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DigitalTwinsforRobotSystemsinManufacturing

AliAhmadMalik,GuodongShao,andJaneTarakhovsky

AbstractTheincreasingneedforindustrialautomationisdrivingtheadoptionofrobotics,withnewdevelopmentssuchashuman–robotcollaborationthroughautonomousmobilerobotsandcollaborativeroboticarms.Whileautomationimprovesproductqualityandworkingconditionsandlowersmanufacturingcosts,itcanalsolimitmanufacturingadaptability.Therefore,whenintegratingrobotsinmanufacturing,thereisapressingneedtosimplifythemethodstodevelop,install,recon?gure,andoperaterobotsystemstoachievegreateradaptability.ThisiswheretheconceptofDigitalTwin,whichreplicatesthebehaviorofacomplexsysteminavirtualenvironmentforanalysisandoptimization,comesintoplay.Inrobotics,theDigitalTwintechnologyisexpectedtoaddressthechallengesassociatedwithdesigning,testing,commissioning,andrecon?gurations.ThisrequiresaDigitalTwintoaccuratelyrepresentvariousdimensionsofaroboticsystemunderproductionvariables.ThisstudycharacterizesthecomponentsofarobotsystemthatneedtobemodeledinaDigitalTwintocreateatrustworthyvirtualreplicaofaphysicalrobotsystem.ADigitalTwinofthiskindcanbeutilizedthroughoutthelifecycleofthephysicalrobotinstallationacrossvarioususecases.

KeywordsRobotics·DigitalTwin·Flexibleautomation·Manufacturingsystems·Simulation·Systemdesign

A.A.Malik(B)·J.Tarakhovsky

DepartmentofIndustrialandSystemsEngineering,OaklandUniversity,Rochester,MI48309,USA

e-mail:

aliahmadmalik@

J.Tarakhovsky

e-mail:

janetarakhovsky@

G.Shao

EngineeringLaboratory,NationalInstituteofStandardsandTechnology,Gaithersburg,MD20899,USA

e-mail:

guodong.shao@

307

?TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2024

M.GrievesandE.Hua(eds.),DigitalTwins,Simulation,andtheMetaverse,SimulationFoundations,MethodsandApplications,

/10.1007/978-3-031-69107-2_13

308A.A.Maliketal.

1Introduction

Modernmanufacturingsystemsarecomplex,withfacilitiesevolvingintolargenetworksofdata-connectedmechatroniccomponents

[1

].Thecomplexityofthesesystemsstemsfromthequantityofinformationthatspansvariouslifecyclephases,includingdesign,development,commissioning,operations,andend-of-life[

2

,

3

].Theelevatedcomplexityandinterconnectivitymakethelifecyclemanagementofmodern-daymanufacturingsystemsmorechallenging.Conversely,demandisincreasingformanufacturingsystemstopossessresilienceandadaptability

[4

].Addressingthesechallengesincomplexscenariosrequiresenablingsmartmanu-facturingthroughdigitalization,dataconnectivity,andtheintegrationofmachinelearning[

5

].Smartmanufacturing,besidesresilience,canbringcostreduction,enhanceworkers’well-being,andresultinabetterreturnoninvestment(ROI).

Industry4.0,orthefourthindustrialrevolution,isthenetsociotechnologicalimpactofinfusingemergingtechnologiessuchasadditivemanufacturing,machinelearning,robotics,simulations,andtheInternetofThingsinproductsandtheirmanu-facturingsystems.AdvancedroboticautomationstandsoutasoneoftheenablersforIndustry4.0[

6

].Moderninstallationsstrivetomakeroboticautomationmore?exible,adaptable,safe,andcost-effectivethantraditionalroboticsimplementa-tions.However,?exibleapproachesarelackingindevelopingplug-and-playhard-ware,programmingtherobots,controlprogramgeneration,taskscheduling,layoutplanning,safetyassessment,andalignmentwithproductionplans.

Advancementsinvirtualization,sensingtechnologies,andcomputingpowerfacil-itatetherealizationofDigitalTwins(DTs),whichenablethetestingandvalidationthroughoutthedesign,development,andcontrolphasesofacomplexsysteminavirtualspace.Differentscienti?cdomainsincreasinglyrecognizethepotentialvalueofDTsformanagingcomplexityinareassuchasmanufacturing,transportation,aircraft,andspacemissions[

7

].Manufacturingcustomizationandrecon?gurabilityarevitaldomainstomanagethroughDTs[

7

].

Computermodelsprovideameanstoshortenthetimeneededtodesign,redesign,anddeployrobotsystems.Computer-basedvirtualmodelsofphysicalsystemscanbebene?cialfortestingandvalidatingtheproductionbeforeimplementation[

8

].Whilethismethodisconsistentwithtraditionalvirtualmodeling,theemerging“l(fā)ifecycle”approachandreal-timecommunicationbetweenphysicalandvirtualsystemsarepivotalconceptsofDTs[

9

].

ManystudieshavedocumentedthepotentialadvantagesandrelevanceofemployingDTsforrobotsystems[

10

–

12

].Ithasalsobeenobservedthatdevel-opingatrustworthyvirtualreplicaofarobotsystemistime-consuminganddemandsadvancedengineeringskillsandinvestmentindifferentengineeringsoft-ware.CreatinganddeployingaDTofarobotsystemshouldbestructured,simpli?ed,andstandardizedtorealizetheneededROI.ItrequiresidentifyingthecomponentsofarobotsystemthatarerelevanttothepurposeofitsDT.Moreover,the?exibilityoftheDTitselfiscriticaltoensurethattheDTcaneffortlesslybeadaptedtoevolvingcircumstances.

DigitalTwinsforRobotSystemsinManufacturing309

ThischapterpresentstheimportanceofDTsinroboticinstallationswithinmanu-facturingsystems.ThecomponentsoutlinedinaDTofarobotsystemcanassistresearchersandpractitionersindevelopingcost-effective,modular,and?exibleDTs,therebyimprovingtheresilienceofrobotinstallations.Thisisanessentialsteptowardachievingadaptablemanufacturingsystems.

Thekeycontributionsofthischapterareto:

1.PresentthecomponentsofaDTofarobotsystemfor?exibility

2.ExaminethelifecyclephasesofarobotsystemthataDTcansupport

3.ApplyDTsinrobotsystemsinmanufacturingsettings

4.PresentusecasesthatdemonstratetheutilizationofDTsinrobotsystems.

2ChallengesandOpportunitiesinContemporaryManufacturing

Thecontinuousdrivetoshortenproductlifecyclesemergesasasigni?canttransfor-mationinthecontemporarybusinesslandscape[

13

].Emergingsociotechnologicaltrendsrequireshorterproductdevelopmentandlaunchtimelines.Inthissetting,manufacturingcompaniesleverageemerginghardwareandsoftwaretechnologiesandtheirpotentialopportunities[

14

,

15

].

Asidefromtherapidpaceofchanges,manufacturersfaceashortageofskilledworkers.TherecentglobalexposuretotheCOVID-19pandemicalsodisplayedwidespreaddisruptionsinsupplychains[

16

]partlybecauseofashortageofworkersduetosocialdistancingmeasures.Researchstudiesidenti?edthatfuturefactoriescouldbetteraddresssuchchallengesbyadoptingmodular,?exible,andhuman-friendlyautomationsolutions

[17

].

Awaytodevelophuman-friendlyautomationsolutionsisthrough?exible,collab-orativerobots.Technologiesthatfacilitatetheswiftvalidationofnewmanufacturingstrategiesarealsoneeded.Therefore,futuremanufacturingsystemsmustnotonlyberepurposablebutalsobedesigned,developed,commissioned,andrecon?guredatasigni?cantlyfasterpace[

18

].

DTscanbeutilizedtoaddresstheresiliencerequirementswithinamanufac-turingsystem.Forexample,DTscanhelpreducethetimerequiredtovalidatenewmanufacturingstrategies,generateautomationprograms,andprovidemain-tenancesupport.Additionally,DTscanharnessreal-timeandhistoricaldatatoofferinsightsforprocessoptimization.Suchassistancecanpotentiallyenhancethelevelofresiliencethatamanufacturingsystemcanprovide.

310A.A.Maliketal.

3RoboticAutomationinManufacturing

Automationdescribesassigningphysicalandcognitivetaskstomachinesandsoft-waretoboostproductionanddecreasehumaneffort[

19

]Inmanufacturing,adoptingautomationbringsadvantagessuchasenhancingworkplacesafety,ef?ciency,quality,andcost-effectiveness[

20

,

21

].However,thisoftencomesatthecostofreducedproduction?exibility.Attheheartofindustrialautomationliesindustrialrobots.Thesubsequentsectionselaborateonthediversetypesofrobotsemployedinmanu-facturingfacilities.Figure

1

showsvariousindustrialrobottypes,includingspher-ical,SCARA(SelectiveComplianceAssemblyRobotArm),delta,Cartesian,andhumanoidrobots[

22

].Therobotselectionforaspeci?ctaskisbasedonthenatureofthetaskstobeautomated,availablespace,?nancialconstraints,andprocess-relatedconsiderations.Whiletheserobotsenhancemanufacturingef?ciency,theirapplica-tionsarelimitedincertainoperations,suchasassembly,whichonlyconstitutes7.3%ofroboticuse[

23

].

3.1TraditionalIndustrialRobots

Robotsarethepredominantforcedrivingtheindustrialautomationofphysicaltasks[

22

,

24

].Theserobots,characterizedby?xedpositioning,operationwithinenclo-sures,andtime-consumingrecon?gurationprocesses

[25

],fallintothecategoryof?xedautomationsolutions.Theycanhelpachievehighproductionvolumesbutmuststrictlybeseparatedfromhumaninteraction.Theyalsodemonstratelimited?exibility[

26

].Industrialrobotshaveprovensuccessfulinvarioussectors,includingautomotive,medicine,food,andelectronicsmanufacturing[

27

].Theprimaryreasonfortheunsuitabilityofrobotsinassemblyishumansafetyandthechallengesoftheirrecon?guration[

28

].

Fig.1Varioustypesofindustrialrobots

DigitalTwinsforRobotSystemsinManufacturing311

Fig.2Ahumanandrobotcoexistinginmanufacturing[

36

]

3.2CollaborativeRobots

Modernindustrialrobotsarelighter,portable,easytoprogram,andsafe.Thischangecanmeettheneedfor?exibilityintermsofmobility,capability,andcapacity

[29

].Theserobots,designedforcollaborationandcoexistencewithhumans(Fig.

2

),arecommonlyknownas“cobots”orcollaborativerobots

[30

].Acollaborativerobotcanbede?nedasamechanicaldeviceintendedfordirectphysicalinteractionwithhumans,aconcept?rstintroducedbyColgate[

31

]andfurtherdevelopedbyKruger[

32

].Cobotsallowhumansandrobotstoworktogethertoharnessthestrengthsofhumansandmachines.Thisconcept,oftencalledleanautomation,existsattheconvergenceofhuman?exibilityandmachineef?ciency[

33

].

Literatureshowcasesdiverseapplicationsofcobots,spanningpick-and-placeoperations,assemblytasks,welding,inspectionprocesses,andpacking

[34

].More-over,cobotshavebeenexploredasaviablesolutionforrapidlyrepurposingfactoriesinemergencies[

17

].Thepredominantuseofcobotshasbeeninmanufacturingsmallcomponentssuchasthoseassembledintoelectronics,appliances,andelectronicactu-ators[

28

,

35

].Withtheadvancementofsensingandsafetytechnologies,cobotsarebeingconsideredtoautomatelargeandheavycomponents.

3.3AutonomousMobileRobots

Autonomousmobilerobots(AMRs)representadistinctivecategorywithincollabo-rativerobots[

37

].Theyhaveprovenhighlyeffectiveinmaterialhandlingapplications

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inmanufacturingsettings[

40

].Theiradaptabilityandversatilitymakethemwell-suitedfortasksrequiringinteractionswithhumans.Beyondmanufacturing,AMRshavefoundapplicationsinwarehouses,militaryoperations,healthcare,searchandrescuemissions,security,andhomeenvironments

[38

].Thisversatilityunderscoresthepotentialofmobilerobotstoautomateoperationsindiverse?elds.

Differentrobottypeshavestandardfeaturessuchasmechanicalmultijointedreprogrammableactuators,end-of-armtooling,machinevision,positioningtech-nology,andcontrolprograms.Adaptabilityisrecognizedasnecessaryformostmodern-dayrobots.Thefollowingsectionpresentsatypicalphysicalarchitectureofarobotinstallation.

4ArchitectureofaRobotSysteminManufacturing

Roboticsystemsinmanufacturingsettingsareavailableinvariousdesigns,layouts,andcon?gurations,in?uencedbyspeci?cusecases.Atypicalrobotcellcomprisesmultiplehardwareandsoftwarecomponents(Fig.

3

).Articulatedrobotarms,withoneormorereprogrammablemechanicaljoints,representmostrobotinstallations[

39

].Oneormoretools(endeffectors)areattachedtoarobotmanipulator’stoolposttoperformvariousfunctions.Arobotcontrolleroverseestheoperationsoftherobotsystem,connectingallexternalhardwarethroughtheinput/output(I/O)interfacesofthecontroller.Varioussensorsareembeddedintherobotbodyandintegratedexternallytomonitoritsperformanceandrespondtoemergingsituations.

Roboticarmsdesignedforhuman–robotcollaboration(HRC)typicallyhavepowerandforce-limitingbodies,speedandseparationmonitoring,handguiding,andemergencystopasstipulatedintheISO15066safetystandardforHRC[

21

].Theseattributesensuresafeinteractionbetweenroboticarmsandhumans

[40

].AMRscan

Fig.3Typicalcomponentsofarobotsysteminamanufacturingsetting

DigitalTwinsforRobotSystemsinManufacturing313

alsofacilitatethemobilityofaroboticarmwithintherobotsystem.Furthermore,collaborativerobotsmustcomplywiththeISO19649standard[

41

],whichestab-lishestermsrelatedtomobilerobotsoperatingonsolidsurfacesandengaginginindustrialrobotapplications.ToeffectivelyemploytheconceptofDTs,itisvitaltomodelmost,ifnotall,ofthesefeaturesofrobotsystemsintheirdigitalmodels.

5DTsforRobotsinManufacturing

ADTisavirtualrepresentationofthecomponentsanddynamicsofanobservablephysicalsystem[

44

].DTscanmirrorreal-timeoperatingconditionsandpredictthefuturebehaviorofaphysicalsystem[

45

].ThecoreconceptofaDTinvolvescreatingadigitalmodelofaphysicalsystemandlinkingeachcomponentofthedigitalmodeltoitscorrespondingphysicalassets.Inreturn,thevirtualmodelmustactasafront-runnerofthephysicalsystemtopredictorestimateitsfuturebehavior.

ThepresentunderstandingoftheDTconceptoriginatesfromtheideaofa“Con-ceptualIdealforPLM”(ProductLifecycleManagement)[

42

].Itproposesthateverysystemisasubsetoftwoothersystems:thephysicalsysteminthephysicalworldandavirtualsystemexistinginvirtualspace,containingallnecessaryinformationaboutthephysicalsystem.Thebidirectionalrelationshipbetweenthephysicalanddigitalsystemscanenhanceproductdesign,manufacturing,andservicethroughoutthesystem’slifecycle[

43

].

Themethodsofusinganinformationalvirtualmodeltorepresentthecomplexityofaphysicalsystemhaveevolved.Inearliertimes,thevirtualmodelexistedasamentalimage[

1

],limitedinitscapacitytoaddressquestionsaboutthesystem’sperformance.Inthemid-twentiethcentury,creatingvirtualmodelsbecamepossible,startingwith2DCAD(computer-aideddesign)objectsandadvancingto3Dmodelsanddynamicsimulations.Thesevirtualmodelsaretypicallydevelopedearlyinthesystem’slifecycle,i.e.,duringdesign.Thesemodelsoftenbecomeuselesswhenthesystemtransitionstotheoperationphase.

Thelinkageofdigitalmodelstotheirphysicalcounterpartsandtheintegratedintelligencethroughouttheirlifecyclearenowachievable(Fig.

4

).Thisenablesthemtounderstandoperationalbehaviorandassistinaddressingday-to-dayproduc-tionconstraints.Inthiscontext,DTscanbecategorizedintoDTprototypesandDTinstances[

44

].ADTprototypeisusedtore?nesystemdesign,presentingoptimalstaticanddynamicinformationtoachievedesiredoutcomes.Meanwhile,aDTinstanceintegratesmonitoring,service,sensing,andbehavioralinformationaboutthephysicaltwinduringitsoperations.DTinstancesexhibitpredictiveandinter-rogativebehaviors,whichprovebene?cialduringtheoperationalandmaintenancephases.

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Fig.4ScopeofDTsinmanufacturingsystems

6LifecyclePhasesofRobotSystems

Aroboticsystemundergoesacomprehensivelifecycle,commencingwithitsdesignandconcludingatitsend-of-lifestage.Correspondingly,itsDTfollowsaparallellifecycle,adaptingtovariousscenariosandsystemevolutionthroughoutthelifecycle(Fig.

5

).ThesubsequentsectiondescribesthefunctionsofaDTacrossmultiplestagesinaroboticsystem’slifecycle.

Fig.5ConceptofaDigitalTwinsysteminhuman–robotcollaboration

DigitalTwinsforRobotSystemsinManufacturing315

6.1DesignoftheRobotSystem

Indevelopinganewrobotsystem,itiscustomarytoconstructvirtualmodelsbeforetheactualphysicalcounterpartisbuilt.Thisvirtualrepresentation,whichcanalsobereferredtoasaDTprototype,isconceivedtoconceptualizeand?nalizethesystem’sappearance,speci?cations,theselectionofoff-the-shelfcomponents,andtheoveralllayout.Despitetheabsenceofthecorrespondingphysicalcounterpartduringthedesignphase,theDTrepresentstheintendedphysicaltwin.Itenablestheexplorationofvariouswhat-ifscenarios,facilitatingswift,secure,andreliabledesignoutcomes.Thechoiceofrobotmanipulators,workstationdesign,layout,?xtures,and?nancialassessmentsarecriticalquestionsthatmustbeaddressedatthedesignstage.

6.2CommissioningtheRobotSystem

Theresultsderivedfromthedesignphaseprovideinformationfordevelopingthecomponentsofthephysicalsystem.Thedevelopedsystemthenmovestothecommis-sioningstage.Inthecaseofarobotsystem,thisstagemayentailthecreationofwork-stations,?xtures,feedingdevices,andotherhardwareelements.TheBillofMaterials(BOM)andBillofProcesses(BOP)canbegenerated,guidingthedevelopmentofthephysicalsystem.Throughoutthisphase,theconnectionbetweenthephysicalsystemsandtheirDTscanbeestablishedbylinkingtheDTtoanactualcontrollerorprogrammablelogiccontroller(PLC)toidentifypotentialerrors.Thismethodologyisanalogoustovirtualcommissioning(VC).VC,orhardware-in-the-loopsimula-tions,reducesdevelopmenttimebyfacilitatingvirtualtestingandintegrationwellbeforeactualcommissioning.ThephysicalrobotcanbeliveconnectedwithitsDT,allowingittoexecutetasksasdesignedintheDT.

6.3ScheduledandPreventiveMaintenance

Maintenanceisanessentialcomponentofmostproductionsystems.Emergingtech-nologiessuchasaugmentedreality(AR)orchatbotscanbeintegratedwithaDTtooptimizemaintenanceprocedures,whichcanbetterassistmaintenancepersonnelwithenhancedvisualizationtoolsforfaultdetectionandtrainingtasks.Virtualreality(VR)isanothervisualizationtechnologythatcanbeintegratedwiththeDT,particularlyfortraining.

Maintenancecanbene?tfromtheDTtechnologyinwayssuchas:

.Real-timeMonitoring:Datacapturingoperatingparameters,energyconsumption,andsystemhealth.

.PredictiveMaintenance:UseofmachinelearningwithinDTtopredictpotentialfailuresormaintenanceneedsbasedonperformancetrends.

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.ConditionMonitoring:IoTsensorsprovidedataonsystemhealthindicators,whichcanbeintegratedwithDTforcontinuousconditionmonitoring.

.AssetTracking:Theusageandlifecycleofroboticsassets,suchasoperatinghours,replacementhistory,etc.,canbetrackedandhelpwithproactivemaintenancescheduling.

.RemoteDiagnosticsandTroubleshooting:Identifyingissuesremotelycanhelpreducedowntimeandimproveoverallef?ciency.

6.4OperationsandChangeovers

ThemostcompellingapplicationofDTtechnologyliesinitsapplicationthroughouttheoperationallifeofarobotsystem.Overtime,arobotsystemmayneedchangeovers,safetyassessments,productionanalyses,andmodi?cations.Assessingtheoverallequipmenteffectiveness(OEE)isanotherpracticalfacetwhenevaluatingaproductionsystemforcontinuousperformanceoptimization.ADTplaysapivotalroleinelevatingthequalityoftheseprocesses,therebyenhancingtheperformanceandreliabilityoftherobotsystem.ThishelpsjustifytheinvestmentinthecreationandmaintenanceofitsDT.

TheDTdevelopedduringthedesignphaseisextendedtofacilitatereal-timecommunicationwiththephysicalsystemduringoperation,enablingbehavioralanalysisandperformanceoptimization.Atthisstage,thesystemsynchronizesthereal-worlddatawiththeDT,enablingautomatedassessmentcycles.Thiscyber-physicalsystemintegratesproductionplanningandcontroldatabasestosupportschedulingproductionordersandchangeovers.TheDTisvaluableinsimplifyingtherecon?gurationorrepurposingoftherobotsysteminresponsetodemand?uctuations.

7ComponentsofaDTforaRobotSystem

Thissectionpresentsthefundamentalcomponentsormodulescomprisingaroboticsystem’sDT,asillustratedinFig.

6

.Traditionally,varioustoolsarerequiredtosimulateeachofthesecomponents.Connectivityprotocolscanenablenearreal-timecommunicationbetweenthesecomponents,streamliningthedevelopmentofanaccurateDTforaroboticsystem.

7.1StaticCADModeling

Theinitialstepinconstructingavirtualmanufacturingsystemistocreatea3DvisualizationusingCADsoftware.Thereisamultitudeoftoolsavailableforthis

DigitalTwinsforRobotSystemsinManufacturing317

Fig.6ComponentsofDTofrobotsystems

purpose.ThisCADdatacanbeobtaineddirectlyfromtheequipmentmanufacturer,ofteninstandardexchangeableformatssuchasSTEP(StandardfortheExchangeofProductData)[

45

].RobotmanufacturersofferCADmodelsoftheirrobots,andasimilarpracticeisfollowedbymanufacturersofrelatedequipment,suchasgrippers,?xtures,feeders,andtables.Furthermore,manysimulationtoolsfeatureabuilt-inlibraryofproprietaryandgenericfactoryresourceCADmodels.

AcriticalstepinpreparingtheCADdataiscreatinganassembly?leandconsol-idatingtheindividualCADmodelsofvariousdevicesandequipment.Thevirtualassemblymodelmustrepresentthecompletephysicalrobotsystembeinginvesti-gated.Eachcomponentcanbeassignedmaterialpropertiesandvisualizationtoaidinsubsequentanalyses.This?lecanbeexportedtovariousexchangeableformats,withSTEPbeingthemostcommonstandardformat.

7.2ProcessSimulation

TheCADdatacanbeimportedintoacontinuoussimulationenvironment.Creatingadynamicsimulationstartswithde?ningthekinematicsofeachactiveresourcewithinthesystem.Itinvolvesspecifyingpositionandlocationconstraints,jointtypes,jointlimits,andvelocitylimits.Forexample,agrippermayneedtobede?nedforits

318A.A.Maliketal.

motionkinematicjointtypes,limits,andactionposes.Thevisualization/simulationofaDTisachievedthroughthreesteps:(1)creatingthesimulationmodelofarobotsystemalongwithitsoperationsequences,akintoaGanttchart,(2)anevent-drivencontinuoussimulationthatrunsforapre-determinedtimeandcontrolledbyaninternallogicengine,and(3)thesimulationiscontrolledthroughsignalsfromavirtualPLCandotheremulators.ThissimulationbecomestheprimarycomponentoftheDTforvisualization,experimentation,andanalysis.Afterthesimulation,itcanperformanalyses(e.g.,collisiondetection,layoutassessment,cycletimeestimates)andoptimizations.Numerousproprietarytoolsareavailabletocreatethistypeofsimulation,whileopen-sourceenginescanalsobeutilized.

7.3AutomationProgram

PLCsserveasindustrialcomputersforprogrammingandmonitoringindustrialrobotsystems.Acriticalstepincommissioningarobot-basedmanufacturingsystemiscreatingandvalidatingtheautomationprogram.Usually,thisprogramiscreatedlaterinthedevelopmentstages.Developing,testing,andvalidatingtheautoma-tionprograminavirtualspace,alongwithprocesssimulation,enhancestherelia-bilityofthesystem’sperformance.EachPLChasitsprogrammingtool,andopen-sourceprogramdevelopmenttoolsarealsoavailable.Toensureinteroperability,PLCprogramsfollowtheIEC61131standard[

46

].TheIEC61131-3,developedbytheInternationalElectrotechnicalCommission(IEC),setsthestandardforPLCs’syntax,semantics,andinteroperability.ThedevelopedprogramsaredownloadedontoavirtualPLCandinterfacedwiththesimulation.

7.4MechatronicBehavior

Arobotsystemincludessensors,actuators,feeders,?xtures,andothermecha-tronicelements(Fig.

3

).Behavioralmodelingofthesedevicesenablesanaccuratevirtualmodeloftheentiresystem.TheFunctionalMock-upUnit(FMU)isatool-independent,freestandardcraftedfordynamicmodelexchangeandco-simulation.FMUsde?neacontainerandaninterfaceforsharingdynamicsimulationmodelsthroughacombinationofExtensibleMarkupLanguage(XML)?les,binaries,andC-code.Bothcommercialandopen-sourcetoolsareaccessibleforsimulatingthebehaviorofeachdeviceandinterfacingitwiththeprocesssimulation.

DigitalTwinsforRobotSystemsinManufacturing319

Fig.7Product