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通过热量误差补偿来改善数控机床的外文翻译精确度数控机床类中英文翻译外文文献翻译.docx

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通过热量误差补偿来改善数控机床的外文翻译精确度数控机床类中英文翻译外文文献翻译.docx

通过热量误差补偿来改善数控机床的外文翻译精确度数控机床类中英文翻译外文文献翻译

外文原文

IMPROVINGACCURACYOFCNCMACHINE

TOOLSTHROUGHCOMPENSATION

FORTHERMALERRORS

Abstract:

 AmethodforimprovingaccuracyofCNCmachinetoolsthroughcompensationforthethermalerrorsisstudied.Thethermalerrorsareobtainedby1-Dballarrayandcharacterizedbyanautoregressivemodelbasedonspindlerotationspeed.ByrevisingtheworkpieceNCmachiningprogram,thethermalerrorscanbecompensatedbeforemachining.Theexperimentsonaverticalmachiningcentershowthattheeffectivenessofcompensationisgood.

Keywords:

CNCmachinetool  Thermalerror  Compensation

0 INTRODUCTION

Improvementofmachinetoolaccuracyisessentialtoqualitycontrolinmanufacturingprocesses.Thermallyinducederrorshavebeenrecognizedasthelargestcontributortooverallmachineinaccuracyandareprobablythemostformidableobstacletoobtaininghigherlevelofmachineaccuracy.Thermalerrorsofmachinetoolscanbereducedbythestructuralimprovementofthemachinetoolitselfthroughdesignandmanufacturingtechnology.However,therearemanyphysicallimitationstoaccuracywhichcannotbeovercomesolelybyproductionanddesigntechniques.Soerrorcompensationtechnologyisnecessary.Inthepastseveralyears,significanteffortshavebeendevotedtothestudy.Becausethermalerrorsvarywithtimeduringmachining,mostpreviousworkshaveconcentratedonreal-timecompensation.Thetypicalapproachistomeasurethethermalerrorsandtemperatureofseveralrepresentativepointsonthemachinetoolssimultaneouslyinmanyexperiments,thenbuildanempiricalmodelwhichcorrelatesthermalerrorstothetemperaturestatuesbymulti-variantregressionanalysisorartificialneuralnetwork.Duringmachining,theerrorsarepredictedon-lineaccordingtothepre-establishedmodelandcorrectedbytheCNCcontrollerinreal-timebygivingadditionalsignalstothefeed-driveservoloop.However,veryfewpracticalcasesofreal-timecompensationhavebeenreportedtobeappliedtocommercialmachinetoolstoday.Somedifficultieshinderitswidespreadapplication.First,itistedioustomeasurethermalerrorsandtemperatureofmanypointsonthemachinetools.Second,thewiresoftemperaturesensorsinfluencetheoperatingofthemachinemoreorless.Third,thereal-timeerrorcompensationcapabilityisnotavailableonmostmachinetools.

Inordertoimprovetheaccuracyofproduction-classCNCmachinetools,anovelmethodisproposed.Althoughanumberofheatsourcescontributetothethermalerrors,thefrictionofspindlebearingsisregardedasthemainheatsource.Thethermalerrorsaremeasureedby1-Dballarrayandaspindle-mountedprobe.Anautoregressivemodelbasedonspindlerotationspeedisthendevelopedtodescribethetime-variantthermalerror.Usingthismodel,thermalerrorscanbepredictedassoonastheworkpieceNCmachiningprogramismade.Bymodifyingtheprogram,thethermalerrorsarecompensatedbeforemachining.Theeffortandcostofcompensationaregreatlyreduced.ThisresearchiscarriedonaJCS2018verticalmachiningcenter.

1 EXPERIMENTALWORK

Forcompensationpurpose,theprincipalinterestisnotthedeformationofeachmachinecomponent,butthedisplacementofthetoolwithrespecttotheworkpiece.Intheverticalmachiningcenterunderinvestigation,thethermalerrorsarethecombinationoftheexpansionofspindle,thedistortionofthespindlehousing,theexpansionofthreeaxesandthedistortionofthecolumn.

Duetothedimensionalelongationofleadscrewandbendingofthecolumn,thethermalerrorsarenotonlytime-variantinthetimespanbutalsospatial-variantovertheentiremachineworkingspace.

Inordertomeasurethethermalerrorsquickly,asimpleprotablegauge,i.e.,1-Dballarray,isutilized.1-Dballarrayisarigidbarwithaseriesofballsfixedonitwithequalspace.Theballshavethesamediameterandsmallsphericityerrors.Theballarrayisusedasareferenceforthermalerrormeasurement.Alotofpre-experimentsshowthatthethermalerrorsinz-axisarefarlargerthanthoseinx-axisandy-axis,thereforemajorattentionisdrawnonthethermalerrorsinz-axis.Thermalerrorsintheothertwoaxescanbeobtainedinthesameway.

ThemeasuringprocessisshowninFig.1.Aprobeismountedonthespindlehousingand1-Dballarrayismountedontheworkingtable.Initially,thecoordinatesoftheballsaremeasuredundercoldcondition.Thenthespindleisrunatatestingconditionoveraperiodoftimetochangethemachinethermalstatus.Thecoordinatesoftheballsaremeasuredperiodically.Thethermaldriftsofthetoolareobtainedbysubtractingtheballcoordinatesunderthenewthermalstatusfromthereferencecoordinatesunderinitialcondition.Becauseittakesonlyabout1mintofinishonemeasurement,thethermaldriftsofthemachineunderdifferentzcoordinatescanbeevaluatedquicklyandeasily.Accordingtotherateofchange,thethermalerrorsandtherotationspeedaresampledbyevery10min.Sinceonlythedriftsofcoordinatesdeviatedfromthecoldconditionbutnottheabsolutedimensionsofthegaugeareconcerned,accuracyandpreciseinstrumentsuchasalaserinterferometerisnotrequired.Thereareonlyfourmeasurementpointsz1,z2,z3,z4tocoverthez-axisworkingrangewhosecoordinatesare-50,-150,-250,-350respectively.Thermalerrorsatothercoordinatescanbeobtainedbyaninterpolatingfunction.

Previousexperimentsshowthatthethermallyinduceddisplacementbetweenthespindlehousingandtheworkingtableisthesamewiththatbetweenthespindleandtable.SothethermalerrorsΔzmeasuredreflectthoseinrealcuttingconditionwithnegligibleerror.

Inordertoobtainathoroughimpressionofthethermalbehaviorofthemachinetooland

identifytheerrormodelaccurately,ameasurementstrategyisdeveloped.Variousloadsofthespindlespeedareapplied.Theyaredividedintothreecategoriesasthefollowing:

(1)Theconstantspeed;

(2)Thespeedspectrum;(3)Thespeedsimulatingrealcuttingcondition.Theeffectoftheheatgeneratedbythecuttingprocessisnottakenintoaccounthere.However,theinfluenceofthecuttingprocessonthethermalbehaviourofthetotalmachinestructureisregardedtobenegligibleinfinishingprocess.

Inthismachine,themostsignificantheatsourcesarelocatedinthez-axis.Thermalerrorsinzdirectionondifferentxandycoordinatesareapproximatelythesame.Itimpliesthatthepositionsofx-carriageandy-carriagehavenostronginfluenceonthez-axisthermalerrors.

Fig.1(L) Thermalerrormeasurement  1.Spindlemountedprobe 2.1-Dballarray  

Fig.2(R) Thermalerrorsatdifferentzcoordinates 1.z=-50 2.z=-150 3.z=-250 4.z=-350

Fig.2plotsthetime-historyofthermaldriftΔzatdifferentzcoordinatesunderatest.It

showsthattheresultantthermaldriftsareobviousposition-dependent.Thethermaldriftsatz1,z2,z3,z4arecoincidentinitiallybutseparategraduallyastimepassesandtemperatureincreases.

Thereasonisthat,initiallymostofthermaldriftsresultfromtheposition-independentthermalgrowthofthespindlehousingwhichwouldrisefastandgotothermal-equilibriumquicklycomparedtoothermachinecomponentswithlongerthermal-time-constants.However,astimepasses,thoseposition-dependentthermalerrorssuchastheleadscrewandthecolumncontributetotheresultantthermaldriftsofthetoolmoreandmore.Asaresult,thethermaldriftsatdifferentzcoordinateshavedifferentmagnitudeandthermalcharacteristics.However,thethermalerrorsatdifferentcoodinatesvarywithzcoordinatecontinuously.

2 ARMODELFORTHERMALERROR

Precisepredictionofthermalerrorsisanimportantstepforaccurateerrorcompensation.

Sincetheknowledgeofthemachinestructure,theheatsourceandtheboundaryconditionareinsufficient,aprecisequantitativepredictionbasedontheoreticalheattransferanalysisisquitedifficult.Ontheotherhand,empirical-basederrormodelsusingregressionanalysisandneuralnetworkshavebeendemonstratedtopredictthermalerrorswithsatisfactoryaccuracyinmuchapplication.

Thermalerrorsarecausedbyvariousheatsources.Onlytheinfluenceoftheheatcausedbythefictionofspindlewhichisthemostsignificantheatsourceisconsidered.Theinfluenceofexternalheatsourceonmachiningaccuracycanbediminishedbyenvironmenttemperaturecontrol.

Fromtheobtaineddata,itisfoundthatthermalerrorsvarycontinuouslywithtime.The

valueoferroratonemomentisinfluencedbythatofthepreviousmomentandtherotationspeedofspindle.Soamodelrepresentingthebehaviorofthethermalerrorsaswrittenistheform

where Δz(t)———Thermalerrorattimet

k,m———Orderofthemodel

ai,bi———Coefficientofthemodel

n(t-i)———Spindlerotationspeedattimet-i

Theorderkandmaredeterminedbythefinalprediction-errorcriterion.Thecoefficientsai

andbiareestimatedbyartificialneuralnetworktechnique.Aneuralnetworkisamultiplenonlinearregressionequationinwhichthecoefficientsarecalledweightsandaretrainedwithaniterativetechniquecalledbackpropagation.Itislesssensitivethanothermodelingtechniquetoindividualinputfailureduetothresholdingofthesignalsbythesigmoidfunctionsateachnode.TheneuralnetworkforthisproblemisshowninFig.3.(k=1,m=0).Thenumberofhiddednodesisdeterminedbyatrial-anderrorprocedure.

Usingthedataobtained(thermalerrorsandcorrespondencespeed),fourmodelsfortheerrorsatz1,z

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