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

Monaco, E and Boffa, ND and Ricci, F and Rautela, M and Passato, D and Cinque, M (2021) Simulation of waves propagation into composites thin shells by FEM methodologies for training of deep neural networks aimed at damage reconstruction. In: Health Monitoring of Structural and Biological Systems XV 2021, 22-26 Mar 2021, Virtual, Online.

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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. © 2021 SPIE

Item Type:	Conference Paper
Publication:	Proceedings of SPIE - The International Society for Optical Engineering
Publisher:	SPIE
Additional Information:	The copyright for this article belongs to SPIE
Keywords:	Backpropagation; Biological systems; Composite structures; Deep learning; Deep neural networks; Diagnosis; Finite element method; Medical imaging; Neural networks; Signal receivers; Wave transmission, Aerospace composite structures; Analysis techniques; Embedded sensors; Explicit finite element analysis; Learning techniques; Maintenance cost; Numerical results; Structural health monitoring (SHM), Structural health monitoring
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	25 Aug 2021 06:49
Last Modified:	25 Aug 2021 11:48
URI:	http://eprints.iisc.ac.in/id/eprint/69361

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