scholarly journals Acoustic emission monitoring of damage in ceramic matrix composites: Effects of weaves and feature

2018 ◽  
Author(s):  
Greg Ojard ◽  
Matt Mordasky ◽  
Rajesh Kumar
Keyword(s):  
Acoustic Emission ◽  
Ceramic Matrix Composites ◽  
Ceramic Matrix ◽  
Matrix Composites ◽  
Acoustic Emission Monitoring ◽  
Emission Monitoring

Monitoring the fracture behavior in ceramic matrix composites by infrared thermography and acoustic emission

2014 ◽  
Author(s):  
Konstantinos G. Dassios ◽  
Evangelos Z. Kordatos ◽  
Dimitris G. Aggelis ◽  
Dimitris A. Exarchos ◽  
Theodore E. Matikas
Keyword(s):  
Acoustic Emission ◽  
Infrared Thermography ◽  
Fracture Behavior ◽  
Ceramic Matrix Composites ◽  
Ceramic Matrix ◽  
Matrix Composites

scholarly journals Data Merging of AE Sensors with Different Frequency Resolution for the Detection and Identification of Damage in Oxide-Based Ceramic Matrix Composites

Materials ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 4691
Author(s):  
Nicolas Guel ◽  
Zeina Hamam ◽  
Nathalie Godin ◽  
Pascal Reynaud ◽  
Olivier Caty ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Data Fusion ◽  
Ceramic Matrix Composites ◽  
Ceramic Matrix ◽  
Sensor Data ◽  
Frequency Resolution ◽  
Matrix Composites ◽  
Emission Data ◽  
Damage Mechanisms ◽  
Detection And Identification

In this paper, acoustic emission data fusion based on multiple measurements is presented for damage detection and identification in oxide-based ceramic matrix composites. Multi-AE (acoustic emission) sensor fusion is considered with the aim of a better identification of damage mechanisms. In this context, tensile tests were conducted on ceramic matrix composites, fabricated with 3M™ Nextel™ 610 fibers and aluminosilicate matrix, with two kinds of AE sensors. Redundant and complementary sensor data were merged to enhance AE system capability and reliability. Data fusion led to consistent signal clustering with an unsupervised procedure. A correlation between these clusters and the damage mechanisms was established thanks to in situ observations. The complementarity of the information from both sensors greatly improves the characterization of sources for their classification. Moreover, this complementarity allows features to be perceived more precisely than using only the information from one kind of sensor.

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