Dynamic thermomechanical deformation map of FCC microparticles using additive micromanufacturing and machine learning

The core aim of DyThM-FCC is to push the envelope of additive micro-manufacturing (AµM) and micromechanical testing under extreme conditions and identify a constitutive strengthening law by using machine learning (ML).

A novel printing method, based on localized electrodeposition in a voxel-by-voxel manner, will be used to print metallic microparticles of Ni, Cu and Co.

Subsequently, these microparticles will be subjected to thermal treatments to control the internal microstructure, i.e. change the dislocation content and grain size.

These metallic microparticles will be tested under a combination of unprecedented strain rates (SR) up to 1000/s and temperatures from -150°C up to 600°C for assessing their suitability and reliability in extreme applications (e.g. sensors, where are subjected to high frequencies, or catalysis, where high temperatures can be expected).

Depending on the applied SR and temperature several deformation mechanisms can contribute towards the deformation of face centered cubic (FCC) microcrystals as a function of the stacking fault energy (SFE). Ni, Cu and Co has been selected for this study as their SFE is very different, i.e. 128, 75 and 15 mJ/m2, respectivley.

As such, a complete deformation map of pristine microscale pure FCC metals will be obtained as a function of temperature, SR, SFE and defect density, based on the stress-strain signatures, extracted thermal activation parameters such as activation volume/energy and microstructural characterization from scanning and transmission electron microscopy (SEM/TEM).

Finally, all that information will be used to feed a ML strategy in which the parameters of a constitutive law will be extracted.

DyThM-FCC will test the limits of cutting-edge technology in EU research arena by exceptional cooperation between complementing scientific fields.