外文翻译-智能机床

毕业设计中英文翻译学生姓名学号学院专业指导教师2013年5月机械设计制造及其自动化原文4844AUTONOMOUSANDINTELLIGENTMACHINETOOLTHEWHOLEMACHININGOPERATIONOFCONVENTIONALCNCMACHINETOOLSISPREDETERMINEDBYNCPROGRAMSONCETHECUTTINGCONDITIONS,SUCHASDEPTHOFCUTANDSTEPOVER,AREGIVENBYTHEMACHININGCOMMANDSINTHENCPROGRAMS,THEYARENOTGENERALLYALLOWEDTOBECHANGEDDURINGMACHININGOPERATIONSTHEREFORENCPROGRAMSMUSTBEADEQUATELYPREPAREDANDVERIFIEDINADVANCE,WHICHREQUIRESEXTENSIVEAMOUNTSOFTIMEANDEFFORTMOREOVER,NCPROGRAMSWITHFIXEDCOMMANDSARENOTRESPONSIVETOUNPREDICTABLECHANGES,SUCHASJOBDELAY,JOBINSERTION,ANDMACHINEBREAKDOWNFOUNDONMACHININGSHOPFLOORSSHIRASEPROPOSEDANEWARCHITECTURETOCONTROLTHECUTTINGPROCESSAUTONOMOUSLYWITHOUTNCPROGRAMSFIGURE4829SHOWSTHECONCEPTUALSTRUCTUREOFAUTONOMOUSANDINTELLIGENTMACHINETOOLSAIMACAIMACCONSISTSOFFOURFUNCTIONALMODULESCALLEDMANAGEMENT,STRATEGY,PREDICTION,ANDOBSERVATIONALLFUNCTIONALMODULESARECONNECTEDWITHEACHOTHERTOSHARECUTTINGINFORMATIONFIG4829CONCEPTUALSTRUCTUREOFAIMACDIGITALCOPYMILLINGFORREALTIMETOOLPATHGENERATIONATECHNIQUECALLEDDIGITALCOPYMILLINGHASBEENDEVELOPEDTOCONTROLACNCMACHINETOOLDIRECTLYTHEDIGITALCOPYMILLINGSYSTEMCANGENERATETOOLPATHSINREALTIMEBASEDONTHEPRINCIPLEOFTRADITIONALCOPYMILLINGINDIGITALCOPYMILLING,ATRACINGPROBEANDAMASTERMODELINTRADITIONALCOPYMILLINGAREREPRESENTEDBYTHREEDIMENSIONAL3DVIRTUALMODELSINACOMPUTERAVIRTUALTRACINGPROBEISSIMULATEDTOFOLLOWAVIRTUALMASTERMODEL,ANDCUTTERLOCATIONSAREGENERATEDDYNAMICALLYACCORDINGTOTHEMOTIONOFTHEVIRTUALTRACINGPROBEINREALTIMEINTHEDIGITALCOPYMILLING,CUTTERLOCATIONSAREGENERATEDAUTONOMOUSLY,ANDANNCMACHINETOOLCANBEINSTRUCTEDTOPERFORMMILLINGOPERATIONWITHOUTNCPROGRAMSADDITIONALLY,NOTONLYSTEPOVER,BUTALSORADIALANDAXIALDEPTHSOFCUTCANBEMODIFIED,ASSHOWNINFIG4830ALSO,DIGITALCOPYMILLINGCANGENERATENEWTOOLPATHSTOAVOIDCUTTINGPROBLEMSANDCHANGETHEMACHININGSEQUENCEDURINGOPERATION4812FURTHERMORE,THECAPABILITYFORINPROCESSCUTTINGPARAMETERSMODIFICATIONWASDEMONSTRATED,ASSHOWNINFIG48314813REALTIMETOOLPATHGENERATIONANDTHEMONITOREDACTUALMILLINGARESHOWNINTHELOWERLEFTCORNERANDTHEUPPERRIGHTCORNEROFTHISFIGURETHEMONITOREDCUTTINGTORQUE,ADAPTEDFEEDRATE,ANDRADIALANDAXIALDEPTHSOFCUTARESHOWNINTHELOWERRIGHTCORNEROFTHISFIGURETHECUTTINGPARAMETERSCANBEMODIFIEDDYNAMICALLYTOMAINTAINTHECUTTINGLOADFIG4830ADEXAMPLEOFREALTIMETOOLPATHGENERATIONABILATERALZIGZAGPATHSBCONTOURINGPATHSCCHANGEOFSTEPOVERDCHANGEOFCUTTINGDEPTHFIG4831ADAPTIVEMILLINGONAIMACFIG4832RESULTSOFMACHININGPROCESSPLANNINGONAIMACFLEXIBLEPROCESSANDOPERATIONPLANNINGSYSTEMAFLEXIBLEPROCESSANDOPERATIONPLANNINGSYSTEMHASBEENDEVELOPEDTOGENERATECUTTINGPARAMETERSDYNAMICALLYFORMACHININGOPERATIONTHESYSTEMCANGENERATETHEPRODUCTIONPLANFROMTHETOTALREMOVALVOLUMETRVTHETRVISEXTRACTEDFROMTHEINITIALANDFINISHEDSHAPESOFTHEPRODUCTANDISDIVIDEDINTOMACHININGPRIMITIVESORMACHININGFEATURESTHEFLEXIBLEPROCESSANDOPERATIONPLANNINGSYSTEMCANGENERATECUTTINGPARAMETERSACCORDINGTOTHEMACHININGFEATURESDETECTEDFIGURE4832SHOWSTHEOPERATIONSEQUENCEANDCUTTINGTOOLSTOBEUSEDCUTTINGPARAMETERSAREDETERMINEDFORTHEEXPERIMENTALMACHININGSHAPETHEDIGITALCOPYMILLINGSYSTEMCANGENERATETHETOOLPATHSORCLDATADYNAMICALLYACCORDINGTOTHESERESULTSANDPERFORMTHEAUTONOMOUSMILLINGOPERATIONWITHOUTREQUIRINGANYNCPROGRAM485KEYTECHNOLOGIESFORFUTUREINTELLIGENTMACHINETOOLSEVERALARCHITECTURESANDTECHNOLOGIESHAVEBEENPROPOSEDANDINVESTIGATEDASMENTIONEDINTHEPREVIOUSSECTIONSHOWEVER,THEYARENOTYETMATUREENOUGHTOBEWIDELYAPPLIEDINPRACTICE,ANDTHEACHIEVEMENTSOFTHESETECHNOLOGIESARELIMITEDTOSPECIFICCASESACHIEVEMENTSOFKEYTECHNOLOGIESFORFUTUREINTELLIGENTMACHINETOOLSARESUMMARIZEDINFIG4833PROCESSANDMACHININGQUALITYCONTROLWILLBECOMEMOREIMPORTANTTHANADAPTIVECONTROLDYNAMICTOOLPATHGENERATIONANDINPROCESSCUTTINGPARAMETERSMODIFICATIONAREREQUIREDTOREALIZEFLEXIBLEMACHININGOPERATIONFORPROCESSANDMACHININGQUALITYCONTROLADDITIONALLY,INTELLIGENTPROCESSMONITORINGISNEEDEDTOEVALUATETHECUTTINGPROCESSANDMACHININGQUALITYFORPROCESSANDMACHININGQUALITYCONTROLAREASONABLESTRATEGYTOCONTROLTHECUTTINGPROCESSANDAREASONABLEINDEXTOEVALUATEMACHININGQUALITYAREREQUIREDITISTHEREFORENECESSARYTOCONSIDERUTILIZATIONANDLEARNINGOFKNOWLEDGE,KNOWHOW,ANDSKILLREGARDINGMACHININGOPERATIONSAPROCESSPLANNINGSTRATEGYWITHWHICHONECANGENERATEFLEXIBLEANDADAPTIVEWORKINGPLANSISREQUIREDANOPERATIONPLANNINGSTRATEGYISALSOREQUIREDTODETERMINETHECUTTINGTOOLANDPARAMETERSPRODUCTDATAANALYSISANDMACHININGFEATURERECOGNITIONAREIMPORTANTISSUESINORDERTOGENERATEOPERATIONPLANSAUTONOMOUSLYSECTIONS4842485AREQUOTEDFROM4814FIG4833ACHIEVEMENTSOFKEYTECHNOLOGIESFORFUTUREINTELLIGENTMACHINETOOLS译文4844智能机床整个传统数控机床的机械加工是在预定的数控程序下进行的。一旦切削条件（如切削深度和进给量）在数控程序指令中被指定，在机械加工操作中，他们一般不允许被改变。因此，数控程序必须有大量的时间和精力用来提前准备和验证。此外，基于固定命令的数控程序在遇到不可预知的变化时不会响应，比如工作延迟和加工车间中的机器故障。SHIRASE提出了一种新的结构在没有数控程序的情况下可以自动控制切削过程。图4829显示了智能机床概念结构。AIMAC包括四个功能模块称为管理、策略、预测和观察。所有功能模块都与彼此分享切削信息。图4829AIMAC的概念结构数字仿形铣削对刀具轨迹进行实时生成数字仿形铣削技术被研发出来后，它可以直接控制数控机床。数字仿形铣削系统可以根据系统的仿形铣削及时生成刀具路径。在数字仿形铣削中，传统仿形铣削中的跟踪探测器和主模型通过计算机用三维虚拟模型表现出来。虚拟跟踪探测器模拟虚拟主模型，根据动态的虚拟轨迹实时生成刀具坐标。在数字仿形铣削中，刀具坐标可自主生成，数控机床可以在没有数控程序情况下可以按指示执行铣削操作。此外，不仅是行距，而且径向和轴向切削深度也可以修改（见图4830）。同时，数字仿形铣削可以生成新的刀具路径，以避免切削问题和改变操作工程中的加工顺序4812。此外，对于切割过程中的参数修改能力也得到了证实，如图48314813。在这个图的左下角和右上角体现了对刀具轨迹进行即时生成和对当前铣削的监测。在此图的右下角，监测切削扭矩、改变进击速率以及径向和轴向的切削深度。切削参数可以动态修改以保持合适的切削载荷。图4830AD对刀具轨迹即时生成案例A双边曲折路径B轮廓线路径C改变间距D改变切削深度图4831在AIMAC下的自适应铣削图4832在AIMAC下的机械加工工艺柔性加工系统和操作规划系统在机械加工中，柔性加工系统和操作规划系统已经发展到可动态生成切削参数。该系统可以根据总去除量生成生产计划。总去除量是根据产品的初始形状和最终的完成形状决定，他可分成加工基元和加工特性。该系统会根据检测到的加工特性生成切削参数。图4832显示了加工顺序和切削刀具的使用。切削参数确定了试验加工的形状。数字仿形铣削系统可以生成刀具路径或动态CL数据，根据这些结果便可以独立完成铣削加工而不需要任何数控程序。485未来智能机床的关键技术如同上一节，该节提出和研究了一些结构和技术。然而，他们还没有足够的成熟来被广泛地应用在实践中。所以，这些科研成果只能被限制在特定情况下使用。在图483