Stochastic 3D microstructure modeling of twinned polycrystals for investigating the mechanical behavior of 𝛾-TiAl intermetallics

dc.contributor.authorRieder, Philipp
dc.contributor.authorNeumann, Matthias
dc.contributor.authorMonteiro Fernandes, Lucas
dc.contributor.authorMulard, Aude
dc.contributor.authorWillot, FranƧois
dc.contributor.authorSchmidt, Volker
dc.date.accessioned2025-01-15T14:13:02Z
dc.date.available2025-01-15T14:13:02Z
dc.date.issued2024-03-10
dc.description.abstractA stochastic 3D microstructure model for polycrystals is introduced which incorporates two types of twin grains, namely neighboring and inclusion twins. They mimic the presence of crystal twins in -TiAl polycrystalline microstructures as observed by 3D imaging techniques. The polycrystal grain morphology is modeled by means of Voronoi and –more generally– Laguerre tessellations. The crystallographic orientation of each grain is either sampled uniformly on the space of orientations or chosen to be in a twinning relation with another grain. The model is used to quantitatively study relationships between morphology and mechanical properties of polycrystalline materials. For this purpose, full-field Fourier-based computations are performed to investigate the combined effect of grain morphology and twinning on the overall elastic response. For -TiAl polycrystallines, the presence of twins is associated with a softer response compared to polycrystalline aggregates without twins. However, when comparing the influence on the elastic response, a statistically different polycrystalline morphology has a much smaller effect than the presence of twin grains. Notably, the bulk modulus is almost insensitive to the grain morphology and exhibits much less sensitivity to the presence of twins compared to the shear modulus. The numerical results are consistent with a two-scale homogenization estimate that utilizes laminate materials to model the interactions of twins.
dc.description.versionpublishedVersion
dc.identifier.doihttps://doi.org/10.18725/OPARU-54957
dc.identifier.urlhttps://oparu.uni-ulm.de/handle/123456789/55032
dc.identifier.urnhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-55032-1
dc.language.isoen
dc.publisherUniversitƤt Ulm
dc.relation.isSupplementedByhttps://zenodo.org/records/10518711
dc.relation1.doi10.1016/j.commatsci.2024.112922
dc.rightsCC BY 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectStochastic 3D modeling
dc.subjectPolycrystalline material
dc.subjectTessellation
dc.subjectTwinning
dc.subjectCrystallographic twin
dc.subjectElectron back-scattered diffraction
dc.subjectFast Fourier transform
dc.subjectElastic response
dc.subjectFull-field computation
dc.subjectHomogenization
dc.subject.ddcDDC 500 / Natural sciences & mathematics
dc.titleStochastic 3D microstructure modeling of twinned polycrystals for investigating the mechanical behavior of 𝛾-TiAl intermetallics
dc.typeWissenschaftlicher Artikel
source.articleNumber112922
source.identifier.eissn1879-0801
source.identifier.issn0927-0256 |
source.publisherElsevier
source.titleComputational Materials Science
source.volume238
source.year2024
uulm.affiliationGeneralFakultät für Mathematik und Wirtschaftswissenschaften
uulm.affiliationSpecificInstitut für Stochastik
uulm.bibliographieuulm
uulm.categoryPublikationende
uulm.categoryOAplusDeposits
uulm.identifier.wos001217325400001
uulm.peerReviewja
uulm.projectOtherSMILE / SMILE: Datenbasierte stochastische 3D Strukturmodellierung für das automatische Lernen mechanischer Eigenschaften / BMBF / 01IS21091
uulm.typeDCMIText
uulm.updateStatusURNurl_update_general

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