Multi-Excitation Infrared Fusion for Impact Evaluation of Aluminium-BFRP/GFRP Hybrid Composites.

feature fusion fibre metal laminates infrared thermography non-destructive testing

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
05 Sep 2021
Historique:
received: 14 08 2021
revised: 29 08 2021
accepted: 02 09 2021
entrez: 10 9 2021
pubmed: 11 9 2021
medline: 11 9 2021
Statut: epublish

Résumé

Fibre metal laminates are widely implemented in the aerospace industry owing to the merits of fatigue resistance and plastic properties. An effective defect assessment technique needs to be investigated for this type of composite materials. In order to achieve accurate impact-induced damage evaluation, a multi-excitation infrared fusion method is introduced in this study. Optical excitation thermography with high performance on revealing surface and subsurface defects is combined with vibro-thermography to improve the capability of detection on defects. Quantitative analysis is carried out on the temperature curve to assess the impact-induced deformation. A new image fusion framework including feature extraction, feature selection and fusion steps is proposed to fully utilize the information from two excitation modalities. Six fibre metal laminates which contain aluminium-basalt fibre reinforced plastic and aluminium-glass fibre reinforced plastic are investigated. Features from different perspectives are compared and selected via intensity contrast on deformation area for fusion imaging. Both types of defects (i.e., surface and sub-surface) and the internal deformation situation of these six samples are characterized clearly and intuitively.

Identifiants

pubmed: 34502852
pii: s21175961
doi: 10.3390/s21175961
pmc: PMC8434630
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Canada Research Chairs
ID : MiViM
Organisme : Natural Sciences and Engineering Research Council of Canada
ID : Discovery
Organisme : Natural Sciences and Engineering Research Council of Canada
ID : CREATE-ON DUTY
Organisme : University of L'Aquila
ID : PROGETTI DI RICERCA DI INTERESSE DI ATENEO E.F. 2021

Références

Sensors (Basel). 2018 Feb 16;18(2):
pubmed: 29462953

Auteurs

Jue Hu (J)

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, Quebec, QC G1V 0A6, Canada.

Hai Zhang (H)

Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, Quebec, QC G1V 0A6, Canada.

Stefano Sfarra (S)

Department of Industrial and Information Engineering and Economics (DIIIE), University of L'Aquila, Monteluco di Roio, 67100 L'Aquila, Italy.

Stefano Perilli (S)

Independent Researcher, 67100 Santa Rufina di Roio, Italy.

Claudia Sergi (C)

Department of Chemical Engineering Materials Environment, Sapienza-Università di Roma & UdR INSTM, 00184 Roma, Italy.

Fabrizio Sarasini (F)

Department of Chemical Engineering Materials Environment, Sapienza-Università di Roma & UdR INSTM, 00184 Roma, Italy.

Xavier Maldague (X)

Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory, Laval University, Quebec, QC G1V 0A6, Canada.

Classifications MeSH