PhD opportunities

Optimization of deep-drawing processes through a better prediction of elastic spring-back for complex loading paths

Thesis proposal

Area of expertiseMécanique numérique et Matériaux
Doctoral SchoolSFA - Sciences Fondamentales et Appliquées
SupervisorBOUCHARD Pierre-Olivier
Co-supervisorPINO MUÑOZ Daniel
Research unitCentre de Mise en Forme des Matériaux
KeywordsSheet metal forming , Anisotropic Plasticity
AbstractMeeting European goals in terms of carbon emissions rises important challenges for the Automobile industry. Car must be lighter and therefore the use of lighter and/or
thinner materials is fundamental. These metallic sheets often exhibit a strong anisotropic behavior and therefore it is always a challenge to design a forming process in order to
fulfill the geometric and visual quality requirements of the parts.

Within this context, Renault, AutoForm and CEMEF have joined forces in order to propose an end-to-end predictive modeling tool that significantly speeds up the designing process of new parts. The key physical phenomenon that must be controlled concerns the material spring-back. Spring-back can be defined as the elastic deformation that is released once the part is removed from the tool.
ProfileDegree: MSc or MTech in Computational mechanics, non-linear mechanics or related
discipline, with excellent academic record.

Considering the nature of the project, the candidate must be willing to also carry out advanced experimental work.

Skills: Finite Element Method, continuum mechanics, proficiency in English, ability to work within a multi-disciplinary team.
FundingConvention CIFRE