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Article Dans Une Revue Scientific Data Année : 2022

Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields

J. Stinville
  • Fonction : Auteur
M. Charpagne
  • Fonction : Auteur
A. Polonsky
  • Fonction : Auteur
M. Echlin
C. Torbet
  • Fonction : Auteur
K. Nygren
  • Fonction : Auteur
M. Miller
  • Fonction : Auteur
O. Klaas
  • Fonction : Auteur
A. Loghin
  • Fonction : Auteur
I. Beyerlein

Résumé

The development of high-fidelity mechanical property prediction models for the design of polycrystalline materials relies on large volumes of microstructural feature data. Concurrently, at these same scales, the deformation fields that develop during mechanical loading can be highly heterogeneous. Spatially correlated measurements of 3D microstructure and the ensuing deformation fields at the micro-scale would provide highly valuable insight into the relationship between microstructure and macroscopic mechanical response. They would also provide direct validation for numerical simulations that can guide and speed up the design of new materials and microstructures. However, to date, such data have been rare. Here, a one-of-a-kind, multi-modal dataset is presented that combines recent state-of-the-art experimental developments in 3D tomography and high-resolution deformation field measurements.
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Dates et versions

hal-03819409 , version 1 (24-10-2023)

Identifiants

Citer

J. Stinville, J. Hestroffer, M. Charpagne, A. Polonsky, M. Echlin, et al.. Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields. Scientific Data , 2022, 9, pp.460. ⟨10.1038/s41597-022-01525-w⟩. ⟨hal-03819409⟩
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