Experimental study on energy consumption of computer numerical control machine tools

ContentslistsavailableatScienceDirectJournalofCleanerProductionjournalhomepage:www.elsevier.com/locate/jcleproExperimentalstudyonenergyconsumptionofcomputernumericalcontrolmachinetoolsJingxiangLva,c,RenzhongTanga,*,ShunJiaa,d,YingLiubaIndustrialEngineeringCenter,ZhejiangProvinceKeyLaboratoryofAdvancedManufacturingTechnology,ZhejiangUniversity,Hangzhou310027,Zhejiang,ChinabSchoolofAerospace,TransportationandManufacturing,CranfieldUniversity,CranfieldMK430AL,UnitedKingdomcXi'anResearchInstituteofNavigationTechnology,Xi'an710068,Shaanxi,ChinadShandongUniversityofScienceandTechnology,Qingdao266590,ChinaarticleinfoArticlehistory:Received20February2015Receivedinrevisedform12June2015Accepted1July2015Availableonline17July2015Keywords:EnergyconsumptionNon-cuttingmotionsMaterialremovalComputernumericalcontrolmachinetoolsabstractMachiningprocessesareresponsibleforsubstantialenvironmentalimpactsduetotheirgreatenergyconsumption.Accuratelycharacterizingtheenergyconsumptionofmachiningprocessesisastartingpointtoincreasemanufacturingenergyefficiencyandreducetheirassociatedenvironmentalimpacts.Theenergycalculationofmachiningprocessesdependsontheavailabilityofenergysupplydataofmachinetools.However,theenergysupplycanvarygreatlyamongdifferenttypesofmachinetoolssothatitisdifficulttoobtaintheenergydatatheoretically.Theaimofthisresearchwastoinvestigatetheenergycharacteristicsandobtainthepowermodelsofcomputernumericalcontrol(CNC)machinetoolsthroughanexperimentalstudy.FourCNClathes,twoCNCmillingmachinesandonemachiningcenterwereselectedforexperiments.Powerconsumptionofnon-cuttingmotionsandmaterialremovalwasmeasuredandcomparedfortheselectedmachinetools.Here,non-cuttingmotionsincludestandby,cuttingfluidspraying,spindlerotationandfeedingoperationsofmachinetools.Materialremovalin-cludesturningandmilling.Resultsshowthatthepowerconsumptionofnon-cuttingmotionsandmillingisdependentonmachinetoolswhilethepowerconsumptionofturningisalmostindependentfromthemachinetools.Theresultsimplythattheenergysavingpotentialofmachiningprocessesistremendous.©2015ElsevierLtd.Allrightsreserved.1.IntroductionOneofthemostsevereproblemswecurrentlyfaceinthemanufacturingindustryistheenergyconsumption.Energyusedbytheindustrialsectorhasmorethandoubledinthelast50yearsandindustrycurrentlyconsumesabouthalfoftheworld'senergy(Mouzonetal.,2007).Forexample,inChina,theenergyusedinmanufacturingindustryamounted1,884,980,000tonsofcoalequivalentandcontributedto58%ofChina'stotalenergycon-sumptionin2010(NBS,2011).Machiningiswidelyusedinthemanufacturingsector(Hanafietal.,2012).Improvingtheenergyefficiencyofmachiningprocessescanyieldsignificantreductionintheenvironmentalimpact.Inordertoachievethisgoal,theenergyconsumptionofmachiningprocessesneedstobecharacterizedandevaluatedproperly(LiandKara,2011).*Correspondingauthor.Tel.:þ86(0)57187952048.E-mailaddress:tangrz@zju.edu.cn(R.Tang).http://dx.doi.org/10.1016/j.jclepro.2015.07.0400959-6526/©2015ElsevierLtd.Allrightsreserved.Theenergyconsumedduringmachiningprocessescanbedividedintotwoparts:constantenergyconsumedbynon-cuttingoperationsofthemachinetoolandmaterialremovalenergywhichistheactualenergyusedtoremovematerial(DahmusandGutowski,2004).Here,thenon-cuttingoperationsincludestandby,cuttingfluidspraying,spindlerotation,feeding,etc.Thenon-cuttingpowercanvarysignificantlyamongdifferenttypesofmachinetools.Forinstance,thestandbypowercouldrangefrom319Wto4040Wfordifferenttypesofmachinetools(Behrendtetal.,2012).Foroperationssuchasspindlerotationandfeeding,theirpowerisalsoinfluencedbyvariousoperationparameters.Alotofcurrentresearchhasfocusedonthetheoreticallymodelingofpowerconsumptionofmachinetooloperations.However,manyunknownparametersinthetheoreticalmodelsaredifficulttoobtainduetothecomplexityofcomputernumericalcontrol(CNC)machinetools.Thusmeasurementsarenecessarytoobtainthepowermodelswithstatisticalanalysis.CNCmachinetoolsarecomplexelectromechanicalproductswithmultipleenergysourcesandenergyflowlinks.ThereareJ.Lvetal./JournalofCleanerProduction112(2016)3864e38743865

manytypesofCNCmachinetools,includingCNClathes,CNCmillingmachines,CNCgrindingmachines,machiningcenters.EachtypeofCNCmachinetoolalsocontainsawidevarietyofmachines.Therearemanydifferencesinthemechanicalstructure,motorperformanceandmotioncontrolfordifferentmachinetools.ThusenergysupplycharacteristicsmayvaryalotfordifferentCNCma-chinetools.Theaimofthisstudyistoobtainthepowermodelsofmachinetoolmotionsbasedonthemeasuredpowerdataandstatisticalanalysis,andtoinvestigatetheenergysavingpotentialofCNCmachinetools.Thestructureofthispaperisasfollows.InSection2areviewofcurrentmodelingapproachesforenergysupplyiscar-riedout.InSection3,experimentsareconductedondifferentmachinetoolstoobtainthepowerdataofvariousmotions.InSection4,basedonthemeasurementresults,powerofnon-cuttingmotionsandmaterialremovalwasmodeledanddiscussed.Inaddition,specificenergyconsumptionandenergyutilizationratewerediscussedtoexploreenergysavingopportunities.FinallyinSection5theconclusionsaredrawnandfutureworkisdiscussed.2.Literaturereview2.1.MotionsofthemachinetoolsduringmachiningprocessesMachinetoolsconsumemostoftheenergyinmachiningsys-tem.Duringmachiningprocesses,thetasksarecompletedthroughaseriesofmachinetoolmotionswhichconsumeenergy.Whenthemachinetoolisturnedon,thecontrolsystem,spindlesystemandservosystemareinastateofreadiness.Thisstateofreadiness,whichiscalledbasicmotion,isthebasisofothermotionsandexiststhroughoutthewholeprocessofmachinetooloperation.Themotionsusedtogeneratethesurfaceofproductarecalledgener-ationmotions,whichincludetheprimarymotionandfeedmotion(KnightandBoothroyd,2006).Theenergyconsumptionofgener-ationmotionscanbedividedintotwocategories,energyconsumedbyair-cuttingmotionsandenergyconsumedbymaterialremoval.Theair-cuttingmotions,whichincludespindlerotationandfeeding,arethegenerationmotionswithoutcuttingload.Inaddi-tiontobasicmotionandgenerationmotions,thereexistothermotionstoassistthecuttingoperations,includingcuttingfluidspraying,automatictoolchanging(ATC)andsoon,suchkindofmotionsarecalledauxiliarymotions.Accordingtotheaboveanalysis,themotionsofmachinetoolcanbecategorizedintofourtypes,asshowninTable1.2.2.EnergyconsumptionofbasicandauxiliarymotionThepowerofbasicmotion,whichisalsocalledstandbypower,isusuallyobtainedthroughmeasurements.Themeasurementre-sultsofcommercialpress-brakeshowedthatthebasicmotionconsumed43%,27%and83%ofthetotalenergyforthreemachinetools,respectively(Santosetal.,2011).ThestandbypowerofninedifferentCNCmachinetoolswasmeasuredandresultsshowedthatitvariedsignificantlyacrossdifferentmachinetools,rangingfrom319Wupto4040W(Behrendtetal.,2012).Resultsalsoshowedthatthestandbypowerincreasedwiththecomplexityofamachinetool,forthereasonthattherealizationofhighautomationofma-chinetoolsneedsmoreauxiliaryfunctions(suchashydraulicsys-temsandcoolingsystems),resultingingreaterenergyconsumptionforbasicmotion.SimilarresearcheswerecarriedoutbyLietal.(2011),thestandbypoweroftwoCNCgrindingma-chines,aCNClathe,aCNClathewithmillingfunctionality,averticalmillingmachiningcenteranda5-axismachiningcenterweremeasured,rangingfrom1020Wto5450W.ItcanbeseenthatthestandbypowerofdifferentCNCmachinetoolsvariesalot.MostofthemachinetoolsbeingstudiedinliteraturearehighlyautomatedmachinesproducedinEuropeanandAmerican.Thesemachinescontaincomplexhydraulicandcoolingsystems,resultinginlargestandbypowerconsumption.InChina,manylow-endmachinetoolsproducedarecurrentlyused(Li,2014).However,therearefewstudiesonthestandbypowerofthesemachines.TheauxiliarymotionsincludecuttingfluidsprayingandATC.Thecuttingfluidsareindispensibleformanycuttingprocesses,itcanhelpcoolthetoolandworkpiece,reducefrictionbetweenthetooledgeandworkpieceinordertoextendtoollifeandimproveworkpiecesurfacequality(Rao,2000).However,theusageofcut-tingfluidscouldresultinincreasedenergycosts,environmentalpollutionandhumanchronicdiseases,inaddition,itconsumesextrapowerusedbythecoolantpumpmotortospraythecoolantontothetoolandworkpiece.Murrayetal.(2012)measuredtheenergyconsumptionofHuffmanHS-155Rmulti-axisgrindingmachines,inwhich34%ofthetotalenergyisconsumedbycuttingfluidspraying.ThepowerofcoolantpumpmotorforPL700verticalmachiningcenterismeasuredtobe340W.Theenergyconsump-tionofcuttingfluidsprayingaccountedfor23%ofthetotalenergyconsumptionduringthemachiningprocesses(Lietal.,2013).Cuttingfluidsprayingaccountsforalargeproportionoftotalen-ergyconsumptionduringmachining,anditspowerconsumptioncanbeobtainedexperimentally.Theautomatictoolchangesystemcanautomaticallyconvertonetooltoanother,thusshorteningauxiliarytimebetweenadjacentsteps.Toolchanginglastsarela-tivelyshorttime.Forinstance,itlastsfor3.0e4.1sforCK6153iCNClathe(Lvetal.,2014).TheenergyconsumptionofATCisinsignifi-cant,thustheenergyconsumptionofATCisexcludedinthisstudy.2.3.Energyconsumptionofair-cuttingmotionTheair-cuttingmotionisthemachinetoolrunningwithnoload,includingspindlerotationandfeeding.Spindlerotationisoneofthelargestenergyconsumingmotions.Thepowerofspindlerotationandfeedinghasbeenmodeledtheoreticallybysomere-searchers.Themechanicalenergyrequirementofthespindlewasestimatedbymultiplyingtheangularspeedandthetorque(AvramandXirouchakis,2011).InAvram'smodel,boththesteadystateandtransientregimesofthespindleareconsidered,buttheelectricallossesareignored,asaresult,thepredictedpowerisonlyaboutTable1MotionsofCNCmachinetools.TypeBasicmotionAuxiliarymotionAir-cuttingmotionMaterialremovalNameBasicMotionCuttingFluidSprayingAutomaticToolChangingSpindleRotationFeedingCuttingDescriptionStandbyoperationofthemachinetoolSpraythecoolantfluidontothecuttingareaConvertonecuttingtooltoanotherautomaticallySpindlerotateatacertainspeedFeedinX/Y/ZaxisThecuttingtoolcontacttheworkpieceandremovethematerial3866J.Lvetal./JournalofCleanerProduction112(2016)3864e3874

50%oftheactualpower.Thepowerofspindlerotationisexpressedasalinearfunctionofthespindlerotationalspeed(Jiaetal.,2014;LiandKara,2011).Inthismodel,thepowerincreaseswiththespindlerotationalspeed,yetthisisnotalwaysthecase.However,forthecommonlyusedfrequencycontrolspindlemotor,whenitrunsbeyondthebasefrequency,thepowerlossofthemotorissubjecttoaslightdecreasewiththeincreaseofspindlerotationalspeed(AvramandXirouchakis,2011).Forthisreason,piecewiselinearfunctionhasbeenusedtodescribethespindlerotationalpower(BalogunandMativenga,2013),andagenericmodelwasformu-latedasfollows:PSR¼mnþC(1)wherePSRisthespindlerotationpower[W],misthecoefficientofspindlerotationalspeed,nisthespindlerotationalspeed[r/min],andCisaconstant.Feeddrive,whichisusedtopositionthemachinetoolandworkpiece,isanintegralsubsystemofmachinetools.Theposi-tioningaccuracyandfeedspeeddeterminethesurfacequalityofmachinedpartsandproductionefficiency.Thepoweroffeedingisafunctionoffeedrate.Thepoweroffeedingatcertainfeedratewasmeasured.TakethePL700vertical-millingmachinecentermadebyChengduPreciseCNCMachineToolofChinaforexample,thepowerofX,YandZ-axisfeedingwasmeasuredtobe15W,15Wand32W(Heetal.,2012).Lvetal.modeledthepoweroffeedingtobequadraticfunctionoffeedratethroughtheoreticalanalysisofma-chinetoolsfeeddrivestructure(Lvetal.,2014).Themodelisexpressedas:PFD¼C1ÂfrþC2Âfr2(2)wherePFDisthepowerofaxisfeeding[W],frisfeedrate[mm/min],C1andC2arecoefficients.2.4.EnergyconsumptionofmaterialremovalThepowerofmaterialremovalistheactualpowerusedtoremovematerial.Therearegoodtheoreticalcomputationsavailableforcuttingenergy,buttheyaredifficulttoperformduetothedif-ficultiesinthecalculationofalltheparametersinvolvedinthetheoreticalformulas(Kalpakjian,1984).Theempiricalmethodis,therefore,stillwidelyusedforthereliablepredictionofcuttingforcesandenergies(Bhushan,2013;Dingetal.,2010).Empiricalmodelspossesssimpleandeasy-to-getcharacteristicsaswellasprovidehighpredictionaccuracy.Hence,genericexponentialmodelsarechosentodescribetherelationshipbetweenthema-terialremovalpowerandtheprocessparameters.Thematerialremovalpowermodelswerederivedbymultiplyingcuttingforcebycuttingspeed.Here,thecuttingforcemodelswerefromAiandXiao(1994).Forturningprocesses:PT¼CTaxpTfyTvnT(3)wherePTistheturningpower[W],apisthedepthofcut[mm],fisfeed[mm/r],visthecuttingspeed[m/min],CT,xT,yTandnTarecoefficientsoftheturningpower,depthofcut,feedrateandcuttingspeed,respectively.Likewise,formillingprocesses:PM¼CMaxpMfzyMvnMaueM(4)wherePMisthemillingpower[W],apisthedepthofcut[mm],fzisfeedpertooth[mm/tooth],visthecuttingspeed[m/min],aeisthewidthofcut[mm],CM,xM,yM,nManduMarecoefficientsofthemillingpower,depthofcut,feedratepertooth,cuttingspeedandwidthofcut,respectively.Thepowerduringmachiningcanbeeasilymeasuredbypowermonitor,suchasawattmeter(KalpakjianandSchmid,2006),thusthematerialremovalpowercouldbedirectlymodeledthroughpowermeasurementsandmultiplelinearregressionanalysis.3.MethodologyExperimentswereconductedtoobtainthepowerdataofma-chinetoolmotionsatdifferentoperatingparameters.Theobtaineddatawerefurtherusedforstatisticalanalysistoacquirethepowermodelsofmachinetoolmotions.3.1.ExperimentalsetupSevendifferentCNCmachinetoolswereselectedtostudythepowercharacteristicsofdifferentmotions.ThemachinetoolsusedwereincludingfourCNClathes(CK6153i,CK6136i,CAK6150DiandCY-K500),twoCNCmillingmachines(JTVM6540andXK715B)andaverticalmillingcenter(XHK-714F).AllofthesemachinetoolsweremanufacturedinChina.Thetechnicalspecificationparame-tersofthesevenselectedmachinesarelistedinTables2and3.Forcuttingtests,AISI1045steelwasselectedasthetestmaterialbecauseofitswideuseinmanufacturingindustry.FurtherdetailsoftheworkpiecematerialsareshowninTable4.Thecuttinginsertswererecommendedbythetoolmanufacturer.TheworkpiecesizeandtoolconditionsarepresentedinTable5.Thetestcuttingfluidwasacommonlyusedwater-basedemulsionwhichhasaconsis-tencyof1partoilto50partswater.Duringeachtest,thetotalelectricalpowerconsumptionwasmeasuredusingthreevoltagetransducersLEMLV25-PandthreecurrenttransducersLEMLA55-Pwhichwereconnectedtothemainbusoftheelectricalcabinetofthemachinetools.ThevoltagesignalisacquiredandsampledbyusingtwoNI-9215dataacquisitioncardsandacompactNICdaq-9174dataacquisitionchassisatasamplingfrequencyof5000Hzperchannel.TheLabVIEWpro-gramminginterfacewasdevelopedtovisualizeandstoretheac-quiredforceandpowerdata.Thepowervaluesofthemachinetoolwererecordedonceevery0.1s,asshowninFig.1.Thepowerofbasicmotionandcuttingfluidsprayingwascon-stantandobtainedthroughmeasurement.Thepowerofspindlerotation,feedingandcuttingvarieswithdifferentprocessparam-eters.Forspindlerotationexperiments,thespeedrangesweredetermined,andthenthespindlewascontrolledtorotateateachsameintervalintheranges.Forinstance,thespindlerotatingspeedhasbeendefinedwithinarangefrom0to1500rpmduringthespindlerotationexperiment.Thespindlewasrotatingatthespeedof100,200,300,…,1500rpm.Thefeedaxiswasoperatedbythesameexperimentapproach.CuttingexperimentswereconductedwithdifferentcombinationofcuttingparametersusingdesignofexperimentswhichwillbeelaboratedinSection3.2.Allexperi-mentswererepeatedthreetimesandtheaveragevaluesofthethreemeasurementsofwereusedinthepaper.3.2.DesignofexperimentsforcuttingtestsForcuttingtests,designofexperiments(DOE)waschosentoplantheexperiments.Taguchi'sorthogonaldesignwasemployedtostudythefactorsthatinfluencethecuttingpower.Thevalueofturningpowerisdecidedbyvaluesofparametersincludingcuttingspeed,feedanddepthofcut.Hence,thethreeparameterscanbedefinedasprocessvariables.AspresentedinTable6,fourlevelsofcuttingspeed,feedanddepthofcutwereselectedfromthetoolmanufacturers'recommendation.ThedesignmatrixforturningJ.Lvetal./JournalofCleanerProduction112(2016)3864e3874

Table2Technicalspecificationparametersoftheselectedlathes.ParameterMax.turningdiameter[mm]Max.travelrangeXÂZ[mmÂmm]Max.spindlespeed[r/min]Rapidtraverserate[m/min]NumberoftoolstationsCK6136i360160Â2003000X:3Z:44CK6153i530260Â4002000X:4Z:84CAK6150Di610305Â6001500X:5Z:1043867

CY-K500500250Â8802500X:4Z:84Table3TechnicalspecificationparametersoftheselectedCNCmillingmachinesandmachiningcenter.ParameterCNCmillingmachinesJTVM6540Max.travelrangeXÂYÂZ[mmÂmmÂmm]Max.spindlespeed[r/min]Rapidtraverserate[m/min]Capacityofthetoolmagazine650Â370Â4006000X:6Y:6Z:6eXK715B1320Â550Â6001600X:10Y:10Z:10eMachiningcenterXHK-714F650Â400Â4806000X:12Y:12Z:108Table4Workpiecematerialdetails.AISI1045steelYieldstrength(MPa)Tensilestrength(MPa)Elongation(%)Hardness(HB)Chemicalcomposition(wt%)38566524.5/25262C(0.44);Si(0.23);Mn(0.61);P(0.012);S(0.024);Ni(0.02);Cr(0.03);Cu(0.05);Pb(0.0020);Fe(Remainder)Table5Workpiecesizeandtoolconditionsusedintheexperiments.TurningWorkpiecesizeInsertToolholderClearanceangleCuttingedgeangleNoseradiusTooldiameterNumberofcuttingedgesTotallengthofthetoolHeightofthecutterToolmanufacturer∅80mmÂ150mmVNMG160408-YBC351MVJNR2525M160󰀃93󰀃0.8mmeeeeSumitomoMilling100mmÂ60mmÂ60mmeBT40eee14mm4100mm35mmJiaxingYongtuoexperimentsisshowninTable7.AsshowninthematrixinTable7,eachrowrepresentsonetrial.16experimentswereconductedunderdryconditions.Thelengthofcutforeachtestwas30mminaxialdirection.Inmillingtests,cuttingspeed,feedpertooth,depthofcutandwidthofcutwereselectedastheprocessvariables.AspresentedinTable8,fourlevelsofcuttingspeed,feed,depthofcutandwidthofcutwereselectedfromthetoolmanufacturers'recommendation.Fig.1.Schematicdiagramoftheexperimentalset-up.

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Table6Cuttingparametersandtheirlevelsinturningexperiments.CuttingparametersLevel1Level2Level3Level4Cuttingspeed[m/min]50100150200Feed[mm/rev]0.050.10.150.2Depthofcut[mm]0.51.01.52.0Table7Designmatrixforturningexperiments.ExperimentalCuttingparametersorderCuttingspeedFeedDepthof[m/min][mm/rev]cut[mm]1500.050.52500.113500.151.54500.2251000.05161000.10.571000.15281000.21.591500.051.5101500.12111500.150.5121500.21132000.052142000.11.5152000.151162000.20.5ThedesignmatrixformillingexperimentsisshowninTable9.16experimentswereconductedunderwetconditions.Thelengthofcutforeachtestwas60mm.Thepowerofturningormillingisobtainedbysubtractingthemeasuredidlepoweraftercuttingfromthetotalpowerwhenthemachinetooliscuttingmaterial.TakeCK6153iforinstance,powerprofileofturningisshowninFig.2.Forcuttingexperiments,eachrunwasrepeatedthreetimesandtheaveragevaluesofcuttingpowerwereusedinthepaper.3.3.RegressionanalysisofcuttingpowermodelsRegressionanalysiswasusedtoobtainthecuttingpowermodels.ThenonlinearEquation(3)forturningpowercanbecon-vertedintolinearformbylogarithmictransformationandcanbewritteninEquation(5):logðPlogðCÀÁTÞ¼TÞþnTlogðvÞþyTlogðfÞþxTlogap(5)TheaboveEquation(5)canbewrittenasfollows:pT¼cTþnTVþyTFþxTAp(6)wherepTisthelogarithmictransformationoftheoutputpowerPT;V,FandAp,arethelogarithmictransformationoftheinputpa-rametersv,fandap;cT,nT,yTandxTaretheunknowncoefficientstobeestimated.Table8Cuttingparametersandtheirlevelsinmillingexperiments.CuttingparametersLevel1Level2Level3Level4Cuttingspeed[m/min]6080100120Feedpertooth[mm/tooth]0.030.060.090.12Depthofcut[mm]0.511.52Widthofcut[mm]681012Table9Designmatrixformillingexperiments.ExperimentalCuttingparametersorderCuttingspeedFeedDepthofWidthof[m/min][mm/rev]cut[mm]cut[mm]1600.030.562600.06183600.091.5104600.122125800.031.5126800.062107800.090.588800.121691000.0328101000.061.56111000.09112121000.120.510131200.03110141200.060.512151200.0926161200.121.58Fig.2.PowerprofilesofturningforCK6153i.

TheaboveunknowncoefficientscT,nT,yTandxTwereacquiredbymultiplelinearregressionsoftheexperimentaldatausingSPSSsoftware.ThentheturningpowermodelcanbeobtainedbysubstitutingtheacquiredcoefficientsintoEquation(3).Likely,themillingpowermodelsofEquation(4)canbecon-vertedintolinearformbylogarithmictransformationasshowninEquation(7):logðPÀÁMÞ¼logðCMÞþxMlogapþyMlogðfzÞþnMlogðvÞþuMlogðaeÞ(7)TheaboveEquation(7)canbewrittenasfollows:pM¼cMþxMApþyMFzþnMVþuMAe(8)wherepM,Ap,Fz,VandAearethelogarithmictransformationofPM,ap,fz,vandae;cM,xM,yM,nManduMaretheunknownparameterstobeestimated.BasedontheaboveEquation(8),themillingpowermodelcanbeobtainedthroughmultiplelinearregressionanalysisofexperi-mentaldatausingSPSSsoftware.Fig.3.PowerprofilesofbasicmotionandcuttingfluidsprayingforCK6153i.

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Table10Measuredpowerofbasicmotionandcuttingfluidspraying.PowerMachinetoolsCK6153iBasicmotion[W]Cuttingfluidspraying[W]332.1369.5CK6136i335.7132.2CAK6150Di414.0149.5CY-K500220.594.9JTVM6540360.5216.4XK715B684.7180.63869

XHK-714F371.0233.04.Resultsanddiscussion4.1.Powerofbasic,auxiliaryandair-cuttingmotionsThebasic,auxiliaryandair-cuttingmotionsarenon-cuttingmotionsofmachinetools.TakeCK6153iforexample,itspowerprofilesofbasicmotionandcuttingfluidsprayingareshowninFig.3.ThemeasuredpowervaluesofbasicmotionandcuttingfluidsprayingareshowninTable10.ThespindlerotationalspeedofCNCmachinetooliscontrolledbyvariablefrequencymotor.Inordertoincreasetheoutputtorquerangeofspindlesystem,theCNClathesareoftenequippedwith2e4transmissionchains.FortheselectedCNClathes,CK6153ihasfourtransmissionchains,fromhigh-speedtolow-speedtheyareAH,BH,ALandBL;CK6136ihastwotransmissionchains:high-speed(H)andlow-speed(L);CAK6150DiandCY-K500havethreetransmissionchains:high-speed(H),medium-speed(M)andlow-speed(L).ThepowercurvesofspindlerotationatvariousspeedsareshowninFig.4.ComparedtoCNClathes,CNCmillingmachinesandmachiningcentercanachievehigherspindlerotationalspeeds.Atthesamespeed,thepowerofspindlerotationforCNCmillingmachinesandmachiningcentersismuchlessthanthatofCNClathes.ThespindlesystemofCNClatheshasmanytransmissionapparatus,includingbelts,shafts,gears,spindleandchuck,andthetransmissionapparatusweightofCNClatheismuchmorethanthatofCNCmillingmachinesandmachiningcenter.Asaresult,thespindlefrictiontorqueoflathesislargerthanthatofmillingma-chines,andmuchmorepowerisconsumedbyCNClathesduringspindlerotationatthesamespeed.ForCNClathes,thepowerofspindlerotationwithdifferentpowertransmissionchainsvariesconsiderably.Thepowerconsumptionatlow-speedtransmissionchainsismuchlargerthanthatathigh-speedtransmissionchainstokeepthespindlerotatingatacertainspeed,sincethemotorneedstorotatefastertodrivethespindlerotatingatlow-speedtransmissionchains.AccordingtoEquation(1),differentmodelscanbedevelopedthroughpiecewiselinearregressiontopredictspindlerotationpowerduringair-cuttingmotions,whichareprovidedinTable11.Thecut-offpointsinthepiecewisemodelswerethecorrespondingspindlerotationalspeedswhentheslopeofthepowercurveschangessignificantlyinFig.4.ThepowercurvesoffeedingatvariousfeedratesareshowninFig.5.ThefeedingpowerofCNCmillingmachinesandmachiningFig.4.Powerofspindlerotationatvariousspeeds:(a)CK6153i;(b)CK6136i;(c)CAK6150Di;(d)CY-K500;(e)JTVM6540;(f)XK715B;(g)XHK-714F.

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Table11Spindlerotationpowerpredictionmodels.MachinetoolPowermodelsCK6153i8P<1:09nþ41:12ð0Fig.5.Poweroffeedingatvariousfeedrates:(a)CK6153i;(b)CK6136i;(c)CAK6150Di;(d)CY-K500;(e)JTVM6540;(f)XK715B;(g)XHK-714F.

Table12Feedingpowerpredictionmodels.MachinetoolCK6153iCK6136iCAK6150DiCY-K500JTVM6540PowermodelsPXF¼5Â10À6fr2þ0:0135frPZF¼2Â10À6fr2þ0:0311frð0Fig.6.ComparisonofturningpoweramongCK6153i,CK6136iandCAK6150DiCNClathes.

Fig.9.ComparisonofmillingpowerbetweenJTVM6540andXHK-714F.

Table14Turningpowerpredictionmodels.MachinetoolCK6153iCK6136iCAK6150DiPowermodels:917PT¼44:57v0:909f0:657a0p:941PT¼40:64v0:931f0:662a0p:941PT¼30:86v0:984f0:669a0pTable15Millingpowerpredictionmodels.MachinetoolJTVM6540XHK-714FPowermodels:927a0:942PM¼3:353v0:927fz0:764a0pe0:9580:7980:000PM¼4:044vfzap:923a1eFig.7.PredictionaccuracyofturningpowermodelofCK6153i.

4.3.SpecificenergyconsumptionandenergyutilizationrateSpecificenergyconsumption(SEC)isdefinedastheenergyconsumptionofthemachinetoolforremoving1cm3material(KaraandLi,2011).TheSECcanbefurtherdecomposedintotwosegmentsaccordingtowhethertheenergyisusedtoremovethematerial:non-cuttingrelatedSECandcuttingrelatedSEC.KaraandLi(2011)notedthattheSECisaninversefunctionofmaterialremovalrate(MRR).However,theinfluenceofnon-cuttingandcuttingrelatedSECwasnotinvestigated.TakeCK6153iforinstance,thenon-cuttingandcuttingpowerwasmeasuredforeachcuttingexperimentsusingthecuttingpa-rametersinTable7.Thenthenon-cuttingorcuttingrelatedSECwascalculatedusingEquation(10).AsshowninFig.11,thenon-cuttingandcuttingrelatedSECarethefunctionofMRR.Thenon-cuttingpowerconsumptiondonotincreasemuchwiththeincreaseofMRR,asaresult,thenon-cuttingrelatedSECdecreasesrapidlywiththeincreaseofMRR.ThecuttingrelatedSECdoesnotdecreaseobviouslywiththein-creaseofMRRforthereasonthatthecuttingpowerincreasesproportionallywithMRR.Thusthemainchanceforenergydecreasingliesinthedecreaseofnon-cuttingenergyconsumptionofmachinetools.InordertocomparetheefficiencyofenergyusagewithdifferentMRR,energyutilizationrate,whichisdefinedastheratiooftheenergyusedformaterialremovaltothetotalenergyconsumption,isintroducedandrepresentedasthefunctionofMRRinthispaper,asshowninFig.12.AstheMRRincreases,theenergyutilizationrateincreases,from10.9%attheMRRof20.8mm3/stothemaximumof57.9%attheMRRof500mm3/s.Therefore,theenergyconsumptionofmachiningprocessescanbegreatlyreducedbyincreasingcuttingparameters.Notingthatthemaximumenergyutilizationrateisonly57.9%,alargeamountofenergycouldbesavedbyreducingnon-cuttingenergy.Theenergysavingpotentialliesintwoaspects:reducingthenon-cuttingtimeduringmachinetoolusephaseanddesigningmoreenergyefficientmachinetools.Forinstance,themachinetoolshouldbeshutdownifthewaitingtimeistoolong.Themachinetoolmanufacturescouldadoptlightweighttransmissionstructuretoreducethespindlerotationpower,oruseminimumquantitylubricationtoreducecuttingfluidsprayingpower.5.ConclusionsSEC¼PMRR(10)whereSECisspecificenergyconsumption[J/mm3],Pisthenon-cuttingorcuttingpower[W],MRRisthematerialremovalrate[mm3/s].CNCmachinetoolsarewidelyusedinmanufacturingindustryandconsumelotsofenergy.UnderstandingenergyconsumptioncharacteristicsprovidesthebasisforenergysavingofCNCmachinetools.TherehasbeensomeresearchontheoreticalmodelingandanalysisofCNCmachinetoolsenergyconsumption.However,Fig.8.Predictionaccuracyofturningpowermodelsof(a)CK6136iand(b)CAK6150Di.

J.Lvetal./JournalofCleanerProduction112(2016)3864e38743873

Fig.10.Predictedaccuracyofmillingpowermodelsof(a)JTVM6540and(b)XHK-714F.

Fig.11.Non-cuttingandcuttingrelatedSECasafunctionofMRR.

energycharacteristicsmayvaryalotfordifferenttypesofCNCmachinetoolsduetothecomplexityofmachinetoolstructure.ThemotionofCNCmachinetoolistherootcauseofenergyconsumption.Themotionsaredividedintofourtypes:basicmo-tion,auxiliarymotion,air-cuttingmotionandmaterialremoval.FourCNClathes,twoCNCmillingmachinesandaverticalmillingcenterareselectedforstudy.Powerdataofdifferentmotionsfortheselectedmachinetoolswasmeasuredandcomparedinthispaper.Basedontheobtaineddata,powermodelsofair-cuttingmotionandmaterialremovalwereestablishedbyregressionanalysis.Accordingtoexperimentalresults,conclusionsaredrawnasfollows:1.Thepowerconsumptionofbasic,auxiliaryandair-cuttingmo-tionsisdependentonmachinetools.2.Thepowerconsumptionofturningisalmostindependentfromthemachinetools,andthemodelobtainedfromonemachinetoolcanbeusedtopredicttheturningpowerofothermachinetools.However,thepowerconsumptionofmillingvarieswithdifferentmachinetools,themillingpowerofeachmachinetoolneedstobemodeledseparately.3.WiththeincreaseofMRR,thenon-cuttingrelatedSECdecreasesrapidlywhilethecuttingrelatedSECdecreasesslightly.Themainchanceforenergyreductionliesinthedecreaseofnon-cuttingenergyconsumptionofmachinetools.4.Theenergyutilizationrateofmachiningprocessesisverylow,especiallywhenMRRislow.Thuslargeamountofenergycouldbesavedbyincreasingcuttingparameters.Thepowerdataandmodelscanhelpgaininsightintotheen-ergyconsumptioncharacteristicsandconstitutionofCNCmachineFig.12.EnergyutilizationrateasthefunctionofMRR.

tools.EnergysavingdirectionofCNCmachinetoolswasfurtherpointedoutinthispaper.Basedonthepowerdataandmodelsofmachinetoolmotions,energysavingmethodologiesofCNCma-chinetoolsduringtheirusephasewillbedevelopedinfuture.AcknowledgmentThisworkwassupportedbytheNationalNaturalScienceFoundationofChina(GrantNo.51175464)andtheNingboScienceandTechnologyInnovationTeam(GrantB81006).TheauthorswouldliketoconveytheirsincerethankstoMr.YangKaidongfromTsinghuaUniversity,Mr.ShaoSaijunfromTheUniversityofHongKong,Mr.ZhouJilieandMr.WangQiangfromthemetalworkingcenterofZhejiangUniversityfortheirvaluablecontributionsdur-ingtheexperiments.Wealsothankalltheanonymousreviewersfortheirhelpfulsuggestionsonthequalityimprovementofourpaper.NomenclatureAccpredictionaccuracyaewidthofcut[mm]Aelogarithmictransformationofaeapdepthofcut[mm]AplogarithmictransformationofapCconstantinthespindlerotationpowermodelC1coefficientforfeedingpowerC2coefficientforfeedingpowerCMcoefficientformillingpowercMlogarithmictransformationofCMCTcoefficientforturningpowercTlogarithmictransformationofCTffeed[mm/r]Flogarithmictransformationofffrfeedrate[mm/min]fzfeedpertooth[mm/tooth]FzlogarithmictransformationoffzmcoefficientofspindlerotationalspeedMRRthematerialremovalrate[mm3/s]nspindlerotationalspeed[r/min]nMcoefficientoffeedratepertoothinmillingpowermodelnTcoefficientofcuttingspeedinturningpowermodelPthenon-cuttingorcuttingpower[W]PAHSRspindlerotationpowerforAHtransmissionchain[W]PALSRspindlerotationpowerforALtransmissionchain[W]PBHSRspindlerotationpowerforBHtransmissionchain[W]PBLSRspindlerotationpowerforBLtransmissionchain[W]PFDaxisfeedingpower[W]PHspindlerotationpowerforHtransmissionchain[W]PSRMmillingpower[W]pMlogarithmictransformationofPMPmesmeasuredpower[W]Ppredthepredictedpower[W]3874

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