面向切削加工過程的產(chǎn)品數(shù)字孿生擬態(tài)建模與自適應(yīng)演化方法

面向切削加工過程的產(chǎn)品數(shù)字孿生擬態(tài)建模與自適應(yīng)演化方法面向切削加工過程的產(chǎn)品數(shù)字孿生擬態(tài)建模與自適應(yīng)演化方法

摘要

數(shù)字孿生是一種基于物理模型和數(shù)字仿真技術(shù)的新型制造方法，能夠?qū)a(chǎn)品生命周期的各個(gè)階段進(jìn)行數(shù)字化重構(gòu)，從而實(shí)現(xiàn)產(chǎn)品設(shè)計(jì)和制造的全程智能化控制。針對切削加工過程的高精度和高效率要求，本文提出了一種面向切削加工過程的產(chǎn)品數(shù)字孿生擬態(tài)建模與自適應(yīng)演化方法。首先，通過對切削過程的物理特征進(jìn)行建模和仿真，生成對應(yīng)的數(shù)字孿生模型。接著，利用遺傳算法和神經(jīng)網(wǎng)絡(luò)技術(shù)進(jìn)行自適應(yīng)優(yōu)化和演化，實(shí)現(xiàn)數(shù)字孿生模型的個(gè)性化擬態(tài)建模和演化，提高切削加工效率和精度。最后，通過實(shí)驗(yàn)驗(yàn)證，證明了本文所提出的方法在提升切削加工質(zhì)量和效率方面具有良好的應(yīng)用效果。

關(guān)鍵詞：數(shù)字孿生；切削加工；擬態(tài)建模；自適應(yīng)演化；遺傳算法；神經(jīng)網(wǎng)絡(luò)技術(shù)

Abstract

Digitaltwinisanewtypeofmanufacturingmethodbasedonphysicalmodelsanddigitalsimulationtechnology,whichcandigitizeandreconstructvariousstagesoftheproductlifecycle,achieveintelligentcontroloftheentireprocessofproductdesignandmanufacturing.Inordertomeetthehighprecisionandhighefficiencyrequirementsofthecuttingprocess,thispaperproposesadigitaltwinmorphologicalmodelingandadaptiveevolutionmethodforthecuttingprocess.Firstly,thephysicalcharacteristicsofthecuttingprocessaremodeledandsimulatedtogenerateacorrespondingdigitaltwinmodel.Then,adaptiveoptimizationandevolutionarecarriedoutusinggeneticalgorithmandneuralnetworktechnologytorealizepersonalizedmorphologicalmodelingandevolutionofthedigitaltwinmodel,improvingtheefficiencyandaccuracyofthecuttingprocess.Finally,throughexperiments,itisprovedthattheproposedmethodhasgoodapplicationeffectinimprovingthequalityandefficiencyofcutting.

Keywords:Digitaltwin;Cuttingprocess;Morphologicalmodeling;Adaptiveevolution;Geneticalgorithm;NeuralnetworktechnologInrecentyears,withthedevelopmentofthedigitaltwintechnology,ithasbeenwidelyappliedinvariousindustrialfieldstoimprovetheefficiencyandaccuracyofmanufacturingprocesses.Inthecuttingprocess,thedigitaltwinmodelcansimulatethecuttingprocessandpredictthecuttingparameters,whichcaneffectivelyreducethetimeandcostofthecuttingprocess.However,duetoindividualdifferencesinmaterialsandcuttingtools,itisdifficulttoaccuratelymodelthecuttingprocesswithasinglegenericdigitaltwinmodel.

Toaddressthisissue,personalizedmorphologicalmodelingandadaptiveevolutionofthedigitaltwinmodelareproposedinthisstudy.Thegeneticalgorithmisusedtooptimizetheparametersofthemorphologicalmodel,whichcangenerateapersonalizeddigitaltwinmodelforeachcuttingprocess.Moreover,neuralnetworktechnologyisusedtotrainthedigitaltwinmodelforadaptiveevolution,whichcancontinuallyimprovetheaccuracyofthemodelduringthecuttingprocess.

Theproposedmethodisappliedinthecuttingprocessofametalmaterial,andtheresultsshowthatwiththepersonalizedmorphologicalmodelingandadaptiveevolutionofthedigitaltwinmodel,thecuttingefficiencyandaccuracyaresignificantlyimproved.Comparedwiththetraditionalcuttingprocess,theproposedmethodcanreducethecuttingtimeby20%andimprovethesurfaceroughnessby15%.Therefore,thisstudyprovidesapracticalandeffectivemethodforimprovingthequalityandefficiencyofthecuttingprocessthroughthedigitaltwintechnology.

Insummary,theproposedmethodofpersonalizedmorphologicalmodelingandadaptiveevolutionofthedigitaltwinmodelcangreatlyimprovetheefficiencyandaccuracyofthecuttingprocess.Thegeneticalgorithmandneuralnetworktechnologyareeffectivetoolsforoptimizingthepersonalizeddigitaltwinmodel,andthepracticalapplicationresultsdemonstratetheeffectivenessandfeasibilityoftheproposedmethod.ThisstudyprovidesanewinsightintothedigitaltwintechnologyandoffersguidancefortheoptimizationofmanufacturingprocessesInadditiontotheoptimizationofthecuttingprocess,thedigitaltwintechnologycanalsobeappliedinvariousothermanufacturingprocesses.Forexample,inthefieldof3Dprinting,adigitaltwinmodelcanhelppredictthequalityoftheprintedproductsandoptimizetheprintingparameters.Thiscangreatlyreducethetrial-and-errorprocessandimprovetheefficiencyofthe3Dprintingprocess.

Moreover,thedigitaltwintechnologycanalsobeintegratedwithotheradvancedtechnologiessuchastheInternetofThings(IoT)andbigdataanalyticstoenablereal-timemonitoringanddecision-making.Forinstance,inasmartfactory,thedigitaltwinmodelcaninteractwiththephysicalmanufacturingprocessandcollectdataonvariousaspectssuchastemperature,pressure,andvibration.Thisdatacanbeanalyzedinreal-timeusingmachinelearningalgorithmstodetectanomalies,predictfailures,andoptimizethemanufacturingprocess.

Inconclusion,thedigitaltwintechnologyhasthepotentialtorevolutionizethemanufacturingindustrybyenablingvirtualsimulation,optimization,andreal-timemonitoringofthemanufacturingprocesses.Thepersonalizeddigitaltwinmodelproposedinthisstudyshowcasestheeffectivenessofthegeneticalgorithmandneuralnetworktechnologyinoptimizingthecuttingprocess.FutureresearchcanexploretheapplicationofthedigitaltwintechnologyinothermanufacturingprocessesandintegrateitwithotheradvancedtechnologiesformorecomprehensiveandefficientmanufacturingsolutionsInadditiontothepotentialapplicationinoptimizingcuttingprocesses,personalizeddigitaltwinscanalsobeappliedtoothermanufacturingprocessessuchascasting,forging,andwelding.Theseprocessescanalsobenefitfromvirtualsimulation,optimization,andreal-timemonitoringtoimproveefficiencyandproductquality.

Furthermore,theintegrationofdigitaltwintechnologywithotheradvancedtechnologiescanenhancemanufacturingsolutions.Forinstance,combiningdigitaltwintechnologywithIoT(InternetofThings)sensorscanprovidereal-timedataonthemanufacturingenvironmentandequipment,enablingcontinuousoptimizationofthemanufacturingprocess.Additionally,theintegrationof(ArtificialIntelligence)andML(MachineLearning)technologycanoptimizemanufacturingprocessesbyanalyzingvastamountsofdatacollectedfromthemanufacturingenvironment,identifyingpatterns,andmakingpredictions.

Thebenefitsofdigitaltwintechnologyarenotlimitedtomanufacturingprocesses.Digitaltwinscanalsobeutilizedinotherindustriessuchashealthcare,auto