材料科学
复合材料
温度循环
断裂韧性
环氧树脂
纤维增强塑料
断裂(地质)
韧性
分层(地质)
自行车
热的
结构工程
生物
气象学
俯冲
构造学
工程类
历史
古生物学
物理
考古
作者
Viviane Jordão Sano Prado,Luiz Cláudio Pardini
标识
DOI:10.1177/00219983241304149
摘要
Carbon fiber/epoxy composites (CFRP) are nowadays extensively used in aircraft structures. During the lifecycle of an aircraft, composites are subjected to temperature and humidity variation over time and that can lead to performance loss. Efforts to understand the environmental conditioning impact on CFRP properties have been widely investigated, leading to the main objective of this study, which was investigating whether damages caused by different environmental conditioning individually are intensified when they are combined. During an aircraft lifetime, several different environmental conditions may occur, and to simulate these effects, this research tested coupons that were exposed to long-term hygrothermal conditioning, drying and thermal cycling and their combined effects on fracture toughness in mode-I and mode-II. Dynamic mechanical and fractographic analysis were also performed on the specimens. Thermal cycling on unaged samples (reference) resulted in average reductions of 5% and 15%, for mode-I and mode-II fracture toughness, respectively, compared to the reference samples. Furthermore, combined effect of thermal cycling and hygrothermal conditionings resulted in reductions around 44% (mode-I) and 15% (mode-II) compared to the reference. The combination of different environmental conditionings was more detrimental to fracture toughness in Mode-I, as confirmed by fractographic analysis. In contrast, Mode-II showed no change in results when exposed to a combination of conditionings, suggesting that isolated analysis of the results can be misinterpreted. Combined effects of thermal cycling and hygrothermal conditioning affected fracture toughness more than either effect alone. Mode-I testing proved to be more sensitive to identifying environmental exposure effects on properties than mode-II.
科研通智能强力驱动
Strongly Powered by AbleSci AI