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ORIGINALARTICLEAclusteringbasedmodelingschemeofthemanufacturingresourcesforprocessplanningHuanMinXuDongBoLiReceived12December2006/Accepted30April2007/Publishedonline14June2007SpringerVerlagLondonLimited2007AbstractProcessplanningdetermineshowaproductistobemanufacturedand,therefore,isoneofthekeyelementsinthemanufacturingprocess.ThebasiccruxofcomputeraidedprocessplanningCAPP,however,isthediversityofmanufacturingbackgroundandthecomplexityofprocessplanning.Sincetheprocessplansaregeneratedondemandwiththemanufacturingresourcesonashopfloor,modelingmethodofthemanufacturingresourcesisoneofmostimportantissuesincomputeraidedprocessplanning.Hence,themajorobjectiveofthisresearchistocreateaclusteringbasedmodelingschemeofthemanufacturingresourcesforprocessplanning.Thismodelingschemecombinesclusteringmethodwithaveragingmethodsoastoreasonablypartitionandclassifythemanufacturingresourcesonashopfloor.Theproposedapproachprovidesoneofsearchstrategiesofmachinetoolsforprocessplanning,especiallyfortheselectionofmachinetools.Finally,anillustrativeexampleisappliedtodescribetheclusteringbasedmodelingscheme.KeywordsProcessplanning.Clustering.Similarity.ComputeraidedprocessplanningCAPP1IntroductionProcessplanningdetermineshowaproductistobemanufactured.Itis,thus,themostimportanttaskinproductionpreparationsandthefoundationofallproductionactivities.Inthepast,theprocessplanningactivityhastraditionallybeenexperiencebasedandhasbeenperformedmanuallybyhumanprocessplannerswithoutcomputerinvolvedinit.Manualprocessplanning,however,involvessomeproblemsforinstanceaslackofskilledplanners,inconsistencyofprocessplansduetovariabilityamongtheplannersjudgementandexperience,lowefficiencyinplangeneration,slowresponsetothechangesinpracticalmanufacturingenvironment,andsoon.Toalleviatetheseproblems,acomputeraidedapproachhasbeentaken.SincetheideaofusingcomputersinprocessplanningactivitywasdiscussedbyNiebel1,notableeffortshavebeenmadetowardthefieldofautomatedprocessplanning.OtherearlyinvestigationsonthefeasibilityofautomatedprocessplanningcanbefoundinSheckandBerra2,3.ToperformprocessplanningautomaticallyonacomputerortointegrateCAD/CAPP/CAM,moreandmoreresearchersandengineershavedevotedtheirattentiontodifferentaspectsofCAPPinthepastfewdecades.Inrecentyears,CAPPhasbeenrecognizedasakeyelementincomputerintegratedmanufacturingCIM.ThemajorityoftheresearchfocusesonsomeproblemsofCAPPsuchaspartfeaturesrecognitionorextraction4–15,processknowledgerepresentation16–21,processreasoningandinference22–26,andthelike.ThisresearchhasenhancedthediversityoftheknowledgerepresentationandinferenceapproachesinprocessplanningandimprovedtheperformanceofCAPPsystems,todifferentextents,respectively.Consequently,alltheseresearchhaveacceleratedandfacilitatedthedevelopmentofautomaticprocessplanning.Theultimategoaloftheresearchistoenablefullyautomatedprocessplanningsystem,withouthumand.SincetremendousefforthasbeenmadetodevelopCAPPsystems,thebenefitsofCAPPinreallifemanufacturingIntJAdvManufTechnol200838154–162DOI10.1007/s001700071075zH.M.XuD.B.LiSchoolofMechanicalEngineering,NanjingUniversityofScienceandTechnology,Nanjing210094,PeoplesRepublicofChinaemailalexandra_xu2003yahoo.com.cnD.B.Liemaildb_callayahoo.com.cnenvironmentsareyettobeseen.However,thetotallyautomaticprocessplanningusingcomputersisstillfarfrombeingapracticalindustrialapplication.Besidesthecomplexityofprocessplanning,oneofthemainreasonsforthisisthelackofsystematicinvestigationoftheprinciplesandmethodologyofmodelingthemanufacturingresourcesinCAPP.SincethebasiccruxofCAPPisthediversityofmanufacturingbackgroundandthecomplexityofprocessplanning,modelingthemanufacturingresourcesisoneoftheprimaryconcernsofCAPP.Moreover,oneofthemajordifficultiesinCAPPinarealmanufacturingenvironmentishowtobuildthemodelofthemanufacturingresources,whichcanproperlyorganizemanufacturingmachines.Therefore,themajorobjectiveofthispaperistocreateaclusteringbasedmodelingschemeofthemanufacturingresourcesforprocessplanning.Mostmodelingapproachesofmanufacturingresources,however,havebeenstudiedtocopewithproductionplanningandcontrolproblemsorresourceallocationtounleashtheunderutilizedresourceflexibility,etc.27–31.Therefore,thisstudypresentsamodelingapproachthatcombinesaclusteringmethodwithaveragingmethodsoastoreasonablypartitionandclassifythemanufacturingresourcesonashopfloor,whichlaysthefoundationforthemetamodelingparadigmofthemanufacturingresourcesusingmathematicallogic32.Intheremainderofthissection,thestructureofthispaperispresented.ThefirstsectionbrieflyintroducestheresearchbackgroundofprocessplanningandCAPPandalsoexplainsthereasonforrequiringfundamentalresearchintoanewmodelingmethodologyofthemanufacturingresources.Thesecondsectionanalyzesprocessplanningandthemanufacturingresources.Thethirdsectionpresentsaclusteringbasedmodelingschemeofthemanufacturingresourcesonashopfloor.Thefourthsectiondescribestheclusteringprocessofthemanufacturingresources.Thefifthsectiondetailsanapplicationcasetoillustratetheclusteringprocess.Finally,thesixthsectiondiscussesthegainsachievedbytheclusteringbasedmodelingschemeofthemanufacturingresourcesforprocessplanning.2ProcessplanningandthemanufacturingresourcesProcessplanning,asdefinedbyChangetal.,isanactofpreparingdetailedoperationinstructionstotransformanengineeringdesigntoafinalpartorprocessingdocumentationforthemanufactureofapiecepartorassembly33,34.Processplanningmaybeengagedundertwodifferentconditions24oneisforanewplantorworkshoptobebuilttheotherforanexistingplantorworkshop.Intheformercase,itispossibletoselectsolelythemostreasonablemachinetools,accordingtothemanufacturingneeds.Inthelattercase,themachinetoolsshouldbeselectedfromtheexistingequipmentavailableintheplantortheworkshopunlessthepurchaseofsomenewequipmentisplanned.Itmeansthataprocessplannermustpossessnecessaryknowledgeaboutavailableequipmentwhenplanningaprocessineithercase.However,themachiningprocessplanning,whichrequiresavarietyofknowledgeofthemanufacturingresources,isactuallyacomplexprocessconsistingofmanydifferenttasks33–37.Fromthetechnologicalpointofview,themachiningprocessplanningmayinvolveseveralorallofthefollowingtasksoractivities24,33–Selectionoftheblank–Selectionofmachiningprocesses–Sequencingofmachiningoperations–Selectionofmachinetoolsandcuttingtools–Determiningsetuprequirements–Calculationsofcuttingparameters,toolpathplanning–Selectionofmachiningparameters–GenerationofNCorCNCpartprograms–DesignofjigsandfixturesHowevertoperformprocessplanning,theaboveactivitiesareelementaryfunctionsofmakingaprocessplaninaccordancewithavailablemanufacturingresourcesonashopfloor.Alternatively,theseactivitiesaretheprimaryconcernsofandthefundamentalissuesforprocessplanning.Itis,thus,evidentthatthemodelingapproachofthemanufacturingresourcesgreatlyinfluencestheaforementionedfunctionsofprocessplanning.Whilealargenumberoftechnicalpapersonthemanufacturingresourceshavebeenpublished,eachcoveringimportantfacetsofthisproblem.Themajorobjectiveofmostofthemistoenhancethefunctionsandtoincreasetheproductivityofcertainexistingmachinetoolstomeettherequirementsoftheplannedprocesses.Fromtheviewpointofprocessplanning,theultimategoalofstudyingthemanufacturingresourcesinthispaperistoprovideamethodologyofmodelingthemanufacturingresources,morespecifically,machinetoolsontheshopfloor.Furthermore,therearevariousmanufacturingprocessesusedforconvertingrawmaterialsintofinishedparts,forinstanceascasting,forging,welding,punching,forming,machining,heattreatment,plating,coating,etc.Amongtheprocesses,themachiningprocessplaysanimportantroleinthemanufactureofparts.Themachiningprocessofapartmayinvolvesomeorallofthefollowingmachiningmethodsturning,milling,drilling,boring,reaming,grinding,broaching,gearcutting,etc.,dependingontherequiredshape,dimensions,accuracyandsurfacequalityofthepart24.Inordertoensurethequalityofacompletedpart,itisnecessarytoproperlyselectmachiningprocessesandmachinetoolsandcuttingtoolsandtodeterminemachiningIntJAdvManufTechnol200838154–162155parameters.Anappropriatemodelingapproachofthemanufacturingresources,therefore,facilitatestheaforementionedactivitiesofprocessplanning.Aclusteringbasedmodelingschemeofthemanufacturingresourcesonashopfloorispresentedinthefollowingsection.3Aclusteringbasedmodelingschemeofthemanufacturingresourcesonashopfloor3.1ThemodelingarchitectureofthemanufacturingresourcesThemodelingscenarioFig.1assumesthatasetOofNobjects,whichrepresentallthemanufacturingresourcesonarealshopfloor.EachobjectinthesetOisdenotedbyobject1,object2,...,andobjectN,respectively,whileeachobjectinthesetOhasonepredominantmachiningfunctionatleast.Forexample,thepredominantfunctionofthedrillingmachineisdrilling.O¼object1object2C1C1C1objectNfgð1ÞThemodelingarchitectureofthemanufacturingresourcesconsistsofthreesteps,suchasclassifying,extractingandclustering,whichareshowninFig.1.TheprocessincludingthreestepsinFig.1abovearedescribedasfollows.3.2ClassifyingbymachiningmethodsThefirststepisthattheseobjectsinthesetOareclassifiedaccordingtotheaforementionedmachiningmethods,suchasturning,milling,drilling,boring,reaming,grinding,broaching,gearcutting,etc.Thatmeanspartitioningtheseobjectsintoasetofclasses,whichdenotebyOK,K1,2,⋯,SseeFig.1.TheclassesresultingfromclassifyingarerepresentedasfollowsO1¼O11O12C1C1C1O1aC8C9O2¼O21O22C1C1C1O2bC8C9C1C1C1C1C1C1C1C1C1C1C1C1C1C1C1C1C1C1OK¼OK1OK2C1C1C1OKcC8C9C1C1C1C1C1C1C1C1C1C1C1C1C1C1C1OS¼OS1OS2C1C1C1OSdC8C9whereab⋯c⋯dNwiththenaturalnumbersofa,b,c,dbeingdifferentorthesamevaluessincedifferentshopfloorswithspecificmanufacturingresources.3.3ExtractingtheattributesofeachclassThesecondstepisextractingthecommonattributesofeachclassasshowninFig.1.Sinceobjectsareclassifiedintothedifferentclassesbythemachiningmethods,objectsinthesameclasshavesomecommonspecificattributes.Forexample,themaximumdrillingdiameterofaholeisoneofcommonimportantattributesofalldrillingmachines.Moreover,eachextractedattributeshouldsatisfythefollowingrequirements–Standingforprocesscapability–Beingoneofkeyspecifications–HavingthespecificanddefinitevalueorthedomainofvalueHowever,thevalueofthisattributevaryindifferentobjects,forinstanceasdifferentdrillingmachinewithdifferentmaximumdrillingdiameterofahole.TheOObject1,Object2,,ObjectNClassifyingbyMachiningmethodsExtractingtheattributesofeachsubset,respectivelyA1...Clusteringbytheattributes,respectivelyClustersC11,C12,...SubsetO1SubsetOSSubsetO2...CLASSIFYINGEXTRACTINGTHEATTRIBUTESCLUSTERINGASA2ClustersC21,C22,ClustersCS1,CS2,Fig.1Aclusteringbasedmodelingschemeofthemanufacturingresources156IntJAdvManufTechnol200838154–162aforementionedattributesofclassOK,K1,2,⋯,S,arerepresentedbythecorrespondingsetofAK,K1,2,⋯,S.TheattributesofeachclassaredenotedbyA1¼A11A12C1C1C1A1pþnoA2¼A21A22C1C1C1A2qnoC1C1C1C1C1C1C1C1C1C1C1C1C1C1C1AK¼AK1AK2C1C1C1AKrC8C9C1C1C1C1C1C1C1C1C1C1C1C1C1C1C1AS¼AS1AS2C1C1C1AStC8C9Althoughdifferentmanufacturingresourcescontaindifferentattributes,themethodologyremainsthesameregardlessofspecificobjectsandattributes.Toclarifytheclusteringprocess,apartialexampleOKwillbeappliedinthefollowingpart.ThesetOKofobjects,whichconsistsofasetAKofattributesAK1AK2C1C1C1AKr,aretobeclustered.AnobjectOKiinthesetOKcanbelogicallyrepresentedasaconjunctionofattributevaluepairsAKi1¼VKi1C2C3AKi2¼VKi2C2C3C1C1C1AKij¼VKijhiC1C1C1AKir¼VKirC2C3,whereiandjarepositiveintegerswith1⩽i⩽cand1⩽j⩽r.Withoutambiguity,werepresentOKiasavectorVKi1VKi2...VKirC2C3.EachvalueofattributesofeachobjectOKiinthesetOKisdenotedbyitscorrespondingelementinthefollowingmatrixVKVK¼VOKAKC0C1¼OK1OK2...OKcAK1AK2C1C1C1AKrVK11VK12C1C1C1VK1rVK21VK22C1C1C1VK2r............VKc1VKc2C1C1C1VKcr0BBBBBBBB1CCCCCCCCAð2ÞwhereKisapositiveintegerwith1⩽K⩽S.3.4TheclusteringprocessThethirdstepisclusteringontheobjectsineachclassOK,dependingontheattributesinthesetofAK,whereKisanintegerfor1⩽K⩽S.TheclusteringresultdenotesbytheclusterssuchasC11C12C1C1C1C21C21C1C1C1.Theclusteringalgorithmwillbedetailedinthenextsection.4TheclusteringalgorithmTheclusteringmethodinFig.1,whichisoneofthekeyissuesofthemodelingarchitectureofthemanufacturingresources,meanspartitioningtheseobjectsintoasetofclustersCi,i1,2,⋯,mFig.1.Theclusteringalgorithmisasimpleyeteffectivestatisticalclusteringapproachofthemanufacturingresources.Herearethestepsofthealgorithm1.Normalizethedataofobjectstobeclusteredsoastoeliminatedimensionaldifferencebetweendifferentattributes.Alternatively,makingallvaluesoftheseattributesdimensionlessisthefirstoneofimportantissuesintheclusteringofthemanufacturingresources.Normalizedfunctions,whichconsistofmeanfunctionandnormalizedfunctionrespectively,aredefinedasVKj¼1cXci¼1VKijð3Þυij¼VKijVKjð4ÞHence,theabovematrixVKcanbemappedtothefollowingmatrixυ.υ¼υ11υ12C1C1C1υ1rυ21υ22C1C1C1υ2r............υc1υc2C1C1C1υcr0BBBBB1CCCCCAð5Þ2.CalculatethedistancebetweendifferentobjectsbyEuclideanmetricfunction.ItisdefinedasdKil¼dOKiOKlC0C1¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXrj¼1uijC0uljC0C12vuutð6ÞThedistanceisnormalizedbythefollowingequationDKil¼DOKiOKlC0C1¼ffiffiffiffiffiffiffiffiffi1rdilrð7Þwherer,c,i,larepositiveintegers,risthenumberofattributesofthesetOK,cisthenumberofobjectsofthesetOK,and1≤i,l≤c.3.Calculatethesimilarityvaluesbetweendifferentobjects.ItisdefinedassKil¼1C01cDKilð8ÞwhereCisaconstantwithC≥MaxDil,sKil201½C138andsKii¼11C20ilC20c.IntJAdvManufTechnol200838154–162157
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