Assessing the capability of in situ nondestructive analysis during layer based additive manufacture

ContentslistsavailableatScienceDirect

Additive

Manufacturing

j o ur na l h o me pa g e :w w w . e l s e v i e r . c o m / l o c a t e / a d d m a

Assessing

the

capability

of

in-situ

nondestructive

analysis

during

layer

based

additive

manufacture

Matthias

Hirsch

_,

_Rikesh

_Patel

_,

_Wenqi

_Li

_,

_Guangying

_Guan

_,

_Richard

_K.

_Leach

_,

Steve

D.

Sharples

_,

_Adam

_T.

_Clare

a_{OpticsandPhotonicsGroup,FacultyofEngineering,TheUniversityofNottingham,NottinghamNG72RD,UnitedKingdom}

b_{AdvancedComponentEngineeringLaboratory(ACEL),FacultyofEngineering,TheUniversityofNottingham,NottinghamNG72RD,UnitedKingdom}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received2August2016 Receivedinrevisedform 16September2016 Accepted17October2016 Availableonlinexxx

Keywords:

Nondestructiveevaluation Additivemanufacturing Processcontrol In-situanalysis

a

b

s

t

r

a

c

t

Unlikemoreestablishedsubtractiveorconstantvolumemanufacturingtechnologies,additive manufac-turingmethodssufferfromalackofin-situmonitoringmethodologieswhichcanprovideinformation relatingtoprocessperformanceandtheformationofdefects.In-processevaluationforadditive manu-facturingisbecomingincreasinglyimportantinordertoassuretheintegrityofpartsproducedinthis way.Thispaperaddressesthegenericperformanceofinspectionmethodssuitableforadditive man-ufacturing.Keyprocessandmeasurementparametersareexploredandtheimpactsthesehaveupon productionratesaredefined.Essentialworkingparametersarehighlighted,withinwhichthespatial opportunityandtemporalpenaltyformeasurementallowforcomparisonofthesuitabilityofdifferent nondestructiveevaluationtechniques.Anewmethodofbenchmarkingin-situinspectioninstruments andcharacterisingtheirsuitabilityforadditivemanufacturingprocessesispresentedtoactasadesign tooltoaccommodateenduserrequirements.Twoinspectionexamplesarepresented:spatiallyresolved acousticspectroscopyandopticalcoherencetomographyforscanningselectivelasermeltingand selec-tivelasersinteringparts,respectively.Observationsmadefromtheanalysespresentedshowthatthe spatialcapabilityarisingfromscanningparametersaffectsthetemporalpenaltyandhenceimpactupon productionrates.Acasestudy,createdfromsimulateddata,hasbeenusedtooutlinethespatial per-formanceofagenericnondestructiveevaluationmethodandtoshowhowadecreaseindatacapture resolutionreducestheaccuracyofmeasurement.

©2016TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Throughthecontinueddevelopmentofadditive manufactur-ing(AM)processespartmanufactureforhighvalueapplicationsis continuingtogaintraction(e.g.inaerospace,medicalandtooling industries)[1].AnAMprocessuseslocalisedmaterialadditionon alayerperlayerbasistobuildupthree-dimensional(3D)parts[2]. Mordfinetal.definethreemainassumptionsaboutmanufactured materials[3]:(1)allmaterialscontaindefects;(2)thesedefectsare expectedanddonotdefinitivelymeanthepartisunfitforuse(i.e. forservicelife);and(3)thedetectabilityofdefectsincreaseswith thesizeofthedefects.Theseassumptionsholdtrueforparts pro-ducedwithAMprocesses,henceitcanbededucedthatinspection isessentialinhighvaluecomponentsinordertoassurethatthe manufacturedpartisfitforuse.

∗Correspondingauthor.

E-mailaddress:[email protected](A.T.Clare).

Inspection can be conducted destructively, where statistical informationcanbegatheredinordertogiveaconfidence inter-valforapartproducedundersimilarconditions(e.g.basematerial consistency, temperatureand atmosphere).However, for many applicationsinthehighvalueaddedindustries,destructive inspec-tion maynot besuitable (where individual partinformation is required).Nondestructiveevaluation(NDE)ofapartis,therefore, oftentheonlymethodthatcanbeemployedtogatherthedefect datarequired.Evertonetal.havereviewedrecentresearchon in-situmonitoringtechniquesofmetalAMprocesses[4];anoverview isgivenofthedirectandindirectmeasurementinstrumentation currentlyemployedtomeasurepartsandmachineoperationinAM, whichhasbeenfoundtobelimitedtoasmallnumberof commer-cialsystems;however,thereisarangeofinspectiontechniquesthat arecurrentlybeingdeveloped[4]andyetthereisnooverarching contributionwhichexplorestheirutilisation.

MeasurementmethodscurrentlybeingconsideredforAMcan besortedintotwoprincipalcategories:indirectanddirect.Indirect measurement techniques investigate effects on part

manufac-http://dx.doi.org/10.1016/j.addma.2016.10.004

areevaluatedinconventionalmanufacturing.However,AM pro-videsagoodopportunityforpartstobeinspectedastheyarebeing built−in-situ.NDEmethodsthatcaninspectthesurfaceand/or thesubsurfaceregioncannowbeusedtobuildupanimageofthe internalstructureofamanufacturedpart,ensuringitisproduced tomeetdesignparameters.Inaddition,measurementsmadeand analysedin-situcandetecterrorswithinthebuildprocess; feed-backcouldthenbeusedtopausethebuildtoavoidscrappageof theflawedcompletepart,reducingmaterialwaste.Alternatively, thescandatacanbeusedtoenabletotheAMmachinetoreact autonomouslyandreworkthedefectivelayer,‘saving’thebuild[8]. TheseapproachesmayimprovetheeconomicviabilityofusingAM processesandimproveitsadoptionintomorefields[9].Whilethe underpinningmachinetooltechnologytoallowin-siturepairisnot availableincurrentgenerationmachines,thispresentsan interest-ingresearchareawhichwillsignificantlyenhancethecapabilityof AMtools.

In-processmonitoringisanimportantnextstepinAMmethods duetouser,machineandmaterialinducederrorsaffectingthe suc-cessofmanufacturefromageometricalandmaterialpointofview [4].Thestudypresentedhereinvestigatesgenericparametersof NDEtoolsandtheirinfluenceontheproductivityontherapidly advancingAMprocesswhenincorporatedin-situ.Analysingthe spatialopportunityandtemporalpenaltyassociatedwithit,that theAMprocesspresentsforameasurementsystem,isakey con-cernwhendesigningandselectinginstruments.AnNDEcapability analysisapproachisdemonstratedintwocasestudiesand simu-lateddefectdataisusedtooutlinetheeffectsoflowNDEspatial capabilities.

1.1. Currentinspectionmethods

In order to frame the methodology of determining and optimising inspection strategies, it is useful to review current measurement strategies. Indirect NDEmethods currentlybeing investigated include thermal and optical analysis − conducted eitherin-situoronline,haveshowntoyielddatathatcanbeused forfeedback inselective lasermelting (SLM)[10,11].Meltpool analysisenablespartfailuredetection.Theprocesswillinfer for-mationofdefectsbasedonobservationsofthemeltpool.Inwork byKraussetal.,artificialdefectsinarangeof40␮mto500␮m wereintroducedintoanSLMbuildatthedesignstage[12].During partmanufacture withInconel718powder,thebuildwas ana-lysedusingthermography(detectingwithaninfraredcamera)with defectsoflessthan100␮midentifiedbydetectingavariationinthe coolingrate.However,asanindirectmeasurementmethod,the siz-ingandnatureofthedefectswerenotobtained.Doubenskaiaetal. haveshownthatanopticalsystemcanbeusedforsolidversus unfusedpowderdifferentiation(whilstalsodeterminingthe geom-etryofthepartin-situ)[13].Similarly,Schwerdtfegeretal.have shownthatinfraredimaging,usedin-situinelectron-beam pow-derbedfusionprocesses,yieldslayerperlayerdatathatoutlines areasofdefectsasareductioninintensity[14]−inthisstudythe minimumsamplingsizecorrespondedto830␮m.Thesizerange

cessmonitoringwithsurfaceandsubsurfaceinformation.Thiswas shown byGuan etal. scanningpolymer test samplesproduced usinganSLSsystemwithembeddedartificialdefects[8].Surface defectsandroughnesswavelengthswithaminimumof9␮mcould beresolvedandidentificationofloosepowderundersintered mate-rialwaspossibledownto200␮mbelowthesurface.Subsurface defectsupto100␮minsizecouldbeidentified.

Directex-situpartinterrogationmethodsincludetheuseof X-raycomputedtomography(XCT)(see[7]forathoroughreview), whichcandelivermeasurementsconsistingofa highresolution data set of the build. For a full 3D data acquisition, Tammas-Williams et al. utilised a high resolution XCT system to scan electron-beampowderbedfusionsampleswithbothcoarseand fine scans to detect defects and relate the distribution to the processingenvironment[15].TheXCTanalysisofelectron-beam powderbedfusionsampleshasbeenshowntoenablethe deter-minationofdefectslargerthan120␮m.Maskeryetal.conducted aporecharacterisationandquantificationstudyonAlSi10Mg

sam-plesproducedusingSLM[18].TheXCTresultsweresegmented andporeswerecharacterisedbasedonrelativecountandshape descriptors.Relativeporositiesofthesampleswerecalculatedto belessthan0.1%andpredictionsofservicelifeweremadebased ontheporedistributions,showingthatXCTisavalidapproachto SLMmonitoring.

2. DefiningNDEcapability

GiventhelevelofresearchactivityintheareaofNDEforAM parts,alongsidetheemergenceofnewAMmachines,thereisaneed toevaluatehowmeasurementtechniquesperforminservice.The timelineofabasicin-situanalysisforanAMprocessisoutlined inFig.1(a).Inthiscase,employinganNDEmethodbetweenthe manufactureofasinglelayerwillextendthetimetopart comple-tion;themanufactureandmeasurementprocessneedtooccurin succession.Onlineanalysis,outlinedinFig.1(b),assumesthatNDE measurementscanbeconductedduringthemanufacturingprocess andprocessmeasurementdataon-the-fly,reducingthebottleneck apparentinthelayercompletiontime.Fullonlinemonitoring sys-temscanonlyyieldindirectmeasurementsasthesolidificationof thematerialinthemanufacturingstagehastohaveoccurredbefore collectingdirectinformation.

Therequirementsformeasurementinstrumentationare depen-dent onthe type of AMprocess, thematerials tobe used,the expecteddefectsandtheendusertolerances.Thisincludesthe spa-tialopportunityandtemporalpenaltyaffordedtotheinstrument. AdefinitionofcapabilityofanNDEinstrumentisneededinorder toascertainthatitmeetstheserequirements.

Onerequirementthatanendusermayhaveforproductionis thepartcompletiontime,tmanufacture,andisgivenby

tmanufacture=tbuild+treset (1)

Fig.1.(a)measurementopportunitytimelineforin-situmeasurementofAMprocesses,whereallprocessingstepsarelinearandparallelprocessingmaynotbepossible. (b)measurementopportunitytimelineforonlinemeasurementofAMprocesseswheresomeparallelprocessingcanbedoneinordertooptimiseonNDEevaluationtime inrelationtoAMbuildtime.

manufacturingspeed,andincludesthetimetakenforthematerial tosolidifyfromamoltenstate.Theresetstageisthetimebetween thelayerbuildstagewherenomeltingoccurs,whereprocesses suchasretractionofthepartinthez-axis,recoatingwithpowder material(inpowderbedfusionprocesses)orresettingtoorigin pointsforthenextlayer(indirectedenergydepositionprocesses) occurs.

IfeachlayerismeasuredwithNDEinstrumentation,theoverall manufacturingtime(tmanufactureNDE)extendsbyafactorthatcanbe definedasthetemporalpenalty(Pt)suchthat

t_{manufactureNDE=}t_{manufacture×}Pt. (2)

ScantimesoftheNDEinstrumentarebasedonvariablesdefined asscan speeds(tscan) perdata point, N,and processing/latency speeds (tlatency) per data point, N. For the model presented in

Fig.1(a),a sequential interaction of production and analysis is observed.TheAMmanufacturingwithNDEtimebecomes

tmanufactureNDE=tbuild+N

tscan+tlatency

+treset. (3) FromEq.(3),thetemporalcapabilitycanbedefined,wherealower valueindicatesafasterscanningoftheAMpartinproduction,thus

Pt=N

tscan+tlatency

/(tbuild+treset)+1. (4) ThemodelpresentedinFig.1(b),onlineanalysis,outlinesthat thescanningofthelayerisoccurringinparalleltomanufacture.As such,scantimeandlatencytimepenaltiescanbedisregardedand theoveralltemporalpenaltybecomes1.

Inorder fortheNDEinstrumentationtobedeemedsuitable forintegrationintoaparticularAMmethodology,theaccuracyof thedataitfeedsbackneedstobedefined.Furthermore,thereisa relationshipbetweenthescantime(temporalpenalty)andthe res-olutionofthedata(e.g.pixelsize)oftheinstrumentationthatcan beoptimised.Theimportanceoftheresolutionofdataisshownin anexampleinFig.2,whereamaterialdefectisshownatits orig-inalsize.Theeffectofresizingthisimageisshowntocompletely loseinformationaboutthisdefectandadescriptionofitbecomes impossible.

To quantify the resolution of the data, a spatial capability index(Cs)isdefinedasfollows.Assumingthedataacquisitionof theNDEmethodhasbeenoptimisedtoyielddatawhich isnot under-sampled,theequivalentpixel size (discrimination ofthe instrument)is dependentonboththestep size(h)and resolu-tion(r)ofthemeasuringinstrument.Thestepsize(h)isdefined asthedistancebetweensampleddatapointsandthedefinitionof resolution(r),here,istheminimumdistancebetweentwo resolv-ablepoints,i.etheRayleighcriterion.Scanninginthexyplanewill yieldthedatasetofthelayerbut,sincesomeprocesses(suchas OCT,SRASand XCT)havetheabilitytopenetrate subsurfaceor near-subsurface,thereis asubsurface datasetresolvedin 2D(a tomograph).Thestepsizecould,hence,notbeuniforminall

direc-Fig.2.Anexampleofhowresolutionofthedataisessential.Anoriginaldefectis shownandcontinuouslyresizedtoshowtheeffectoflossofdatatoapointwhere noinformationaboutthedefectcanbeobtained.

tionsandassuchmustbedefinedforeachdimensionseparately, whichwouldthenyielddistinctspatialcapabilitiesforeach dimen-sion.

h=hX,hY,hZ. (5)

Foragivenprocess,therequiredspatialcapabilityofan instru-ment needs to be defined by the minimum defect size, Dmin,

requiredtobeinvestigatedinordertodetermineifthepart pro-ducedisfitforservice,e.g.inSLMdefectsofapproximately10␮m areexpected[15,16].IfhX,hY,hZ<Dminandtheratioofstepsizeand

resolutionisadequate,thentheinstrumentcanbeclassedviable foruse.However,generallymorethanonepixelisnecessaryfor thedeterminationofwhetheradefectispresentornot.Forthis,a multiplyingfactor,theminimumclustersize(Clmin)needstobe defined.Combiningtheminimumdefectsizeandminimumcluster sizeresultsinacapabilityoftheNDEmethodtoobtaindata,the spatialcapability:(ClminhX,hY,hZ)/r<Dmin.Ifthisspatialcapability isexpressedasafactor,itcanberewrittenas

Cs=rDmin/Clmin(hX,hY,hZ). (6) Aminimumrequiredspatialcapabilityinrelationtothe mini-mumdefectsizeis1atwhichpointtheNDEprocessingparameters will yield data with no lack of information. The outcomes of employingsub-optimalparameters(Cs below1)areoutlinedin Section4.3.

eval-tiononthespatialcapabilityispresented,basedonsimulatedsets ofscandatamimickingcommonlyobservedSLMbaseddefects.

Forthefirstcasestudy,anNDEtechniqueknownasspatially resolvedacousticspectroscopy(SRAS)isusedtoshowthe differ-encebetweenahighresolutionandlowresolutionscan.SRAShas beenoutlinedasaviablecandidateasanNDEmethodfor metal-basedAM[19]andcanbeusedforthedeterminationofsurfaceand nearsubsurfacefeaturedetection.Furthermore,currentresearchis ongoingtomakeSRASapplicableforobtainingsurfacedefectand graininformationonroughsurfaces[20]and,hence,applicablefor AM.Inthepast,SRAShasbeenemployedtoscanoveroptically smoothsurfacesinordertodeterminemicrostructureandgrain orientationofmetalsforhighvalueapplicationsthroughchanges insurfaceacousticwave(SAW)velocityorsignaldropout. Specif-icallySRAShasbeenemployedonAMsampleswiththeintentof developingitintoanin-situinvestigationtechnology[21].SRAS isalaserultrasoundtechniquethatusestwoseparatelasers;one lasergeneratesaSAWontoasampleprojectedthroughadefined grating,andthesecondlaserisusedtopickuptheperturbation causedbythewave,enablingmappingthesurfacethroughpoint scans[22].Processingdatawasobtainedthroughthetimestampsof datacollectionandsetupofscanparameters.Theprocessingtime wasextrapolatedthroughtimestampsofanalysisdatacreation.

For the second case study, example scanning scenarios are shown,usingopticalcoherencetomography(OCT)asanNDE tech-niqueforSLSofpolymerpowdermaterials.IntheworkbyGuan etal.,itwasshownthatOCTiscapabletoscanfordefectsand geom-etryfeaturesonpolymer-basedAMprocesses,bothonthesurface andsubsurface[8].TheOCTsignalisderivedfromback-scattering oflight,hence,ahigherintensityisaresultofhigherback scatter-ingpropertiesofthesample.Theimagedepthisdependentonthe penetrationdepth,whichisdeterminedbythepropertiesofOCT lightsourceandtheopticalpropertiesofthetargetsample.The OCTcasestudywasconstructedtoanalysethespatialcapability andtemporalpenaltyoftwocommerciallyavailableOCTsystems (ZeissCirrusHD-OCT5000[23]andTopcon3DOCT-2000[24]).

3.1. Spatialcapabilityonsimulateddefectdata

Asimulateddatasetwascreatedtodemonstratetheeffectsof changeininstrumentspatialcapability.Thedatasetwasbasedon atypicaldefectarrayforanSLMcomponentwithdefectsizesof approximately100␮m.Asamplecubeofside10mmwasdefined forthecreationoflayer-basedscaninformation.Inordertomimic a data set that could be obtained with an NDE instrument, a ‘tomograph-like’stackofimageswascreatedatascaleof25␮m/px. Theselayersweredefinedtohaveathicknessof30␮m;a com-monlayerthicknessinSLMmanufacture.Thedataforeachlayer wasgenerated withthesolidnoiserender function ata random seedwithinanopen-sourcesoftwarepackagedetailedelsewhere [25].Apseudo-randomnumbergeneratorbasedoncurrenttimewas utilisedtoproduce seedsin therangeof0 to2 ˆ31-1,producing cloud-likevariations.

Forthepurposeofshowingtheeffectofareductioninscaleon spatialcapability,thedatasetimageresolutionwasresizedinsteps

ofthepore.Thefirstdescriptor,fcirc,isdefinedas

f_circ=4A/P2 ₍₇₎

whereAisthecross-sectionalareaandPistheperimeterlength ofthepore.Themeasurefcircliesintherange0<fcirc≤1,where

1representsaperfectlycircularcross-section.Thesecondshape descriptor,faspectisdefinedas

faspect=dminor/dmajor (8)

where the dminor and dmajor are the minimum and maximum orthogonaldimensionsofthepores’bestfittingellipse.faspectcan beexpressedwithvalues0<faspect≤1,whereavalueof1indicates aperfectlysphericalporeandapproachingthevalueof0indicates aneedle-likeshape.

4. Casestudies

InordertooutlinehowtheNDEcapabilitiescanbecomparedfor AMpurposes,casestudiesarepresentedinthissection.Scanning environmentswillbepresentedanddiscussedinrelationtotheir applicabilitytoAMprocessesassupportedbytheliterature.

4.1. Casestudy1:SRASforSLM

SRAShasbeenoutlinedasaviablecandidatefor the inspec-tionofpartsproducedusingtheSLMAMprocesspreviously[21]. AschematicofthesetupofaSRASinstrumentisshowninFig.3(a). ThedetectedperturbationfrequencyindicatestheSAWvelocity, wherethevelocitycontrastorwaveabsencecanbeusedtoobtain boththematerialsurfacemicrostructureanddefectinformation. DependingontheSAWwavelength,somesubsurfacedefect infor-mationcanalsobeobtained.ThetypicalSRASscansyielddataas showninFig.3:theopticalmap(b),whichdirectlyindicatesthe surfacecracksandporesandthevelocitymap(c),whichimages themicrostructureofthesamplealongwithdefects.

TheSRAS setupis a scalablesystemand can becustomised dependingontheuser’srequirements;thereisatrade-offbetween accuracyandspeed.Intheworkpublished,therearetwosetsof SRASworkingparametersthatareused,showninTable1(with

saw=24␮m).IntheworkpresentedbySmithetal.,scanswere conductedex-situonpreparedTi-6Al–4Vsamples.InTable1,the scantimeandlatencytimeoftheinstrumenthavebeencombined. Thedesignoftheinstrumentallowsforindividualselectionofstep sizeaccordingtothemovementaxes utilised.Theresolution is dependentonthewavelengthoftheSAWs.Onlyasinglevaluefor stepsizeisgivenwhichindicatesthath=hx=hywithnoz-axisstep sizepresent.Thescanswereabletogathernearsubsurface informa-tion−thesubsurfacesensitivitydepthisrelatedtothewavelength ofthecreatedSAW,approximately24␮m.

Firstly,acoarsescanwasconductedtoobtainapproximate sur-facelayerinformation. Thedefined minimumdefect, Dmin, was

Fig.3.(a)schematicoftheSRASsystem,(b)opticalimageand(c)acousticimageobtainedfromSRASscanonAMsamplefrompublishedwork[21].

Fig.4. StepsofmanufacturewithNDEinstrumentation:(a)thedesignofthepart;(b)slicingoftheparttocreatemachinecode;(c)manufactureofthepartonalayerper layerbasisand(d)analysisalsoonalayerperlayerbasis.

Table1

SRASscanningparametersutilisedforSLMsamplemeasurements.AfterSmithetal.[21].

Example Description

MinimumDefect,

Dmin(␮m)

LayerSize(␮m) StepSize,

h(␮m)

Resolution,r

(␮m)

MinimumCluster Size,Clmin

Numberofdata points,N

tscan(s)+tlatency

(s)

SRAScoarse 50 X:10000

Y:10000

25 100 4 160000 0.0008

SRASfine 20 X:10000

Y:10000

5 100 3 4000000 0.0008

Thesamplemanufacturingtimeofthetestspecimen (dimen-sions:10×10×10mm)wascalculatedtobe2072s,arisingfrom ahatchspacingof75␮m,beamspeedof600mms−1_,layer thick-nessof30␮mandrecoatingtimeof4s.Usingthisinformation,the temporalpenalty(perlayer)forthecoarseandfinescansusingEq. (4)werecalculatedtobe59and1442,respectively.UsingEq.(6), theresultingspatialcapabilitiesareequalto67and100forthe coarseandfinescan,respectively.Furthermore,dataabout sub-surfacedefectsisalsoavailableusingSRAS₋thespatialcapability forsubsurfaceinformationwascalculatedtobe69and21forthe coarseandfinescan,respectively.

Fromthepresenteddata,itcanbeseenthatthespatial capa-bilityoftheSRASinstrumentcanbeconsideredsuitableforboth scanenvironmentsinthisrequirementdefinition(discussed fur-therinthesimulateddatacasestudy).Iftheuserrequiresasmaller Dmin,thespatialcapabilitywouldneedtobechangedaccordingly

− this in turnwill affect thetemporal penalty of the measur-inginstrument.Specifically,forSRASintegrationeffortsintoSLM manufacture,thetemporalpenaltymaycurrentlybeprohibitive. Therefore,furtherworkisstillrequiredtooptimisetheSRASdata

acquisitionspeed withoutcompromisingonaccuracy.However, sinceSRASisascalablesystem,thespatialcapabilityandits tem-poralpenalty can beadjusted to suit theapplication of in-situ monitoringinAM.

4.2. Casestudy2:OCTforSLS

OCThasbeenshowntobeaviablecandidateforSLSpart inter-rogation[8].Thiscasestudyfocussesonhowanend usercould obtaininformationofhowcapablethisinspectionmethodis,with freelyavailableinformation.ThetwocommerciallyavailableOCT systemschosenaretheCirrusHD-OCT5000(referredtoasOCT1) andtheTopcon3DOCT2000(OCT2).AssumingaSLStestspecimen wasproducedwithdimensionsof(10×10×10)mmwithalayer thicknessof100␮m,meltinglaserscanningspeedof2500mms−1

Fig.5. ProbabilityofDetectionofdefects.Thenumberofporesdetectedreducesdrasticallywithareductioninthespatialcapabilityoftheinstrumentwhilsttherelative densityofporesincreasestoapproach100%density(bulkdensity).Theinsetsshowa3Dinterpolationofthesimulatedscandata.

Table2

ExamplesofOCTscanparametersforNDEcapabilitycalculation.

Example Description

MinimumDefect,

Dmin(␮m)

LayerSize (␮m)

StepSize,h(␮m) Resolution,r

(␮m)

MinimumCluster Size,Clmin

Numberofdata points,N

tscan(s)+tlatency

(s)

OCT1 30 X:10000

Y:10000

15 5 4 1332000 3.7×10−5

OCT2 30 X:10000

Y:10000

46 6 4 355550 2×10−5

Fig.6.Histogramsrepresentingtheporeareadeterminedbypixelcountingat dif-ferentspatialcapabilities.Theabsolutenumberofdeterminedporesisreduced alteringthedistribution.

Table2.Onlyasinglevalueforstepsizeisgivenwhichindicates thath=hx=hywithnoz-axisstepsizepresent.

Fromtheabovepresenteddata,boththespatialcapabilityand temporalpenalty(forx−y)canbecalculated.Intheliterature,it hasbeenoutlinedthatOCTcanreturnasubsurfacedataset[8]but thematerial-specificinformationisnotavailablethroughthedata sheets.FortheOCT1example,thetemporalpenaltyperlayerwas foundtobe8.5;thespatialcapabilitywasfoundtobe2.5.This indicatesthat,whilsttheinterrogationwouldconsiderablyextend themanufacturingtimeofthesamplecubes,thedatathatcould beobtainedfromtheNDEinstrumentwouldbesufficientforthe

requirements.Incontrast,theOCT2exampleyieldedatemporal penaltyof5.4 perlayerandspatialcapability of0.97. Thetime penaltyforutilisingthisscanningsystemwouldbelower,butthe dataobtainedwouldbeinsufficientfortherequirementsstated above.

Inaddition,theseOCTscanstrategy exampleshelp in deter-miningthebestapproachforintegrationofOCTsystemsforSLS applicationsbasedonuserrequirements.InthecaseofOCT1,the spatialcapabilityissufficientbuteffortscouldbefocussedon opti-misingtheinterrogationtimeperlayer.Inthecase ofOCT,the spatialcapabilitycouldbedeemedinsufficient,soaredesignofthe opticalsystemcouldberecommendedinordertomeet specifica-tions.

4.3. Simulateddefectstudy

Thissectionoutlinestheeffectsofnon-idealspatialcapabilities ofmeasurementinstrumentation,withafocusondetectabilityof defects,aswouldbeobservedinAM.Astatisticalporeanalysisis conductedonthesimulatedsurfaceinformationthatwascreated asdescribedinSection3.1.Thedatamimicsthat,whichcouldbe obtainedfromanAMmanufacturingprocesssuchasSLM.The pro-cessingstepsofsuchamanufacturingmethodareshowninFig.4.A commondesign-to-partsequenceconsistsofthemodellingofthe partinCAD(Fig.4(a)),slicingoftheparttocreatethemachinecode forproduction(Fig.4(b)),manufactureofthepart(Fig.4(c)),and analysis(Fig.4(d)),wherethelattertwostepsoccuronalayerper layerbasis,asisdiscussedpreviously.

Throughthedesignof thespatialcapabilityformula, avalue between0<Cs<1indicatesanon-optimisedscanenvironment.A

Csvalueof1,bydefinition,showsthattheNDEprocessis

Fig.7.Histogramsof(a)circularityand(b)aspectratioofdetectedporesinthesimulateddatasets.TheshapedescriptorsaredefinedaccordingtoEqs.(7)and(8).

hastohavebeensetaccordingtothemicrostructuralrequirements ofthepartinproduction.Forthepurposesofthisstudy,the sim-ulatedscandatawasresizedtoyieldnon-idealspatialcapability valuesandarecomparedtoanidealcasescenario.Theminimum defectsizewasdefinedas100␮mandminimumclustersizeis2×2 pixels,foralldiscriminationchanges.Therelativedensityofpores presentversusbulkmaterialareapresentwascalculatedthrough pixelcountingasametricdescribingtherelationshipofapparent poresobservedandabulkdensityofthesimulatedpart(100%dense inanidealcasescenario).

WhenthespatialcapabilityofanNDEmethodisinsufficient,the numberofdetectabledefectsreducesdramatically.Thisisshown withtheprobabilityofdetectiondatapresentedinFig.5,wherea spatialcapabilityof0.5(discriminationof50␮m/pxinthiscase) onlyallowsforthedetectionofapproximately73%ofthepores presentin thedata set.Witha furtherreduction in discrimina-tion,thenumberofdetectableporesapproacheszero.Thisisshown withtherelativepartdensitymeasurementonthesecondordinate inFig.5,whereareductionindiscriminationyieldsameasured volumetricdensityapproaching100%,untilnomoredefectsare detectable.ThisanalysisalignswithworkbyMaskeryetal.who characterisedandquantifiedporosityofAl-Si10-Mgsamplecubes

producedwithSLM[18].Intheirstudy,therelativedensityofthe test cubes,analysed withXCT,closely resemblesthebehaviour observedinthesimulateddataataspatialcapabilityof1.Theinsets inFig.5,a3Dinterpolationofthelayerscandata,showhowthe decreaseinspatialcapabilityaffectstheavailablepixeldata.Aclear visualreductioninpores(portrayedingrey)canbeobserved.

Inordertooutlinehowthedegradingavailabilityofdataaffects measurementofdefects,distributionhistogramsoftheporeareas areshowninFig.6.Aclearreductioninabsolutenumberofpores detectedcanbeobservedforthereducingspatialcapabilities.As soonasthedatasetisresizedtoanon-idealcapability,the dis-tributionbehaviourbecomeserraticandnotrepresentativeofthe defectspresentinthesimulateddata.Itmustbementionedthatthe simulatedpores,similarlytorealdefectsobservedinSLM manu-facture,arenon-perfectspheres.This,hence,warrantstheneedfor investigatinghowporeshapedescriptorsdeviatewiththe alter-ationindiscrimination.

Fig.7 shows the circularityand theaspect ratiohistograms forthesimulatedscandata.Trendstowardsamorecircularpore shape(Fig.7(a))canbeobserved.Again,theabsolutenumberof detectableporesisreflectedbuttheresultsareskewedtowardsan fcircvalueof1,whichismostprominentataspatialcapabilityof

0.2andabove.Theaspectratio,faspect,showninFig.7(b)outlines abroadrangewithfewporesperfectlyround.Thisisalsotobe expectedinarealscenario.Withthespatialcapabilityofthe sim-ulateddatareducing,however,therandomnatureoftheporesis notrepresentedanymore,indicatingapredictablebehaviour.The shapedescriptorsalongsidetheporeareadistributionare essen-tialindefiningshapegeometriesanditcanbeclearlyseenthata reductioninspatialcapabilityhindersthis.

Theabovediscussedsimulateddataoutlinestheeffectswhen thespatialcapabilityisbelow 1.AssumingtheNDEinstrument scanparametershavebeenadaptedappropriately,tocorrelatewith theminimumdefectsize,thecapabilityshouldbeaminimumof 1toavoid lossofdata.However,it mustbepointedout thata spatialcapabilityabove1maynotbeidealbecauseitisclosely linkedwiththescantime.Itshouldbeconsideredwhetherthere isatrade-offthatenablesanincreaseinscanningtimewhilstnot losinginformation.

5. Conclusions

ThecurrentdrivetoutiliseAMtechnologiesforhighvalue appli-cationsisaleadingfactorindevelopingin-situintegrationofNDE methods.Currently,theapplicabilityoftheseprocessesforlayer perlayerscanninghasnotbeenrigorouslyassessed.Inorderto quantitativelyassessthecapabilityofaNDEmethodinanin-situ oronlineintegrationwithanAMprocess,aspatialcapabilityand temporalpenaltymeasureneedtobedeveloped.Thespatial capa-bilitydescribestherelationshipbetweentheminimumdefectsizes, inAMproduction,and scanparametersthattheNDEmethodis requiredtoresolveaccurately.Specifically,someparametersmay not beuniformin alldirections whichis accommodatedin the model.Thespatialcapabilityisdefinedby

Cs=rDmin/Clmin(hX,hY,hZ).

Thetemporalpenaltyassessesthechangeintimefor manu-facturewithandwithoutNDEinstrumentation.Thisrelationship accommodatessequentialprocessingaswellasparallelprocessing inthatthescanlatencytimeoftheNDEprocesscanbedisregarded. Inthiscaseatimepenaltyof1istheresult.Utilisingtheapproachof measuringfortemporalpenaltycanaidinoptimisingthescanning regimeforallowableextratimeintheAMprocess.Thetemporal penaltyisdefinedby

Pt=N

tscan+tlatency

defectsreducesdrasticallybutalsotheshapedescriptorsusedfor characterisingdefectsyieldfalsifieddata.

WithutilisingthisNDEcapabilityapproach,optimisationofthe manufacturingandinterrogationenvironmentcanoccurthrough aquantifiedcomparativeanalysis.

Acknowledgements

This work was supported by “UK Research Centre in Non-destructiveEvaluation”(EPSRCGrantNo.EP/L022125/1), “Metrol-ogyforPrecisionandAdditiveManufacturing”(EPSRCGrantNo. EP/M008983/1)and “In-situMonitoringofComponentIntegrity DuringAdditiveManufacturingUsingOpticalCoherence Tomog-raphy”(EPSRCGrantNo.EP/L01713X/1).

References

[1]T.Caffrey,I.Campbell,T.Wohlers,Wohlersreport,in:AdditiveManufacturing and3DPrintingStateoftheIndustry,AnnualWorldwideProgressReport, WohlersAssociates,Inc,FortCollins,2016.

[2]G.K.Lewis,E.Schlienger,Practicalconsiderationsandcapabilitiesforlaser assisteddirectmetaldeposition,Mater.Des.21(4)(2000)417–423. [3]L.Mordfin,NondestructiveEvaluation,MaterialsandProcesses,PartB:

Processes,MarcelDekkerInc,1985.

[4]S.K.Everton,M.Hirsch,P.Stravroulakis,R.K.Leach,A.T.Clare,Reviewof in-situprocessmonitoringandin-situmetrologyformetaladditive manufacturing,Mater.Des.(2016).

[5]A.Townsend,N.Senin,L.Blunt,R.K.Leach,J.S.Taylor,Surfacetexture metrologyformetaladditivemanufacturing:areview,Precis.Eng.(2016). [6]P.I.Stavroulakis,R.K.Leach,Invitedreviewarticle:reviewofpost-process

opticalformmetrologyforindustrial-grademetaladditivemanufactured parts,Rev.Sci.Instrum.87(4)(2016)041101.

[7]A.Thompson,I.Maskery,R.K.Leach,X-raycomputedtomographyforadditive manufacturing:areview,Meas.Sci.Technol.27(7)(2016)072001. [8]G.Guan,M.Hirsch,Z.H.Lu,D.T.D.Childs,S.J.Matcher,R.Goodridge,K.M.

Groom,A.T.Clare,Evaluationofselectivelasersinteringprocessesbyoptical coherencetomography,Mater.Des.88(2015)837–846.

[14]J.Schwerdtfeger,R.F.Singer,C.Körner,InsituflawdetectionbyIR-imaging duringelectronbeammelting,RapidPrototyp.J.18(4)(2012)259–263. [15]S.Tammas-Williams,H.Zhao,F.Léonard,F.Derguti,I.Todd,P.B.Prangnell,

XCTanalysisoftheinfluenceofmeltstrategiesondefectpopulationin Ti–6Al–4éVcomponentsmanufacturedbySelectiveElectronBeamMelting, Mater.Charact.102(2015)47–61.

[16]X.Zhou,D.Wang,X.Liu,D.Zhang,S.Qu,J.Ma,G.London,Z.Shen,W.Liu, 3D-imagingofselectivelasermeltingdefectsinaCo–Cr–Moalloyby synchrotronradiationmicro-CT,ActaMater.98(2015)1–16.

[17]H.Rieder,A.Dillhöfer,M.Spies,J.Bamberg,T.Hess,Onlinemonitoringof additivemanufacturingprocessesusingultrasound,in:11thEuropean ConferenceonNon-DestructiveTesting(ECNDT2014),Prague,Czech Republic,2014.

[18]I.Maskery,N.T.Aboulkhair,M.R.Corfield,C.Tuck,A.T.Clare,R.K.Leach,R.D. Wildman,I.A.Ashcroft,R.J.M.Hague,Quantificationandcharacterisationof porosityinselectivelylasermeltedAl–Si10–MgusingX-raycomputed tomography,Mater.Charact.111(2016)193–204.

[19]D.Clark,S.D.Sharples,D.C.Wright,Developmentofonlineinspectionfor additivemanufacturingproducts,InsightNonDestruct.Test.Cond.Monit.53 (11)(2011)610–613,4.

[20]S.O.Achamfuo-Yeboah,R.A.Light,S.D.Sharples,Opticaldetectionof ultrasoundfromopticallyroughsurfacesusingacustomCMOSsensor,J. Phys.:Conf.Ser.581(2015)012009.

[21]R.J.Smith,M.Hirsch,R.Patel,W.Li,A.T.Clare,S.D.Sharples,Spatiallyresolved acousticspectroscopyforselectivelasermelting,J.Mater.Process.Technol. (2016).

[22]R.J.Smith,W.Li,J.Coulson,M.Clark,M.G.Somekh,S.D.Sharples,Spatially resolvedacousticspectroscopyforrapidimagingofmaterialmicrostructure andgrainorientation,Meas.Sci.Technol.25(5)(2014)055902.

[23]CarlZeissMeditecAG,CIRRUSHD-OCTAdvancingSMARTOCT.http://www. zeiss.com/meditec/ende/products—solutions/ophthalmology-optometry/ glaucoma/diagnostics/oct/oct-optical-coherence-tomography/cirrus-hd-oct. html,(accessed25.07.16.).

[24]TopconCorporation,OpticalCoherenceTomography3DOCT-2000Series. http://www.topcon-medical.eu/files/EUDownloads/Products/3DOCT-2000/ 3DOCT2000seriesen.brochure.pdf,(accessed25.07.16.).

[25]R.W.Solomon,Freeandopensourcesoftwareforthemanipulationofdigital images,Am.J.Roentgenol.92(6)(2009)W330–W334.

[26]J.N.Kapur,P.K.Sahoo,A.K.Wong,Anewmethodforgray-levelpicture thresholdingusingtheentropyofthehistogram,Comput.Vis.Graph.Image Process.29(3)(1985)273–285.