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

Author:

Ma Mengyuan,Cao Hongyi,Jiang MingshunORCID,Sun Lin,Zhang LeiORCID,Zhang Faye,Sui Qingmei,Tian Aiqin,Liang Jianying,Jia Lei

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.

Funder

National Key Research and Development Project

National Natural Science Foundation of China

Key research and development plan of Shandong Province

Publisher

MDPI AG

Subject

General Materials Science

Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Progressive damage modeling in open hole composite laminates with ultrasound-informed drilling-induced delamination;Composites Part A: Applied Science and Manufacturing;2024-09

2. A Multivariate Statistical Approach to Wrinkling Detection in Composites;IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control;2024-09

3. Optimization of Weak Ultrasonic Defect Signal Detection of Carbon Fiber Composites Based on Double-Sided Pulse Reflection Scanning;Journal of Testing and Evaluation;2024-07-01

4. Delamination Detection in CFRP Components from Ultrasound Images Using Convolutional Neural Networks;2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC);2024-05-20

5. Automated ultrasonic testing for near-surface flaws in CFRP;Nondestructive Testing and Evaluation;2024-03-31

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