Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Morand, Lukas | - |
dc.contributor.author | Iraki, Tarek | - |
dc.contributor.author | Helm, Dirk | - |
dc.date.accessioned | 2023-07-20T12:45:27Z | - |
dc.date.available | 2023-07-20T12:45:27Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://fordatis.fraunhofer.de/handle/fordatis/319 | - |
dc.identifier.uri | http://dx.doi.org/10.24406/fordatis/256 | - |
dc.description.abstract | This 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.iso | en | en |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
dc.subject | Crystallographic texture | en |
dc.subject | Metal forming | en |
dc.subject.ddc | DDC::500 Naturwissenschaften und Mathematik::530 Physik::531 Klassische Mechanik, Festkörpermechanik | en |
dc.subject.ddc | DDC::600 Technik, Medizin, angewandte Wissenschaften::670 Industrielle Fertigung::671 Metallverarbeitung und Rohprodukte aus Metall | en |
dc.title | Crystallographic texture-property data set originating from a simulated multi-step metal forming process | en |
dc.type | Tabular Data | en |
dc.contributor.funder | Deutsche Forschungsgemeinschaft DFG | en |
fordatis.group | Werkstoffe, Bauteile | en |
fordatis.institute | IWM Fraunhofer-Institut für Werkstoffmechanik | en |
fordatis.rawdata | true | en |
fordatis.sponsorship.projectid | 415804944 | en |
fordatis.sponsorship.projectname | Maßgeschneiderte Werkstoffeigenschaften durch Mikrostrukturoptimierung: Maschinelle Lernverfahren zur Modellierung und Inversion von Struktur-Eigenschafts-Beziehungen und deren Anwendung auf Blechwerkstoffe | en |
Appears in Collections: | Fraunhofer-Institut für Werkstoffmechanik IWM |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Readme.txt | data description | 1,77 kB | Text | Download/Open |
idx_train.txt | indices that can be used to train machine learning models | 352,14 kB | Text | Download/Open |
idx_test.txt | indices that can be used to test machine learning models | 88,07 kB | Text | Download/Open |
orientations.npy | crystallographic textures as lists of orientations | 923,76 MB | Unknown | Download/Open |
properties.npy | properties corresponding to the orientation lists | 3,61 MB | Unknown | Download/Open |
This item is licensed under a Creative Commons License