PhD opportunities

AI and digital twins in metallurgy

Thesis proposal

Area of expertiseMécanique numérique et Matériaux
Doctoral SchoolSFA - Sciences Fondamentales et Appliquées
SupervisorBERNACKI Marc
Research unitCentre de Mise en Forme des Matériaux
Keywordsdigital twins, computational metallurgy
AbstractMultiscale materials modeling, and more precisely simulations
at the mesoscopic scale, constitute the most promising numerical framework for the next decades of industrial simulations as it
compromises between the versatility and robustness of physicallybased models, computation times, and accuracy. The DIGIMU
consortium, the RealIMotion ANR Industrial Chair and the
DIGIMU software package developed by TRANSVALOR S.A. are
dedicated to this topic at the service of major industrial companies
like Aperam, ArcelorMittal, Aubert&Duval, Constellium, Framatome and Safran.
In this context, the efficient and robust modeling of evolving
interfaces like grain boundary networks is an active research
topic, and numerous numerical frameworks exist. In the context
of hot metal forming, a new promising front-tracking (FT)
method [1,2] was recently developed as illustrated in Fig.1. This
PhD will focus on exploring Machine Learning strategies for
different applications to enhance the solutions proposed within
DIGIMU® for data generation and exploitation. First, 3D
representative polycrystalline microstructure reconstruction from
2D data will be explored by GAN based methods [3]. Secondly,
use of supervised DNN and Deep Reinforcement Learning will be
explored to build fast surrogates on top of high-fidelity simulation
data generated by the new developed front tracking method [2].
These tools shall enable the automatic causal interpretation of
microstructural singularities such as abnormal grain growth (see
Fig 2). The developments will be validated thanks to pre-existing
experimental and numerical data concerning the evolution of
grain boundary interfaces during recrystallization and related
phenomena for different materials. They will also be integrated
in the DIGIMU®
ProfileDegree: MSc or MTech in Applied Mathematics, with excellent academic record.

Skills: Numerical Modeling, programming, proficiency in English, ability to work
within a multi-disciplinary team.
FundingConvention CIFRE