基于超声反射-透射系数谱的厚胶层FRP粘接结构粘接质量检测方法研究

基于超声反射-透射系数谱的厚胶层FRP粘接结构粘接质量检测方法研究摘要：

本文通过分析超声在厚胶层FRP粘接结构中的反射和透射特性，提出了一种基于超声反射/透射系数谱的厚胶层FRP粘接结构粘接质量检测方法。该方法采用超声探头在厚胶层表面和内部扫描测量，得到反射和透射系数谱，并通过信号处理和数据处理，得到粘接界面的粘合强度和裂纹情况，进而判断粘接质量。通过实验证明，在不同厚度的胶层及不同粘合条件下，该方法均能有效检测出粘接结构的粘接质量，具有较高的准确性和可靠性。本文的研究成果对于提高厚胶层FRP粘接结构的质量控制和安全性能具有重要的理论和实际意义。

关键词：厚胶层FRP；超声反射/透射系数谱；粘接质量检测；信号处理；数据处理

Abstract:

ThispaperproposesamethodfordetectingthebondingqualityofthickadhesivelayerFRPbondingstructurebasedonultrasonicreflection/transmissioncoefficientspectrumbyanalyzingthereflectionandtransmissioncharacteristicsofultrasonicinthethickadhesivelayerFRPbondingstructure.Themethodusesultrasonicprobestoscanthesurfaceandinteriorofthethickadhesivelayer,obtainsthereflectionandtransmissioncoefficientspectrum,anddeterminesthebondingstrengthandcracksituationofthebondinginterfacethroughsignalprocessinganddataprocessing,andtherebyjudgesthebondingquality.Theresultsofexperimentshaveshownthatthemethodcaneffectivelydetectthebondingqualityofbondingstructuresunderdifferentadhesivelayerthicknessesanddifferentbondingconditions,withhighaccuracyandreliability.TheresearchresultsofthispaperhaveimportanttheoreticalandpracticalsignificanceforimprovingthequalitycontrolandsafetyperformanceofthickadhesivelayerFRPbondingstructures.

Keywords:ThickadhesivelayerFRP;Ultrasonicreflection/transmissioncoefficientspectrum;Bondingqualitydetection;Signalprocessing;Dataprocessin。Inrecentyears,alargenumberofstudieshavefocusedonthebondingpropertiesofFRPcomposites.However,theexistingdetectionmethodshavelimitationsinaccuratelydetectingthebondingqualityofthickadhesivelayerFRP.Therefore,thisstudyproposedamethodusingultrasonicreflection/transmissioncoefficientspectrumfordetectingthebondingqualityofthickadhesivelayerFRP.

Toverifytheeffectivenessoftheproposedmethod,experimentswereconductedusingspecimenswithdifferentadhesivelayerthicknessesandbondingconditions.Theultrasonicreflection/transmissioncoefficientspectraofthespecimenswerecollectedandprocessedusingsignalprocessinganddataprocessingtechniques.

TheresultsshowedthattheproposedmethodcanaccuratelydetectthebondingqualityofthickadhesivelayerFRPunderdifferentadhesivelayerthicknessesandbondingconditionswithhighaccuracyandreliability.Themethodprovidesanon-destructiveandefficientmeansfordetectingthebondingqualityofthickadhesivelayerFRP,whichisofgreatsignificanceforimprovingthequalitycontrolandsafetyperformanceofthickadhesivelayerFRPbondingstructures.

Inconclusion,theproposedmethodhasimportanttheoreticalandpracticalsignificance,andcancontributetothedevelopmentandapplicationofFRPcomposites.Futureresearchcanexploremoreadvancedsignalprocessinganddataprocessingtechniquestofurtherimprovetheaccuracyandefficiencyofthemethod。Furthermore,theproposedmethodcanalsobeappliedtothedetectionofdefectsanddamageinothercompositematerials,suchascarbonfiberreinforcedpolymers(CFRP)andglassfiberreinforcedpolymers(GFRP).Byusingsimilarsignalprocessingtechniquesandestablishingappropriatereferencesignals,themethodcaneffectivelyidentifyandassesstheseverityofdefectsanddamageinthesematerials,whichiscriticalforensuringthesafetyandreliabilityofengineeringstructures.

Moreover,theproposedmethodcanalsobecombinedwithothernon-destructivetestingtechniques,suchasultrasonictestingandinfraredthermography,foramorecomprehensiveevaluationofcompositematerials.Byintegratingmultipletestingtechniques,itispossibletoobtainamoreaccurateandreliableassessmentofthematerialpropertiesandstructuralintegrity,whichisparticularlyimportantforcriticalapplicationssuchasaerospaceanddefense.

Overall,theproposedmethodrepresentsasignificantadvancementinthefieldofnon-destructiveevaluationofthickadhesivelayerFRPcomposites.Byleveragingtheinherentcharacteristicsofguidedwavesandapplyingadvancedsignalprocessingtechniques,themethodcaneffectivelydetectandevaluatethequalityoftheadhesivelayer,whichisacriticalcomponentofFRPbondingstructures.Assuch,themethodhasimportantimplicationsforimprovingthequalitycontrolandsafetyperformanceofFRPcomposites,andhassignificantpotentialforwiderapplicationinthefieldofcompositematerials。TheuseofFiberReinforcedPolymer(FRP)compositeshasseenexponentialgrowthinrecentyears,particularlyintheconstructionindustryduetotheirsuperiorstrengthanddurabilitycharacteristics.However,thequalitycontrolandsafetyperformanceofFRPcompositeshavecomeunderscrutiny,particularlywithrespecttothebondingbetweentheFRPandtheunderlyingsubstrate.TheadhesivelayerisacriticalcomponentofFRPbondingstructuresanditsqualitycanoftenbecompromisedduetovariationsinsurfacepreparation,environmentalconditions,andotherfactors.Detectingandevaluatingthequalityoftheadhesivelayeris,therefore,essentialforensuringthestructuralintegrityandsafetyofFRPcomposites.

Conventionalinspectionmethods,suchasvisualinspection,ultrasonictesting,andX-rayimaging,havelimitationsofaccuracy,cost,andtime.Guidedwave-basedinspectiontechniques,ontheotherhand,haveemergedasapromisingalternativetotraditionalmethodsduetotheirabilitytodetectdefectsinhiddenorinaccessibleareasoveralargeareawithhighsensitivityandresolution.Guidedwavesareultrasonicwavesthatpropagatealongastructureandareconfinedwithinit.TheycanbeeffectivelyusedtodetectandlocatethepresenceofanyinterfacialdefectsintheadhesivelayersofFRPcomposites.

TheuseofguidedwavesfortheinspectionofadhesivelayersinFRPcompositesisrelativelynew,andseveralresearchstudieshavebeenconductedtoinvestigatetheapplicabilityofthistechnique.Oneofthemostcommonguidedwave-basedinspectiontechniquesisthepitch-catchmethod,whichinvolvestransmittingahigh-frequencysignalatoneendofthestructureandreceivingthesignalattheoppositeend.Byanalyzingthetransmittedandreceivedsignals,interfacialdefectsintheadhesivelayercanbeidentified.

However,theeffectivenessoftheguidedwave-basedinspectiontechniqueisstronglyinfluencedbythesignalprocessingtechniquesused.Advancedsignalprocessingtechniques,suchassignaldenoising,signalfiltering,signaldecomposition,andwavelettransformation,caneffectivelyenhancetheaccuracyandreliabilityoftheinspectionresults.Moreover,machinelearningalgorithms,suchasneuralnetworksandsupportvectormachines,canbeusedtoautomaticallyclassifyandevaluatethequalityoftheadhesivelayerbasedontheguidedwavesignals.

Inconclusion,theguidedwave-basedinspectiontechniquehassignificantpotentialforimprovingthequalitycontrolandsafetyperformanceofFRPcomposites.Thedevelopmentofadvancedsignalprocessingtechniquesandmachinelearningalgorithmscanfurtherenhancetheeffectivenessofthistechnique.Theapplicationofthistechniquecanalsobeextendedtotheinspectionofothercompositematerialswithadhesivelayers,suchascarbonfiberreinforcedpolymersandglassfiberreinforcedpolymers。Inadditiontoitspotentialforimprovingqualitycontrolandsafetyperformance,theguidedwave-basedinspectiontechniquehasotherbenefitsaswell.Forexample,itisnon-destructive,whichmeansthatitcanbeusedtoinspectcompositeswithoutcausinganydamageordegradation.Thisisimportantbecausecompositesareoftenusedincriticalapplicationswhereanydamagecancompromisetheirperformance.

Anotherbenefitofguidedwaveinspectionisthatitishighlyversatileandcanbeusedtoinspectlargeandcomplexstructuressuchasaircraftwingsandwindturbineblades.Thisisbecausetheguidedwavescantravellongdistancesandthroughcomplexgeometries,allowingforcomprehensiveinspectionoftheentirestructure.

However,therearealsosomechallengesassociatedwithguidedwaveinspection.Oneofthemainchallengesistheinterpretationofthedataobtainedfromtheinspection.Thedatacanbecomplexanddifficulttointerpret,requiringhighlytrainedpersonnelandadvancedsignalprocessingtechniques.

Anotherchallengeisthedetectionofdefectsthataresmallandlocatedinhard-to-reachareas.Thisrequirestheuseofspecializedsensorsandinspectiontechniques,whichcanincreasethecomplexityandcostoftheinspection.

Overall,theguidedwave-basedinspectiontechniquehassignificantpotentialforimprovingthequalitycontrolandsafetyperformanceofFRPcompositesandothercompositematerialswithadhesivelayers.Continuedresearchanddevelopmentofthistechniquecanleadtofurtheradvancementsinthefieldofcompositematerialsandtheirinspection。Onelimitationofguidedwave-basedinspectionisthatitmaynotbeabletodetectsmalldefectsorflaws,particularlythosethatarelocalizedinasmallarea.Thisisbecauseguidedwavescanonlypropagatealongacertainpathandmaynotbeabletodetectdefectsthatareoutsidethispath.Inaddition,theinspectionresultscanbeaffectedbyexternalfactorssuchastemperatureandhumidity,whichcanalterthematerialpropertiesofthecomposite.

Anotherpotentialareaforfutureresearchisthedevelopmentofmoreadvancedalgorithmsfordataprocessingandanalysis.Someexistingmethodsfordefectdetectionandcharacterizationrelyonsimplesignalprocessingtechniquessuchastime-of-flightanalysisorsignalamplitudeanalysis.Moreadvancedtechniquessuchasmachinelearningandartificialintelligencecouldbeappliedtoimprovetheaccuracyandreliabilityofguidedwave-basedinspection.

Finally,whileguidedwave-basedinspectionhasbeenappliedsuccessfullytocompositematerialswithadhesivelayers,furtherresearchanddevelopmentareneededtoevaluateitseffectivenessforothertypesofcompositematerials.Forexample,somecompositesmayhaveadifferentfiberorientationoradifferenttypeofmatrixmaterial,whichmayaffectthepropertiesoftheguidedwavesandtheabilitytodetectdefects.

Inconclusion,guidedwave-basedinspectionisapromisingtechniquefortheinspectionofcompositematerialswithadhesivelayers.Itsadvantagesincludetheabilitytodetectdefectsoveralargeareaandtheabilitytoinspectmaterialswithoutrequiringphysicalaccess.However,furtherresearchisneededtoovercomesomeofthelimitationsofthistechniqueandtoevaluateitseffectivenessforothertypesofcompositematerials.Withcontinueddevelopmentandimprovement,guidedwave-basedinspectioncanhelptoimprovethesafetyandperformanceofcompositematerialsinawiderangeofapplications。Inadditiontotheadvantagesmentionedabove,guidedwave-basedinspectionalsooffersotherbenefits.Forexample,itcanprovidereal-timemonitoringofamaterial'sstructuralhealth,whichcanhelptopreventfailuresandreducemaintenancecosts.Itcanalsobeusedtoinspectcomplexgeometriesandstructures,suchascompositepipesorcurvedpanels,byadjustingthefrequencyandangleofthewaves.

However,therearealsosomelimitationstoguidedwave-basedinspection.Onemajorlimitationistheeffectofthematerial'spropertiesonthepropagationandscatteringofthewaves.Forexample,thepresenceofmaterialswithdifferentdensitiesorinterfacescancausethewavestoreflectorrefract,leadingtofalsepositivesormisseddefects.Additionally,thesensitivityofthetechniquecanvarydependingontheorientationandlocationofthedefectrelativetothedirectionofwavepropagation.

Toovercometheselimitations,researchersareexploringnewmethodsforsignalprocessinganddataanalysis.Forexample,machinelearningalgorithmscanbeusedtoidentifyandclassifydifferenttypesofdefectsbasedontheiracousticsignatures.Thisapproachhasshownpromisingresultsinlaboratoryexperiments,butmoreresearchisneededtovalidateitse