Internship - R&D engineer - Fine characterization of damage evolution in aluminium thick plate F/H/X (Voreppe, FR, 38341) at Constellium

C-TEC RECRUTE  

INTERNSHIP - R&D ENGINEER F/H/X

Constellium is a global leader designing and manufacturing innovative and high value-added aluminum products and solutions for a broad range of applications dedicated primarily to aerospace, automotive and packaging markets. Constellium Technology Center (C-TEC) based in Grenoble (Voreppe - 38) is Constellium’ s European research center with 246 employees and 20 nationalities.

PROJECT TITLE : «Fine characterization of damage evolution in aluminium thick plate thanks to the coupling of ex situ tomography high temperature tensile tests and finite element modelling» 

Scientific background 
Light alloys, such as aluminum 2050, play a very important role in the aeronautic industry, in particular because of their high mechanical properties and characteristic low density. During the processing of thick plates, hot rolling is applied in order to reduce the thickness and close pores that are formed during casting. Pore evolution depends on the mechanical field (stress/strain) and on the morphology of the pores. While models accurately predict the evolution for simple shaped pores, the situation is much less accurate for geometrically complex pores. Furthermore, new process windows may induce extreme conditions where pore behavior is not well known and nucleation and growth may occur and models have not been yet validated due to the lack of experimental data. Actually Constellium handle some materials laws including a damage model which reproduce the macroscopic response of the sample, but the relations between the mechanical damage parameter and the porosity state has to be studied more deeply.

X-ray micro computed tomography (micro-CT) is a unique tool able to probe in 3D nondestructively the microstructural heterogeneities of a sample with a micrometric resolution. Hence, provided the sample geometry is compatible with the micro-CT acquisition, a 3D description the pores population can be provided, enabling local porosity estimation and pores morphology and size quantification. Previous pore evolution experimental setups are usually performed in situ on very small samples to enable the device to fit inside the instruments. These small tests are not able to ensure a proper decoupling between local pore evolution and mechanical fields. Interrupted high temperature tensile test conducted on standard coupons size would enable intermediate characterization by micro-CT while being representative of the behavior of the material in real parts. Thus, a dual experimental/numerical tensile test would shed light on the relationship between the mechanical damage parameter and the porosity state.

Main Objectives 
Conduct a dual experiment/numerical tensile test combining 3D micro-CT images during the tests and a digital twin finite element simulation :

• to quantify the porosity field in 3D and its evolution along a tensile test
• to quantify the strain field thanks to digital volume correlation [Stamati et al. 2020; Lhuissier et al.2021]
• to quantify the damage parameter field thanks to finite element simulations and Hosford-Coulomb damage model [Mohr and Marcadet 2015]
• to link pore volume evolution to the damage parameter

Preliminary design of the sample shape and size and of the acquisition conditions will also be required.

Key Highlights

• The proposing group has proven expertise in in-situ and ex-situ tomography acquisition and analysis.
• Exposure to performing X-ray tomography experiments and for the subsequent image reconstructions.
• Exposure to performing finite element simulations.
• Close collaboration with Constellium.

Profile : 

Education level: students engaged in their final year of Master’s or Material / Mechanical Engineer’s degree

Competencies & technical & soft skills requirement :

• Background in material science or a closely related discipline,
• Interested in experimental research activities, including numerical data processing and numerical,
• Modelling,
• Solid basic knowledge in programming languages like Python or comparable,
• Sound knowledge of image processing and segmentation methodologies,
• Good communication and team-work skills.