循环张力载荷下聚合物复合材料声发射源的识别

A. Bryansky, O. Bashkov
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引用次数: 0

摘要

高分子复合材料(PCM)的结构提供了高的力学性能,但同时对内部缺陷的形成高度敏感。因此,在设计、制造产品和评估产品的使用可靠性时,人们非常重视无损检测的方法,其中声发射(AE)方法已被证明可以研究材料在外界影响下的结构变化。采用声发射法对T11-GVS9玻璃纤维布和DION 9300 FR粘结剂制成的玻璃纤维样品进行了循环拉伸试验,并对其进行了典型损伤识别。本文解决了声发射信息参数的选择问题,并采用聚类方法对声发射源的性质和形成动力学进行了识别。作者使用Kohonen自组织映射(SOM)和计算的傅立叶谱对循环测试中记录的声发射信号进行聚类。基于对产生的簇的峰值频率分析,研究人员确定了它们的性质,并计算了临界积累的周期。在表征声发射源时,作者使用了对不同分解水平的小波谱进行的峰值频率分析。根据自己的研究成果和他人的研究成果,确定了试样在试验过程中的损伤积累阶段。研究发现,识别为粘接失效的声发射信号的配准可用于识别材料破坏的开始,表征基体中局部微损伤的形成,纤维断裂可用于预测PCM的破坏。
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IDENTIFICATION OF ACOUSTIC EMISSION SOURCES IN A POLIMER COMPOSITE MATERIAL UNDER THE CYCLE TENSION LOADING
The structure of polymer composite materials (PCM) provides high mechanical properties but, at the same time, is highly sensitive to the formation of internal defects. Therefore, when designing, manufacturing products, and assessing their reliability in service, much attention is paid to the methods of non-destructive testing, among which the method of acoustic emission (AE) has proven itself to study structural changes in material under external influence. The paper deals with the identification of typical damages in fiberglass samples made of T11-GVS9 glass fiber cloth and DION 9300 FR binder and tested under cyclic tension using the AE method. In the work, the authors solved the problem of selecting the AE informative parameters and used a clustering method to identify the nature and the formation kinetics of the AE sources. The authors performed clustering using the Kohonen self-organization map (SOM) with the Fourier spectra calculated for the AE signals recorded during cyclic tests. Based on the peak frequencies analysis of the produced clusters, the researchers determined their nature and calculated the periods of critical accumulation. When characterizing the AE sources, the authors used the peak frequencies analysis of the wavelet spectra performed for different levels of decomposition. The authors determined the damage accumulation stages of samples during testing based on own research and research by other authors’ results. The study established that registration of AE signals identified as adhesion failure can be used to identify the onset of the material destruction and characterized the local formation of micro-damages in the matrix and fracture of fibers can be used to predict the destruction of PCM.
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