薄壁零件柔性裝配線數(shù)字孿生模型構(gòu)建

薄壁零件柔性裝配線數(shù)字孿生模型構(gòu)建薄壁零件柔性裝配線數(shù)字孿生模型構(gòu)建

摘要

在制造業(yè)中，數(shù)字孿生技術(shù)已被廣泛應(yīng)用于產(chǎn)品設(shè)計(jì)、工藝規(guī)劃和生產(chǎn)過程控制等方面。針對薄壁零件的柔性裝配線，數(shù)字孿生模型的構(gòu)建可以有效提高裝配過程的可靠性和效率。本文基于裝配線中的傳感器數(shù)據(jù)和CAD模型，提出了一種薄壁零件柔性裝配線數(shù)字孿生模型的構(gòu)建方法。首先將CAD模型進(jìn)行網(wǎng)格化處理，并根據(jù)傳感器數(shù)據(jù)獲取零件的真實(shí)狀態(tài)信息；然后利用有限元分析技術(shù)對零件的力學(xué)行為進(jìn)行模擬，并結(jié)合機(jī)器學(xué)習(xí)算法對裝配過程中的故障進(jìn)行預(yù)測和診斷；最后，將模擬結(jié)果與實(shí)際數(shù)據(jù)進(jìn)行比對，驗(yàn)證模型的有效性和可行性。實(shí)驗(yàn)結(jié)果表明，該模型可以有效地模擬薄壁零件在裝配過程中的受力及變形情況，預(yù)測與診斷裝配故障的準(zhǔn)確率較高，具有可靠性和實(shí)用性，為薄壁零件柔性裝配線的優(yōu)化提供了重要的理論依據(jù)和技術(shù)支持。

關(guān)鍵詞：數(shù)字孿生；裝配線；薄壁零件；有限元分析；機(jī)器學(xué)習(xí)

Abstract

Inthemanufacturingindustry,digitaltwintechnologyhasbeenwidelyusedinproductdesign,processplanning,andproductionprocesscontrol.Fortheflexibleassemblylineofthin-walledparts,theconstructionofadigitaltwinmodelcaneffectivelyimprovethereliabilityandefficiencyoftheassemblyprocess.Thispaperproposesamethodforconstructingadigitaltwinmodelofaflexibleassemblylineofthin-walledpartsbasedonsensordataandCADmodels.Firstly,theCADmodelismeshed,andthereal-timestateinformationofthepartsisobtainedaccordingtothesensordata.Then,finiteelementanalysistechnologyisusedtosimulatethemechanicalbehavioroftheparts,combinedwithmachinelearningalgorithmstopredictanddiagnoseassemblyfailures.Finally,thesimulationresultsarecomparedwiththeactualdatatoverifytheeffectivenessandfeasibilityofthemodel.Experimentalresultsshowthatthemodelcaneffectivelysimulatethestressanddeformationofthin-walledpartsintheassemblyprocess,withhighaccuracyinpredictinganddiagnosingassemblyfailures,andhasreliabilityandpracticality,providinganimportanttheoreticalbasisandtechnicalsupportfortheoptimizationoftheflexibleassemblylineofthin-walledparts.

Keywords:Digitaltwin;assemblyline;thin-walledpart;finiteelementanalysis;machinelearningWiththedevelopmentofdigitaltwintechnology,moreandmoreattentionhasbeenpaidtoitsapplicationinthemanufacturingindustry.Inrecentyears,theflexibleassemblylineofthin-walledpartshasbecomeanimportantresearchtopicinthefieldofmanufacturingengineering.However,itischallengingtoensuretheaccuracyandreliabilityoftheassemblyprocessofthin-walledpartsduetotheirlowstiffnessandlargedeformation.

Toaddressthisissue,thisstudyproposedadigitaltwin-basedmethodtooptimizetheflexibleassemblylineofthin-walledparts.Themethodcombinedfiniteelementanalysisandmachinelearningtechniquestomodelandsimulatethestressanddeformationofthin-walledpartsintheassemblyprocess.Specifically,thedigitaltwinmodelwasestablishedbasedonthegeometryandmaterialpropertiesoftheparts,andthefiniteelementmethodwasusedtosimulatetheassemblyprocessandcalculatethestressanddeformationoftheparts.Moreover,machinelearningalgorithms,suchasdecisiontreeandrandomforest,wereemployedtopredictanddiagnosetheassemblyfailures,basedonthedatacollectedfromtheassemblyprocess.

Theexperimentalresultsshowedthattheproposedmethodcouldeffectivelysimulatethestressanddeformationofthin-walledpartsintheassemblyprocess,withhighaccuracyinpredictinganddiagnosingassemblyfailures.Moreover,themethodhadgoodreliabilityandpracticality,providinganimportanttheoreticalbasisandtechnicalsupportfortheoptimizationoftheflexibleassemblylineofthin-walledparts.Infutureresearch,theproposedmethodcanbeappliedtoothermanufacturingprocesses,suchasweldingandforming,tofurtherimprovetheefficiencyandqualityofthemanufacturingindustryInadditiontotheproposedmethod'sapplicationintheflexibleassemblyofthin-walledparts,thereispotentialforitsuseinothermanufacturingprocesses.Forexample,itcanbeappliedinweldingtoimprovetheefficiencyandqualityofweldingprocesses.Weldingisacrucialprocessinmanufacturing,andanydefectsorerrorscanleadtoproductfailure,makingitnecessarytodevelopeffectivemethodsforpredictinganddiagnosingassemblyfailures.

Theproposedmethodcanalsobeusedinformingprocesses,whereitcanaccuratelypredictanddiagnoseassemblyfailures.Formingprocessesinvolveaseriesofmetalworkingtechniquesthatareusedtodeformmetalintoaspecificshapeorsize.Theseprocessesincludestamping,bending,androlling.Withtheapplicationoftheproposedmethodinformingprocesses,manufacturerscansignificantlyreducethelikelihoodoferrorsanddefectsinthefinalproduct.

Moreover,enhancingtheefficiencyandqualityofmanufacturingprocessesusingthisproposedmethodcanleadtogreatersustainability.Byreducingtheamountofresourcesconsumedandminimizingwaste,manufacturerscanimprovetheirenvironmentalfootprint.Themethodcanalsobeusedasatooltodevelopnewtechnologiesthataremoreenvironmentallyfriendly,leadingtoincreasedsustainabilityinmanufacturing.

Overall,theproposedmethodrepresentsanimportantbreakthroughinthemanufactureofthin-walledparts.Itsaccuracyandreliabilitymakeitaninvaluabletoolforpredictinganddiagnosingassemblyfailures,improvingefficiency,andensuringhigh-qualityoutput.Itspotentialapplicationinothermanufacturingprocessesrepresentsasignificantadvancementintheindustry,withthepotentialtoenhancesustainabilitywhileimprovingthequalityofgoodsproducedOnepotentialapplicationofthismethodisintheaerospaceindustry,wherelightweight,yetdurablecomponentsarecriticalforensuringthesafetyandefficiencyofaircraft.Withthismethod,manufacturerscanaccuratelypredictanddiagnosepotentialassemblyfailures,reducingtheriskofcostlyorevencatastrophicaccidents.Additionally,theabilitytomanufacturethin-walledpartswithhighaccuracyandreliabilitycanresultinsignificantweightsavings,reducingfuelconsumptionandemissions.

Anotherpotentialapplicationisinthemedicaldeviceindustry,whereprecisionandaccuracyarecrucialforensuringthesafetyandeffectivenessofdevicesusedinpatientcare.Theabilitytomanufacturethin-walledcomponentswithhighprecisioncanresultinbetterperformanceandlonger-lastingproducts,ultimatelyimprovingpatientoutcomes.

Themethodmayalsoprovevaluableintheautomotiveindustry,wherelightweightyetstrongcomponentscanimprovefuelefficiencyandoverallperformanceofvehicles.Byaccuratelypredictinganddiagnosingassemblyfailures,manufacturerscanreducetheriskofrecalls,savingtimeandmoney.

Overall,theproposedmethodhasbroadapplicationsinmanufacturingandhasthepotentialtofundamentallychangehowthin-walledpartsareproduced.Itsaccuracyandreliabilitymakeitaninvaluabletoolforensu