Karagadde, S.

Lee, Peter D.

Puncreobutr, Chedtha

Yuan, Lang

English

Engineering Conferences International

Engineering Conferences InternationalECI Digital ArchivesHarnessing The Materials Genome: AcceleratedMaterials Development via Computational andExperimental ToolsProceedingsFall 10-3-2012Predicting Microstructure-Property Relationshipsin Structural Materials via Multiscale ModelsValidated by In-Situ Synchrotron ObservationPeter D. LeeManchester X-ray Imaging FacilityLang YuanManchester X-ray Imaging FacilityChedtha PuncreobutrManchester X-ray Imaging FacilityS. KaragaddeManchester X-ray Imaging FacilityFollow this and additional works at: http://dc.engconfintl.org/materials_genomePart of the Biomedical Engineering and Bioengineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inHarnessing The Materials Genome: Accelerated Materials Development via Computational and Experimental Tools by an authorized administrator ofECI Digital Archives. For more information, please contact franco@bepress.com.Recommended CitationPeter D. Lee, Lang Yuan, Chedtha Puncreobutr, and S. Karagadde, "Predicting Microstructure-Property Relationships in StructuralMaterials via Multiscale Models Validated by In-Situ Synchrotron Observation" in "Harnessing The Materials Genome: AcceleratedMaterials Development via Computational and Experimental Tools", J.-C. Zhao, The Ohio State Univ.; M. Asta, Univ. of CaliforniaBerkeley; Peter Gumbsch Institutsleiter Fraunhofer-Institut fuer Werkstoffmechanik IWM; B. Huang, Central South University Eds,ECI Symposium Series, (2013). http://dc.engconfintl.org/materials_genome/12Henry G. J. Moseleyb. 1887d. 1915Predicting Microstructure-Property Henry G. J. Moseleyb. 1887d. 1915The University of ManchesterRelationshops via multiscale models validated by synchrotron observationsPeter D. Lee, Lang Yuan, Chedtha Puncreobutr, S. Karagadde Manchester X-ray Imaging Facilitywww.mxif.manchester.ac.ukNature versus Nurture in the MGI…Why simulate Microstructural Evolution (ICME)?To track genome evolution across length scales and processes90σmax (MPa)3. Machining:-309030σmax (MPa) 2. Heat Treatment:residual stresses50290170L (µm)1. Casting:Alloy/Microstructure dependent propertiesAlloyGenome?Page 2-3030residual stressesεmax (×10-3)01.20.64. Service:Cyclic stresses/strains132Nf (×106)Crack Initiation5. Component Performance: Fatigue Life PredictionA BCLee&Hunt,MRS, MCWASP, Acta, 1994-7 Lee et al, Mat. Sci. Eng. A,, 2004Maijer, Lee et al, Met. Trans, 2004John Allison, ICME. JOM, 2006I’ve modelled alloy processing for 25 years  - What lessons have I learned?1. When using multi-scale, through process modelling (or ICME), there are sufficient unknown parameters one can tune, you usually get the answer you want… 2. For structural materials, it is not only the innate alloy properties, but also how you manufacture the component that matters I.e. Nurture can be more important than Nature if you want to get the most out of a Material’s Genome. 3Typical number of Nurturing steps…Thermal TreatmentThermal TreatmentMelt Homogenize TMP TMP Machining Weld ServiceSolidifyASTM GS0 4 8With Rolls-Royce; Special Metals & Wyman-GordonUniv. of Cambridge (Tin) and Birmingham (Ward)Kermanpur, Tin, Lee, JOM 56(3) 2004, 72-78.  or Tin, Lee, et al Met. Trans. A., 2005. Evolution of theMaterial GenomeDuring Nurturing!Is optimising Nature, then providing good Nurturing enough?• 1989 Kegworth air crash, caused by fan blade loss, manufacturing defect• 1985, Manchester, failed combustor weld repair -porosityLesson 3 - Lifing is often limited by a deviant microstructural feature, rather than the average, even though it may have the same genome…My Conclusion…The Materials Genome Project needs to map out not only the average behaviour, but also the distribution in behaviour, including the rebels6Example 1: Predicting deviant microstructures in Ni-based SX turbine blades blades: or the Freckle RebelBeckermann, Flemings Symposium, 2001Solidification of Ga-25wt%In alloy, G .5K/mm, R 8.1 µm/sX-ray In-situ observation,Courtesy HZDR,DEµMatIC Simulationwt% GaN Shevchenko et al 2012Mater. Sci. Eng. 33 012035www3.imperial.ac.uk/advancedalloys/Solidification of Ga-25wt%In alloy, G .5K/mm, R 8.1 µm/sX-ray In-situ observation,Courtesy HZDR,DEµMatIC SimulationUpwards liquid flow increases solute concentration in the channel with local N Shevchenko et al 2012Mater. Sci. Eng. 33 012035www3.imperial.ac.uk/advancedalloys/wt% Garemelting, remelting secondary arms and stopping primary arms100010000Rayleigh numberRayleigh number100001000To scale the microstructural model to a macroscopic level, we can approximate it via the Rayleigh number, to predict the rebels…RebelsSimulations1101000 1 2 3 4 5 6 7Rayleigh numberData group from experimentsRa_crit ≈ 35Experimental data1. Tewari,19962. Sarazin, 19883. Bergman, 19974. Wang, 19885. Streat, 19746. Sarazin, 1990Rayleigh number100101Data group from experiments0 1 2 3 4 5 6 7Yuan L and Lee PD, Acta Mater., 2012Average GuysExample 2Understanding why eating your spinach is not always good for your strength, or predicting Fe-intermetallic Rebels…Why worry about deviant microstructures like pores and Fe-intermetallics?They initiate failure during serviceYi, J.Z. et al.  Met. Trans. 34A, 2003.And they alter formabilityduring manufacturingWhat needs to be simulated?α Al +SiβΙβΙΙβΙΙΙα Al + Al2Cuα AlGrain nucleation and GrowthSolute partitioning(Si & H)at solid/liquid frontIntermetallic nucleationAnd growthPage 13α Alα Al + Siββ ββΙΙΙΙ ΙΙΙΙββ ββΙΙ ΙΙEutectic growthGrowthrestricted by solidFor speed we model with 10µm elements, and approximated anisotropy• Nucleation: nanometres• Dendrite tip radius: 1 micron• Coarsening: 10 microns• Solute diffusion: 10 micronsEmpirical fnFs>0.5, approximate...Borderline...Fs>0.5, ~Scheil • H diffusion: 100 microns• Pores: 10-100’s microns• Intermetallics: 10-100’s microns• Grain size: 100-1000 micronsbtwn dendrites Page 14PβSynchrotron CT Characterization of Fe Intermetallic Morphology compared to model prediction100µm100µm400µmTypical predictionHow do we capture the genome and span scales?Via model-based constitutive equationsMaximum Intermetallic LengthMicrons1000’s micromodelpredictionsRegression Fit1000Solidification Time)ln Lmax= b0+ b1ln tsCFeinitand statistical variation0Solidification Time(Ln ts )Initial Fe Content CFeinitChallenges: 1. Improved statistical tracking of multi-variant distributions2. Fitting highly coupled phenomena (e.g. Pressure) Page 16Coupling deviant microstructure to LifingCastingHeatTreatmentCyclicLoadsManufacturing: Service:Residual StressMicrostructure CyclicMachiningFatigue Life290Lmax (µm)170Page 17and Defect Stress/Strain290Lmax (µm)5017090σmax (MPa)-303090σmax (MPa)-3030+ +1.2εmax (×10-3)00.6+ = ???50Li et al, MCWASP 2006, MMTA 2007Crack InitiatorsCrack outline1 mmCrack under opening load of 100 MPaFe-intermetallicσaSurface Si10 µmσaPoreSiσa20 µmσaPage 18( )2 221 100 11 amax ia ys tt tf i p fCN N N k akC aβσα σελ σ σλ− ×− + − +    = + = + + −           Gao YX et al., Acta Mat, 2005Li P et al., AEM, 2006εpl (×10-3)0.24.010εpl (×10-3)0.22.04.8σmax (MPa)80140200Maximum size of deviant microstructures: Pores or Fe-rich intermetallics - LmaxChallenges: models for flow stress of each phase,interface strength and  adding debonding model, etc…Does it work? Accurate prediction of failure location was achieved (deviant feature - pores).AB3Nf (×106)12Crack InitiationCLABCCIn situ observation shows Fe reduces hot forming/increases hot-tearing of A319, why?0.2wt.%Fe 0.6wt.%FeComparison of analysis techniquesImaging DVC Tracked QuantificationWe can directly compare to predicted influence of triaxiality on localisation of damage0.330.260.200.010.000.13IIITriaxiality(σh/σeq)1st hypothesis, the intermetallics reduce interdendritic flow – we can use image based modelling to directly simulate the flow275 µm 275 µmDendrite Dendrite + IntermetallicsFlow Simulation results – <10% reduction in flowGeometry file k [um^2] k [d] Input pressure [Pa] Output pressure [Pa] Flow rate [um^3.s^-1] Viscosity [Pa.s]Without Plate 178.5231 180.88853 130000 100000 4.87E+11 0.001With Plate 167.14487 169.35954 130000 100000 4.86E+11 0.001Synchrotron imaging with direct simulations demonstrated flow is only a minor effect, so now we need another hypothesis!275 µmHowever, the synchrotron observations helped answer other questions:1. when/where do the intermetallics nucleate, and 2. do pores nucleate on intermetallicsCooled at 3°C/minDo pores nucleate on intermetallics?Cooled at 3°C/minConclusionsIn situ observation shows us the kinetics of microstructural formation, clarifying dominate mechanisms and causalityNurture can matter as much as NatureWe need to look out for the rebels when during the Materials Genome Initiative 28Acknowledgements• Ford, Tata Steel, GE • Diamond Light Source & I12 Team• EPSRC (grant EP/I02249X/1)• Research Complex at Harwell• The MXIF Team:• UoM• RCaH• Diamond• Imperial CollegeQuestions?29

Predicting Microstructure-Property Relationships in Structural Materials via Multiscale Models Validated by In-Situ Synchrotron Observation

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Predicting Microstructure-Property Relationships in Structural Materials via Multiscale Models Validated by In-Situ Synchrotron Observation

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