Data-driven Stochastic 3D Microstructure modeling for learning mechanical properties

PhD thesis funded by a grant from French-German research agencies, in partnership with Ulm University

Centre de Morphologie Mathématique, Mines ParisTech (Fontainebleau) - Centre des Matériaux, Mines ParisTech (Évry)

Personnes à contacter par le candidat ;


The objective  of this  research project is twofold. First,  we want to assess and understand  the influence of the polycrystalline microstructure on fracture, and on the ductile and brittle response of  γ-TiAl  alloys.  Second,  and more generally, we want to  implement tools  allowing rapid characterization and exploration of the mechanical behavior of polycrystalline structures.

The research  program centers  around three main tasks,  3D and 4D imaging, microstructure modeling, and the prediction and exploration of fracture by statistical learning.  The second task, which concerns the simulation of virtual microstructures, will take place at the University of Ulm through a parallel thesis, which is part of the same research project, while the other two tasks will be the subject of the thesis at the Mines.

The first task concerns the acquisition and analysis of images of polycrystalline microstructures and local orientations in 3D of samples of γ-TiAl materials as well as in-situ microtomography at the synchrotron allowing access to the cracking facies in 3D+time.

In the second task, polycrystalline microstructures  will be characterized  and virtually simulated  using  realistic  random partition models, taking into account particle size distribution, grain shapes and crystallographic texture. On a smaller scale, the model will be completed by the addition of twining.

The third task will concern the prediction of the failure behavior, performed using a combined phase-field/FFT method, the predictions of which will be compared with experimental data from two materials with distinct mechanical behavior.