晶粒组织的三维模型构建与定量表征研究

晶粒组织的三维模型构建与定量表征研究摘要：

晶粒组织是材料微结构中最基本、最重要的特征之一。构建晶粒组织的三维模型并对其进行定量表征具有理论和工程实践价值。本文基于计算机图像处理技术，提出了一种晶体结构三维重建方法，将二维显微镜图像转化为三维晶粒组织模型。接着我们与传统手工重建方法做了对比，其中包括空间点云重建、Delaunay三角剖分、以及直接点扩散法，结果表明本文提出的方法具有更高的重建精度和鲁棒性。针对三维晶粒组织模型，我们采用网络结构基于图卷积神经网络的无监督拓扑学习方法，自动挖掘模型中晶粒之间的拓扑关系，并进行拓扑性质的特征提取和表征。最后，通过应用所提出的方法于高熵合金晶粒组织的重建和表征，得到了高质量的三维模型和更为精确的定量特征，验证了方法的有效性和优越性。因此，该方法可以应用于材料科学领域中各类材料的晶粒组织重建和表征研究。

关键词：晶粒组织；计算机图像处理；三维构建；表征研究；图卷积神经网络。

Abstract:

Grainmicrostructureisoneofthemostfundamentalandcriticalfeaturesinmaterialmicrostructure.Constructingathree-dimensionalmodelofgrainmicrostructureandquantitativelycharacterizingithavetheoreticalandengineeringvalue.Inthispaper,athree-dimensionalreconstructionmethodofcrystalstructureisproposedbasedoncomputerimageprocessingtechnology,whichcanconverttwo-dimensionalmicroscopicimagesintothree-dimensionalgrainmicrostructuremodels.Thenwecompareditwithtraditionalmanualreconstructionmethods,includingspacepointcloudreconstruction,Delaunaytriangulation,anddirectpointdiffusionmethod.Theresultsshowthattheproposedmethodhashigherreconstructionaccuracyandrobustness.Basedonthegeneratedthree-dimensionalgrainmicrostructuremodel,weemployagraphconvolutionalneuralnetwork(GCN)basedunsupervisedtopologicallearningmethodtoautomaticallyminethetopologicalrelationshipsamonggrainboundariesinthemodelandextractandcharacterizetopologicalproperties.Finally,themethodwasappliedtothereconstructionandcharacterizationofhigh-entropyalloygrainmicrostructure,resultinginhigh-qualitythree-dimensionalmodelsandmoreaccuratequantitativeproperties,whichhasshowntheeffectivenessandsuperiorityoftheproposedmethod.Therefore,thismethodcanbeappliedtothereconstructionandcharacterizationofgrainmicrostructureinvariousmaterialssciencefields.

Keywords：grainmicrostructure；computerimageprocessing；three-dimensionalreconstruction；characterizationresearch；graphconvolutionalneuralnetworkInrecentyears,thestudyofmicrostructurehasbecomeincreasinglyimportantinthefieldofmaterialsscience.Grainmicrostructureisoneoftheimportantmicrostructuresinmetallicmaterials,anditscharacterizationandreconstructionhavegreatsignificancefortheresearchofmaterialpropertiesandpropertiesoptimization.

Computerimageprocessingtechnologyhasbeenwidelyusedinthefieldofmicrostructureanalysis.However,thetraditionaltwo-dimensionalimageanalysismethodshavelimitationsindescribingthecomplexthree-dimensionalmicrostructure.Withthedevelopmentofthree-dimensionalreconstructiontechnology,thereconstructionandcharacterizationofgrainmicrostructurehavebeengreatlyimproved.

Graphconvolutionalneuralnetwork,asanewtypeofdeeplearningalgorithm,hasshownapowerfulabilitytoprocessdataingraphstructure.Byusingthismethod,researcherscanextractthefeaturesofthegrainmicrostructureinthree-dimensionalspace,andproducehigh-qualitythree-dimensionalmodels.

Intheapplicationofthismethod,researcherscanobtaintheoriginalimageofthegrainmicrostructurethroughamicroscope,andusecomputerimageprocessingtechnologytopreprocesstheimage.Then,theycanconstructathree-dimensionalgraphstructurebasedontheimagedata,andusegraphconvolutionalneuralnetworktorealizethereconstructionandcharacterizationofgrainmicrostructure.

Theapplicationofthismethodhasbeendemonstratedinthereconstructionandcharacterizationofgrainmicrostructureinvariousmetallicmaterials.Theresultsshowthatthethree-dimensionalmodelsobtainedbythismethodaremoreaccurateandreliablethantraditionalmethods.Therefore,thismethodhasbroadapplicationprospectsinthefieldofmaterialsscienceMoreover,thegraphconvolutionalneuralnetworkmethodcanalsobeextendedtostudytheevolutionofgrainmicrostructureduringmaterialsprocessing.Byusinginsituobservationsandsimulations,theevolutionofthegrainmicrostructurecanbecapturedandanalyzed.Thiscanprovideimportantinsightsintothemechanismsofgraingrowth,recrystallization,andtextureevolutioninmetallicmaterials.

Inaddition,thegraphconvolutionalneuralnetworkmethodcanalsobecombinedwithotheradvancedcharacterizationtechniques,suchaselectronbackscatterdiffraction(EBSD)andX-raydiffraction(XRD),tofurtherimprovetheaccuracyandreliabilityofgrainmicrostructurecharacterization.Forexample,byintegratingEBSDdatawiththree-dimensionalmodelsobtainedbygraphconvolutionalneuralnetwork,thelocalorientationandmisorientationdistributionofgrainscanbeanalyzedindetail.

Overall,thegraphconvolutionalneuralnetworkmethodisapowerfultoolforthereconstructionandcharacterizationofgrainmicrostructureinmetallicmaterials.Itsabilitytocapturecomplexspatialcorrelationsandtopologicalstructuresofgrainmicrostructuremakeitsuperiortotraditionalmethods.Withthedevelopmentofhigh-performancecomputingandadvancedimagingtechniques,webelievethatthismethodwillplayanincreasinglyimportantroleinthefieldofmaterialsscienceInadditiontoitsapplicationinthereconstructionandcharacterizationofgrainmicrostructure,thegraphconvolutionalneuralnetwork(GCNN)methodhasalsoshowngreatpotentialinotherareasofmaterialsscience.

OnepromisingapplicationofGCNNisinthepredictionofmaterialproperties.BytrainingaGCNNonalargedatasetofmaterialswithknownproperties,thenetworkcanlearntopredictthepropertiesofnewmaterialsbasedontheirstructuralfeatures.Thisapproachhasbeenusedtopredictthemechanicalandthermalpropertiesofvariousmaterials,includingmetals,ceramics,andpolymers.

AnotherareawhereGCNNhasshownpromiseisintheanalysisofmoleculardynamicssimulations.Moleculardynamicssimulationsarewidelyusedtostudythebehaviorofatomsandmoleculesinmaterials,butanalyzingthevastamountsofdatageneratedbythesesimulationscanbechallenging.GCNNcanbeusedtoextractrelevantfeaturesfromthesimulationdataandidentifypatternsandcorrelationsthatwouldbedifficulttodetectusingtraditionalmethods.

GCNNhasalsobeenusedtoanalyzeanddesignnewmaterialsatthenanoscale.Byincorporatingfeaturessuchassurfacearea,poresize,andnanoparticleshapeintothenetwork,GCNNcanbeusedtopredicttheperformanceofmaterialsinapplicationssuchascatalysis,energystorage,anddrugdelivery.

Overall,thegraphconvolutionalneural