基于CEEMD和GWO-SVR的铣削振动信号前瞻预测-豆柴文库

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基于CEEMD和GWO-SVR的铣削振动信号前瞻预测
Title:ProactivePredictionofMillingVibrationSignalsBasedonCEEMDandGWO-SVR
Abstract:
Millingvibrationisacriticalfactoraffectingmachiningefficiencyandproductquality.Earlypredictionofmillingvibrationsignalscanhelpoptimizemachiningparametersandreducetheriskoftooldamage.Inthispaper,anovelapproachbasedonthecombinationofCompleteEnsembleEmpiricalModeDecomposition(CEEMD)andGreyWolfOptimizer(GWO)withSupportVectorRegression(SVR)isproposedtoachieveproactivepredictionofmillingvibrationsignals.Theeffectivenessoftheproposedapproachisdemonstratedthroughexperimentalresultsandcomparisonwithotherpredictionmethods.
1.Introduction(200words)
Millingisawidelyusedmachiningprocessinvariousindustriessuchasautomotive,aerospace,andmanufacturing.However,excessivevibrationduringmillingcanleadtoreducedtoollife,poorsurfacefinish,andincreasedmachiningtime.Therefore,accuratepredictionandcontrolofmillingvibrationsarecrucialforoptimizingmachiningperformanceandensuringproductquality.
2.LiteratureReview(400words)
Severaltechniqueshavebeendevelopedforvibrationsignalprediction,includingtimedomainanalysis,frequencydomainanalysis,andmachinelearning-basedmethods.However,existingapproachesoftenfailtocapturethenonlinear,non-stationary,andnon-Gaussiancharacteristicsofmillingvibrationsignals.Toaddressthesechallenges,theCEEMDmethodcaneffectivelydecomposecomplexsignalsintointrinsicmodefunctions(IMFs)withdistinctfrequencybands.
3.Methodology(400words)
Theproposedproactivepredictionframeworkconsistsoftwomaincomponents:CEEMDandGWO-SVR.First,themillingvibrationsignalisdecomposedintodifferentIMFsusingCEEMD.EachIMFrepresentsaspecificvibrationpattern.GWOisthenappliedtooptimizetheSVRparametersanddeterminethebestcombinationofhyperparametersforeachIMF.TheoptimizedSVRmodelsareusedtoforecastfuturevibrationsignals.
4.ExperimentalResults(300words)
Tovalidatetheeffectivenessoftheproposedapproach,experimentaldatafrommillingtestsareused.Thepredictionperformanceisevaluatedusingmetricssuchasrootmeansquareer