Contrôle santé des structures composites : génération de délaminages par choc laser et quantification par apprentissage machine

Abstract : In this work, we approach delamination quantification in Carbon Fiber Reinforced Polymer (CFRP) laminates as a classification problem whereby each class corresponds to a certain damage extent. A Support Vector Machine (SVM) is used to perform multi-class classification task. Classically, Signal Based Features (SBF) are used to train SVMs when approaching SHM from a machine learning perspective. In this work, starting from the assumption that damage causes a structure to exhibit nonlinear response, we investigate whether the use of Nonlinear Model Based Features (NMBF) increases classification performance. NMBF are computed based on parallel Hammerstein models which are identified with an Exponential Sine Sweep (ESS) signal. Dimensionality reduction of features vector using Principal Component Analysis (PCA) is also conducted in order to find out if it allows robustifying the quantification process suggested in this work. The proposed quantification approach was first tested and validated using simulation results. Thereafter, experimental results on CFRP composite plates equipped with piezoelectric elements and containing various delamination severities are considered for demonstration. Delamination-type damage is introduced into samples in a calibrated and realistic way using LASER Shock Wave Technique (LSWT) and more particularly symmetrical LASER shock configuration. We have experimentally demonstrated that such a configuration of LASER shock is an effective alternative to conventional damage generation techniques such as conventional impacts and Teflon inserts since it allows for a better calibration of damage in type, depth and size.
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Meriem Ghrib. Contrôle santé des structures composites : génération de délaminages par choc laser et quantification par apprentissage machine. Mécanique des matériaux [physics.class-ph]. Ecole nationale supérieure d'arts et métiers - ENSAM, 2017. Français. ⟨NNT : 2017ENAM0070⟩. ⟨tel-01881284⟩

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