introduction to machine vision...conventional barcodes have gained wide acceptance for retail...

INTRODUCTION TOMACHINE VISIONA guide to automating process & quality improvements

AUTOMATIONa division of HTE Technologies

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


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).


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).


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


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


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


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


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.


INSPECTION

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

Figure 7. Gaugingapplicationscanmeasureparttolerancestowithin0.0254millimeters.

Figure 8. Machinevisionsystemscandetectdefectsorfunctionalflaws.


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


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


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.


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


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


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


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.


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


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.


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.


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.


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.


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

VISION FOR EVERY INDUSTRYCognexvisionsystemsperform100%inspection,ensurebrandqualityandimproveyourproductionprocesses.Withoveronemillionsystemsinstalledworldwide,Cognexmachinevisionsystemsareacceptedinnearlyeveryindustryandusedbymostmajormanufacturers.

The manufacturing processes for building virtually every system and component within an automobile can benefit from the use of machine vision.

AutomotiveMachine-vision-enabled robots provide scalable, final assembly of mobile phones, tablets, and wearable devices. Cognex vision technology enables high precision touchscreen display manufacturing and 3D quality inspection.

Mobile Devices

Improve production and packaging operations with high-speed image acquisition, advanced color tools, and 3D inspection systems.

Consumer Products

Food and beverage applications require vision that can perform precisely, accurately and quickly to keep up with the fast-paced production lines.

Food & Beverage

Quality inspection is critical to success. Liability for defective products, inconsistent quality, rapidly changing costs and pending regulations, all challenge medical device manufacturers.

Medical Devices

The need to comply with patient safety and traceability requirements is imperative, and machine vision helps meet compliance goals.

Pharmaceutical

Cognex vision provides the precise, sub-pixel alignment and identification essential to every step of the semiconductor manufacturing process, despite increasingly fine geometries and process effect challenges.

SemiconductorMachine vision provides the high-speed alignment and traceability for electronics assembly, even on the newest miniaturized components and flexible circuits.

Electronics

© Copyright 2018, 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-2018–EN

CompaniesaroundtheworldrelyonCognexvisionandbarcodereadingtooptimizequality,drivedowncosts,andcontroltraceability.

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