Deep learning algorithms for delamination identification on composites panels by wave propagation signals analysis
Ernesto Monaco, Fabrizio Riccidownload PDF
Abstract. Performances are a key concern in aerospace vehicles, requiring safer structures with as little consumption as possible. Composite materials replaced aluminum alloys even in primary structures to achieve higher performances with lighter components. However, random events such as low-velocity impacts may induce damages that are typically more dangerous and mostly not visible than in metals. Structural Health Monitoring deals mainly with sensorised structures providing signals related to their “health status” aiming at lower maintenance costs and weights of aircrafts. Much effort has been spent during last years on analysis techniques for evaluating metrics correlated to damages’ existence, location and extensions from signals provided by the sensors networks. Deep learning techniques can be a very powerful instrument for signals patterns reconstruction and selection but require the availability of consistent amount of both healthy and damaged structural configuration experimental data sets, with high materials and testing costs, or data reproduced by validated numerical simulations. Within this work will be presented a supervised deep neural networks trained by experimental measurements as well as numerically generated strain propagation signals. The final scope is the detection of delamination into composites plates for aerospace employ. The approach is based on the production of images trough signal processing techniques and on employ of an image recognition convolutional network. The network is trained and tested on combinations of experimental and numerical data.
Structural Health Monitoring, Composites Structures, Deep Neural Networks
Published online 11/1/2023, 4 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Ernesto Monaco, Fabrizio Ricci, Deep learning algorithms for delamination identification on composites panels by wave propagation signals analysis, Materials Research Proceedings, Vol. 37, pp 409-412, 2023
The article was published as article 90 of the book Aeronautics and Astronautics
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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