the fracture features by employing multiple image processing techniques for quantitative analysis. A similar distribution of the 3D strain obtained from the volumetric digital image correlation demonstrated the feasibility of the developed spatiotemporal algorithm. Moreover, this algorithm provided in-depth and quantitative analysis of fracture features, which provided insights into the microscale failure mechanism and thus shed light on the improvement of failure criteria for CFRP composites with complex microstructures.
AB - This study proposed a spatiotemporal algorithm to quantitatively characterize the in-situ 3D fracture behavior of carbon-fiber-reinforced polymer (CFRP) composites at microscale. In-situ micro X-ray computed tomography (µXCT) integrated with a tensile stage was applied to capture the 3D fracture evolution of the CFRP composites, where the initiation and propagation of fracture features (e.g., fiber tip-end crack and fiber/matrix debonding) were identified. After the reconstruction of the 3D material microstructure, the proposed spatiotemporal algorithm thereafter extracted the fracture features by employing multiple image processing techniques for quantitative analysis. A similar distribution of the 3D strain obtained from the volumetric digital image correlation demonstrated the feasibility of the developed spatiotemporal algorithm. Moreover, this algorithm provided in-depth and quantitative analysis of fracture features, which provided insights into the microscale failure mechanism and thus shed light on the improvement of failure criteria for CFRP composites with complex microstructures.
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U2 - 10.1016/j.compstruct.2018.07.022
DO - 10.1016/j.compstruct.2018.07.022
M3 - Article
AN - SCOPUS:85049472200
SN - 0263-8223
VL - 203
SP - 30
EP - 37
JO - Composite Structures
JF - Composite Structures
ER -