卷积神经网络机器学习外文文献翻译中英文2020.docx
- 文档编号:178898
- 上传时间:2022-10-05
- 格式:DOCX
- 页数:16
- 大小:17.66KB
卷积神经网络机器学习外文文献翻译中英文2020.docx
《卷积神经网络机器学习外文文献翻译中英文2020.docx》由会员分享,可在线阅读,更多相关《卷积神经网络机器学习外文文献翻译中英文2020.docx(16页珍藏版)》请在冰豆网上搜索。
卷积神经网络机器学习相关外文翻译中英文2020
英文
Predictionofcompositemicrostructurestress-straincurvesusing
convolutionalneuralnetworks
CharlesYang,YoungsooKim,SeunghwaRyu,GraceGuAbstract
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,synthesizedviaseif-assemblyprocesses,haverelativelyperiodicandregularstructures,theirmechanicalpropertiescanbepredictediftheloadtransfermechanismofarepresentativeunitcellandtheroleoftheself-similarhierarchicalstructureareunderstood.However,theapplicabilityofanalyticaltheoriesislimitedinquantitativelypredictingcompositepropertiesbeyondtheelasticlimitinthepresenceofdefects,becausesuchtheoriesrelyontheconceptofrepresentativevolumeelement(RVE),astatisticalrepresentationofmaterialproperties,whereasthestrengthandfailureisdeterminedbytheweakestdefectintheentiresampledomain.Numericalmodelingbasedonfiniteelementmethods(FEM)cancomplementanalyticalmethodsforpredictinginelasticpropertiessuchasstrengthandtoughnessmodulus(referredtoastoughness,hereafter)whichcanonlybeobtainedfromfullstress-straincurves.
However,numericalschemescapableofmodelingtheinitiationandpropagationofthecurvilinearcracks,suchasthecrackphasefieldmodel,arecomputationallyexpensiveandtime-consumingbeeauseaveryfinemeshisrequiredtoaccommodatehighlyconcentratedstressfieldnearcracktipandtherapidvariationofdamageparameterneardiffusivecracksurface.Meanwhile,analyticalmodelsrequiresignificanthumaneffortanddomainexpertiseandfailtogeneralizetosimilardomainproblems.Inordertoidentifyhigh-performingcompositesinthemidstoflargedesignspaceswithinrealistictime-frames,weneedmodelsthatcanrapidlydescribethemechanicalpropertiesofcomplexsystemsandbegeneralizedeasilytoanalogoussystems.Machinelearningoffersthebenefitofextremelyfastinferencetimesandrequiresonlytrainingdatatolearnrelationshipsbetweeninputsandoutputse.g.,compositemicrostructuresandtheirmechanicalproperties.Machinelearninghasalreadybeenappliedtospeeduptheoptimizationofseveraldifferentphysicalsystems,includi
- 配套讲稿:
如PPT文件的首页显示word图标,表示该PPT已包含配套word讲稿。双击word图标可打开word文档。
- 特殊限制:
部分文档作品中含有的国旗、国徽等图片,仅作为作品整体效果示例展示,禁止商用。设计者仅对作品中独创性部分享有著作权。
- 关 键 词:
- 卷积 神经网络 机器 学习 外文 文献 翻译 中英文 2020