Localization of low velocity impacts on CFRP laminates based on FBG sensors and BP neural networks. Issue 26 (26th October 2022)

• Record Type: Journal Article
• Authors: Wen, Xianglong; Sun, Quanzhi; Li, Wenhu; Ding, Guoping; Song, Chunsheng; Zhang, Jinguang
• Is Part Of: Mechanics of advanced materials and structures. Volume 29:Issue 26(2022)
• Page Start: 5478
• Page End: 5487
• Publication Date: 2022-10-26
• Subjects: localization -- impact -- BP neural network -- FBG sensor -- CFRP laminates -- sensing network configuration -- principal component analysis; Composite materials -- Mechanical properties -- Periodicals; Composite construction -- Periodicals; 620.118
• DOI: 10.1080/15376494.2021.1956653
• Languages: English
• ISSNs: 1537-6494

Abstract

Abstract: Carbon fiber reinforced plastic (CFRP) structures are vulnerable to low-speed impacts, which will lead to almost invisible impact damage. Therefore, the timely localization of impact is of great significance to damage detection and maintenance of the structure. In this article, a low velocity impact supervisory and testing system based on fiber Bragg grating (FBG) sensors was built up for CFRP laminates to obtain the low velocity impact strain sensitivity model. Meanwhile, genetic algorithm was applied to optimize the configuration of the FBG sensing network. The eigenvectors of the impact signals were extracted by applying fast Fourier transform (FFT) transform and principal component analysis (PCA) technology used as the input of the back propagation (BP) neural network model, while the corresponding impact coordinates were used as the output, to train the model. After training, the impact position prediction model based on BP neural network was obtained, thereby achieving the impact localization for CFRP laminates successfully with an average localization error of 2.1 cm.