Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder

2022 ◽  
Vol 163 ◽  
pp. 108148
Author(s):  
Hyunseong Lee ◽  
Hyung Jin Lim ◽  
Travis Skinner ◽  
Aditi Chattopadhyay ◽  
Asha Hall
Author(s):  
Assunta Sorrentino ◽  
Angelo De Fenza

In this paper, an improvement of the elliptical triangulation method for damage detection using Lamb waves is presented. The damage is the main cause of structural failure and often occurs on structures. In order to avoid sudden failure, a special attention was given in the past decades to the damage detection in structures. In order to obtain efficient damage detection techniques, the structural health monitoring has been the main research topic of many scientists worldwide. The elliptical triangulation method, proposed in this paper, is a non-destructive method based on measurement of Lamb waves. This method, through the calculation of the time of flight of the signals and the actuator-sensor positioning, allows to identify position and dimension of the damage. The application of the method to the metallic structures and to the composite material structures is presented in this paper. The complexity connected with the uncertainty of the waves’ propagation speed due to the anisotropy of the composite materials has been explored through an iterative approach. The initialization of the wave propagation speed at first tentative iteration is the key issue for the convergence of the method. Seven different conditions were used to validate the method on both metallic and composite structures combining two damage shapes, two damage dimensions (effective damaged area), and three different positions. Upon evaluating the effectiveness, the method has been applied at two composite panels in order to detect by test the post-impact damages. Tests results have been compared with the numerical ones. The feasibility of the elliptical triangulation method to detect the damage (evaluating the damage position and area) has been proved using the ultrasonic C-Scan.


2005 ◽  
Author(s):  
Ajit K. Mal ◽  
Frank J. Shih ◽  
Fabrizio Ricci ◽  
Sauvik Banerjee

2011 ◽  
Author(s):  
Yingtao Liu ◽  
Masoud Yekani Fard ◽  
Seung B. Kim ◽  
Aditi Chattopadhyay ◽  
Derek Doyle

2015 ◽  
Author(s):  
Gerges Dib ◽  
Ermias Koricho ◽  
Oleksii Karpenko ◽  
Mahmood Haq ◽  
Lalita Udpa ◽  
...  

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