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High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm

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Publisher MDPI
Date 2020 Sep 4
PMID 32878129
Citations 4
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Abstract

This paper presents a method based on signal correlation to detect delamination defects of widely used carbon fiber reinforced plastic with high precision and a convenient process. The objective of it consists in distinguishing defect and non-defect signals and presenting the depth and size of defects by image. A necessary reference signal is generated from the non-defect area by using autocorrelation theory firstly. Through the correlation calculation results, the defect signal and non-defect signal are distinguished by using Euclidean distance. In order to get more accurate time-of-flight, cubic spline interpolation is introduced. In practical automatic ultrasonic A-scan signal processing, signal correlation provide a new way to avoid problems such as signal peak tracking and complex gate setting. Finally, the detection results of a carbon fiber laminate with artificial delamination through ultrasonic phased array C-scan acquired from Olympus OmniScan MX2 and this proposed algorithm are compared, which showing that this proposed algorithm performs well in defect shape presentation and location calculation. The experiment shows that the defect size error is less than 4%, the depth error less than 3%. Compared with ultrasonic C-scan method, this proposed method needs less inspector's prior-knowledge, which can lead to advantages in automatic ultrasonic testing.

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References
1.
Slonski M, Schabowicz K, Krawczyk E . Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks. Materials (Basel). 2020; 13(7). PMC: 7177575. DOI: 10.3390/ma13071557. View

2.
Sharma G, Kumar A, Jayakumar T, Purnachandra Rao B, Mariyappa N . Ensemble Empirical Mode Decomposition based methodology for ultrasonic testing of coarse grain austenitic stainless steels. Ultrasonics. 2014; 57:167-78. DOI: 10.1016/j.ultras.2014.11.008. View

3.
Song Y, Kube C, Zhang J, Li X . Higher-order spatial correlation coefficients of ultrasonic backscattering signals using partial cross-correlation analysis. J Acoust Soc Am. 2020; 147(2):757. DOI: 10.1121/10.0000615. View

4.
Praveen A, Vijayarekha K, Abraham S, Venkatraman B . Signal quality enhancement using higher order wavelets for ultrasonic TOFD signals from austenitic stainless steel welds. Ultrasonics. 2013; 53(7):1288-92. DOI: 10.1016/j.ultras.2013.03.013. View

5.
Benammar A, Drai R, Guessoum A . Ultrasonic flaw detection using threshold modified S-transform. Ultrasonics. 2013; 54(2):676-83. DOI: 10.1016/j.ultras.2013.09.004. View