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2D-CSiC拉伸损伤的声发射信号聚类分析.docx

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2D-CSiC拉伸损伤的声发射信号聚类分析 Title:ClusterAnalysisofAcousticEmissionSignalsfor2D-CSiCTensileDamageAssessment Abstract: Inrecentyears,2Dcarbon-siliconcarbide(2D-CSiC)compositeshavegainedsignificantattentionduetotheirremarkablemechanicalpropertiesandpotentialapplicationsinhigh-performancestructures.However,assessingtheintegrityanddamageevolutionof2D-CSiCmaterialsischallenging,primarilyduetothecomplexandheterogeneousnatureoftheirmicrostructure.Inthisstudy,weproposeanovelapproachtoanalyzetheacousticemission(AE)signalsgeneratedduringtensileloadingof2D-CSiCcompositesusingclusteranalysistechniques.Theobjectiveistoenhancetheunderstandingofthedamagemechanismandenablereliablereal-timestructuralhealthmonitoringof2D-CSiCmaterials. Introduction: Thedevelopmentofadvancedmaterialswithimprovedmechanicalpropertiesanddamagetoleranceiscrucialforthecontinuedadvancementofaerospace,automotive,andenergyindustries.Amongthesematerials,2D-CSiCcompositeshaveemergedaspromisingcandidates,offeringhighstrength,stiffness,andthermalstability.Acousticemission(AE)monitoringhasproventobeaneffectivenon-destructivetestingmethodforassessingthestructuralintegrityofvariousmaterials.Inthisstudy,weinvestigatetheapplicationofAEanalysisincharacterizingthedamageevolvementin2D-CSiCcompositessubjectedtotensileloading. Methodology: Theexperimentalprocedureincludesthefabricationof2D-CSiCcompositesandsubsequenttensiletesting.Duringthetensileloading,AEsignalsarecontinuouslymonitoredandrecorded,capturingtheoccurrenceofmicrocracks,fiber/matrixinterfacedebonding,andotherdamagemechanisms.TheAEsignalsarethenpreprocessedtoremovenoiseandspurioussignals.Next,featureextractiontechniquesareappliedtoobtainasetofquantitativefeaturesfromtheAEsignals,representingtheirfundamentalcharacteristics. ClusterAnalysis: ClusteranalysisisemployedtogroupsimilarAEsignalsinordertoidentifydifferentstagesofdamageevolution.VariousclusteringalgorithmssuchasK-means,hierarchicalclustering,anddensity-basedspatialclusteringofapplicationswithnoise(DBSCAN)areu