Simulation of waves propagation into composites thin shells by FEM methodologies for training of deep neural networks aimed at damage reconstruction

E. Monaco; N. D. Boffa; F. Ricci; M. Rautela; D. Passato; M. Cinque

doi:10.1117/12.2583572

22 March 2021 Simulation of waves propagation into composites thin shells by FEM methodologies for training of deep neural networks aimed at damage reconstruction

E. Monaco, N. D. Boffa, F. Ricci, M. Rautela, D. Passato, M. Cinque

Author Affiliations +

Proceedings Volume 11593, Health Monitoring of Structural and Biological Systems XV; 1159318 (2021) https://doi.org/10.1117/12.2583572
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only

Abstract

Structural Health Monitoring (SHM) deals mainly with structures instrumented by secondary bonded or embedded sensors that, acting as both signal generators and receivers, are able to “interrogate” the structure about its “health status”. Sensorised structures appear promising for reducing the maintenance costs and the weight of aerospace composite structures, without any reduction of the safety level required. Much effort has been spent during last years on signal analysis techniques in order to extract from signals provided by the sensors networks many parameters, metrics, and images correlated to damages existence, location and extensions. As in many other technological fields, like medical image diagnostics, deep learning techniques in general and artificial neural networks in particular can be a very powerful instrument for damage patterns reconstruction and selection provided that a sufficient and consistent amount of data related to healthy and damaged configuration of the item under test are available. Within this work explicit finite element analysis has been employed to simulate waves propagation within composite plates with and without delaminations due to impacts. The numerical results have been previously validated with analytical solutions and experimental signals then have been used to populate the data sets necessary for deep learning. This paper will present the preliminary results achieved by the authors.

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E. Monaco, N. D. Boffa, F. Ricci, M. Rautela, D. Passato, and M. Cinque "Simulation of waves propagation into composites thin shells by FEM methodologies for training of deep neural networks aimed at damage reconstruction", Proc. SPIE 11593, Health Monitoring of Structural and Biological Systems XV, 1159318 (22 March 2021); https://doi.org/10.1117/12.2583572

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