晶粒組織的三維模型構(gòu)建與定量表征研究

晶粒組織的三維模型構(gòu)建與定量表征研究摘要：

晶粒組織是材料微結(jié)構(gòu)中最基本、最重要的特征之一。構(gòu)建晶粒組織的三維模型并對(duì)其進(jìn)行定量表征具有理論和工程實(shí)踐價(jià)值。本文基于計(jì)算機(jī)圖像處理技術(shù)，提出了一種晶體結(jié)構(gòu)三維重建方法，將二維顯微鏡圖像轉(zhuǎn)化為三維晶粒組織模型。接著我們與傳統(tǒng)手工重建方法做了對(duì)比，其中包括空間點(diǎn)云重建、Delaunay三角剖分、以及直接點(diǎn)擴(kuò)散法，結(jié)果表明本文提出的方法具有更高的重建精度和魯棒性。針對(duì)三維晶粒組織模型，我們采用網(wǎng)絡(luò)結(jié)構(gòu)基于圖卷積神經(jīng)網(wǎng)絡(luò)的無(wú)監(jiān)督拓?fù)鋵W(xué)習(xí)方法，自動(dòng)挖掘模型中晶粒之間的拓?fù)潢P(guān)系，并進(jìn)行拓?fù)湫再|(zhì)的特征提取和表征。最后，通過(guò)應(yīng)用所提出的方法于高熵合金晶粒組織的重建和表征，得到了高質(zhì)量的三維模型和更為精確的定量特征，驗(yàn)證了方法的有效性和優(yōu)越性。因此，該方法可以應(yīng)用于材料科學(xué)領(lǐng)域中各類材料的晶粒組織重建和表征研究。

關(guān)鍵詞：晶粒組織；計(jì)算機(jī)圖像處理；三維構(gòu)建；表征研究；圖卷積神經(jīng)網(wǎng)絡(luò)。

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