Rapid assessment of AM metal performance through dislocation physics and machine learning

Develop a new machine learning model that would predict mechanical response with a given microstructure evolution based on dislocation mediated plasticity simulations. The developed ML model will be validated by applying to experimental results of transmission electron microcopy (TEM).

This method of the discrete dislocation dynamics ParaDiS can support a wide variety of 3d dislocation behaviors and complex material systems. By coupling the state-of art present in ParaDiS with machine learning models, we can build deep, physics-based neural networks suitable to application on real microstructures observed in experiments. Moreover, our expected capability of the project is general and can be applied to unique NASA missions.

This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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