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Cluster analysis of acoustic emission signals and tensile properties of carbon/glass fiber-reinforced hybrid composites

机译:碳/玻璃纤维增​​强混杂复合材料的声发射信号和拉伸性能的聚类分析

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摘要

Understanding the damage and failure of carbon/glass epoxy hybrid woven composites under tensile loading based on acoustic emission signals is a challenging task in their practical uses. In this study, an approach based on fuzzy c-means algorithm is proposed to process the acoustic emission signals from tensile loading of composites monitored by combining acoustic emission technology and digital image correlation method. The results show that the acoustic emission signals from tensile loading can be divided into three clusters. The three clusters correspond to three kinds of damage modes including matrix cracking, fiber/matrix debonding, delamination, and fiber breakage. By comparing the acoustic characteristics of these classes, a correlation procedure between the clusters and the damage mechanisms observed is proposed. Meanwhile, it can be found that debonding and fiber break signals for glass fiber correspond to a lower frequency range than that for carbon fiber. Moreover, the method combining acoustic emission and digital image correlation can effectively monitor the damage process of the specimen both on the inside and outside, which can provide a reference for the health monitoring of composite structure.
机译:基于声发射信号来了解碳/玻璃环氧混合机织复合材料在拉伸载荷下的损坏和破坏在其实际应用中是一项艰巨的任务。本研究提出了一种基于模糊c-均值算法的方法,结合声发射技术和数字图像相关方法,对复合材料的拉伸载荷监测声发射信号进行处理。结果表明,来自拉伸载荷的声发射信号可分为三个簇。这三个簇对应三种损坏模式,包括基体开裂,纤维/基体剥离,分层和纤维断裂。通过比较这些类别的声学特性,提出了簇与观测到的损伤机理之间的相关程序。同时,可以发现,与碳纤维相比,玻璃纤维的剥离和纤维断裂信号对应的频率范围更低。此外,声发射与数字图像相关性相结合的方法可以有效地监测标本的内外损伤过程,为复合结构的健康监测提供参考。

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