A combined global-local approach for delamination assessment of composites using vibrational frequencies and FBGs. (15th March 2022)

• Record Type: Journal Article
• Authors: He, Mengyue; Ramakrishnan, Karthik Ram; Wang, Yishou; Zhang, Zhifang; Fu, Jiyang
• Is Part Of: Mechanical systems and signal processing. Volume 167:Part B(2022)
• Publication Date: 2022-03-15
• Subjects: Delamination detection -- Fiber reinforced polymer -- Scanning laser Doppler vibration -- Fiber Bragg grating -- Support vector machine -- Extreme learning machine; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108577
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A hybrid detection method is proposed for assessing delamination in composites. Two-step method has combined the global frequency and local FBGs for damage detection. FBGs were distributed near the damage area predicted by frequency index to further predict damage with better accuracy. Support vector machine and the extreme learning machine were used as inverse algorithms. Abstract: In this paper, a combined global-local strategy was developed for structural health monitoring (SHM) of fiber reinforced polymer (FRP) composites to assess the internal delamination damage, which has combined vibrational frequency shifts as the global index and the fiber Bragg grating (FBG) wavelength shifts as the local index. The delamination detection was carried out in two steps. The first step was based on the global damage index, which is the changes in multiple modes of frequencies measured by a non-contact scanning laser Doppler vibrometer (SLDV) system. Machine learning (ML) algorithms including support vector machine (SVM) and extreme learning machine (ELM) algorithms were used to initially predict the delamination interface, location, and size through frequency shifts. Numerical and experimental verification results show that SVM has a higher prediction accuracy than ELM when only a small number of samples exist, and the classification of SVM is better than its regression function to be used in the prediction of discrete delamination interfaces. To verify the damaged area predicted by frequency changes and further update the delamination edges with better accuracy, the second step has taken the changes in the wavelength of multiple FBG sensors as the local damage index. Multistage loads were applied at equidistant positions of the FRP specimens to induce deformations and the wavelength shifts in FBGs were used to determine the boundary of delamination. The results showed that delamination in FRP composites can be assessed more precisely using the global-local two-step SHM strategy, which can compensate for the shortcomings of only using vibration-based or FBG-based monitoring techniques. Besides, the SVM algorithm showed excellent predictive performance in both steps and had great potential in delamination damage monitoring.