Applying acoustic emission method to understand the damage and failure mechanism of hybrid composites is challenge for industry. For this study, nine types of carbon/glass fiber-reinforced hybrid laminate composite specimens with different structure and indentation were subjected to tensile experiments. The AE signals collected during the tensile process were post-processed using cluster analysis based on Fuzzy C-Means algorithm. It was found that the AE signals can be divided into three types which correspond to three damage modes, and the AE peak frequency characteristics of each damage mode were found for different specimens. Micro-computed tomography imaging was obtained for specimens with indention before loading and after failure. There is seen to be a good correlation between the damage seen with the Micro-CT imaging, the mechanisms identified and the data cluster analyzed by using AE signals. Results prove that AE signals are reliable and can be used for composite structure health monitoring.

应用声发射方法来了解混合复合材料的损伤和失效机制是工业面临的挑战。在这项研究中，对九种不同结构和压痕的碳/玻璃纤维增​​强混合层压复合材料试样进行了拉伸试验。在拉伸过程中收集的 AE 信号使用基于模糊 C 均值算法的聚类分析进行后处理。发现AE信号可以分为三种类型，分别对应三种损伤模式，并针对不同的试件找出了每种损伤模式的AE峰值频率特性。在加载之前和失败之后获得了具有压痕的样本的显微计算机断层扫描成像。可以看出 Micro-CT 成像所见的损伤之间存在良好的相关性，通过使用 AE 信号识别的机制和数据集群分析。结果证明AE信号可靠，可用于复合结构健康监测。