Machine learning enabled damage classification in composite laminated beams using mode conversion quantification

Vivek T. Rathod; Subrata Mukherjee; Yiming Deng

doi:10.1117/12.2559677

19 May 2020 Machine learning enabled damage classification in composite laminated beams using mode conversion quantification

Vivek T. Rathod, Subrata Mukherjee, Yiming Deng

Author Affiliations +

Proceedings Volume 11380, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIV; 113800B (2020) https://doi.org/10.1117/12.2559677
Event: SPIE Smart Structures + Nondestructive Evaluation, 2020, Online Only

Abstract

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.

Conference Presentation

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Vivek T. Rathod, Subrata Mukherjee, and Yiming 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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