数控机床相关毕业设计翻译通过热量误差补偿来改善数控机床的精确度.docx

数控机床相关毕业设计翻译通过热量误差补偿来改善数控机床的精确度.docx

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数控机床相关毕业设计翻译通过热量误差补偿来改善数控机床的精确度

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,z2,z3and