ndlespeed,depthofcutandfeedrateonthecuttingforceandsurfaceroughnesswerestudied.Theinvestigationshowedthatendmillswithlefthandhelixanglesaregenerallylesscosteffectivethanthosewithrighthandhelixangles.Thereisnosignificantdifferencebetweenupmillinganddownmillingwithregardtothecuttingforce,althoughthedifferencebetweenthemregardingthesurfaceroughnesswaslarge.[]havestudiedtheaffectofthetoolrotationangleservedthatthenoseradiusplaysasignificantroleinaffectingthesurfacefinish.Thereforethedevelopmentofagoodmodelshouldinvolvetheradialrakeangleandnoseradiusalongwithotherrelevantfactors.Establishmentofefficientmachiningparametershasbeenaproblemthathasconfrontedmanufacturingindustriesfornearlyacentury,andisstillthesubjectofmanystudies.Obtainingoptimummachiningparametersisofgreatconcerninmanufacturingindustries,wheretheeconomyofmachiningoperationplaysakeyroleinthecompetitivemarket.Inmaterialremovalprocesses,animproperselectionofcuttingconditionscausesurfaceswithhighroughnessanddimensionalerrors,anditisevenpossiblethatdynamicphenomenaduetoautoexcitedvibrationsmaysetin[].Inviewofthesignificantrolethatthemillingoperationplaysintoday‟smanufacturingworld,thereisaneedtooptimizethemachiningparametersforthisoperation.So,anefforthasbeenmadeinthispapertoseetheinfluenceoftoolgeometry(radialrakeangleandnoseradius)andcuttingconditions(cuttingspeedandfeedrate)onthesurfacefinishproducedduringendmillingofmediumcarbonsteel.Theexperimentalresultsofthisworkwillbeusedtorelatecuttingspeed,feedrate,radialrakeangleandnoseradiuswiththemachiningresponsei.e.surfaceroughnessbymodeling.Themathematicalmodelsthusdevelopedarefurtherutilizedtofindtheoptimumprocessparametersusinggeneticalgorithms.、ReviewProcessmodelingandoptimizationaretwoimportantissuesinmanufacturing.Themanufacturingprocessesarecharacterizedbyamultiplicityofdynamicallyinteractingprocessvariables.Surfacefinishhasbeenanimportantfactorofmachininginpredictingperformanceofanymachiningoperation.Inordertodevelopandoptimizeasurfaceroughnessmodel,itisessentialtounderstandthecurrentstatusofworkinthisarea.Davisetal.[]haveinvestigatedthecuttingperformanceoffiveendmillshavingvarioushelixangles.CuttingtestswereperformedonalloyLforthreemillingprocesses(face,slotandside),inwhichcuttingforce,surfaceroughnessandconcavityofamachinedplanesurfaceweremeasured.Thecentralcompositedesignwasusedtodecideonthenumberofexperimentstobeconducted.Thecuttingperformanceoftheendmillswasassessedusingvarianceanalysis.Theaffectsofspindlespeed,depthofcutandfeedrateonthecuttingforceandsurfaceroughnesswerestudied.Theinvestigationshowedthatendmillswithlefthandhelixanglesaregenerallylesscosteffectivethanthosewithrighthandhelixangles.Thereisnosignificantdifferencebetweenupmillinganddownmillingwithregardtothecuttingforce,althoughthedifferencebetweenthemregardingthesurfaceroughnesswaslarge.[]havestudiedtheaffectofthetoolrotationangle，改变了接触长度之间芯片和工件表面。也很明显，由于径向前角从◦至◦变化，表面粗糙度降低，然后增加。因此，可以得出结论，径向前角在◦至◦范围内将提供更好表面光洁度。图还表明，表面粗糙度使刀尖半径先减小，然后增加。这表明，有一定范围内，寻找最佳值径向前角和刀尖半径可获取尽可能好表面质量。实验还发现，表面粗糙度下降，增加了切割速度及加工时间。可以观察到，表面粗糙度最低时是米/分钟速度，毫米/分钟进给速度，◦径向前角和.毫米刀尖半径。了解过程可以看处，实验结果可用于开发数学模型，用丹参。在这项工作中，在商业上可用数学软件包（MATLAB），是用来计算回归常数和指数。.粗糙度模型利用实验结果，已经获得了估计表面粗糙度经验公式，考虑符合重大参数，即切削速度，进给速度，径向前角和刀尖半径。从一阶模型得到，从上述功能关系用丹参方法如下：等式从Eq获得。替代x代码值,x、x和x根据lns,lnf,lnα和lnr算得，分析中变异（方差分析）和F-比检验已完成，适合作精确数学模型。由于计算值F比率都远低于标准值F比率为表面粗糙度如表所示，模型是足够在信心水准下，以代表之间关系，加工回应，并考虑将加工参数年底铣削过程。多元回归系数一阶模型被指定为.。这表明，一阶模型可以解释变异表面粗糙度，为.。作为一阶模型具有较低可预测性，二阶模型已经研制成功就看它是否能代表更好。二阶表面粗糙度模型通过以下资料建立。如Y型，是估计反应，表面粗糙度对对数尺度,x,x,x和x是对数变换速度，伺服，径向前角和刀尖半径。数据方差分析，为二阶表面粗糙度模型表所示。因为Fcal大于F.，在标准下，响应变量与自变量，有一定关系。多元回归系数二阶模型被裁定为.。在此基础上多元回归系数（er）,可以得出结论认为，二阶模型足以代表这一进程。因此二阶模型被认为是一个客观功能，为优化利用遗传算法。这二阶模型，还验证了用卡方检验。计算卡方值该模型是.和他们表价值在χ.是.，如表所示，这表明，.变异性，表面粗糙度解释了这一模式。用二阶模型，其表面粗糙度组成部分所产生端铣可估计合理性及准确性。这种模式将利用遗传算法（GA）得以优化。.优化端铣优化加工参数，不仅增加了实用性，为加工经济，还对产品质量TOA有很大程度影响。在这方面已作出努力来估计最佳工具，几何形状和加工条件，以产生最佳表面质量，内部制约因素。凡xil和xiu上下界过程中变数及x，x，X，x是对数变换切削速度，进给，径向前角和刀尖半径。GA开发利用Matlab。这种方法使一个二进制编码系统为代表变数切削速度（s）和进给速度（f），径向前角（α）和刀尖半径（R），即每这些变数都代表了一个位二进制当量，限制总字串长度为.据了解，作为一个染色体。变数就派代表作为基因（substrings）在染色体上。随机产生个染色体（人口规模是个），完成制约变数，是采取在各代。第一代是所谓初始种群。一旦编码变数就已经做了，那么，实际解码值为变数。xi是解码后实际价值切削速度，进给速度，径向前角和刀尖半径，X（l）i是下限和X（u）i是上限和李应生是子长，这是相当于十年样子。用现在这一代人个染色体，健身价值，是按以下转变：这里f（x）是健身功能和RA是目标函数。出于这个健身价值，有四个选择使用轮盘轮甄选计划。染色体对应于这四个健身价值观是采取家长。然后交叉和变异繁殖方法，是适用于产生个新染色体，为下一代。这个过程生成新人口从旧人口，是所谓一代人。很多这样一代，都运行到最大数目，是几代人达到或平均个选定健身价值，在每一代人变得平稳。这确保了优化所有变量（切削速度，进给速度，径向前角和刀尖半径），是同步进行。最后统计显示，在结束所有迭代。为了优化目前问题，利用加文，以下参数已选定，以取得最佳解决方案与最不计算努力。表显示，一些最起码价值，表面粗糙度预测，由加文程序方面投入，加工范围，并表显示了最佳加工条件，为相应最低值，表面粗糙度表所示。该MRR给出表计算其中f是工作台进给速度（毫米/分钟），机管局正轴向切深（毫米）和AR是径向切深（毫米）。可以得出这样结论，从优化结果GA程序，是有可能选择相结合切削速度，进给速度，径向前角和刀尖半径为取得最佳表面光洁度给予一个合理良好物质去除率。对GA计划提供最佳加工条件，相应给予最低值表面粗糙度。应用遗传算法方法，以获得最佳加工条件，将是非常有益，在计算机辅助工艺设计（CAPP）阶段，在生产高品质货品，紧公差，由各种各样值。改换行动，并在自适应控制自动机床。与已知边界表面粗糙度及加工条件，加工，可表演了一个比较高成功率，与选定加工条件。结论调查研究表明，该参数切削速度，进给，径向前角和刀尖半径是主要受行动者影响，表面粗糙度中碳钢端铣。该文提供了n动力，以发展分析模型，根据实验结果，可以获得表面粗糙度模型，用响应面方法论。通过把刀具几何模型，该模型有效性有了较大提高。优化模型采用遗传算法，导致在一个相当有用方法获取加工参数，以获得最佳表面质量。servedthatthenoseradiusplaysasignificantroleinaffectingthesurfacefinish.Thereforethedevelopmentofagoodmodelshouldinvolvetheradialrakeangleandnoseradiusalongwithotherrelevantfactors.Establishmentofefficientmachiningparametershasbeenaproblemthathasconfrontedmanufacturingindustriesfornearlyacentury,andisstillthesubjectofmanystudies.ObtainingoptimummachiningparametersisofgreatconcerninmanufacturingindSelectionofoptimumtoolgeometryandcuttingconditionsusingasurfaceroughnesspredictionmodelforendmillingAbstract:Influenceoftoolgeometryonthequalityofsurfaceproducediswellknownandhenceanyattempttoassesstheperformanceofendmillingshouldincludethetoolgeometry.Inthepresentwork,experimentalstudieshavebeenconductedtoseetheeffectoftoolgeometry(radialrakeangleandnoseradius)andcuttingconditions(cuttingspeedandfeedrate)onthemachiningperformanceduringendmillingofmediumcarbonsteel.Thefirstandsecondordermathematicalmodels,intermsofmachiningparameters,weredevelopedforsurfaceroughnesspredictionusingresponsesurfacemethodology(RSM)onthebasisofexperimentalresults.ThemodelselectedforoptimizationhasbeenvalidatedwiththeChisquaretest.Thesignificanceoftheseparametersonsurfaceroughnesshasbeenestablishedwithanalysisofvariance.Anattempthasalsobeenmadetooptimizethesurfaceroughnesspredictionmodelusinggeneticalgorithms(GA).TheGAprogramgivesminimumvaluesofsurfaceroughnessandtheirrespectiveoptimalconditions.、IntroductionEndmillingisoneofthemostcommonlyusedmetalremovaloperationsinindustrybecauseofitsabilitytoremovematerialfastergivingreasonablygoodsurfacequality.Itisusedinavarietyofmanufacturingindustriesincludingaerospaceandautomotivesectors,wherequalityisanimportantfactorintheproductionofslots,pockets,precisionanddies.Greaterattentionisgiventodimensionalaccuracyandsurfaceroughnessofproductsbytheindustrythesedays.Moreover,surfacefinishinfluencesmechanicalpropertiessuchasfatiguebehaviour,wear,corrosion,lubricationandelectricalconductivity.Thus,measuringandcharacterizingsurfacefinishcanbeconsideredforpredictingmachiningperformance.Surfacefinishresultingfromturningoperationshastraditionallyreceivedconsiderableresearchattention,whereasthatofmachiningprocessesusingcutters,requiresattentionbyresearchers.Astheseprocessesinvolvelargenumberofparameters,itwouldbedifficulttocorrelatesurfacefinishwithotherparametersjustbyconductingexperiments.Modelinghelpstounderstandthiskindofprocessbetter.Thoughsomeamountofworkhasbeencarriedouttodevelopsurfacefinishpredictionmodelsinthepast,theeffectoftoolgeometryhasreceivedlittleattention.However,theradialrakeanglehasamajoraffectonthepowerconsumptionapartfromtangentialandradialforces.Italsoinfluenceschipcurlingandmodifieschipflowdirection.Inadditiontothis,researchers[]havealsoobservedthatthenoseradiusplaysasignificantroleinaffectingthesurfacefinish.Thereforethedevelopmentofagoodmodelshouldinvolvetheradialrakeangleandnoseradiusalongwithotherrelevantfactors.Establishmentofefficientmachiningparametershasbeenaproblemthathasconfrontedmanufacturingindustriesfornearlyacentury,andisstillthesubjectofmanystudies.Obtainingoptimummachiningparametersisofgreatconcerninmanufacturingindustries,wheretheeconomyofmachiningoperationplaysakeyroleinthecompetitivemarket.Inmaterialremovalprocesses,animproperselectionofcuttingconditionscausesurfaceswithhighroughnessanddimensionalerrors,anditisevenpossiblethatdynamicphenomenaduetoautoexcitedvibrationsmaysetin[].Inviewofthesignificantrolethatthemillingoperationplaysintoday‟smanufacturingworld,thereisaneedtooptimizethemachiningparametersforthisoperation.So,anefforthasbeenmadeinthispapertoseetheinfluenceoftoolgeometry(radialrakeangleandnoseradius)andcuttingconditions(cuttingspeedandfeedrate)onthesurfacefinishproducedduringendmillingofmediumcarbonsteel.Theexperimentalresultsofthisworkwillbeusedtorelatecuttingspeed,feedrate,radialrakeangleandnoseradiuswiththemachiningresponsei.e.surfaceroughnessbymodeling.Themathematicalmodelsthusdevelopedarefurtherutilizedtofindtheoptimumprocessparametersusinggeneticalgorithms.、ReviewProcessmodelingandoptimizationaretwoimportantissuesinmanufacturing.Themanufacturingprocessesarecharacterizedbyamultiplicityofdynamicallyinteractingprocessvariables.Surfacefinishhasbeenanimportantfactorofmachininginpredictingperformanceofanymachiningoperation.Inordertodevelopandoptimizeasurfaceroughnessmodel,itisessentialtounderstandthecurrentstatusofworkinthisarea        1SelectionofoptimumtoolgeometryandcuttingconditionsusingasurfaceroughnesspredictionmodelforendmillingAbstract:Influenceoftoolgeometryonthequalityofsurfaceproducediswellknownandhenceanyattempttoassesstheperformanceofendmillingshouldincludethetoolgeometry.Inthepresentwork,experimentalstudieshavebeenconductedtoseetheeffectoftoolgeometry(radialrakeanglea

文档预览结束

找同类相似 下载到手机