Acoustic emission source location in unidirectional carbon-fiber-reinforced plastic plates with virtually trained artificial neural networks

2011 ◽  
Vol 122 (6) ◽  
pp. 3506-3513 ◽  
Author(s):  
G. Caprino ◽  
V. Lopresto ◽  
C. Leone ◽  
I. Papa
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Carbon Fiber ◽  
Source Location ◽  
Emission Source ◽  
Carbon Fiber Reinforced Plastic ◽  
Fiber Reinforced Plastic ◽  
Reinforced Plastic ◽  
Acoustic Emission Source

scholarly journals Damage assessment of a titanium skin adhesively bonded to carbon fiber–reinforced plastic omega stringers using acoustic emission

2021 ◽  
pp. 147592172110017
Author(s):  
Milad Saeedifar ◽  
Mohamed Nasr Saleh ◽  
Peter Nijhuis ◽  
Sofia Teixeira de Freitas ◽  
Dimitrios Zarouchas
Keyword(s):  
Acoustic Emission ◽  
Carbon Fiber ◽  
Digital Image Correlation ◽  
Digital Image ◽  
Image Correlation ◽  
Carbon Fiber Reinforced Plastic ◽  
Adhesively Bonded ◽  
Fiber Reinforced Plastic ◽  
Reinforced Plastic ◽  
Carbon Fiber Reinforced

This study is devoted to the use of acoustic emission technique for a comprehensive damage assessment, that is, damage detection, localization, and classification, of an aeronautical metal-to-composite bonded panel. The structure comprised a titanium panel adhesively bonded to carbon fiber–reinforced plastic omega stringers. The panel contained a small initial artificial debonding between the titanium panel and one of the carbon fiber–reinforced plastic stringers. The panel was subjected to a cyclic increasing in-plane compression load, including loading, unloading, and then reloading to a higher load level, until the final fracture. The generated acoustic emission signals were captured by the acoustic emission sensors, and digital image correlation was also used to obtain the strain field on the surface of the panel during the test. The results showed that acoustic emission can accurately detect the damage onset, localize it, and also trace its evolution. The acoustic emission results not only were consistent with the digital image correlation results, but also managed to detect the damage initiation earlier than digital image correlation. Finally, the acoustic emission signals were clustered using particle swarm optimization method to identify the different damage mechanisms. The results of this study demonstrate the capability of acoustic emission for the comprehensive damage characterization of aeronautical bi-material adhesively bonded structures.

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