Strain-based delamination prediction in fatigue loaded CFRP coupon specimens by deep learning and static loading data. (15th July 2022)

• Record Type: Journal Article
• Authors: Cristiani, Demetrio; Falcetelli, Francesco; Yue, Nan; Sbarufatti, Claudio; Di Sante, Raffaella; Zarouchas, Dimitrios; Giglio, Marco
• Is Part Of: Composites. Number 241(2022)
• Publication Date: 2022-07-15
• Subjects: Convolutional neural network -- B. delamination -- B. fatigue -- A. prepreg; Composite materials -- Periodicals; Materials science -- Periodicals; Composite materials; Periodicals; Electronic journals; 620.118
• DOI: 10.1016/j.compositesb.2022.110020
• Languages: English
• ISSNs: 1359-8368

Abstract

Abstract: Machine learning (ML) methods for the structural health monitoring (SHM) of composite structures rely on sufficient domain knowledge as they typically demand to extract damage-sensitive features from raw data before training the ML model. In practice, prior knowledge is not available in most cases. Deep learning (DL) methods, on the other hand, can obtain higher-level features from raw input data and have proven superior in several applications. This paper proposes a Convolutional Neural Network (CNN) based approach for the delamination prediction in CFRP double cantilever beam (DCB) specimens using raw local array strain measurements via distributed optical fiber sensors. The conventional CNN architecture is modified to perform regression, as the delamination size is a continuous value. 1D and 2D CNN architectures are deployed and compared and different techniques are exploited to encode 1D spatial strain pattern series as 2D images. Raw strain patterns collected during static testing are used to train the CNNs, while testing is performed on unseen raw fatigue strain patterns, showing the CNN ability to automatically extract discriminative features from the non-pre-processed static strain pattern-based signals that generalize to raw fatigue signals as well. This strategy has the potential to reduce fatigue testing expenditures while also shortening the time required to gather training data.