SBIR/STTR Award attributes
Most existing simulation tools are deficient in modeling gas flow effects, which have been proven to significantly affect the build quality of metal additive manufacturing (AM). In Phase I, the research team will extend our current Integrated Computational Materials Engineering (ICME) modeling toolkit by including a multiscale and multiphysics simulation modules to predict the effect of gas flow on metal additive manufacturing (AM) processes for improving the quality of the parts. The framework carefully integrates the discrete element method, chamber-scale aerodynamics, and powder-scale thermal multiphase flow models to comprehensively resolve the multi-physics in the metal AM process and quantify the gas flow effects on melt pool dimensions, surface roughness, solidification rate, powder spattering, and pore formation/propagation. The developed framework will be thoroughly first validated on laser powder bed fusion (LPBF) using existing data and new experiments conducted by the team. Then, the model will be extended to model multi-layer multi-track LPBF and directed energy deposition (DED) processes. Physics-informed machine learning-based surrogate models will also be constructed to enable the performance driven process design. The developed framework will directly quantify the gas flow effects by taking AM parameters and material properties as inputs and help establish practical mitigation strategies for the gas-induced powder spattering and pore formation.
