机械可靠性英文文献及翻译.docx

1、机械可靠性英文文献及翻译MechanicalReliabilitySimulationJianfengXu,YalianXie,DanXuShanghaiInstituteofProcessAutomationInstrumentationShanghai,PRCGenerallyamechanicalsystemiscomposedAbstractThisarticleprovidesanumericalmethodtosimulatethereliabilityofacomplicatedmechanicalsystemwithmultipleinputrandomparameters.T

2、hemethodbasesonthetheoryofmechanicalreliabilityandusesacommercialFEMsoftware,ANSYS.ThequantitativereliabilityofthemechanicalsystemisobtainedbyMonteCarlosimulation.Themethodisverifiedbytheoreticalanalysisandisappliedtoarealstructure.Thismethodhaspreventedthetediousmathematicalcalculationandismoreecon

3、omicalthanexperimentalmethods.Itcanbeusedasacomplementoftraditionalexperimentalmethods.KeywordsMechanical Reliability; FiniteElementMethod;MonteCarloSimulationI.INTRODUCTIONBasedonthenationalstandardGB/T3187-19941,reliabilityhasbeendefinedas“theprobabilitythataproductwillsatisfactorilyperformitsinte

4、ndedfunctionundergivencircumstancesforaspecifiedperiodoftime”.Reliabilityhasbeenviewedasoneofthemostcriticalparametersfortheproducts.Forexample,itissaidinUSthatreliabilitywillbethefocusofcompetitionforthefutureproducts.MostJapanesecompaniestakemorereliableproductastheirmainobjectiveforcompanydevelop

5、ment.Therefore,toaccuratelypredictthereliabilityoftheproduct,andtooptimizethedesignbasedonthereliabilityrequirementsisverymeaningful.bymanyparts.Asystemsreliabilityisdeterminedbymanyparameters,suchasthenumberofcomponents,thequalityofthecomponentsandalsohowthosecomponentsareassembled.Itisalsorelatedt

6、othematerialproperties,manufacturingtechnologies,dimensiontolerancesandsoon.Mostofthereferencescurrentlyavailablearefocusedonthetheoreticalderivationforreliabilitycalculation,however,duethecomplexity,itNormallythestructuralmaximumstressandthematerialstrengtharedistributedwithcertainprobabilitydensit

7、y.Figure1showsanexampleofthedistribution,inrequirestediousmathematicalcalculation.Anothermethodtoobtainthereliabilityofawhichf()andg(s)representthesystemisbyexperiments,whichrequiresenoughsamplesandtestingtime,thustheexperimentalmethodcanonlybeapplicabletothosemostcriticalsystems.Thisarticleusesmech

8、anicalstrengthreliabilityasanexample,illustrateshowFEMcanbeappliedtothereliabilitysimulation.maximumstressandmaterialstrength,respectively.InFigure1(a),sinceisalwayslargerthanreliabilityofthestructureis1.However,inFigure1(b),itisnotThemethodemployscommercialFEMsoftware,ANSYS,andtakesadvantageofthein

9、tegratedMonteCarlosimulationfunction.Asimpleexampleisanalyzedtoverifythecorrectnessguaranteedthat).Thereisang(s)belargerthanf(ofthemethod.Theverifiedmethodisthenappliedtoarealstructuretoobtainthequantitativereliabilityofacomplexsystemwithmultipleinputrandomparameters.II.STRENGTH-STRESSINTERFERENCETH

10、EORYinterferencezoneinthedistribution,asshownintheshadedareaandzoomedinFigure2.Thusthereliabilityofthestructureislessthan1.Basedonthemathematicalderivation,thereliabilitycanbecalculatedbyintegrationwiththedistribution.978-1-61284-666-8/11$26.00 2011IEEE1147R=P( s)=+ g(s)dsd+ f()III.METHODDESCRIPTION

11、ThecommercialFEMpackageANSYShasaPDSf() g(s)(a)(ProbabilisticDesignSimulation)module2.Themoduledefinesdimension,materialpropertiesandotherparametersasrandomvariableswithcertaindistributions.Theoutputstressanddisplacementcanbedefinedasresultsvariables.SamplesarecollectedbasedonMonteCarlomethodtoobtain

12、thedistributionoff()(b)g(s)outputvariables.WiththePDSmodule,asuitableanalysisfileshouldbefirstlygeneratedtodefinethesimulationprocessandtodefinetheinputandoutputvariables.Ifnecessary,correlationbetweeninputparameterscanalsobedefined.MonteCarlomethodisthenemployedtocollectenoughsamplesforsimulation.S

13、inceeachrunisindependent,theFigure1Stressandstrengthdistributiong(s) f()Figure2Stress-strengthinterferencezoneTocreateanalysisfileandtodefineparametersTodefinetheinputrandomparametersanddistributionsTodefinecorrelationsbetweeninputTodefineoutputrandomvariablesTodefinemethodforprobabilisticsimulation

14、ResponsesurfacesimulationResultsreviewFigure3.Flowchartforstrengthmethodissuitableforparallelruntosavetime.Generally50200samplesarecollectedinMonteCarlomethodtoinsurethecorrectnessoftheresults.Insomecases,theoutputvariableisasmoothfunctionoftherandominputvariables.withlimitedtime.Basicallytheflowcha

15、rttosimulatethestrengthreliabilitywithANSYScanbeshowninFigure3,inwhichthedashedstepsindicatetheyareoptional.IV.VERIFICATIONSANDAPPLICATIONSThecorrectnessofthemethodisverifiedbyatensionrodwhichisanexampleinthereference3.TheInsuchcases,theresponsecanbeapproximatelyevaluatedbyatensionPN(29400,441)N,mat

16、erialmathematicalfunction.Byassessingthecoefficientsinthefunctionwiththesamplingpointsandruns,responsestrengthSN(1054.48,41.36)Nmm2,radiusoftherodsurfacecanbedeterminedandnumerousrunscanbeconductedrN(3.195,0.016)mm,theoreticaltoobtainbetterdistributionscalculationshowsthereliabilityoftherodis0.999.1

17、148reliabilityofapressurevessel.TheshapeandparameterdistributionsareshowninFigure6.BarelementPFigure4.FEMmodel(a)MonteCarloSimulationthestrength-stressdifference,whereKPaisunit.The method is then applied toanalyze the(b)ResponseSurfaceSimulationFigure5.Histogramofstrength-stressdifferenceWithANSYS,t

18、herodismodeledasabarelementwhichisfixedinonend.Thetensionisappliedattheotherend.TheaxialstressiscalculatedinANSYS.Thetensionforce,radiusandmaterialstrengtharedefinedasrandominputvariablesinPDS.Theoutputvariableisdefinedasthedifferencebetweenthestrengthandmaximumstress.Avaluelessthan0indicateastrengt

19、hfailure.OnehundredsamplesarecollectedwithMonteCarlomethod.Resultsshowthatthedifferenceapproximatelyfollowsanormaldistributionwithnormal137.73Nandvariance45.42N.Lateron,responsesurfaceareobtainedbasedontheprevioussamplesand10000runsareconducted.Thenormalandvariancecanbebetterobtainedas137.65Nand44.5

20、2N,respectively.Fromwhichitisconcludedthatthereliabilityoftherodis0.9988and0.9990withthetwomethods.Thisresultmatcheswellwiththetheoreticalcalculationwhichdemonstratesthecorrectnessofthemethod.Figure4showstheFEMmodel,andFigure5isthehistogramofrLFixedrU1990,2010mmLU9990,10010mmThicknessU7.5,8.5mmPress

21、ureU2800,3200KPaYoungsModuleN(200,10)GPaYieldStresN(1000,50)MPaFigure6PressureVesselInPDSmodule,therandominputparametersaredefinedinaccordancewithFigure6.With50MonteCarlosamplingpoints,thesimulationshowsthatthedifferencebetweenstrengthandstresscanbeapproximatelydescribedasanormaldistributionN(229.22

22、,67.251)MPa.AfurtherresponsesurfacesimulationdemonstratesthatthedifferencematchthedistributionasN(228.86,65.823)MPa,asshowninFigure7.Fromthesedistributions,thereliabilityofthe1149pressurevesselcanobtainedas0.99967and0.99974,respectively.(a)MonteCarloSimulation(b)ResponseSurfaceSimulationFigure7.Stre

23、ngth-stressdeviationhistogramofpressurevesselV.CONCLUSIONSFEMandMonteCarlosimulationmethodcanbeintegratedtoobtainthemechanicalreliabilityofacomplexstructurewithmultipleinputrandomparameters.Althoughthisarticleconsidersonlythestaticcases,themethodcanalsobeappliedtodynamiccaseswithsuitableanalysisfile

24、.Themethoddescribedinthisarticlepreventstediousmathematicalcalculation.Thestructurereliabilitycanbeobtainedwithlesstimeandmoney.Thismethodcanbeacomplementofthetraditionalexperimentalmethods.REFERENCES1ReliabilityandMaintainabilityterms,GB/T3187-942ANSYSHelp,ANSYSCorp3LiuP.,BasicofReliabilityEngineer

25、ing,MetrologyPress,2002中文翻译：机械可靠性仿真JianfengXu,YalianXie,DanXu上海工业自动化仪表研究所上海，中华人民共和国摘要：本文提供了一种多输入随机参数模拟复杂机械系统的可靠性计算方法。该方法基于可靠性理论，采用商业有限元软件ANSYS。机械系统的可靠性定量是通过蒙特卡罗模拟。该方法是通过理论分析验证和实际应用的结构。该方法避免了繁琐的数学计算和比实验方法更经济。它可以作为传统实验方法的一种补充。关键词：机械可靠性；有限元；蒙特卡罗仿真方法。一、引言根据国家标准GBT3187-19941，可靠性被定义为“在一定的条件下在指定的一段时间内，一个产品

26、很好地履行其预期的功能的概率”。可靠性一直被看作是一个产品的最重要的参数。例如，据说在美国，可靠性将是今后产品竞争的焦点。大多数日本企业以更可靠的产品作为公司发展的主要目标。因此，为了准确地预测产品的可靠性，并优化基于可靠性要求的设计是非常有意义的。一般的机械系统是由许多部分。一个系统的可靠性取决于许多参数，如组件的数量，质量的部件和这些部件组装。这也与材料的性质，相关的制造技术，尺寸公差等有关。大多数现有的文献主要集中在可靠性理论推导计算，然而，由于它的复杂性，需要繁琐的数学计算。还有一种实验法来获得系统的可靠性试验，需要足够的样本和测试时间，因此，实验方法只能适用于那些最重要的系统。本文采

27、用机械强度可靠性为例，说明了有限元法可以应用于可靠性仿真。该方法采用商业有限元软件，ANSYS，并利用MonteCarlo模拟功能集成。一个简单的实例分析验证了该方法的正确性。这种验证的方法随后便应用到实际结构，来获得多输入随机参数的一个复杂系统的可靠性定量。二、拉压干涉理论。通常情况下，结构的最大应力和材料强度的分布有一定的概率密度。图1显示的分布的一个例子，在f()和g(s)分别代表最大应力和材料强度的情况下。图1（a）中因为g(s)总是大于f()，结构的可靠度是1。然而，在图1（b），它是不能保证g(s)大于f() 。图中分布着一个干扰区，如图所示，在阴影区域和放大的图2。因此，结构的可靠度小于1。基于数学推导的可靠性，可以按照分布做一体化计算。R=P( s)= + f()