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dc.contributor.authorMorand, Lukas-
dc.contributor.authorIraki, Tarek-
dc.contributor.authorHelm, Dirk-
dc.date.accessioned2023-07-20T12:45:27Z-
dc.date.available2023-07-20T12:45:27Z-
dc.date.issued2023-
dc.identifier.urihttps://fordatis.fraunhofer.de/handle/fordatis/319-
dc.identifier.urihttp://dx.doi.org/10.24406/fordatis/256-
dc.description.abstractThis publication contains a set of 76980 samples of crystallographic textures (as lists of orientations) and corresponding properties (Youngs modulus E and an anisotropy measure R*, similar to the Lankford coefficients, in three room directions). The data originates from a simulated multi-step metal forming process. The simulation was constrained to perform seven successive process steps of 10% strain at the material point in different directions. In each step, the orientation of the tension operation is chosen randomly from a set of 25 uniformly distributed orientations in the orientation space SO(3).en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectCrystallographic textureen
dc.subjectMetal formingen
dc.subject.ddcDDC::500 Naturwissenschaften und Mathematik::530 Physik::531 Klassische Mechanik, Festkörpermechaniken
dc.subject.ddcDDC::600 Technik, Medizin, angewandte Wissenschaften::670 Industrielle Fertigung::671 Metallverarbeitung und Rohprodukte aus Metallen
dc.titleCrystallographic texture-property data set originating from a simulated multi-step metal forming processen
dc.typeTabular Dataen
dc.contributor.funderDeutsche Forschungsgemeinschaft DFGen
fordatis.groupWerkstoffe, Bauteileen
fordatis.instituteIWM Fraunhofer-Institut für Werkstoffmechaniken
fordatis.rawdatatrueen
fordatis.sponsorship.projectid415804944en
fordatis.sponsorship.projectnameMaßgeschneiderte Werkstoffeigenschaften durch Mikrostrukturoptimierung: Maschinelle Lernverfahren zur Modellierung und Inversion von Struktur-Eigenschafts-Beziehungen und deren Anwendung auf Blechwerkstoffeen
Appears in Collections:Fraunhofer-Institut für Werkstoffmechanik IWM

Files in This Item:
File Description SizeFormat 
Readme.txtdata description1,77 kBTextDownload/Open
idx_train.txtindices that can be used to train machine learning models352,14 kBTextDownload/Open
idx_test.txtindices that can be used to test machine learning models88,07 kBTextDownload/Open
orientations.npycrystallographic textures as lists of orientations923,76 MBUnknownDownload/Open
properties.npyproperties corresponding to the orientation lists3,61 MBUnknownDownload/Open


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