机械加工刀具中英文对照外文翻译文献

中英文对照外文翻译文献PAGE17中英文对照外文翻译英文原文SelectionofoptimumtoolgeometryandcuttingconditionsusingasurfaceroughnesspredictionmodelforendmillingAbstractInfluenceoftoolgeometryonthequalityofsurfaceproducediswellknownandhenceanyattempttoassesstheperformanceofendmillingshouldincludethetoolgeometry.Inthepresentwork,experimentalstudieshavebeenconductedtoseetheeffectoftoolgeometry(radialrakeangleandnoseradius)andcuttingconditions(cuttingspeedandfeedrate)onthemachiningperformanceduringendmillingofmediumcarbonsteel.Thefirstandsecondordermathematicalmodels,intermsofmachiningparameters,weredevelopedforsurfaceroughnesspredictionusingresponsesurfacemethodology(RSM)onthebasisofexperimentalresults.ThemodelselectedforoptimizationhasbeenvalidatedwiththeChisquaretest.Thesignificanceoftheseparametersonsurfaceroughnesshasbeenestablishedwithanalysisofvariance.Anattempthasalsobeenmadetooptimizethesurfaceroughnesspredictionmodelusinggeneticalgorithms(GA).TheGAprogramgivesminimumvaluesofsurfaceroughnessandtheirrespectiveoptimalconditions.1IntroductionEndmillingisoneofthemostcommonlyusedmetalremovaloperationsinindustrybecauseofitsabilitytoremovematerialfastergivingreasonablygoodsurfacequality.Itisusedinavarietyofmanufacturingindustriesincludingaerospaceandautomotivesectors,wherequalityisanimportantfactorintheproductionofslots,pockets,precisionmouldsanddies.Greaterattentionisgiventodimensionalaccuracyandsurfaceroughnessofproductsbytheindustrythesedays.Moreover,surfacefinishinfluencesmechanicalpropertiessuchasfatiguebehaviour,wear,corrosion,lubricationandelectricalconductivity.Thus,measuringandcharacterizingsurfacefinishcanbeconsideredforpredictingmachiningperformance.Surfacefinishresultingfromturningoperationshastraditionallyreceivedconsiderableresearchattention,whereasthatofmachiningprocessesusingmultipointcutters,requiresattentionbyresearchers.Astheseprocessesinvolvelargenumberofparameters,itwouldbedifficulttocorrelatesurfacefinishwithotherparametersjustbyconductingexperiments.Modellinghelpstounderstandthiskindofprocessbetter.Thoughsomeamountofworkhasbeencarriedouttodevelopsurfacefinishpredictionmodelsinthepast,theeffectoftoolgeometryhasreceivedlittleattention.However,theradialrakeanglehasamajoraffectonthepowerconsumptionapartfromtangentialandradialforces.Italsoinfluenceschipcurlingandmodifieschipflowdirection.Inadditiontothis,researchers[1]havealsoobservedthatthenoseradiusplaysasignificantroleinaffectingthesurfacefinish.Thereforethedevelopmentofagoodmodelshouldinvolvetheradialrakeangleandnoseradiusalongwithotherrelevantfactors.Establishmentofefficientmachiningparametershasbeenaproblemthathasconfrontedmanufacturingindustriesfornearlyacentury,andisstillthesubjectofmanystudies.Obtainingoptimummachiningparametersisofgreatconcerninmanufacturingindustries,wheretheeconomyofmachiningoperationplaysakeyroleinthecompetitivemarket.Inmaterialremovalprocesses,animproperselectionofcuttingconditionscausesurfaceswithhighroughnessanddimensionalerrors,anditisevenpossiblethatdynamicphenomenaduetoautoexcitedvibrationsmaysetin[2].Inviewofthesignificantrolethatthemillingoperationplaysintoday’smanufacturingworld,thereisaneedtooptimizethemachiningparametersforthisoperation.So,anefforthasbeenmadeinthispapertoseetheinfluenceoftoolgeometry(radialrakeangleandnoseradius)andcuttingconditions(cuttingspeedandfeedrate)onthesurfacefinishproducedduringendmillingofmediumcarbonsteel.Theexperimentalresultsofthisworkwillbeusedtorelatecuttingspeed,feedrate,radialrakeangleandnoseradiuswiththemachiningresponsei.e.surfaceroughnessbymodelling.Themathematicalmodelsthusdevelopedarefurtherutilizedtofindtheoptimumprocessparametersusinggeneticalgorithms.2ReviewProcessmodellingandoptimizationaretwoimportantissuesinmanufacturing.Themanufacturingprocessesarecharacterizedbyamultiplicityofdynamicallyinteractingprocessvariables.Surfacefinishhasbeenanimportantfactorofmachininginpredictingperformanceofanymachiningoperation.Inordertodevelopandoptimizeasurfaceroughnessmodel,itisessentialtounderstandthecurrentstatusofworkinthisarea.Davisetal.[3]haveinvestigatedthecuttingperformanceoffiveendmillshavingvarioushelixangles.CuttingtestswereperformedonaluminiumalloyL65forthreemillingprocesses(face,slotandside),inwhichcuttingforce,surfaceroughnessandconcavityofamachinedplanesurfaceweremeasured.Thecentralcompositedesignwasusedtodecideonthenumberofexperimentstobeconducted.Thecuttingperformanceoftheendmillswasassessedusingvarianceanalysis.Theaffectsofspindlespeed,depthofcutandfeedrateonthecuttingforceandsurfaceroughnesswerestudied.Theinvestigationshowedthatendmillswithlefthandhelixanglesaregenerallylesscosteffectivethanthosewithrighthandhelixangles.Thereisnosignificantdifferencebetweenupmillinganddownmillingwithregardtothecuttingforce,althoughthedifferencebetweenthemregardingthesurfaceroughnesswaslarge.Bayoumietal.[4]havestudiedtheaffectofthetoolrotationangle,feedrateandcuttingspeedonthemechanisticprocessparameters(pressure,frictionparameter)forendmillingoperationwiththreecommerciallyavailableworkpiecematerials,11L17freemachiningsteel,62-35-3freemachiningbrassand2024aluminiumusingasingleflutedHSSmillingcutter.Ithasbeenfoundthatpressureandfrictionactonthechip–toolinterfacedecreasewiththeincreaseoffeedrateandwiththedecreaseoftheflowangle,whilethecuttingspeedhasanegligibleeffectonsomeofthematerialdependentparameters.Processparametersaresummarizedintoempiricalequationsasfunctionsoffeedrateandtoolrotationangleforeachworkmaterial.However,researchershavenottakenintoaccounttheeffectsofcuttingconditionsandtoolgeometrysimultaneously;besidesthesestudieshavenotconsideredtheoptimizationofthecuttingprocess.Asendmillingisaprocesswhichinvolvesalargenumberfparameters,combinedinfluenceofthesignificantparametersanonlybeobtainedbymodelling.MansourandAbdallaetal.[5]havedevelopedasurfaceroughnessmodelfortheendmillingofEN32M(asemi-freecuttingcarboncasehardeningsteelwithimprovedmerchantability).Themathematicalmodelhasbeendevelopedintermsofcuttingspeed,feedrateandaxialdepthofcut.Theaffectoftheseparametersonthesurfaceroughnesshasbeencarriedoutusingresponsesurfacemethodology(RSM).Afirstorderequationcoveringthespeedrangeof30–35m/minandasecondorderequationcoveringthespeedrangeof24–38m/minweredevelopedunderdrymachiningconditions.Alauddinetal.[6]developedasurfaceroughnessmodelusingRSMfortheendmillingof190BHNsteel.Firstandsecondordermodelswereconstructedalongwithcontourgraphsfortheselectionofthepropercombinationofcuttingspeedandfeedtoincreasethemetalremovalratewithoutsacrificingsurfacequality.Hasmietal.[7]alsousedtheRSMmodelforassessingtheinfluenceoftheworkpiecematerialonthesurfaceroughnessofthemachinedsurfaces.Themodelwasdevelopedformillingoperationbyconductingexperimentsonsteelspecimens.Theexpressionshows,therelationshipbetweenthesurfaceroughnessandthevariousparameters;namely,thecuttingspeed,feedanddepthofcut.Theabovemodelshavenotconsideredtheaffectoftoolgeometryonsurfaceroughness.Sincetheturnofthecenturyquitealargenumberofattemptshavebeenmadetofindoptimumvaluesofmachiningparameters.Usesofmanymethodshavebeenreportedintheliteraturetosolveoptimizationproblemsformachiningparameters.JainandJain[8]haveusedneuralnetworksformodelingandoptimizingthemachiningconditions.Theresultshavebeenvalidatedbycomparingtheoptimizedmachiningconditionsobtainedusinggeneticalgorithms.Sureshetal.[9]havedevelopedasurfaceroughnesspredictionmodelforturningmildsteelusingaresponsesurfacemethodologytoproducethefactoraffectsoftheindividualprocessparameters.Theyhavealsooptimizedtheturningprocessusingthesurfaceroughnesspredictionmodelastheobjectivefunction.Consideringtheabove,anattempthasbeenmadeinthisworktodevelopasurfaceroughnessmodelwithtoolgeometryandcuttingconditionsonthebasisofexperimentalresultsandthenoptimizeitfortheselectionoftheseparameterswithinthegivenconstraintsintheendmillingoperation.3MethodologyInthiswork,mathematicalmodelshavebeendevelopedusingexperimentalresultswiththehelpofresponsesurfacemethodology.Thepurposeofdevelopingmathematicalmodelsrelatingthemachiningresponsesandtheirfactorsistofacilitatetheoptimizationofthemachiningprocess.Thismathematicalmodelhasbeenusedasanobjectivefunctionandtheoptimizationwascarriedoutwiththehelpofgeneticalgorithms.3.1MathematicalformulationResponsesurfacemethodology(RSM)isacombinationofmathematicalandstatisticaltechniquesusefulformodellingandanalyzingtheproblemsinwhichseveralindependentvariablesinfluenceadependentvariableorresponse.Themathematicalmodelscommonlyusedarerepresentedby:whereYisthemachiningresponse,ϕistheresponsefunctionandS,f,α,raremillingvariablesand∈istheerrorwhichisnormallydistributedabouttheobservedresponseYwithzeromean.Therelationshipbetweensurfaceroughnessandotherindependentvariablescanberepresentedasfollows，whereCisaconstantanda,b,canddareexponents.Tofacilitatethedeterminationofconstantsandexponents,thismathematicalmodelwillhavetobelinearizedbyperformingalogarithmictransformationasfollows:TheconstantsandexponentsC,a,b,canddcanbedeterminedbythemethodofleastsquares.Thefirstorderlinearmodel,developedfromtheabovefunctionalrelationshipusingleastsquaresmethod,canberepresentedasfollows:whereY1istheestimatedresponsebasedonthefirst-orderequation,Yisthemeasuredsurfaceroughnessonalogarithmicscale,x0=1(dummyvariable),x1,x2,x3andx4arelogarithmictransformationsofcuttingspeed,feedrate,radialrakeangleandnoseradiusrespectively,∈istheexperimentalerrorandbvaluesaretheestimatesofcorrespondingparameters.Thegeneralsecondorderpolynomialresponseisasgivenbelow:whereY2istheestimatedresponsebasedonthesecondorderequation.Theparameters,i.e.b0,b1,b2,b3,b4,b12,b23,b14,etc.aretobeestimatedbythemethodofleastsquares.Validityoftheselectedmodelusedforoptimizingtheprocessparametershasbeentestedwiththehelpofstatisticaltests,suchasF-test,chisquaretest,etc.[10].3.2OptimizationusinggeneticalgorithmsMostoftheresearchershaveusedtraditionaloptimizationtechniquesforsolvingmachiningproblems.Thetraditionalmethodsofoptimizationandsearchdonotfarewelloverabroadspectrumofproblemdomains.Traditionaltechniquesarenotefficientwhenthepracticalsearchspaceistoolarge.Thesealgorithmsarenotrobust.Theyareinclinedtoobtainalocaloptimalsolution.Numerousconstraintsandnumberofpassesmakethemachiningoptimizationproblemmorecomplicated.So,itwasdecidedtoemploygeneticalgorithmsasanoptimizationtechnique.GAcomeundertheclassofnon-traditionalsearchandoptimizationtechniques.GAaredifferentfromtraditionaloptimizationtechniquesinthefollowingways:1.GAworkwithacodingoftheparameterset,nottheparameterthemselves.2.GAsearchfromapopulationofpointsandnotasinglepoint.3.GAuseinformationoffitnessfunction,notderivativesorotherauxiliaryknowledge.4.GAuseprobabilistictransitionrulesnotdeterministicrules.5.ItisverylikelythattheexpectedGAsolutionwillbetheglobalsolution.Geneticalgorithms(GA)formaclassofadaptiveheuristicsbasedonprinciplesderivedfromthedynamicsofnaturalpopulationgenetics.Thesearchingprocesssimulatesthenaturalevaluationofbiologicalcreaturesandturnsouttobeanintelligentexploitationofarandomsearch.ThemechanicsofaGAissimple,involvingcopyingofbinarystrings.Simplicityofoperationandcomputationalefficiencyarethetwomainattractionsofthegeneticalgorithmicapproach.Thecomputationsarecarriedoutinthreestagestogetaresultinonegenerationoriteration.Thethreestagesarereproduction,crossoverandmutation.InordertouseGAtosolveanyproblem,thevariableistypicallyencodedintoastring(binarycoding)orchromosomestructurewhichrepresentsapossiblesolutiontothegivenproblem.GAbeginwithapopulationofstrings(individuals)createdatrandom.Thefitnessofeachindividualstringisevaluatedwithrespecttothegivenobjectivefunction.Thenthisinitialpopulationisoperatedonbythreemainoperators–reproductioncrossoverandmutation–tocreate,hopefully,abetterpopulation.Highlyfitindividualsorsolutionsaregiventheopportunitytoreproducebyexchangingpiecesoftheirgeneticinformation,inthecrossoverprocedure,withotherhighlyfitindividuals.Thisproducesnew“offspring”solutions,whichsharesomecharacteristicstakenfromboththeparents.Mutationisoftenappliedaftercrossoverbyalteringsomegenes(i.e.bits)intheoffspring.Theoffspringcaneitherreplacethewholepopulation(generationalapproach)orreplacelessfitindividuals(steadystateapproach).Thisnewpopulationisfurtherevaluatedandtestedforsometerminationcriteria.Thereproduction-crossovermutation-evaluationcycleisrepeateduntiltheterminationcriteriaaremet.中文翻译选择最佳工具，几何形状和切削条件

利用表面粗糙度预测模型端铣摘要：刀具几何形状对工件表面质量产生的影响是人所共知的，因此，任何成型面端铣设计应包括刀具的几何形状。在当前的工作中，实验性研究的进行已看到刀具几何（径向前角和刀尖半径）和切削条件（切削速度和进给速度），对加工性能，和端铣中碳钢影响效果。第一次和第二次为建立数学模型，从加工参数方面，制订了表面粗糙度预测响应面方法（丹参），在此基础上的实验结果。该模型取得的优化效果已得到证实，并通过了卡方检验。这些参数对表面粗糙度的建立，方差分析极具意义。通过尝试也取得了优化表面粗糙度预测模型，采用遗传算法（GA）。在加文的程式中实现了最低值，表面粗糙度及各自的值都达到了最佳条件。

1导言端铣是最常用的金属去除作业方式，因为它能够更快速去除物质并达到合理良好的表面质量。它可用于各种各样的制造工业，包括航空航天和汽车这些以质量为首要因素的行业，以及在生产阶段，槽孔，精密模具和模具这些更加注重尺寸精度和表面粗糙度产品的行业内。此外，表面光洁度还影响到机械性能，如疲劳性能，磨损，腐蚀，润滑和导电性。因此，测量表面光洁度，可预测加工性能。车削过程对表面光洁度造成的影响历来倍受研究关注，对于加工过程采用多刀，用机器制造处理，都是研究员需要注意的。由于这些过程涉及大量的参数，使得难以将关联表面光洁度与其他参数进行实验。在这个过程中建模有助于更好的理解。在过去，虽然通过许多人的大量工作，已开发并建立了表面光洁度预测模型，但影响刀具几何方面受到很少注意。然而，除了切向和径向力量，径向前角对电力的消费有着重大的影响。它也影响着芯片冰壶和修改芯片方向人流。此外，研究人员[1]也指出，在不影响表面光洁度情况下，刀尖半径发挥着重要作用。因此，发展一个很好的模式应当包含径向前角和刀尖半径连同其他相关因素。对于制造业，建立高效率的加工参数几乎是将近一个世纪的问题，并且仍然是许多研究的主题。获得最佳切削参数，是在制造业是非常关心的，而经济的加工操作中及竞争激烈的市场中发挥了关键作用。在材料去除过程中，不当的选择切削条件造成的表面粗糙度高和尺寸误差，它甚至可能发生动力现象：由于自动兴奋的震动，可以设定在[2]。鉴于铣削运行在今天的全球制造业中起着重要的作用，就必要优化加工参数。因此通过努力，在这篇文章中看到刀具几何（径向前角和刀尖半径）和切削条件（切削速度和进给速度），表面精整生产过程中端铣中碳钢的影响。实验显示，这项工作将被用来测试切削速度，进给速度，径向前角和刀尖半径与加工反应。数学模型的进一步利用，寻找最佳的工艺参数，并采用遗传算法可促进更大发展。2回顾建模过程与优化，是两部很重要的问题，在制造业。生产过程的特点是多重性的动态互动过程中的变数。表面光洁度一直是一个重要的因素，在机械加工性能预测任何加工操作。为了开发和优化表面粗糙度模型，有必要了解目前在这方面的工作的状况。迪维斯等人[3]调查有关切削加工性能的五个铣刀具有不同螺旋角。分别对铝合金L65的3向铣削过程（面，槽和侧面）进行了切削试验，并对其中的切削力，表面粗糙度，凹状加工平面进行了测量。所进行的若干实验是用来决定该中心复合设计的。切削性能的立铣刀则被评定采用方差分析。对主轴速度，切削深度和进给速度对切削力和表面粗糙度的影响进行了研究。调查显示铣刀与左手螺旋角一般不太具有成本效益比。上下铣方面切削力与右手螺旋角，虽然主要区别在于表面粗糙度大，但不存在显著差异。拜佑密等人[4]研究过工具对旋转角度，进给速度和切削速度在机械工艺参数（压力，摩擦参数）的影响，为端铣操作常用三种商用工件材料，11L17易切削钢，62-35-3易切削黄铜和铝2024年使用单一槽高速钢立铣刀。目前已发现的压力和摩擦法对芯片-工具接口减少，增加进给速度，并与下降的气流角，而切削速度已微不足道，对一些材料依赖参数，工艺参数，归纳为经验公式，作为职能的进给速度和刀具旋转角度为每个工作材料。不过，研究人员也还有没有考虑到的影响，如切削条件和刀具几何同步，而且这些研究都没有考虑到切削过程的优化。因为端铣过程介入多数f参量，重大参量的联合只能通过塑造得到。曼苏尔和艾布达莱特基地[5]已开发出一种表面粗糙度模式，为年底铣EN32M（半自由切削碳硬化钢并改进适销性）。数学模型已经研制成功，可用在计算切削速度，进给速度和轴向切深。这些参数对表面粗糙度的影响已进行了响应面分析法（丹参）。分别制定了一阶方程涵盖的速度范围为30-35米/分，一类二阶方程涵盖速度范围的24-38米/分的干切削条件。艾尔艾丁等人[6]开发出一种表面粗糙度模型，用丹参，为端铣190BHN钢。为选择适当的组合，切割速度和伺服，增加金属