Fracture behavior and damage mechanisms identification of SiC/glass ceramic composites using AE monitoring

被引：37
|
作者：
Kostopouios, V. [1 ]
Loutas, T.
Dassios, K.
机构：
[1] Univ Paris 01, Dept Mech Engn & Aeronaut, Appl Mech Lab, F-75231 Paris 05, France
[2] Fdn Res & Technol Hellas, Inst Chem Engn & High Temperature Chem Proc, GR-26500 Patras, Greece
来源：
COMPOSITES SCIENCE AND TECHNOLOGY | 2007年 / 67卷 / 7-8期
关键词：
large scale bridging; pull-out; acoustic emission; unsupervised pattern recognition technique;
D O I：
10.1016/j.compscitech.2005.02.002
中图分类号：
TB33 [复合材料];
学科分类号：
摘要：
Large Scale Bridging in SiC/MAS-L (ceramic glass matrix) composites was investigated by using DEN specimens under tensile loading conditions with in situ Acoustic Emission monitoring. The AE data were successfully classified using Unsupervised Pattern Recognition Algorithms and the resulted clusters were correlated to the dominant damage mechanisms of the material. The evolution in time of the different damage mechanisms is feasible after the pattern recognition classification. Microscopic examination was used to correlate the clusters to the damage mechanism they correspond and. thus to provide the failure mode identification based on AE data. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1740 / 1746
页数：7
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