Keynote Speaker (by invitation only) at Digital Twin Symposium 2019, presented by Manyalibo Matthews
Metal powder bed fusion additive manufacturing (AM), while continuing to play an important strategic role across a diverse application space, still lacks the necessary control to obtain parts that meet strict performance-driven criteria for qualification and certification. A new science-based approach is proposed that leverages multiple scientific and engineering disciplines to address this shortcoming through use of comprehensive modeling tools and tailored application of laser sources. An enabling component of our approach is an AM Digital Twin that evolves within a multi-scale computational ecosystem and is based on the ALE3D-based (Arbitrary Lagrangian-Eulerian 3D) high-fidelity mesoscopic model which simulates the laser energy deposition and effects on melt-pool dynamics. The main output is an accurate thermal history profile that is coupled to a highly efficient macroscale cellular automata (CA) method for microstructure grain growth and orientation. This in turn is refined with a higher-fidelity microscale microstructure prediction, based on the Adaptive Mesh Phase Evolution (AMPE) phase-field code that resolves grain morphology down to the dendrite level. Finally, the resolved micro- and meso-structures associated with the metal AM process are correlated with experimental measurements of the material property response given a choice of AM process parameters. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344.