Abstract In this paper, multiscale acoustic emission (AE) signal analysis was applied to acoustic emission data processing to classify the AE signals produced during the tensile process of C/SiC mini-composites. An established unsupervised clustering algorithm was provided to classify an unknown set of AE data into reasonable classes. In order to correctly match the obtained classes of the AE signals with the damage mode of the sample, three scales of materials were involved. Single fiber tensile test and fiber bundle tensile test were firstly performed to achieve the characteristics of AE signal of fiber fracture. Parameter analysis and waveform analysis were added to extract the different features of each class of signals in the In-situ tensile test of C/SiC mini-composite. The change of strain field on the sample surface analyzed by DIC (Digital Image Correlation) revealed the corresponding relationship between matrix cracking and AE signals. Microscopic examinationwas used to correlate the clusters to the damage mode. By analyzing the evolution process of signal activation for each class against the load, it also provided a reliable basis for the correlation between the obtained classes of the AE signals and the damage mechanism of the material.

中文翻译：

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C/SiC 微型复合材料的多尺度声发射和使用模式识别的损伤识别

摘要 本文将多尺度声发射(AE)信号分析应用于声发射数据处理，对C/SiC微复合材料拉伸过程中产生的声发射信号进行分类。提供了一种已建立的无监督聚类算法，以将一组未知的 AE 数据分类为合理的类别。为了将获得的 AE 信号类别与样品的损伤模式正确匹配，涉及三个尺度的材料。首先进行单纤维拉伸试验和纤维束拉伸试验，获得纤维断裂的AE信号特征。加入参数分析和波形分析，提取C/SiC微型复合材料原位拉伸试验中各类信号的不同特征。通过DIC（Digital Image Correlation）分析样品表面应变场的变化揭示了基体开裂与AE信号之间的对应关系。显微镜检查用于将簇与损伤模式相关联。通过分析各类信号激活对载荷的演化过程，也为获得的AE信号类与材料损伤机理的相关性提供了可靠的依据。