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.

The present study delved into the effect of impactor diameter on low velocity impact response and damage characteristics of CFRP. Moreover, the phased array ultrasonic technique (PAUT) was adopted to identify the impact damages based on double-sided scanning. Low-velocity impact tests were carried out using three hemispherical impactors with different diameters. The relationship of impact response and impactor diameters was analyzed by ultrasonic C-scans and S-scans, combined with impact response parameters. Subsequently, the damage characteristics were assessed in terms of dent depth, delamination area and extension shape via the thickness, and the relationships between absorbed energy, impactor displacement, dent depth and delamination area were elucidated. As revealed from experiment results, double-sided PAUT is capable of representing the internal damage characteristics more accurately. Moreover, the impactor diameter slightly affects the impact response under small impact energy, whereas it significantly affects the impact response under large impact energy.

Carbon fiber reinforced plastics inevitably develop defects such as delamination, inclusions, and impacts during manufacturing and usage, which can adversely affect their performance. Ultrasonic phased array inspection is the most effective method for conducting nondestructive testing to ensure their quality. However, the diversity of defects within carbon fiber reinforced plastics makes it challenging for the current ultrasonic phased array inspection techniques to accurately identify these defects. Therefore, this paper presents a method for the ultrasonic phased array nondestructive testing and classification of various internal defects in carbon fiber reinforced plastics based on convolutional neural networks. We prepared an ultrasonic C-scan dataset containing multiple types of internal defects, analyzed the defect features in the ultrasonic C-scan images, and established an autoencoded classifier network to recognize manufacturing defects and impact defects of varying sizes. The experiments showed that the proposed method demonstrates superior defect feature extraction capabilities and can more accurately identify both impact and manufacturing defects.

This paper presents a signal correlation–based two-sided detection method to detect delamination defects in widely used carbon fiber reinforced plastics with high accuracy and a convenient process. This method can improve the accuracy of ultrasonic testing and distinguish non-defective signals from defective signals, especially those with small amplitude, and to present the depth and size of defects by images. We combined the echo signals from both sides of ultrasonic detection at the same location to extract the weak defect signals and achieved the detection of materials with high ultrasonic attenuation. First, empirical mode decomposition filtering is performed on the ultrasonic raw signal. Then, the defect echoes are identified by smart thresholding and combined with the results of ultrasonic detection on both sides. Next, the defect depth and size are calculated, and the defect image is drawn. Finally, the ultrasonic phased array C-scan obtained by the ultrasonic phased array equipment was compared with the detection results of carbon fiber laminates with artificial delamination by the algorithm of the present invention, showing that the proposed algorithm defect depth calculation error is less than 4 %, the defect size calculation error is less than 0.5 mm, and it performs well in defect shape presentation and position calculation.