A global interactive attention-based lightweight denoising network for locating internal defects of CFRP laminates. (November 2022)

• Record Type: Journal Article
• Authors: Yang, Bo; Zhang, Yang; Wang, Shilong; Xu, Weichun; Xiao, Meng; He, Yan; Mo, Fan
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: CFRP -- Defect location -- Signal analysis -- Deep learning -- Global interaction attention; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105436
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Carbon fiber reinforced plastic (CFRP) has become one of the main structural materials for aerospace vehicles. However, some internal defects are prone to occur and have potential to cause significant losses of life and property. Currently, the detection of internal defects for CFRP mainly relies on ultrasonic, and other technologies, while they have disadvantages of low efficiency, and poor adaptability. Therefore, this paper explores a novel method to locate internal defects of CFRP laminates by analyzing vibration signals. Firstly, a signal acquisition scheme is designed. Then, a global interactive attention-based lightweight denoising network (GIALDN) is designed to analyze vibration signals and locate internal defects of CFRP laminates. In GIALDN, the threshold denoising method is used to eliminate noise-related features and improve feature discrimination; a global interactive attention module is designed, which makes the network pay more attention to the valid features while realizing the global interactive connection and obtains the rich contextual features; combining with the convolution layer of de-pooling strategy and multi-layer convolution using the residual connection, the backbone of the network is formed. Finally, an experimental platform is established to test the performance of GIALDN. Results show that the location accuracy of GIALDN can reach 98.68%, which is more than 15% higher than those of VGGnet11 and FaultNet, and is also superior to those of LSTM, RNN, Rsenet18, SEresnet18 and Densenet121. Lastly, the location accuracies of GIALDN on CFRP laminates with the same thickness and different stacking sequences are investigated and a good model applicability can be observed. Highlights: GIALDN is designed for locating the internal defects in CFRP laminates. LDM is proposed to effectively eliminate noise and improve feature discrimination. GIAM is proposed to obtain the inter-data connection and rich contextual features. De-pooling and 1DCNN strategies are developed to improve the model performance. The accuracy of GIALDN is 98.68%, which is higher than the existing models.