Machine learning for micromechanics: a novel approach – MAMIENOVA
The ultimate objective of materials science is to be able to adapt microstructures to reach desired properties. However, no consistent constitutive models were made to date essentially because of the need to statistically link the microscopic and macroscopic scales. In this project, we propose an original methodology where a crystalline plasticity code will be coupled to a supervised learning algorithm to obtain a system capable of suggesting the distribution of operating mechanisms in a polycrystal with its microstructural parameters in order to obtain desired macroscopic mechanical properties. This new model resulting from the learning process will be instructed from a large set of experimental data obtained by scanning electron microscopy and translated into an input-output system. This project will have a major impact in current societal issues by enabling energy savings and limited costs associated with the tuning of microstructures targeting specific mechanical performances.
Monsieur Antoine GUITTON (Université de Lorraine)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Université de Lille
LORIA Institut national de la recherche en informatique et automatique
LEM3 Université de Lorraine
Help of the ANR 608,466 euros
Beginning and duration of the scientific project: December 2022 - 48 Months