Abstract Scope |
Understanding the process-microstructure-property relationship is critical for metal additive manufacturing (AM). At the University of Utah we have established a multi-physics modeling framework to predict such a relationship. As the first step, a macro-scale computational fluid dynamics model combined with the discrete element method is established to simulate the dynamic motion of molten pool, depression zone, and powder particles. Then a meso-scale Cellular Automata model is established to predict the grain structure in the AM builds by simulating the nucleation and competitive growth of all grains during molten pool solidification. Finally, the predicted grain structure is automatically sub-sampled to perform virtual mechanical testing using a meso-scale crystal-plasticity FFT model, accounting for grain-boundary strengthening. The effective stress-strain response of each sub-sampled volume is automatically analyzed to extract effective mechanical properties, which are used to generate property maps showing the spatial variability of mechanical properties throughout the simulated-build volume. This framework provides a useful approach to improve the scientific understanding and engineering practice for metal AM processes. |