Material Quality and Process Monitoring in Metal Additive Manufacturing by Lee Clemon A ...

154 Pages·2017·11.08 MB·English

MaterialQualityandProcessMonitoringinMetalAdditiveManufacturing
by
LeeClemon
Adissertationsubmittedinpartialsatisfactionofthe
requirementsforthedegreeof
DoctorofPhilosophy
in
Engineering-MechanicalEngineering
inthe
GraduateDivision
ofthe
UniversityofCalifornia,Berkeley
Committeeincharge:
ProfessorTarekZohdi,Co-chair
ProfessorDavidDornfeld,Co-chair
ProfessorRobertoHorowitz
ProfessorPeterBartlett
Spring2017
MaterialQualityandProcessMonitoringinMetalAdditiveManufacturing
Copyright2017
by
LeeClemon
1
Abstract
MaterialQualityandProcessMonitoringinMetalAdditiveManufacturing
by
LeeClemon
DoctorofPhilosophyinEngineering-MechanicalEngineering
UniversityofCalifornia,Berkeley
ProfessorTarekZohdi,Co-chair
ProfessorDavidDornfeld,Co-chair
Additivemanufacturingisrapidlybecomingavailabletodesigners,hobbyists,andindustrialman-
ufacturers. Unfortunately, process control and predictability remain beyond the grasp of most
users and even machine tool builders. This manuscript employs design and material quality mon-
itoring to explore solutions to these challenges. Metallurgical study and design limitations were
explored through intentional design. Design for additive manufacturing was shown to provide
insight into both process constraints for aesthetics and metallurgical properties. In assessing the
process-structure-property relationships of metal additive manufacturing, test designs must con-
sider the heat and mass transport phenomena as well as post-fabrication analysis requirements,
several such designs were presented. On monitoring, an algorithm with inspiration from the field
of statistical learning was proposed. This method serves as a computationally feasible approach
tomonitoringreal-timematerialquality. ThisalgorithmemploysadynamicBayesnetworkmodel
andcanbeshowntoreducetoaKalmanfilterundersomeassumptions. Thus,directcomparisonof
the proposed algorithm with known control methods is possible. Graphical analysis was also con-
ductedonthevariousparameterrelationshipsreportedintheliteratureforpowder-bedfusion. The
analysis revealed optimal clustering of the parameters and serves as a guide for future researchers
to look beyond the melt pool for quality control. In support of in-process monitoring, a numerical
simulation and validation experiment were constructed related to the acoustic emissions of parti-
cle impacts during directed energy deposition. Representative particle spray is simulated with the
discrete element method. Impacts upon the build surface were used as inputs to a finite-difference
wave propagation model to approximate the acoustic signature of the spray. The results of these
several studies support manufacturing process monitoring for additive manufacturing. They also
serve as a base for further investigation into material property tailoring and generally improved
systemcontrolforadditivemanufacturing.
i
ToBecca
ii
Contents
Contents ii
ListofFigures iv
ListofTables ix
Acknowledgments x
I Introduction and Background 1
1 Introduction 2
1.1 IntroductiontoAdditiveManufacturing . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 IntroductiontoMetalAdditiveManufacturing . . . . . . . . . . . . . . . . . . . . 4
1.3 Structureofthisdocument . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
II Metallurgy in Directed Energy Deposition 9
2 MetalPowders 10
2.1 CharacterizationofPowders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 PowderConsolidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 DesignEnabledMaterialStudy 20
3.1 MicrostructureEvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 PartDesignforMetallurgicalStudyinDirectedEnergyDeposition . . . . . . . . . 23
IIIMonitoring For Additive Manufacturing 32
4 MonitoringinMetalAdditiveManufacturing 33
4.1 SolidificationStrategyinMetalAdditiveManufacturing . . . . . . . . . . . . . . . 33
4.2 MaterialMonitoringMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
iii
4.3 MeltPoolMonitoringandModeling . . . . . . . . . . . . . . . . . . . . . . . . . 42
5 MaterialQualityInference 46
5.1 GraphicalModelsforInference . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.2 ProposedModelforMonitoringAdditiveManufacturing . . . . . . . . . . . . . . 48
IVSimulation of Powder Spray 71
6 Simulation: PowderDepositiononaPlate 72
6.1 Particle-ParticleCollisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.2 Contactwithaplate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.3 Fluideffectsonparticles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.4 NumericalSimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7 Experiment: VibrationResponsetoParticleImpacts 92
7.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
7.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
8 RemarksandFutureExtensions 111
8.1 DiscussionforMonitoringHybridAdditive-SubtractiveManufacturing . . . . . . . 111
8.2 ManuscriptConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
8.3 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Bibliography 114
Appendices 125
A WavesandWavePropagation 126
A.1 AnalyticalSolutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
A.2 Dispersionincluded . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
iv
List of Figures
1.1 Overview of typical additive manufacturing workflow: file conversion to 2.5-D slices,
thentomachine,finallytoconstructionofplasticpart.[27] . . . . . . . . . . . . . . . 4
1.2 Directed Energy Deposition process diagrams showing laser, gas, and powder path-
ways[14]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Powder-bed Fusion process diagram showing powder delivery method and example
buildsection[99]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Hybrid additive-subtractive manufacturing images of (left) Directed Energy Deposi-
tion,(topright)drilling,and(bottomright)milling[130] . . . . . . . . . . . . . . . . 7
2.1 Analytical bounds for thermal conductivity in Argon (20 mW/m-K) - 304SST (14
W/m-K) system showing as the volume fraction of 304SST increases (powder com-
pacts),thermalconductivityincreases. . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 SchematicofvaryingpackeddensitythroughoutbuildofPowder-bedFusionofunsin-
teredmaterial,densitydecreasesfrombottomtotop. . . . . . . . . . . . . . . . . . . 12
2.3 Flowability test setups: (top-left) fixed height static test, (top-right) fixed width static
test, (bottom-left) tilt table dynamic test, and (bottom-right) rotary cylinder dynamic
test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 SelectivecorrosiveattackincontaminatedareasofSST316Lspecimen. . . . . . . . . 14
2.5 Distribution of powder diameters (∼10-100 µm) for three powder-bed fusion supply
lotsasreportedbyLiu,etal. [78] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.6 Distribution of powder diameters (∼10-100 µm) for two separate powder-bed fusion
sourcesasreportedbyWhitingandFox[131] . . . . . . . . . . . . . . . . . . . . . . 15
2.7 FieldemissionscanningelectronmicroscopeimageofrawAlSiMgpowder,typicalof
metaladditivemanufacturingpowderfeedstock. . . . . . . . . . . . . . . . . . . . . 15
2.8 Sprayatomizationdiagram,typicallygasorwaterisusedasthecoolingmedium. [26] 16
2.9 HierarchyofbindingmechanismsfoundinpowdersinteringforAdditiveManufacturing 17
2.10 Graphanddiagramofconsolidationphasesofcompositematerials[43]. . . . . . . . . 18
2.11 (a) Several powders fused together, (b) single powder with a liquid layer that has par-
tially separated. The diagram between (a) and (b) shows (1) the unmelted core of a
powder,(2)adistinctliquidlayer,and(3)thevoidspacebetweenorbs. [43]. . . . . . 18
2.12 Images of (a) fully melted powders versus (b) sintered ‘green’ surface at the same
magnification,highlightingtheporositydifferencesbetweentheprocesses[43]. . . . . 19
v
3.1 Nucleation and grain growth in metals. (a) nucleation, (b) grain growth, (c) grains
meetformingboundaries,(d)grainboundariesasviewedbymicroscope[100] . . . . 21
3.2 Sensitization diagram for SST304, with the red path indicating a single high cooling
ratematerialprocess. Baseimageadaptedfrom[89] . . . . . . . . . . . . . . . . . . . 22
3.3 Sensitization diagram for SST304, with artificial pathway indicating reheating of an
AMmaterialelement. Baseimageadaptedfrom[89] . . . . . . . . . . . . . . . . . . 22
3.4 Previously proposed test artifacts for powder-bed fusion machine testing. Left recre-
ationby[95],right[80] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.5 Previouslyproposedtestartifactsforpowder-bedfusionmachinetesting[95] . . . . . 24
3.6 Previouslyproposedtestartifactsforpowder-bedfusionmachinetesting. Leftasrecre-
atedby[95],right[70] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.7 Commontestartifactformachiningcenterssometimesreferredtoasthe“circle-diamond-
square”[1,95] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.8 Hexagonal stair-stepped ‘stadium’ or ‘cake’ design for investigating geometry and
lengthscaleeffectsonmicrostructure. . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.9 Designs of mating discrete variations of posts (cylinders) and holes to assess positive
andnegativefeatureresolutionandrepeatability(left)isometricview,(right)topview 27
3.10 Resolutionandrepeatabilityofpositivedomes(left)andnegativedivots(right)in3-D
printedmaterials[24] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.11 Imageofwallseparationinactualpartandidealcaseinmodelindicatingthatfabrica-
tionresolutionislimitedbytheprocess. [24] . . . . . . . . . . . . . . . . . . . . . . 28
3.12 Verticalstructureswithvaryingradii[140] . . . . . . . . . . . . . . . . . . . . . . . 29
3.13 Fabricatedhexagoninternalstair-stepped‘stadium’shapewithprintdimensions. [139] 29
3.14 Slicedpartwithindicationofaxialtensilespecimenextractionlocations. [139] . . . . 30
3.15 Tensile test micrograph collected from hexagonal test part samples showing fracture
surface across multiple layers (a→c) and in-plane with the layers (b→d). Specimen
locationslabeledaxialandtransverseinFigure3.14,respectively. [139] . . . . . . . . 30
4.1 Scanningpatternscommoninmetaladditivemanufacturing[101]. . . . . . . . . . . . 34
4.2 Scanningpatternsformetaladditivemanufacturingstudyofdensification[101]. . . . 34
4.3 ScanspeedandstrategyeffectsonporosityofAlSiMg[2]. . . . . . . . . . . . . . . . 35
4.4 Scan speed and strategy effects on porosity of AlSiMg numerical summary for scan
speedsof500,750,and1000mm/s[2]. . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.5 Porositymeasurementsthroughoutdepthofpartfornon-optimized(top)andoptimized
(bottom)tests[114]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.6 AtmospherechemicalmakeupeffectsonductilityofAMparts[127]. . . . . . . . . . 38
4.7 AtmospherechemicalmakeupeffectsontensilestrengthofAMparts[127]. . . . . . 39
4.8 Monitoringschemeformeasuringpowderflowrateasdeliveredtobuildarea[58]. . . 39
4.9 Schemeformonitoringmeltpoolradiusandtemperaturecoaxialwithlaser[58]. . . . 40
4.10 Visual and infrared images of stationary melt pool using proposed axial monitoring
system[58]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
vi
4.11 Calibration curve for monitoring melt pool temperature using voltage input to laser
[33]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.12 Scheme for monitoring the composition of a multi-material deposition process using
spectralemissionsfromplasmaplume[33]. . . . . . . . . . . . . . . . . . . . . . . . 42
4.13 Example data of spectral emissions from plasma plume and indication of spikes that
representdifferencesinmaterialcomposition[87]. . . . . . . . . . . . . . . . . . . . 43
4.14 Spectral line intensity ratios show linear relationship within each phase of material,
butacrossphases,linearitybreaksdown[87]. . . . . . . . . . . . . . . . . . . . . . . 43
4.15 2-DmodelformeltpooldepthandsolidificationduringlasertraverseinPBF[47]. . . 44
4.16 Spatial mapping of surface temperature in melt pool (left), and IR image (right) of
DEDmachine[54]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.17 SimulationofmeltpoolflowduringlasertraverseinPBF[105]. . . . . . . . . . . . . 45
5.1 Illustration of voxel segmentation of material in DED. Dotted lines represent to-be-
deposited material, the purple dots indicate voids; the red line indicates a growing
crack. Defectsemitsignalsthatmaybeusedforprocessmonitoring. . . . . . . . . . 47
5.2 DynamicBayesiannetworkforlatentvariableofmaterialformation. Inputparameters,
q,andobservableemissions,y,areusedtocomputethelikelihoodofhiddenstates,l. 49
i i i
5.3 MoralizedgraphicalmodelfromFigure5.2. . . . . . . . . . . . . . . . . . . . . . . . 50
5.4 ReformulatedgraphicalmodelfromFigure5.3. . . . . . . . . . . . . . . . . . . . . . 50
5.5 Maximalcliquesarerepeatedinthechain,boldoutlineshighlightrepresentativecliques
forclarity;dashedlinesmarkrepeats. . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.6 DynamicBayesiannetworkgraphicalmodelwithlatentvariablefordetermineddistri-
butionweightsofy mixture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
i
5.7 Graphical model with potential functions labeling maximal cliques and junctions be-
tweenmaximalcliques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.8 Fixedq givesacommonHiddenMarkovModelgraphicalmodelwithlatentvariable. 53
t
5.9 Potentialfunctionsandseparatorssetsforreference. . . . . . . . . . . . . . . . . . . 55
5.10 MaterialqualitymonitoringbreakoutdiagramwithHMMforeachvoxelelement . . . 59
5.11 Combined and reformulated powder-bed fusion parameter and property relations. Ini-
tialisolatedgroupsreportedbyMani,etal. [84]. KeyislistedinTable5.6 . . . . . . . 67
5.12 Data storage rate for storing 10 double precision variables and 10 voxels at each time
step. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.1 Contactdiagramfortwoparticlescolliding . . . . . . . . . . . . . . . . . . . . . . . . 76
6.2 Contactdiagramforaparticlecollidingwithawall . . . . . . . . . . . . . . . . . . . 78
6.3 Axial carrier gas velocity as ratio of full gas flow as the particles approach the impact
location. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.4 Powderparticlesinjectedatthenozzletipwithcarriergasvelocityarrows. . . . . . . . 84
6.5 Bin grid for discrete element method, blue bins are searched for particles that may
interactwithparticlesintheredbin. . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
vii
6.6 A single impulse wave initiated at the center of one edge, propagated across the small
plateandreflectedfromtheedges. Eachofthefourpanelsshowstheplateafter0(top
left), 1 (top right), 3 (bottom left), and 6 (bottom right) reflections from the walls at
either end of the Z-axis. The plate was 10 cm in the X direction and 4 cm in the Z
direction;axesindicatethenumberofspatialpointsineachdirection. . . . . . . . . . 90
6.7 Maximum amplitude decay (left), and frequency spectrum (right) of a single impulse
waveintheplateshowninFigure6.6.8 . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.8 Theoretical voltage profile (left), and simulated measurement of voltage (right) of a
singleimpactofparticleontheplateshowninFigure6.6. . . . . . . . . . . . . . . . 91
7.1 Image of particle 1 (white spherical beads) and particle 2 (circular metal pellets) used
indroptests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
7.2 Voltage response from a single particle 1 dropped in isolation onto the target plate 1,
10kHzsamplerate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
7.3 Voltageresponsefromisolatedparticle1impactsontotargetplate1. . . . . . . . . . . 97
7.4 Voltage response from compiled isolated particle 1 impacts onto plate 1 with mean
exponentialdecayfitline,thresholdat4V(Voltage=1650e−.004τ)whereτ isthetime
inunitsofsamplefrequency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7.5 Signalanalysisofisolatedparticle1impactsontoplate1 . . . . . . . . . . . . . . . . 98
7.6 Signalanalysisplotsofisolatedparticle2impactsonplate1 . . . . . . . . . . . . . . 99
7.7 Signalanalysisplotsofisolatedparticle1impactsonplate2with10kHzsamplerate . 101
7.8 Signalanalysisplotsofisolatedparticle1impactsonplate2with50kHzsamplerate . 102
7.9 Voltageresponsefromasinglewhitesphericalbeaddroppedinisolationontothethick
targetplate,100kHzsamplerate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.10 Voltage response from many white spherical beads dropped in isolation onto the thick
targetplatewithexponentialdecayfitline(Voltage∝V e−kt). . . . . . . . . . . . . . 103
0
7.11 Spectrogramofmixedparticleflowsbothisolateddroptestsandstreamsareshown. . . 104
7.12 Spectrogram of streams of each particle type. Particles were poured onto the surface,
thetoptwopanelsareforparticle1andthebottomtwoforparticle2. . . . . . . . . . 105
7.13 Spectrogramofisolatedimpacts(lefttop)particle1onplate1,(leftbottom)particle1
onplate2,(righttop)particle2onplate2,and(rightbottom)particle2onplate1. . . . 106
7.14 Qualitativedisplayofparticlemass,gasvelocity,andmassvariancetrade-offsinpiezo
sensor voltage signal. Colors correspond to voltage signal from blue at the minimum
toredatthemaximum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.15 Piezosensorvoltagereadingandfrequencyspectrumsimulationofsinglesteelpowder
particlehittingthinplateat10m/s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.16 Example statistical process control chart (left) with oneout-of-control data point. The
upper control limit (UCL) and lower control limit (LCL) indicate what is acceptable
asdefinedonthesupplementprobabilitydistribution(right). . . . . . . . . . . . . . . 110