Determining Subsurface Defects’ Depth in CFRP Composites Using Lock-In Thermography

2021 ◽  
pp. 114-117
Author(s):  
Carlos Manzano ◽  
Jonathan Zheng ◽  
Vinod Kumar ◽  
Andrew Ngo
Keyword(s):  
Cfrp Composites ◽  
Lock In ◽  
Subsurface Defects

Evaluation of subsurface defects in fiber glass composite plate using lock-in technique

2012 ◽  
Vol 13 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Ghiseok Kim ◽  
Soonsung Hong ◽  
Geon Hee Kim ◽  
Kyung-Young Jhang
Keyword(s):  
Composite Plate ◽  
Glass Composite ◽  
Fiber Glass ◽  
Lock In ◽  
Subsurface Defects

Research on the Quantitative Analysis of Subsurface Defects for Nondestructive Testing by Ultrasound Lock-In Thermography

2011 ◽  
Vol 301-303 ◽  
pp. 635-640
Author(s):  
Jun Yan Liu ◽  
Qing Ju Tang ◽  
Yang Wang
Keyword(s):  
Quantitative Analysis ◽  
Steel Plate ◽  
Threshold Value ◽  
Canny Operator ◽  
Subsurface Defect ◽  
Artificial Neural Network Ann ◽  
Lock In ◽  
Takagi Sugeno ◽  
Good Agreement ◽  
Subsurface Defects

This paper describes the quantitative analysis of the shape, boundary, and depth of subsurface defects by ultrasound lock-in thermography. The phase difference between defective areas and non-defective areas illustrates the qualitative analysis of the shape and the boundary of the subsurface defect. In order to accurately estimate the shape, boundary and depth of the defects, the optimal threshold value method is proposed to identify the shape and boundary of the subsurface defects based on the canny operator of image processing. A self–adaption artificial neural network (ANN) with Takagi-Sugeno modeling is proposed to determine the depth of the subsurface defect. Experimental results for a steel plate with artificial subsurface defects show good agreement with actual values.

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