De-Bonding Numerical Characterization and Detection in Aeronautic Multi-Element Spars.
adhesive bonding
composite structures
damage characterization
sensors
smart devices
structural health monitoring
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
30 May 2022
30 May 2022
Historique:
received:
28
03
2022
revised:
16
05
2022
accepted:
19
05
2022
entrez:
10
6
2022
pubmed:
11
6
2022
medline:
14
6
2022
Statut:
epublish
Résumé
Structural health monitoring has multifold aims. Concerning composite structures, the main objectives are perhaps reducing costs by shifting from scheduled to on-demand maintenance and reducing weight by removing redundant precautions as the insertion of chicken fasteners to for ensuring joint safety in cases of bonding layer fail. Adhesion defects may be classified along different types, for instance distinguishing between glue deficiency or de-bonding. This paper deals with a preliminary numerical characterization of adhesive layer imperfections on a representative aircraft component. The multipart composite spar is made of two plates and two corresponding C-beams, bonded together to form an almost squared boxed section beam. A numerical test campaign was devoted to extract relevant information from different defect layouts and to try to assess some parameters that could describe their peculiarities. A focus was then given to macroscopic evidence of fault effects behavior, as localization, reciprocal interference, impact on structural response, and so on. A proprietary code was finally used to retrieve the presence and size of the imperfections, correlating numerical outcomes with estimations. Activities were performed along OPTICOMS, a European project funded within the Clean Sky 2 Joint Technology Initiative (JTI).
Identifiants
pubmed: 35684774
pii: s22114152
doi: 10.3390/s22114152
pmc: PMC9185655
pii:
doi:
Substances chimiques
Adhesives
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Clean Sky 2, JTI-SAT, OPTICOMS Project
ID : 699740
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