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We propose a model assisted method to identify damage types and severity based on mode converted wave strength. Machine learning techniques are employed to develop classification models complemented by the finite element simulation models. Finite element simulation models provide the training data for various cases of damage and severity involving common types of damages in composites. Damage classification models are based on mode conversion strength versus frequency curves of participating four wave modes. For damage recognition and classification, a multi-layer Convoluted Neural Network (CNN) has been trained using the back-propagation paradigm on the generated dataset.
Vivek T. Rathod,Subrata Mukherjee, andYiming Deng
"Machine learning enabled damage classification in composite laminated beams using mode conversion quantification", Proc. SPIE 11380, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIV, 113800B (19 May 2020); https://doi.org/10.1117/12.2559677
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