139 

AdvancesinProductionEngineering&Management ISSN1854‐6250

Volume12|Number2|June2017|pp139–150 Journalhome:apem‐journal.org

https://doi.org/10.14743/apem2017.2.246 Originalscientificpaper

  

A novel approach of applying copper nanoparticles in minimum quantity lubrication for milling of Ti‐6Al‐4V 

Songmei, Y.a,b,*, Xuebo, H.a,b, Guangyuan, Z.a,b, Amin, M.a,b aBeihang University, School of Mechanical Engineering and Automation, Beijing, P.R. China  bBeijing Engineering Technological Research Center of High‐efficient & Green CNC Machining Process and Equipment, Beijing, P.R. China   

A B S T R A C T   A R T I C L E   I N F O

Presently,nanoparticlesaremixedintolubricants toenhancethelubricatingandcoolingproperties.Someresearchworksareavailableonminimumquan‐titylubrication(MQL)machiningperformanceofnanofluidssuspendedwithMoS2,Al2O3andxGnPnanoparticles.However,thedeficiencyhasbeenfoundinapplyingofmetalparticleslikecopper(Cu)nanoparticles.Inthisresearch,nanofluids have been prepared by mixing four types of nanoparticle (Cu,Graphite,MoS2andAl2O3)intonatural‐77vegetableoilwithtwoconcentra‐tions (1%and2%). Taguchi’s orthogonal arrayhas used for experimentaldesign.ThemachiningperformanceofnanofluidsareevaluatedwithregardtothereductionincuttingforceandsurfaceroughnessduringMQLmillingofTi‐6Al‐4Valloys.Analysisofvariance(ANOVA)hascarriedouttoinvestigatethe relative influence of machining parameters. From the analysis, Cu andGraphitenanoparticleshave shownhigher effects for reducing cutting forceandsurfaceroughness.TheresultsofANOVAhaveshownthat the typeandconcentration of nanoparticles influence the cutting force significantly. Theconfirmationtestshavecarriedoutandfoundthatcopper‐nanofluidreducedcutting force and surface roughness by 8.84 % and 14.74 %, respectively.Graphite‐nanofluid reduced cutting force and surface roughness by 5.51%and21.96%,respectively.

©2017PEI,UniversityofMaribor.Allrightsreserved.

  Keywords:CoppernanoparticlesMinimumquantitylubrication(MQL)CuttingforceSurfaceroughnessAnalysisofvariance(ANOVA)

*Correspondingauthor:yuansm@buaa.edu.cn(Songmei,Y.)

Articlehistory:Received16January2017Revised7May2017Accepted12May2017

  

1. Introduction and literature review 

Traditional manufacturing transform gradually to green manufacturing in recent years. Eco‐friendlymachining technique such asMQL has been promoted for its bettermachinability athigher cutting speedcomparedwith conventional floodcooling.Nevertheless, its capability tocarryawaytheheatislimited[1‐3].Withthedevelopmentofnanoscienceandnanotechnology,someresearchersmixednanoparticlesintobasefluidtoimprovethecharacters,suchasthermalconductivity,specificheat,andheattransfercoefficientinthefieldofheattransferandthisap‐proachmayprovideanalternativewaytoimprovethecoolingperformanceofMQL[4‐6].

Itisobviousthattheheatconductivitycoefficientofsolidismuchhigherthanthatofliquid.Thecoolingperformancemaybeincreasedbyaddingnano‐sizedsolidparticles intolubricant,withoutabrasionandclogging[7].What’smore,theballbearingeffect,protectivefilm,mendingeffectandpolishingeffectofnanoparticlesalsowillreducethefrictioncoefficientandenhancethe surface quality [8]. Researchers had investigated the tribological properties ormachiningperformanceof some commonnanoparticles includemetallic particles (Cu,Bi),metallic oxideparticles(Al2O3,CuO,TiO2)andnano‐diamond(ND)etc[9‐11].Meanwhile,somesolidmateri‐

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alssuchasmolybdenumdisulfide(MoS2),graphiteandboronnitridealsohavebeenused forlubricant[12‐14].Forinstance,MoS2andxGnPatnanoscalehaveamultilayerstructure,whichmake themeffective to reduce the fictionon the chip‐toolor tool‐workpiece contact interface[13]. However, it’s questionable and doubtful that MoS2 has a low dissociation temperature,approximatelyat350 , inoxidizingenvironments.ComparedwithMoS2,xGnPhasnotonlyamuch higher dissociation temperature but also the high aspect ratio, these main advantagesmakeabetterlubricitywhengraphiteisappliedintoMQLprocess[12].Justlikegraphite,hex‐agonalBoronNitride(hBN)hasalayeredstructurewhereeachlayerisweaklybondedtoadja‐centlayers.Thehigherdissociationtemperatureandtheaspectratioofdiameter/thicknesscanbethemainadvantagesoftheplateletformofsolidlubricant[14].Besidesthetribologicalprop‐erties,therearealsosomeotherselectionstandardswhenresearchersselectthetypesofnano‐particle,suchasthenontoxicproperties[10].

Theconductivitycoefficientofcopperisupto401W/(m·K),whichismuchhigherthanthebase fluid.Therefore, the lubricant suspendedwith copperparticlesmay reduce the tempera‐tureofcuttingzoneefficiently[15].Besides,asasoftmetal,thehardnessofthetoolandwork‐pieceisharderthancopper,soitwon’tscratchthemachinedsurface.Whentheparticlesreachto the contact interfaces of cutting zone, they can shape easily, reduce the friction and evenmendthesurface.G.Liuetal.investigatedthemendingeffectofcoppernanoparticleaddedintolubricantoil[16].TheyfoundthatcoppernanoparticlesdodisplayanexcellentmendingeffectthroughthePin‐on‐diskexperimentsandSEMobservations.YanJetal.exploredthefeasibilityoffourtypesofnanoparticles(MoS2,GF,CuandCuO)indiamondturningofreaction‐bondedSiC[17].Theresultsshowthatgreasecontaining10%Cunanoparticlesproducedthehighestsur‐facequalityandthelowesttoolwear.ResearcherscontributedtheexcellentlubricationtoCu’ssignificantly high micro plasticity. These researches and findings make the possibility thatnanofluidsuspendedwithnano‐scalecopperparticlescanimprovetheMQLmachiningperfor‐mancecomparedwithpurelubricant,maybeevenbetterthanothertypesofnanoparticles.

Basedonthepresentwork,however,thereareonlyafewevaluationpaperswhichfocusonthedifferentMQLmachiningperformanceofdifferenttypesofnanoparticle,especiallyformetal(Cu)nanoparticles.So furtherresearch isneeded. In thispaper,anorthogonalexperimentaredesignedtoexploretheperformanceofnano‐enhancedlubricantwithCunanoparticles,aswellasotherthreetypesofnanoparticleswithtwoconcentrations.Meanwhile,theeffectsofmachin‐ingparameterssuchasmillingspeed,depthofcutandfeedrateareinvestigated.ThedesignofexperimentsisperformedusingtheTaguchimethod.Optimalprocessparametersareobtainedusingtherangeanalysis.Moreover,ANOVAiscarriedouttoobtainthesignificanceofparame‐tersinfluencingonthecuttingforce(millingforce)andsurfaceroughnessintheMQLmillingoftitaniumalloy.Aftertheoptimization,theconfirmationtestsareconducted.

2 Experimental procedure 

2.1 Workpiece and nano‐enhanced lubriacant 

Titanium (Ti‐6Al‐4V)alloywasused as theworkpiecematerial,with chemical compositionof6.5 %Al, 4.25%V, 0.04 %Fe, 0.02 % C, 0.015 %N, 0.16%O, 0.0018%H, and the remainingamountisTi.Thenano‐enhancedlubriacant(nanofluids)werepreparedbyaddingfourdiffer‐enttypesofnanoparticleswithanaveragesizeof40nmtothenatural‐77vegetableoil,followedbymechanical rabbling in order to suspend the particles homogeneously in themixture. Thefour types of nanoparticles are MH‐Cu, MH‐Graphite, MH‐MoS2 and MH‐Al2O3, produced byNanjingEmperorNanoMaterialCO.,Ltd(China).Thenatural‐77vegetableoilisthegreenlubri‐cant, produced by iLC company (Italy). The concentration of each lubricantwas 1% or 2%,measuredbyvolume.A suspension stability experimentwasperformedbefore themachiningprocessandthenanofluidshavefoundstableinthemachiningprocessasdesired.Fig.1showsthenanofluidsusedinMQLmachiningprocess.

A novel approach of applying copper nanoparticles in minimum quantity lubrication for milling of Ti‐6Al‐4V 

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2.2 Orthogonal experiment design 

The techniqueofdefiningand investigatingallpossibleconditions inanexperiment involvingmultiple factors isknownas thedesignof experiments (DOE).Theexperimental testsarede‐signedonthebasisofTaguchi’sorthogonalarray 4 2 , forfour factors(typesofnano‐particle,millingspeed,depthofcutandfeedrate)withfourlevels(4 )andonefactor(concen‐tration)with two levels (2 ) [18,19].RangeanalysisandANOVAareperformedtodeterminethe order and relative influence of the machining parameters respectively, through the SPSSsoftware.ThefactorsandtheirlevelssetupfortheexperimentshaveshowninTable1.

Fig.1Nanofluidsusedinmachiningprocess

Table1Factorsandlevelssetupfortheexperiment

Factors Level‐1 Level‐2 Level‐3 Level‐4(A)typesofnanoparticle MH‐Cu MH‐Graphite MH‐MoS2 MH‐Al2O3(B)millingspeed(m/s) 1 1.5 2 2.5(C)depthofcut(mm) 0.2 0.4 0.6 0.8(D)feedrate(mm/z) 0.025 0.05 0.075 0.1(E)concentration 1 % 2 % ‐ ‐

2.3 Experimental setup 

Theverticalmachiningcentre(VMC0850B,Shenyang,China)wasemployedfortheslotmillingprocess.Thismachiningcentrehasthree‐axiswithamaximumrotationalspindlespeedof6000rpm.ThemillingtoolfittedwithtwoteethinsertsACM300anddiameterof16mmhaveusedforexperiments.Theexperimentalsetup,machiningspotandtheMQLdeviceareshowninFig.2.TheMQLdevicewasdesignedbyBeihangUniversityand its relatedparametersareshown inTable2[20,21].

Fig.2Experimentalsetup,machiningspotandtheMQLdevice

Table2TheparametersofMQLdevice

Nano‐enhancedlubricantflowrate 24mL/hAirpressure 0.4Mpa 4.0barDistancefromthenozzletiptothecuttingzone 20mmNozzleelevationangle α 60°Nozzledirectioninrelationtofeeddirection 0°and 120°

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Thecuttingforcemeasuringsystem(9257B,Kistler,Switzerland)fittedwithhighspeedpre‐cisiondynamometer,5070AchargeamplifierandDynoWareV2.41haveappliedandthecuttingforceshaverecordedingraphicalsignal.ThesignalofcuttingforcewereanalysedbyDynoWaresoftware.Theresultantofforce,FrelatedtotheforceinX,Y,ZdirectionshasbeenfoundfromEq.1.Thesurfaceroughness(Sa)weremeasuredbya3Dopticalprofiler(CCIMP,TAYLORHOB‐SON,England).Thisequipmentisanadvancedtypeofmeasurementinterferometer(anoncon‐tact 3D Profiler),which canmeasure polished or rough, curved, flat or stepped surfaces. Theresultantofsurfaceroughness(Sa) is theaverageofvaluesgot fromthreemeasurements.TheorthogonalarrayandtheexperimentdatahaverecordedintheTable3.

(1)

Table3Orthogonalarrayandexperimentaldata

Exp.num

Factors Experimentalparameters Experimentalresults

A B C D E A B(m/s) C(mm) D(mm/z) E(%) F(N) Sa(μm)

1 1 1 1 1 1 Cu 1 0.2 0.025 1 175.49 0.3592 1 2 2 2 1 Cu 1.5 0.4 0.05 1 251.55 0.4223 1 3 3 3 2 Cu 2 0.6 0.075 2 376.17 0.4644 1 4 4 4 2 Cu 2.5 0.8 0.1 2 444.18 0.5195 2 1 2 3 2 Graphite 1 0.4 0.075 2 345.84 0.4896 2 2 1 4 2 Graphite 1.5 0.2 0.1 2 251.84 0.4527 2 3 4 1 1 Graphite 2 0.8 0.025 1 329.05 0.4938 2 4 3 2 1 Graphite 2.5 0.6 0.05 1 320.80 0.3109 3 1 3 4 1 MoS2 1 0.6 0.1 1 420.08 0.59210 3 2 4 3 1 MoS2 1.5 0.8 0.075 1 421.13 0.53811 3 3 1 2 2 MoS2 2 0.2 0.05 2 323.05 0.53712 3 4 2 1 2 MoS2 2.5 0.4 0.025 2 336.99 0.48213 4 1 4 2 2 Al2O3 1 0.8 0.05 2 416.72 0.86014 4 2 3 1 2 Al2O3 1.5 0.6 0.075 2 301.23 0.35815 4 3 2 4 1 Al2O3 2 0.4 0.1 1 316.09 0.60716 4 4 1 3 1 Al2O3 2.5 0.2 0.075 1 237.90 0.353

3 Results and discussion

3.1 Milling force 

Fig.3indicatestheinfluenceonmillingforceofthefactorsatdifferentlevels.The3DresponsesurfaceplotsinFig.4havebeendrawnforallthelevelsofthefivefactors,bytakingthetwofac‐torsasvariableatthesametime.FromthemaineffectplotinFig.3(a),thehigherinfluenceofMH‐Cu,MH‐GraphiteandMH‐Al2O3particleshavebeenfoundinreducingthemillingforce.Theapplicationofthesethreetypesofnanoparticleshasshownreducedmillingforcemorethan60NascomparedtoMH‐MoS2particles.Moreover,theflatlinebetweenMH‐CuandMH‐Graphitehasindicatedthesimilareffectsonmillingforce(millingforceof310Ninbothcases).Besidesthetypesofnanoparticles,theconcentrationalsoremainsadominantfactorinthemillingpro‐cessinFig.3(e).Theillustrationshowsthatthemillingforcedon’talwaysdecreasewithhigherconcentration.Ithasfoundthatnanofluidswith1%concentrationdecreasethemillingforceby11.6%ascomparedtothenanofluidswiththeconcentrationof2%.Fromthe3Dresponsesur‐faceplot inFig.4(d), thesmallestmilling forcehas foundthat factorA(typesofnanoparticle)withCuandfactorE(concentration)with1%forMQLmillingprocess.

From Fig. 3(b), themilling force found decreased up to 35 Nwith the increase ofmillingspeedfrom1m/sto1.5m/s.However,themillingforcefoundincreasedagainwhenthemillingspeed increased to2m/sand2.5m/s. Finally, this forcehas reduced slightly in comparisonswiththeforcerecordedat1m/s.Regardingtothisfact,someresearchcontributethisphenom‐

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enontothesyntheticandcomplexinfluenceofcuttingtemperatureandvibrationinthemillingprocess[22].

ThemillingforcehasfoundincreasedwiththeincreaseofthecuttingdepthandthefeedrateasshowninFig.3(c)and(d),respectively.FromFig.4(c),ithasfoundthatthesmallestdepthofcut and feed rate results the smallestmilling force.Moreover, the difference of the tilt of theplateforeachfactorinFig.4(a),(b)and(c)alsoshowsthatthedepthofcuthasmoreinfluenceonthemillingforceascomparedtofeedrate.

 

Fig.3Themillingforceandrelatedfactorswithdifferentlevels

Rangeanalysisformillingforce

TheresultsofrangeanalysisformillingforcehaverecordedinTable4.AccordingtotheRvol‐ume (155.700>72.3575> 63.4650>40.4910>33.0950), the influence order of five controllablefactorsonmillingforcehasfoundasC→D→A→E→B.Fromtherangeanalysis,factorC(depthofcut)hasfoundwiththemaximuminfluenceandfactorB(millingspeed)hasfoundwiththelow‐

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estinfluenceontheforce.Thetiltoftheplotformillingspeedanddepthofcut(showninFig.4(a),(b)and(c))indicatethesimilarresults.

FromtherangeanalysisinTable4,theoptimalcuttingparametersforFweredeterminedasA1B2C1D1E1. Considering the little difference between level 1 (MH‐Cu) and level 2 (MH‐Graphite) of the type of nanoparticles (factor A), just 0.035, the optimal cutting parameterscouldalsobeA2B2C1D1E1.

ANOVAformillingforce

TheANOVAhascarriedout toanalyse theeffectsof typesandconcentrationsofnanoparticle,millingspeed,depthofcutandfeedrateonthemilling force.Theresultsof thisanalysishaverecordedinTable5.

Table4Resultsofrangeanalysisformillingforce

factor\level Level1 Level2 Level3 Level4 R

A 311.8475 311.8825 375.3125 317.9850 63.4650

B 339.5325 306.4375 336.0900 334.9675 33.0950

C 247.0700 312.6175 354.5700 402.7700 155.700

D 285.6900 328.0300 345.2600 358.0475 72.3575

E 309.0110 349.5030 ‐‐ ‐‐ 40.4910

Table5ResultsofANOVAformillingforceVariationofsource

Sumofsquares(SS)

Degreeoffreedom(DOF)

Meanofsquares(MS)

Fratio Significance

A 11412.524 3 3804.175 55.99 significant

B 2822.461 3 940.820 13.85 non‐significant

C 52305.940 3 17435.313 256.60 highlysignificant

D 11938.312 3 2186.055 32.17 significant

E 6558.165 1 6558.165 96.52 significant

Error(e) 135.895 2 67.948

Total 85173.297 15

Fig.4The3Dresponsesurfaceplotonthemillingforce

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The data of F Distribution Table as follows: F . 3, 2 19.2 , F . 1, 2 18.5 ,F . 3, 2 99.2,F . 1, 2 98.5. The statistical significance of each factor for themillingforce are gained by comparing the data with the F ratio in Table 5 as follows:F 55.99F . 19.2 , F 13.85 F . 19.2 , F 256.60 F . F . 99.2 , F 32.17F . 19.2,F 96.52 F . 18.5. From the ANOVA analysis, we can conclude that thedepthofcuthasahighlysignificantinfluenceonthemillingforce,whilethetypesofnanoparti‐cle, the concentration and the feed rate significantly influence the milling force. The millingspeedhasshownnon‐significantinfluenceascomparedtotheotherfactorsontheforce.

3.2 Surface roughness 

Fig.5hasshowntheinfluenceoffactorsonsurfaceroughnesswithdifferentlevels.The3Dre‐sponsesurfaceplotshaveshowninFig.6.FromthemaineffectplotintheFig.5(a),(e)and3DresponsesurfaceplotinFig.6(d),ithasbeenfoundthatthetypesofnanoparticles(factorA)andthe concentration (factor E) have a remarkable influence on the surface roughness. The littledifferencehasbeenfoundforMH‐CuandMH‐GraphitewiththevalueofSa=0.44μm.ThisvalueislowerthanthevalueofSa=0.54μmthatisfoundbyaddingMH‐MoS2andMH‐Al2O3.Withtheincreaseofconcentrationfrom1%to2%,Safoundincreasedcorrespondinglyby13.2%.Thesurfaceroughnesshasfoundchangewithinthescopeof0.3‐0.6μmforalltheexperiment.

Fig.5Thesurfaceroughnessandrelatedfactorswithdifferentlevels

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Fromthe3DresponsesurfaceplotshowninFig.6(a),(b)and(c),thesurfaceroughnesshasfound increasedwith the increase of depth of cut and feed rate.With the increase ofmillingspeedfrom1m/sto2.5m/s,thesurfaceroughnesshasobservedtodecrease.ButitmaynotbemonotoneseenfromthemaineffectplotinFig.5(b),(c)and(d).Thedifferencebetweenthetiltoftheplotformillingspeed,depthofcutarenotgreaterandisslightlyhigherthanthefeedrate.FromFig.6,ithasfoundthatthecombinationofthemillingspeedsetatlevel4,depthofcutsetat level1andfeedratesetat level1,thebestsurfacequality(thesmallestsurfaceroughness)canbeachieved.

Rangeanalysisforsurfaceroughness

TheresultsofrangeanalysisforsurfaceroughnesshavereportedinTable6.AccordingtotheRvolume(0.17762>0.15896>0.11915>0.10822>0.06081),theinfluenceorderoffivecontrolla‐blefactorsonsurfaceroughnesshasfoundasC→B→D→A→E.Fromtherangeanalysis,itcanbefoundthatthedepthofcuthasthehighestinfluenceonSa,thetypesandconcentrationofnano‐particledon’t influence the roughnessmuch.The result isdifferent fromsomeother researchwhichindicatethatthefeedraterankfirst intheinfluenceorder[23].Wemaycontributethisphenomenontotheinfluenceofnanoparticlesortheunreasonablechoiceofdepthofcut,whichcauseexcessvibrationinthemachiningprocess.

From Table 6, the optimal cutting parameters for surface roughness have been found asA2B4C1D1E1.Consideringthelittledifferencebetweenlevel1andlevel2ofthetypeofnano‐particles(factorA),just0.00493,theoptimalcuttingparameterscouldalsobeA1B4C1D1E1.

ANOVAforsurfaceroughness

Inthissection,theANOVAhascarriedouttoanalysetheeffectsoftypesandconcentrationsofnanoparticle,millingspeed,depthofcutandfeedrateonthesurfaceroughness.TheresultsofthisanalysishavetabulatedinTable7.

Fig.6The3Dresponsesurfaceplotonthesurfaceroughness

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Table6Resultsofrangeanalysisforsurfaceroughness

factor\level Level1 Level2 Level3 Level4 R

A 0.44105 0.43612 0.53729 0.54434 0.10822

B 0.57499 0.44254 0.52525 0.41603 0.15896

C 0.42499 0.50004 0.43116 0.60261 0.17762

D 0.42316 0.53233 0.46100 0.54231 0.11915

E 0.45930 0.52011 ‐‐ ‐‐ 0.06081

Table7ResultsofANOVAforsurfaceroughnessVariationofsource

Sumofsquares(SS)

Degreeoffreedom(DOF)

Meanofsquares(MS)

Fratio significance

A 0.04195 3 0.01398 1.753 non‐significant

B 0.06476 3 0.02159 2.706 non‐significant

C 0.08188 3 0.02729 3.421 non‐significant

D 0.03935 3 0.01312 1.644 non‐significant

E 0.01479 1 0.01479 1.854 non‐significant

Error(e) 0.01596 2 0.00798

Total 0.25868 15ThedataofFDistributionTablefollowsas:F . 3, 2 19.2,F . 1, 2 18.5.Thestatisti‐

calsignificanceofeachfactorforsurfaceroughnessisobtainedbycomparingthedatawiththeFratio in Table 7 as follows:F 1.753 F . 19.2,F 2.706 F . 19.2,F 3.421F . 19.2,F 1.644 F . 19.2,F 1.854 F . 19.2. From the ANOVA, it hasfoundobviously that the typesofnanoparticles,milling speed,depthof cut, feed rate and theconcentrationhavenon‐significantinfluenceonthesurfaceroughness.

FortheMeanofsquares(MS)offactorAandDandEissmallerthanthedoubleoferror,it’sbetter to classify them into the error, the results of ANOVA analysis after the adjustment isshowninTable8.

WecangetthefollowingdatafromFDistributionTable:F . 3, 9 3.86.Bycomparingthedata with the F ratio in:F 2.706 F . 3.86,F 3.421 F . 3.86, we can get thesameconclusionsthatthefivefactorshavenon‐significant influenceonthesurfaceroughness.Previousresearchersclaimedthesimilarresultsabouteffectofcuttingparametersonthesur‐faceroughness[24].

Table8ResultsofANOVAforsurfaceroughnessaftertheadjustment

Variationofsource

Sumofsquares(SS)

Degreeoffreedom(DOF)

Meanofsquares(MS)

Fratio significance

B 0.06476 3 0.02159 2.706 non‐significant

C 0.08188 3 0.02729 3.421 non‐significant

Error(e) 0.11205 9 0.01245

Total 0.25868 15

4. Confirmation tests

Inordertoconfirmtheaccuracyoftheoptimalparametersgotbytheorthogonalexperiments,theconfirmationtestsarearranged.Theconfidenceinterval(CI)isemployedtoverifythequali‐tycharacteristicsoftheconfirmationexperiment.Theconfidenceintervalforthepredictedop‐timalvaluesiscalculatedusingEq.2,Eq.3andEq.4[18,19,25]:

C. I.,

(2)

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(3)

1 ∑ (4)

Where , variance ratio for DOF and at the level of significanceα. The confi‐dence level is(1 α). DOFofmean(whichalwaysequals1), DOFoferrorterm, variance of error term, number of equivalent replications, number of the orthogonalexperiments ( 16), level number of each factor ( 4or2). The optimal parameter isA1B2C1D1E1formillingforceandA2B4C1D1E1forsurfaceroughness.The95%and99%con‐fidence interval for the milling force areC. I. 143.016 33.165andC. I. 143.016 76.526,respectively. The 95 % and 99 % confidence interval for the surface roughness areC. I.0.2008 0.1670andC. I. 0.2008 0.2403,respectively.Moreover,thefeasibilityofnano‐MQLisdeterminedcomparedwiththeperformanceofMQL.Iftherearenonanoparticlesinthelubri‐cant,theparametersaredefinedasA0B×C×D×E0.TheexperimentalsetupandtheresultshaveshownintheTable9.3DviewofthesurfaceofanexperimenthasshowninFig.7.

Table9Experimentalsetupofconfirmationtests

Condition ParametersResults

F(N) Sa(μm)

MQLA0B2C1D1E0 128.05 ‐A0B4C1D1E0 ‐ 0.346

Nano‐enhancedMQL

A1B2C1D1E1 116.73 ‐A1B4C1D1E1 ‐ 0.295A2B2C1D1E1 121.0 ‐A2B4C1D1E1 ‐ 0.270

It’sseenthatnano‐copperhasthebestmachiningperformancewithregardtocuttingforceandnano‐Graphitehasthebestmachiningperformancewithregardtosurfaceroughnessintheexperimnt.ComparedwithMQLmachining,nanofluidsuspendedwithnano‐coppercanreducethe cutting force and surface roughness by 8.84% and 14.74%, respectively. Nanofluid sus‐pendedwithnano‐Graphitecanreducethecuttingforceandsurfaceroughnessby5.51%and21.96%,respectively.ComparedwithMQLmachining,nano‐enhancedMQLgotbettermachin‐ingperformancebothoncuttingforceandsurfaceroughness.Besides,theresultsoftheconfir‐mationtestsalllocatedintherangeof95%and99%CI,whichindicatedthattheoptimalcom‐binationoftheparametersareeffectively.

Fig.73Dviewofthesurfaceofanexperiment

A novel approach of applying copper nanoparticles in minimum quantity lubrication for milling of Ti-6Al-4V

5. Conclusion In this paper, Cu nanoparticles, as well as other three types of nanoparticles were applied into the research and the optimal of cutting parameters in nano-enhanced MQL milling Ti-6Al-4V to improve machining performance were investigated. The conclusions have been drawn as fol-lows:

• Nanoparticles of copper and graphite have a distinct and similar effect on reducing the cutting force and the surface roughness. The types of nanoparticles play a more important role than the concentration for better machining performance. What’s more, higher con-centrations didn’t lead to better machining performance.

• It can be seen from the range analysis that the types and the concentration of the nanopar-ticles have much more influence than the milling speed on the cutting force. But the con-trary is the case for surface roughness. And obviously, depth of cut always play a key role both on the cutting force and surface roughness.

• From the ANOVA analysis, we can conclude that depth of cut has a highly significant influ-ence on the milling force, the types of nanoparticles, the concentration and the feed rate influence the cutting force significantly. The milling speed doesn’t have much effect on the force. While the all five factors have non-significant influence on the surface roughness.

• The confirmation tests show that compared with MQL machining, copper nanofluid re-duced the cutting force and surface roughness by 8.84 % and 14.74 %, respectively. Graphite nanofluid reduced the cutting force and surface roughness by 5.51 % and 21.96 %, respectively.

The combination of different types of particles as additives added into pure lubricant may be explored as future work. This research work has good application for practical machining at industry level.

Acknowledgement This research was financially supported by National Natural Science Foundation of China (51475030). The authors are indebted to this financial support to accomplish this research.

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