外文翻译采用遗传算法优化加工夹具定位和加紧位置.docx

外文翻译采用遗传算法优化加工夹具定位和加紧位置.docx

《外文翻译采用遗传算法优化加工夹具定位和加紧位置.docx》由会员分享，可在线阅读，更多相关《外文翻译采用遗传算法优化加工夹具定位和加紧位置.docx（16页珍藏版）》请在冰点文库上搜索。

外文翻译采用遗传算法优化加工夹具定位和加紧位置

附录

Machiningfixturelocatingandclampingpositionoptimizationusinggeneticalgorithms

NecmettinKaya*

DepartmentofMechanicalEngineering,UludagUniversity,Go¨ru¨kle,Bursa16059,TurkeyReceived8July2004;accepted26May2005

Availableonline6September2005

Abstract

Deformationoftheworkpiecemaycausedimensionalproblemsinmachining.Supportsandlocatorsareusedinordertoreducetheerrorcausedbyelasticdeformationoftheworkpiece.Theoptimizationofsupport,locatorandclamplocationsisacriticalproblemtominimizethegeometricerrorinworkpiecemachining.Inthispaper,theapplicationofgeneticalgorithms（GAs）tothefixturelayoutoptimizationispresentedtohandlefixturelayoutoptimizationproblem.Ageneticalgorithmbasedapproachisdevelopedtooptimisefixturelayoutthroughintegratingafiniteelementcoderunninginbatchmodetocomputetheobjectivefunctionvaluesforeachgeneration.Casestudiesaregiventoillustratetheapplicationofproposedapproach.Chromosomelibraryapproachisusedtodecreasethetotalsolutiontime.DevelopedGAkeepstrackofpreviouslyanalyzeddesigns;thereforethenumbersoffunctionevaluationsaredecreasedabout93%.Theresultsofthisapproachshowthatthefixturelayoutoptimizationproblemsaremulti-modalproblems.Optimizeddesignsdonothaveanyapparentsimilaritiesalthoughtheyprovideverysimilarperformances.

Keywords:

Fixturedesign;Geneticalgorithms;Optimization

1.Introduction

Fixturesareusedtolocateandconstrainaworkpieceduringamachiningoperation,minimizingworkpieceandfixturetoolingdeflectionsduetoclampingandcuttingforcesarecriticaltoensuringaccuracyofthemachiningoperation.Traditionally,machiningfixturesaredesignedandmanufacturedthroughtrial-and-error,whichprovetobebothexpensiveandtime-consumingtothemanufacturingprocess.Toensureaworkpieceismanufacturedaccordingtospecifieddimensionsandtolerances,itmustbeappropriatelylocatedandclamped,makingitimperativetodeveloptoolsthatwilleliminatecostlyandtime-consumingtrial-and-errordesigns.Properworkpiecelocationandfixturedesignarecrucialtoproductqualityintermsofprecision,accuracyandfinishofthemachinedpart.

Theoretically,the3-2-1locatingprinciplecansatisfactorilylocateallprismaticshapedworkpieces.Thismethodprovidesthemaximumrigiditywiththeminimumnumberoffixtureelements.Topositionapartfromakinematicpointofviewmeansconstrainingthesixdegreesoffreedomofafreemovingbody（threetranslationsandthreerotations）.Threesupportsarepositionedbelowtheparttoestablishthelocationoftheworkpieceonitsverticalaxis.Locatorsareplacedontwoperipheraledgesandintendedtoestablishthelocationoftheworkpieceonthexandyhorizontalaxes.Properlylocatingtheworkpieceinthefixtureisvitaltotheoverallaccuracyandrepeatabilityofthemanufacturingprocess.Locatorsshouldbepositionedasfarapartaspossibleandshouldbeplacedonmachinedsurfaceswhereverpossible.Supportsareusuallyplacedtoencompassthecenterofgravityofaworkpieceandpositionedasfarapartaspossibletomaintainitsstability.Theprimaryresponsibilityofaclampinfixtureistosecurethepartagainstthelocatorsandsupports.Clampsshouldnotbeexpectedtoresistthecuttingforcesgeneratedinthemachiningoperation.

Foragivennumberoffixtureelements,themachiningfixturesynthesisproblemisthefindingoptimallayoutorpositionsofthefixtureelementsaroundtheworkpiece.Inthispaper,amethodforfixturelayoutoptimizationusinggeneticalgorithmsispresented.Theoptimizationobjectiveistosearchfora2Dfixturelayoutthatminimizesthemaximumelasticdeformationatdifferentlocationsoftheworkpiece.ANSYSprogramhasbeenusedforcalculatingthedeflectionofthepartunderclampingandcuttingforces.Twocasestudiesaregiventoillustratetheproposedapproach.

2.Reviewofrelatedworks

Fixturedesignhasreceivedconsiderableattentioninrecentyears.However,littleattentionhasbeenfocusedontheoptimumfixturelayoutdesign.MenassaandDeVries[1]usedFEAforcalculatingdeflectionsusingtheminimizationoftheworkpiecedeflectionatselectedpointsasthedesigncriterion.Thedesignproblemwastodeterminethepositionofsupports.MeyerandLiou[2]presentedanapproachthatuseslinearprogrammingtechniquetosynthesizefixturesfordynamicmachiningconditions.Solutionfortheminimumclampingforcesandlocatorforcesisgiven.LiandMelkote[3]usedanonlinearprogrammingmethodtosolvethelayoutoptimizationproblem.Themethodminimizesworkpiecelocationerrorsduetolocalizedelasticdeformationoftheworkpiece.RoyandLiao[4]developedaheuristicmethodtoplanforthebestsupportingandclampingpositions.Taoetal.[5]presentedageometricalreasoningmethodologyfordeterminingtheoptimalclampingpointsandclampingsequenceforarbitrarilyshapedworkpieces.LiaoandHu[6]presentedasystemforfixtureconfigurationanalysisbasedonadynamicmodelwhichanalysesthefixture–workpiecesystemsubjecttotime-varyingmachiningloads.Theinfluenceofclampingplacementisalsoinvestigated.LiandMelkote[7]presentedafixturelayoutandclampingforceoptimalsynthesisapproachthataccountsforworkpiecedynamicsduringmachining.Acombinedfixturelayoutandclampingforceoptimizationprocedurepresented.Theyusedthecontactelasticitymodelingmethodthataccountsfortheinfluenceofworkpiecerigidbodydynamicsduringmachining.Amaraletal.[8]usedANSYStoverifyfixturedesignintegrity.Theyemployed3-2-1method.TheoptimizationanalysisisperformedinANSYS.Tanetal.[9]describedthemodeling,analysisandverificationofoptimalfixturingconfigurationsbythemethodsofforceclosure,optimizationandfiniteelementmodeling.

Mostoftheabovestudiesuselinearornonlinearprogrammingmethodswhichoftendonotgiveglobaloptimumsolution.Allofthefixturelayoutoptimizationproceduresstartwithaninitialfeasiblelayout.Solutionsfromthesemethodsaredependingontheinitialfixturelayout.Theydonotconsiderthefixturelayoutoptimizationonoverallworkpiecedeformation.

TheGAshasbeenproventobeusefultechniqueinsolvingoptimizationproblemsinengineering[10–12].Fixturedesignhasalargesolutionspaceandrequiresasearchtooltofindthebestdesign.FewresearchershaveusedtheGAsforfixturedesignandfixturelayoutproblems.Kumaretal.[13]haveappliedbothGAsandneuralnetworksfordesigningafixture.Marcelin[14]hasusedGAstotheoptimizationofsupportpositions.Vallapuzhaetal.[15]presentedGAbasedoptimizationmethodthatusesspatialcoordinatestorepresentthelocationsoffixtureelements.FixturelayoutoptimizationprocedurewasimplementedusingMATLABandthegeneticalgorithmtoolbox.HYPERMESHandMSC/NASTRANwereusedforFEmodel.Vallapuzhaetal.[16]presentedresultsofanextensiveinvestigationintotherelativeeffectivenessofvariousoptimizationmethods.TheyshowedthatcontinuousGAyieldedthebestqualitysolutions.LiandShiu[17]determinedtheoptimalfixtureconfigurationdesignforsheetmetalassemblyusingGA.MSC/NASTRANhasbeenusedforfitnessevaluation.Liao[18]presentedamethodtoautomaticallyselecttheoptimalnumbersoflocatorsandclampsaswellastheiroptimalpositionsinsheetmetalassemblyfixtures.KrishnakumarandMelkote[19]developedafixturelayoutoptimizationtechniquethatusestheGAtofindthefixturelayoutthatminimizesthedeformationofthemachinedsurfaceduetoclampingandmachiningforcesovertheentiretoolpath.Locatorandclamppositionsarespecifiedbynodenumbers.Abuilt-infiniteelementsolverwasdeveloped.

Someofthestudiesdonotconsidertheoptimizationofthelayoutforentiretoolpathandchipremovalisnottakenintoaccount.Someofthestudiesusednodenumbersasdesignparameters.

Inthisstudy,aGAtoolhasbeendevelopedtofindtheoptimallocatorandclamppositionsin2Dworkpiece.DistancesfromthereferenceedgesasdesignparametersareusedratherthanFEAnodenumbers.FitnessvaluesofrealencodedGAchromosomesareobtainedfromtheresultsofFEA.ANSYShasbeenusedforFEAcalculations.Achromosomelibraryapproachisusedinordertodecreasethesolutiontime.DevelopedGAtoolistestedontwotestproblems.Twocasestudiesaregiventoillustratethedevelopedapproach.Maincontributionsofthispapercanbesummarizedasfollows:

（1）developedaGAcodeintegratedwithacommercialfiniteelementsolver;

（2）GAuseschromosomelibraryinordertodecreasethecomputationtime;

（3）realdesignparametersareusedratherthanFEAnodenumbers;

（4）chipremovalistakenintoaccountwhiletoolforcesmovingontheworkpiece.

3.Geneticalgorithmconcepts

GeneticalgorithmswerefirstdevelopedbyJohnHolland.Goldberg[10]publishedabookexplainingthetheoryandapplicationexamplesofgeneticalgorithmindetails.Ageneticalgorithmisarandomsearchtechniquethatmimicssomemechanismsofnaturalevolution.Thealgorithmworksonapopulationofdesigns.Thepopulationevolvesfromgenerationtogeneration,graduallyimprovingitsadaptationtotheenvironmentthroughnaturalselection;fitterindividualshavebetterchancesoftransmittingtheircharacteristicstolatergenerations.

Inthealgorithm,theselectionofthenaturalenvironmentisreplacedbyartificialselectionbasedonacomputedfitnessforeachdesign.Thetermfitnessisusedtodesignatethechromosome’schancesofsurvivalanditisessentiallytheobjectivefunctionoftheoptimizationproble