卷积神经网络机器学习外文文献翻译中英文Word下载.docx

卷积神经网络机器学习外文文献翻译中英文Word下载.docx

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卷积神经网络机器学习外文文献翻译中英文@#@卷积神经网络机器学习相关外文翻译中英文2020@#@英文@#@Predictionofcompositemicrostructurestress-straincurvesusingconvolutionalneuralnetworks@#@CharlesYang，YoungsooKim，SeunghwaRyu，GraceGu@#@Abstract@#@Stress-straincurvesareanimportantrepresentationofamaterial'@#@smechanicalproperties,fromwhichimportantpropertiessuchaselasticmodulus,strength,andtoughness,aredefined.However,generatingstress-straincurvesfromnumericalmethodssuchasfiniteelementmethod（FEM）iscomputationallyintensive,especiallywhenconsideringtheentirefailurepathforamaterial.Asaresult,itisdifficulttoperformhighthroughputcomputationaldesignofmaterialswithlargedesignspaces,especiallywhenconsideringmechanicalresponsesbeyondtheelasticlimit.Inthiswork,acombinationofprincipalcomponentanalysis（PCA）andconvolutionalneuralnetworks（CNN）areusedtopredicttheentirestress-strainbehaviorofbinarycompositesevaluatedovertheentirefailurepath,motivatedbythesignificantlyfasterinferencespeedofempiricalmodels.WeshowthatPCAtransformsthestress-straincurvesintoaneffectivelatentspacebyvisualizingtheeigenbasisofPCA.Despitehavingadatasetofonly10-27%ofpossiblemicrostructureconfigurations,themeanabsoluteerrorofthepredictionis<@#@10%oftherangeofvaluesinthedataset,whenmeasuringmodelperformancebasedonderivedmaterialdescriptors,suchasmodulus,strength,andtoughness.Ourstudydemonstratesthepotentialtousemachinelearningtoacceleratematerialdesign,characterization,andoptimization.@#@Keywords：

@#@Machinelearning，Convolutionalneuralnetworks，Mechanicalproperties，Microstructure，Computationalmechanics@#@Introduction@#@Understandingtherelationshipbetweenstructureandpropertyformaterialsisaseminalprobleminmaterialscience,withsignificantapplicationsfordesigningnext-generationmaterials.Aprimarymotivatingexampleisdesigningcompositemicrostructuresforload-bearingapplications,ascompositesofferadvantageouslyhighspecificstrengthandspecifictoughness.Recentadvancementsinadditivemanufacturinghavefacilitatedthefabricationofcomplexcompositestructures,andasaresult,avarietyofcomplexdesignshavebeenfabricatedandtestedvia3D-printingmethods.Whilemoreadvancedmanufacturingtechniquesareopeningupunprecedentedopportunitiesforadvancedmaterialsandnovelfunctionalities,identifyingmicrostructureswithdesirablepropertiesisadifficultoptimizationproblem.@#@Onemethodofidentifyingoptimalcompositedesignsisbyconstructinganalyticaltheories.Forconventionalparticulate/fiber-reinforcedcomposites,avarietyofhomogenizationtheorieshavebeendevelopedtopredictthemechanicalpropertiesofcompositesasafunctionofvolumefraction,aspectratio,andorientationdistributionofreinforcements.Becausemanynaturalcomposites,synthesizedviaself-assemblyprocesses,haverelativelyperiodicandregularstructures,theirmechanicalpropertiescanbepredictediftheloadtransfermechanismofarepresentativeunitcellandtheroleoftheself-similarhierarchicalstructureareunderstood.However,theapplicabilityofanalyticaltheoriesislimitedinquantitativelypredictingcompositepropertiesbeyondtheelasticlimitinthepresenceofdefects,becausesuchtheoriesrelyontheconceptofrepresentativevolumeelement（RVE）,astatisticalrepresentationofmaterialproperties,whereasthestrengthandfailureisdeterminedbytheweakestdefectintheentiresampledomain.Numericalmodelingbasedonfiniteelementmethods（FEM）cancomplementanalyticalmethodsforpredictinginelasticpropertiessuchasstrengthandtoughnessmodulus（referredtoastoughness,hereafter）whichcanonlybeobtainedfromfullstress-straincurves.@#@However,numericalschemescapableofmodelingtheinitiationandpropagationofthecurvilinearcracks,suchasthecrackphasefieldmodel,arecomputationallyexpensiveandtime-consumingbecauseaveryfinemeshisrequiredtoaccommodatehighlyconcentratedstressfieldnearcracktipandtherapidvariationofdamageparameterneardiffusivecracksurface.Meanwhile,analyticalmodelsrequiresignificanthumaneffortanddomainexpertiseandfailtogeneralizetosimilardomainproblems.Inordertoidentifyhigh-performingcompositesinthemidstoflargedesignspaceswithinrealistictime-frames,weneedmodelsthatcanrapidlydescribethemechanicalpropertiesofcomplexsystemsandbegeneralizedeasilytoanalogoussystems.Machinelearningoffersthebenefitofextremelyfastinferencetimesandrequiresonlytrainingdatatolearnrelationshipsbetweeninputsandoutputse.g.,compositemicrostructuresandtheirmechanicalproperties.Machinelearninghasalreadybeenappliedtospeeduptheoptimizationofseveraldifferentphysicalsystems,includinggraphenekirigamicuts,fine-tuningsp