INTRODUCTION TOMACHINE VISIONA guide to automating process & quality improvements

Introduction to Machine Vision 2

What is machine vision? .................... 3

Benefits of machine vision ................. 5

Machine vision applications ............... 6

Guidance ......................................... 7

Identification .................................... 8

Gauging........................................... 9

Inspection ...................................... 10

Components of a machine vision system ................................................11

Lighting.......................................... 13

Back lighting ............................ 13

Axial diffuse lighting ................. 13

Structured light......................... 13

Directionallighting ................... 14

Dark-fieldillumination ......... 14

Bright-fieldillumination........ 14

Diffuseddomelighting ............. 14

Strobelighting .......................... 14

Lenses........................................... 15

Imagesensor ................................ 15

Visionprocessing .......................... 16

Communications ........................... 16

Different types of machine vision systems? ........................................... 17

1DVisionSystems ........................ 17

2DSystems ................................... 18

Area scan vs. line scan ............ 19

3DSystems ................................... 20

Machine vision platforms ................. 21

PC-basedmachinevision ............. 21

Visioncontrollers ........................... 21

Standalonevisionsystems............ 22

Visionsensorsand image-basedbarcodereaders ...... 22

Conclusion ........................................ 23

TABLE OF CONTENTS

Introduction to Machine Vision 3

WHAT IS MACHINE VISIONAccordingtotheAutomatedImagingAssociation(AIA),machinevisionencompassesallindustrialandnon-industrialapplicationsinwhichacombinationofhardwareandsoftwareprovideoperationalguidancetodevicesintheexecutionoftheirfunctionsbasedonthecaptureandprocessingofimages.Thoughindustrialcomputervisionusesmanyofthesamealgorithmsandapproachesasacademic/educationalandgovernmental/militaryapplicationsofcomputervision,constraintsaredifferent.

Industrialvisionsystemsdemandgreaterrobustness,reliability,andstabilitycomparedwithanacademic/educationalvisionsystemandtypicallycostmuchlessthanthoseusedingovernmental/militaryapplications.Therefore,industrialmachinevisionimplieslowcost,acceptableaccuracy,highrobustness,highreliability,andhighmechanical,andtemperaturestability.

Machinevisionsystemsrelyondigitalsensorsprotectedinsideindustrialcameraswithspecializedopticstoacquireimages,sothatcomputerhardwareandsoftwarecanprocess,analyze,andmeasurevariouscharacteristicsfordecisionmaking.

Asanexample,considerafill-levelinspectionsystematabrewery(Figure1).Eachbottleofbeerpassesthroughaninspectionsensor,whichtriggersavisionsystemtoflashastrobelightandtakeapictureofthebottle.Afteracquiringtheimageandstoringitinmemory,visionsoftwareprocessesoranalyzesitandissuesapass-failresponsebasedonthefilllevelofthebottle.Ifthesystemdetectsanimproperlyfilledbottle—afail—itsignalsadivertertorejectthebottle.Anoperatorcanviewrejectedbottlesandongoingprocessstatisticsonadisplay.

Machinevisionsystemscanalsoperformobjectivemeasurements,suchasdeterminingasparkpluggaporprovidinglocationinformationthatguidesarobottoalignpartsinamanufacturingprocess.Figure2showsexamplesofhowmachinevisionsystemscanbeusedtopassorfailoilfilters(right)andmeasurethewidthofacentertabona bracket(left).

Introduction to Machine Vision 4

Vision System Display Strobe

Sensor

Good oil filter (allholesareopen)

Reject oil filter (someholesareblocked)

37.255 mm

Figure 1.Bottlefill-levelinspectionexample

Thefill-levelinspectionsysteminthisexamplepermitsonlytwopossibleresponses,whichcharacterizesitasabinarysystem:

1.Passiftheproductisgood

2.Failiftheproductisbad.

Figure 2.

Machinevisionsystemscanprocessreal-timemeasurementsandinspectionsontheproductionline, suchasamachinedbracket(left)oroilfilters(right).

Introduction to Machine Vision 5

BENEFITS OFMACHINE VISIONWherehumanvisionisbestforqualitativeinterpretationofacomplex,unstructuredscene,machinevisionexcelsatquantitativemeasurementofastructuredscenebecauseofitsspeed,accuracy,andrepeatability.Forexample,onaproductionline,amachinevisionsystemcaninspecthundreds,oreventhousands,ofpartsperminute.Amachinevisionsystembuiltaroundtherightcameraresolutionandopticscaneasilyinspectobjectdetailstoosmalltobeseenbythehumaneye.

Inremovingphysicalcontactbetweenatestsystemandthepartsbeingtested,machinevisionpreventspartdamageandeliminatesthemaintenancetimeandcostsassociatedwithwearandtearonmechanicalcomponents.Machinevisionbringsadditionalsafetyandoperationalbenefitsbyreducinghumaninvolvementinamanufacturingprocess.Moreover,itpreventshumancontaminationofcleanroomsandprotectshumanworkersfromhazardousenvironments.

Machinevisionhelpsmeetstrategicgoals

Machine vision helps meet strategic goals

Strategic Goal Machine Vision Applications

Higherquality Inspection,measurement,gauging,andassemblyverification

Increasedproductivity RepetitivetasksformerlydonemanuallyarenowdonebyMachineVisionSystem

Productionflexibility Measurementandgauging/Robotguidance/Prioroperationverification

Lessmachinedowntimeandreducedsetuptime Changeoversprogrammedinadvance

Morecompleteinformationandtighterprocesscontrol Manualtaskscannowprovidecomputerdatafeedback

Lowercapitalequipmentcosts Addingvisiontoamachineimprovesitsperformance,avoidsobsolescence

Lowerproductioncosts Onevisionsystemvs.manypeople/Detectionofflawsearlyintheprocess

Scrapratereduction Inspection,measurement,andgauging

Inventorycontrol OpticalCharacterRecognitionandidentification

Reducedfloorspace Visionsystemvs.operator

Introduction to Machine Vision 6

MACHINE VISIONAPPLICATIONSTypicallythefirststepinanymachinevisionapplication,whetherthesimplestassemblyverificationoracomplex3Droboticbin-picking,isforpatternmatchingtechnologytofindtheobjectorfeatureofinterestwithinthecamera’sfieldofview.Locatingtheobjectofinterestoftendeterminessuccessorfailure.Ifthepatternmatchingsoftwaretoolscannotpreciselylocatethepartwithintheimage,thenitcannotguide,identify,inspect,count,ormeasurethepart.Whilefindingapartsoundssimple,differencesinitsappearanceinactualproductionenvironmentscanmakethatstepextremelychallenging(Figure3).Althoughvisionsystemsaretrainedtorecognizepartsbasedonpatterns,eventhemosttightlycontrolledprocessesallowsomevariabilityinapart’sappearance(Figure4).

Toachieveaccurate,reliable,andrepeatableresults,avisionsystem’spartlocationtoolsmustincludeenoughintelligencetoquicklyandaccuratelycomparetrainingpatternstotheactualobjects(patternmatching)movingdownaproductionline.Partlocationisthecriticalfirststepinthefourmajorcategoriesofmachinevisionapplications.Thecategoriesareguidance,identification,gauging,andinspection,whichcanberememberedbytheacronym(GIGI).

Figure 3.

Appearancechangesduetolightingorocclusioncanmakepartlocationdifficult.

Normal BothDarker Lighter

IncompleteBackground Focus Dirt

Figure 4.

Partpresentationorposedistortioneffectscanmakepartlocationdifficult.

Normal

Wider

Smaller

Higher

Larger

Linear Distortion

Rotated

Non Linear

Introduction to Machine Vision 7

GUIDANCE

Guidancemaybedoneforseveralreasons.First,machinevisionsystemscanlocatethepositionandorientationofapart,compareittoaspecifiedtolerance,andensureit’satthecorrectangletoverifyproperassembly.Next,guidancecanbeusedtoreportthelocationandorientationofapartin2Dor3Dspacetoarobotormachinecontroller,allowingtherobottolocatethepartorthemachinetoalignthepart.Machinevisionguidanceachievesfargreaterspeedandaccuracythanmanualpositioningintaskssuchasarrangingpartsonoroffpallets,packagingpartsoffaconveyorbelt,findingandaligningpartsforassemblywithothercomponents,placingpartsonaworkshelf,orremovingpartsfrombins. Guidancecanalsobeusedforalignmenttoothermachinevisiontools.Thisisaverypowerfulfeatureofmachinevisionbecausepartsmaybepresentedtothecamerainunknownorientationsduringproduction.Bylocatingthepartandthenaligningtheothermachinevisiontoolstoit,machinevisionenablesautomatictoolfixturing.Thisinvolveslocatingkeyfeaturesonaparttoenableprecisepositioningofcaliper,blob,edge,orothervisionsoftwaretoolssothattheycorrectlyinteractwiththepart.Thisapproachenablesmanufacturerstobuildmultipleproductsonthesameproductionlineandreducestheneedforexpensivehardtoolingtomaintainpartpositionduringinspection.

Sometimesguidancerequiresgeometricpatternmatching.Patternmatchingtoolsmusttoleratelargevariationsincontrastandlighting,aswellaschangesinscale,rotation,andotherfactorswhilefindingthepartreliablyeverytime.Thisisbecauselocationinformationobtainedbypatternmatchingenablesthealignmentofothermachinevisionsoftwaretools.

Figure 5a.Examplesofimagesusedinguidance.

Tomato sauce packets Printed circuit board 90 degree elbow

Introduction to Machine Vision 8

Figure 5b.Patternmatchingcanbechallenging.

IDENTIFICATION

Amachinevisionsystemforpartidentificationandrecognitionreadsbarcodes(1-D), datamatrixcodes(2-D),directpartmarks(DPM),andcharactersprintedonparts,labels,andpackages.Anopticalcharacterrecognition(OCR)systemreadsalphanumericcharacterswithoutpriorknowledge,whereasanopticalcharacterverification(OCV)systemconfirmsthepresenceofacharacterstring.Additionally,machinevisionsystemscanidentifypartsbylocatingauniquepatternoridentifyitemsbasedoncolor,shape,orsize.

DPMapplicationsmarkacodeorcharacterstringdirectlyontothepart.Manufacturersinallindustriescommonlyusethistechniqueforerror-proofing,enablingefficientcontainmentstrategies,monitoringprocesscontrolandquality-controlmetrics,andquantifyingproblematicareasinaplantsuchasbottlenecks.Traceabilitybydirectpartmarkingimprovesassettrackingandpartauthenticityverification.Italsoprovidesunit-leveldatatodrivesuperiortechnicalsupportandwarrantyrepairservicebydocumentingthegenealogyofthepartsinasub-assemblythatmakeupthefinishedproduct.

Trained Part

Out of Focus

Scale Change/ Dim Lighting

Confusing Background 180º Rotation

OcclusionReversed Polarity

Introduction to Machine Vision 9

Conventionalbarcodeshavegainedwideacceptanceforretailcheckoutandinventorycontrol.Traceabilityinformation,however,requiresmoredatathancanfitinastandardbarcode.Toincreasethedatacapacity,companiesdeveloped2-Dcodes,suchasDataMatrix,whichcanstoremoreinformation,includingmanufacturer,productidentification,lotnumber,andevenauniqueserialnumberforvirtuallyanyfinishedgood.

GAUGING

Amachinevisionsystemforgaugingcalculatesthedistancesbetweentwoormorepointsorgeometricallocationsonanobjectanddetermineswhetherthesemeasurementsmeetspecifications.Ifnot,thevisionsystemsendsafailsignaltothemachinecontroller,triggeringarejectmechanismthatejectstheobjectfromtheline.

Inpractice,afixed-mountcameracapturesimagesofpartsastheypassthecamera’sfieldofviewandthesystemusessoftwaretocalculatedistancesbetweenvariouspointsintheimage.Becausemanymachinevisionsystemscanmeasureobjectfeaturestowithin0.0254millimeters,theyaddressanumberofapplicationstraditionallyhandledbycontactgauging.

Figure 6.IdentificationtechniquescanrangefromsimplebarcodescanningtoOCR.

Introduction to Machine Vision 10

INSPECTION

Amachinevisionsystemforinspectiondetectsdefects,contaminants,functionalflaws,andotherirregularitiesinmanufacturedproducts.Examplesincludeinspectingtabletsofmedicineforflaws,displaystoverifyiconsorconfirmpixelpresence,ortouchscreenstomeasurethelevelofbacklightcontrast.Machinevisioncanalsoinspectproductsforcompleteness,suchasensuringamatchbetweenproductandpackageinthefoodandpharmaceuticalindustries,andcheckingsafetyseals,caps,andringsonbottles.

Figure 7. Gaugingapplicationscanmeasureparttolerancestowithin0.0254millimeters.

Figure 8. Machinevisionsystemscandetectdefectsorfunctionalflaws.

Introduction to Machine Vision 11

COMPONENTS OF MACHINE VISIONThemajorcomponentsofamachinevisionsystem(Figure9)includethelighting,lens,imagesensor,visionprocessing,andcommunications.Lightingilluminatestheparttobeinspectedallowingitsfeaturestostandoutsotheycanbeclearlyseenbycamera.Thelenscapturestheimageandpresentsittothesensorintheformoflight.Thesensorinamachinevisioncameraconvertsthislightintoadigitalimagewhichisthensenttotheprocessorforanalysis.

Visionprocessingconsistsofalgorithmsthatreviewtheimageandextractrequiredinformation,runthenecessaryinspection,andmakeadecision.Finally,communicationistypicallyaccomplishedbyeitherdiscreteI/Osignalordatasentoveraserialconnectiontoadevicethatislogginginformationorusingit.

Mostmachinevisionhardwarecomponents,suchaslightingmodules,sensors,andprocessorsareavailablecommercialoff-the-shelf(COTS).MachinevisionsystemscanbeassembledfromCOTS,orpurchasedasanintegratedsystemwithallcomponentsina single device.

Thefollowingpageslistthevariouskeycomponentsofamachinevisionsystemincluding:lighting,lenses,visionsensor,imageprocessing,visionprocessing,communications

Introduction to Machine Vision 12

Figure 9. Maincomponentsofamachinevisionsystem.

Vision System

Light Source

Monitor

Camera

Lens

Image Sensor

Inspected Parts

Input/Output

• Serial • Parallel •ISA,PCI,VMEbus

Operational Pointing Device

•Touchscreen •Mouse •Trackball

Introduction to Machine Vision 13

LIGHTING

Lightingisonekeytosuccessfulmachinevisionresults.Machinevisionsystemscreateimagesbyanalyzingthereflectedlightfromanobject,notbyanalyzingtheobjectitself.Alightingtechniqueinvolvesalightsourceanditsplacementwithrespecttothepartandthecamera.Aparticularlightingtechniquecanenhanceanimagesuchthatitnegatessomefeaturesandenhancesothers,bysilhouettingapartwhichobscuressurfacedetailstoallowmeasurementofitsedges,forexample.

Back lightingBacklightingenhancesanobject’soutlineforapplicationsthatneedonlyexternaloredgemeasurements.Backlightinghelpsdetectshapesandmakesdimensionalmeasurementsmorereliable.

Axial diffuse lightingAxialdiffuselightingcoupleslightintotheopticalpathfromtheside(coaxially).Asemi-transparentmirrorilluminatedfromtheside,castslightdownwardsonthepart.Thepartreflectsthelightbacktothecamerathroughthesemi-transparentmirrorresultinginaveryevenlyilluminatedandhomogeneouslookingimage.

Structured lightStructuredlightistheprojectionofalightpattern(plane,grid,ormorecomplexshape)ataknownangleontoanobject.Itcanbeveryusefulforprovidingcontrast-independentsurfaceinspections,acquiringdimensionalinformationandcalculatingvolume.

Introduction to Machine Vision 14

Dark-field illuminationDirectionallightingmoreeasilyrevealssurfacedefectsandincludesdark-fieldandbright-fieldillumination.Dark-fieldilluminationgenerallypreferredforlow-contrastapplications. Indark-fieldillumination,specularlightisreflectedawayfromthecamera,anddiffusedlightfromsurfacetextureandelevationchangesarereflectedintothecamera.

Bright-field illuminationBright-fieldilluminationisidealforhigh-contrastapplications.However,highlydirectionallightsourcessuchashigh-pressuresodiumandquartzhalogenmayproducesharpshadowsandgenerallydonotprovideconsistentilluminationthroughouttheentirefieldofview.Consequently,hot-spotsandspecularreflectionsonshinyorreflectivesurfacesmayrequireamorediffusedlightsourcetoprovideevenilluminationinthebrightfield.

Diffused dome lightingDiffuseddomelightinggivesthemostuniformilluminationoffeaturesofinterest,andcanmaskirregularitiesthatarenotofinterestandmaybeconfusingtothescene.

Strobe lightingStrobelightingisusedinhigh-speedapplicationstofreezemovingobjectsforexamination.Usingastrobelightalsohelpstopreventblurring.

Formoreinformationonlightingtechniques,pleasedownloadtheCognexexpertguide “HowtoChoosetheRightLightingforMachineVisionApplications”availableat cognex.com/lightingexpertguide

Introduction to Machine Vision 15

LENSES

Thelenscapturestheimageanddeliversittotheimagesensorinthecamera.Lenswillvaryinopticalqualityandprice,thelensuseddeterminesthequalityandresolutionofthecapturedimage.Mostvisionsystemcamerasoffertwomaintypesoflenses:interchangeablelensesandfixedlenses.InterchangeablelensesaretypicallyC-mountsorCS-mounts.Therightcombinationoflensandextensionwillacquirethebestpossibleimage.Afixedlensaspartofastandalonevisionsystemtypicallyusesautofocus,whichcouldbeeitheramechanicallyadjustedlensoraliquidlensthatcanautomaticallyfocusonthepart.Autofocuslensesusuallyhaveafixedfieldofviewatagivendistance.

IMAGE SENSOR

Thecamera’sabilitytocaptureacorrectly-illuminatedimageoftheinspectedobjectdependsnotonlyonthelens,butalsoontheimagesensorwithinthecamera.Imagesensorstypicallyuseachargecoupleddevice(CCD)orcomplementarymetaloxidesemiconductor(CMOS)technologytoconvertlight(photons)toelectricalsignals(electrons).Essentiallythejoboftheimagesensoristocapturelightandconvertittoadigitalimagebalancingnoise,sensitivityanddynamicrange.Theimageisacollectionofpixels.Lowlightproducesdarkpixels,whilebrightlightcreatesbrighterpixels.It’simportanttoensurethecamerahastherightsensorresolutionfortheapplication.Thehighertheresolution,themoredetailanimagewillhave,andthemoreaccuratemeasurementswillbe.Partsize,inspectiontolerances,andotherparameterswilldictatetherequiredresolution.

Formoreinformationonlenses,pleasedownloadtheCognexexpertguide “UsingOpticstoOptimizeYourMachineVisionApplication”availableat cognex.com/lensexpertguide

Formoreinformationonsensorresolution,pleasedownloadtheCognexexpertguide “UsingOpticstoOptimizeYourMachineVisionApplication”availableat cognex.com/lensexpertguide

Introduction to Machine Vision 16

VISION PROCESSING

ProcessingisthemechanismforextractinginformationfromadigitalimageandmaytakeplaceexternallyinaPC-basedsystem,orinternallyinastandalonevisionsystem.Processingisperformedbysoftwareandconsistsofseveralsteps.First,animageisacquiredfromthesensor.Insomecases,pre-processingmayberequiredtooptimizetheimageandensurethatallthenecessaryfeaturesstandout.Next,thesoftwarelocatesthespecificfeatures,runsmeasurements,andcomparesthesetothespecification.Finally,adecisionismadeandtheresultsarecommunicated.

Whilemanyphysicalcomponentsofamachinevisionsystem(suchaslighting)offercomparablespecifications,thevisionsystemalgorithmssetthemapartandshouldtopthelistofkeycomponentstoevaluatewhencomparingsolutions.Dependingonthespecificsystemorapplication,visionsoftwareconfigurescameraparameters,makesthepass-faildecision,communicateswiththefactoryfloor,andsupportsHMIdevelopment.

COMMUNICATIONS

Sincevisionsystemsoftenuseavarietyofoff-the-shelfcomponents,theseitemsmustcoordinateandconnecttoothermachineelementsquicklyandeasily.TypicallythisisdonebyeitherdiscreteI/Osignalordatasentoveraserialconnectiontoadevicethatislogginginformationorusingit.DiscreteI/Opointsmaybeconnectedtoaprogrammablelogiccontroller(PLC),whichwillusethatinformationtocontrolaworkcelloranindicatorsuchasastacklightordirectlytoasolenoidwhichmightbeusedtotriggerarejectmechanism. DatacommunicationbyaserialconnectioncanbeintheformofaconventionalRS-232serialoutput,orEthernet.Somesystemsemployahigher-levelindustrialprotocollikeEthernet/IP,whichmaybeconnectedtoadevicelikeamonitororotheroperatorinterfacetoprovideanoperatorinterfacespecifictotheapplicationforconvenientprocessmonitoringandcontrol.

FormoreinformationcommunicationsandI/O,pleasedownloadtheCognexTechNote“GetControlofYourVisionandIDSystems”availableat cognex.com/getcontroltechnote

Introduction to Machine Vision 17

DIFFERENT TYPES OF MACHINE VISION SYSTEMS

1D VISION SYSTEMS

1Dvisionanalyzesadigitalsignalonelineatatimeinsteadoflookingatawholepictureatonce,suchasassessingthevariancebetweenthemostrecentgroupoftenacquiredlinesandanearliergroup.Thistechniquecommonlydetectsandclassifiesdefectsonmaterialsmanufacturedinacontinuousprocess,suchaspaper,metals,plastics,andothernon-wovensheetorrollgoods,asshowninFigure10.

Broadlyspeaking,thereare3categoriesofmachinevisionsystems:1D,2Dand3D.

Figure 10. 1Dvisionsystemsscanonelineatatimewhiletheprocessmoves.Intheaboveexample,adefectinthesheetisdetected.

Introduction to Machine Vision 18

Figure 11.

2Dvisionsystemscanproduceimageswithdifferentresolutions.

Figure 12.

Linescantechniquesbuildthe2Dimageonelineatatime.

2D VISION SYSTEMS

Mostcommoninspectioncamerasperformareascansthatinvolvecapturing2Dsnapshotsinvariousresolutions,asshowninFigure11.Anothertypeof2Dmachinevision–linescan–buildsa2Dimagelinebyline,asshowninFigure12.

Line Light

Conveyor Belt Movement

Encoder Shaft

In-Sight 5000

Line Acquired Built Image

Introduction to Machine Vision 19

Figure 13. Linescancamerascan(a.)unwrapcylindricalobjectsforinspection,(b.)addvisiontospace-constrainedenvironments,(c.)meethigh-resolutioninspectionrequirements,and(d.)inspectobjectsincontinuousmotion.

a. b.

c. d.

AREA SCAN VS. LINE SCAN

Incertainapplications,linescansystemshavespecificadvantagesoverareascansystems.Forexample,inspectingroundorcylindricalpartsmayrequiremultipleareascancamerastocovertheentirepartsurface.However,rotatingthepartinfrontofasinglelinescancameracapturestheentiresurfacebyunwrappingtheimage.Linescansystemsfitmoreeasilyintotightspacesforinstanceswhenthecameramustpeekthroughrollersonaconveyortoviewthebottomofapart.Linescansystemscanalsogenerallyprovidemuchhigherresolutionthantraditionalcameras.Sincelinescansystemsrequirepartsinmotiontobuildtheimage,theyareoftenwell-suitedforproductsincontinuousmotion.

Introduction to Machine Vision 20

Incontrast,3Dlaser-displacementsensorapplicationstypicallyincludesurfaceinspectionandvolumemeasurement,producing3Dresultswithasfewasasinglecamera.Aheightmapisgeneratedfromthedisplacementofthereflectedlasers’locationonanobject.Theobjectorcameramustbemovedtoscantheentireproductsimilartolinescanning.Withacalibratedoffsetlaser,displacementsensorscanmeasureparameterssuchassurfaceheightandplanaritywithaccuracywithin20µm.Figure15showsa3Dlaserdisplacementsensorinspectingbrakepadsurfacesfordefects.

Figure 14.

3Dvisionsystemstypicallyemploymultiplecameras.

Figure 15.

3Dinspectionsystemusingasinglecamera.

3D SYSTEMS

3Dmachinevisionsystemstypicallycomprisemultiplecamerasoroneormorelaserdisplacementsensors.Multi-camera3Dvisioninroboticguidanceapplicationsprovidestherobotwithpartorientationinformation.Thesesystemsinvolvemultiplecamerasmountedatdifferentlocationsand“triangulation”onanobjectivepositionin3-Dspace.

Introduction to Machine Vision 21

MACHINE VISION PLATFORMSMachinevisionimplementationcomesonseveralphysicalplatforms,includingPC-basedsystems,visioncontrollersdesignedfor3Dandmulti-camera2Dapplications,standalonevisionsystems,simplevisionsensors,andimage-basedbarcodereaders.Choosingtherightmachinevisionplatformgenerallydependsontheapplication’srequirements,includingdevelopmentenvironment,capability,architecture,andcost.

PC-BASED MACHINE VISION

PC-basedsystemseasilyinterfacewithdirect-connectcamerasorimageacquisitionboardsandarewellsupportedwithconfigurablemachinevisionapplicationsoftware.Inaddition,PCsprovideawealthofcustomcodedevelopmentoptionsusingfamiliarandwell-supportedlanguagessuchasVisualC/C++,VisualBasic,andJava,plusgraphicalprogrammingenvironments.However,developmenttendstobelongandcomplicated,soisusuallylimitedtolargeinstallationsandappealmostlytoadvancedmachinevisionusersandprogrammers.

VISION CONTROLLERS

VisioncontrollersofferallofthepowerandflexibilityofPC-basedsystem,butarebetterabletowithstandtherigorsofharshfactoryenvironments.Visioncontrollersallowforeasierconfigurationof3Dandmulti-camera2Dapplications,perhapsforone-offtaskswhereareasonableamountoftimeandmoneyisavailablefordevelopment.Thisallowsformoresophisticatedapplicationstobeconfiguredinaverycost-effectiveway.

Introduction to Machine Vision 22

STANDALONE VISION SYSTEMS

Standalonevisionsystemsarecosteffectiveandcanbequicklyandeasilyconfigured.Thesesystemscomecompletewiththecamerasensor,processor,andcommunications.Somealsointegratelightingandautofocusoptics.Inmanycasesthesesystemsarecompactandaffordableenoughtobeinstalledthroughoutthefactory.Byusingstandalonevisionsystemsatkeyprocesspoints,defectscanbecaughtearlierinthemanufacturingprocessandequipmentproblemscanbeidentifiedmorequickly.Mostofferbuilt-inEthernetcommunications,whichenablesuserstonotonlydistributevisionthroughouttheprocess,buttolinktwoormoresystemstogetherinafully-manageable,scalablevisionareanetworkinwhichdataisexchangedbetweensystemsandmanagedbyahost.Anetworkofvisionsystemscanalsobeeasilyuplinkedtoplantandenterprisenetworks,allowinganyworkstationinthefactorywithTCP/IPcapabilitytoremotelyviewvisionresults,images,statisticaldata,andotherinformation.Thesesystemsofferconfigurableenvironmentsthatprovideeasyguidedsetupormoreadvancedprogrammingandscripting.Somestandalonevisionsystemsprovidebothdevelopmentenvironmentsallowingforeasysetupwiththeaddedpower,andflexibilityofprogrammingandscriptingforgreatercontrolofsystemconfigurationandhandlingofvision-applicationdata.

VISION SENSORS AND IMAGE-BASED BARCODE READERS

Visionsensorsandimage-basedbarcodereadersgenerallyrequirenoprogramming,andprovideuser-friendlyinterfaces.Mostareeasilyintegratedwithanymachinetoprovidesingle-pointinspectionswithdedicatedprocessing,andofferbuilt-inEthernetcommunicationsforfactory-widenetworkability.

Introduction to Machine Vision 23

CONCLUSIONMachinevisionistheautomaticextractionofinformationfromdigitalimagesforprocessorqualitycontrol.Mostmanufacturersuseautomatedmachinevisioninsteadofhumaninspectorsbecauseitisbettersuitedtorepetitiveinspectiontasks.Itisfaster,moreobjective,andworkscontinuously.Machinevisioncaninspecthundredsoreventhousandsofpartsperminute,andprovidesmoreconsistentandreliableinspectionresults24hoursaday,7daysaweek.

Measurement,counting,location,anddecodingaresomeofthemostcommonapplicationsformachinevisioninmanufacturingtoday.Byreducingdefects,increasingyield,facilitatingcompliancewithregulationsandtrackingpartswithmachinevision,manufacturerscansavemoneyandincreaseprofitability.

Formoreinformationonhowmachinevisioncanhelpyourorganizationreducewaste,minimizedowntime,andimproveprocessescontactCognex

Orvisittheseonlineresourcesformoreinformation:• CognexMachineVision • CognexVisionSystems • CognexVisionSensors • Cognex3DVision • CognexIndustrialBarcodeReaders

Introduction to Machine Vision 24Conclusion 24

© Copyright 2016, Cognex Corporation. All information in this document is subject to change without notice. Cognex, PatMax, 1DMax, In-Sight, EasyBuilder, DataMan, VisionView, SensorView, Checker and VisionPro are registered trademarks and OCRMax, the Cognex logo, Cognex Connect, Cognex Designer and PatMax RedLine are trademarks of Cognex Corporation. All other trademarks are the property of their respective owners. Printed in the USA. Lit. No. IMVWP-2016-0331 –EN

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