Afficher la notice abrégée

dc.contributor.authorGROZ, Marie-Marthe
IDREF: 228324599
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorSOMMIER, Alain
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorABISSET, Emmanuelle
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorCHEVALIER, Stephane
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorBATTAGLIA, Jean-Luc
IDREF: 084712562
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorBATSALE, Jean-Christophe
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorPRADERE, Christophe
IDREF: 095038132
dc.date2020
dc.date.accessioned2021-05-14T09:32:10Z
dc.date.available2021-05-14T09:32:10Z
dc.date.issued2020
dc.identifier.issn1768-6733
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/75947
dc.description.abstractEnThe main goal of this paper is the estimation of thermal resistive fields in multilayer samples using the classical front face flash method as excitation and InfRared Thermography (IRT) as a monitoring sensor. The complete inverse processing of a multilayer analytical model can be difficult due to a lack of sensitivity to certain parameters (layer thickness, depth of thermal resistance, etc.) or processing time. For these reasons, our present strategy proposes a Bayesian inference approach. Using the analytical quadrupole method, a reference model can be calculated for a set of parameters. Then, the Bayesian probabilistic method is used to determine the maximum likelihood probability between the measured data and the reference model. To keep the processing method robust and fast, an automatic selection of the calculation range is proposed. Finally, in the case of a bilayer sample, both the thickness and resistive 3D layers are estimated in less than 2 min for a space and time matrix of 50,000 pixels by 4000 time steps with a reasonable relative error of less than 5%.
dc.language.isoen
dc.publisherTaylor and Francis
dc.subject.enInfraRed Thermography
dc.subject.enInverse Processing
dc.subject.enQuantitative Thermal Resistance Fields Estimation
dc.subject.enBayesian Inference
dc.title.enThermal resistance field estimations from IR thermography using multiscale Bayesian inference
dc.typeArticle de revue
dc.identifier.doi10.1080/17686733.2020.1771529
dc.subject.halSciences de l'ingénieur [physics]/Matériaux
dc.subject.halPhysique [physics]/Physique [physics]/Instrumentations et Détecteurs [physics.ins-det]
bordeaux.journalQuantitative InfraRed Thermography Journal
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.peerReviewedoui
hal.identifierhal-02998120
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02998120v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Quantitative%20InfraRed%20Thermography%20Journal&rft.date=2020&rft.eissn=1768-6733&rft.issn=1768-6733&rft.au=GROZ,%20Marie-Marthe&SOMMIER,%20Alain&ABISSET,%20Emmanuelle&CHEVALIER,%20Stephane&BATTAGLIA,%20Jean-Luc&rft.genre=article


Fichier(s) constituant ce document

Thumbnail

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée