Improving processing, Microstructures and Performance of 3D printed metals through experimental and modeling investigations assisted by machine learning – IMP3D

We aim at a better micromechanical understanding of links between processing, microstructures, and mechanical properties of metallic alloys produced by additive manufacturing. We consider three critical aspects, which are the three-dimensional, heterogeneous, and multiscale morphology of microstructures, the presence of residual stresses due to the printing process, and the heterogeneous plastic deformation of materials when solicited mechanically. To do so, we aim at addressing three scientific questions and challenges: What are the elementary plasticity mechanisms at small scale that impact the mechanical response, how to estimate the residual stresses at the scale of grain aggregates and, can machine learning methods be useful to guide the design of new microstructures with better mechanical performance? We develop and use robust and complementary experimental and modeling tools to answer these questions. We propose, respectively, the development of a spectral discrete dislocation dynamics code numerically able to simulate the deformation of small grain aggregates with precipitates and pores, the measurement of residual stresses in grains with high-resolution 3D-EBSD assisted by thermomechanical modeling, and the use of a micromechanical model fed by X-ray 3D imaging to generate a material database for training and testing machine learning algorithms. We will consider at first copper-chromium industrial alloys as a model system for our study. At longer term, our strategy can be extended to more complex high performance aluminum alloys.