Rapid, Low Cost, High-quality Component Qualification Using Multi-scale, Multi-physics Analytical Toolset for the Optimization of Metal Additive Manuf

The research objective of the proposed Phase II is to demonstrate a prototype analytical toolset for the optimization of metal additive manufacturing (AM) machine settings. Its purpose is to reduce the time and cost required to maximize the part quality. The optimal selection of AM machine settings will be demonstrated for a Ti-6Al-4V Link for V-22 Osprey aircraft. The desired part quality for this Link is its performance under fatigue loading. Thus, the optimization effort will minimize four measures, which are proxy for fatigue performance, namely, build defects, surface roughness, residual stress, and microstructure inhomogeneity. The primary focus for the optimization will be on the nonlinear behavior of the AM build material located at critical regions and surfaces in the part, which correspond to the high-stress locations under its operational loading and the overhangs in its AM build geometry. The analytical toolset will comprise multi-scale, multi-physics simulations of thermal, mechanical, and metallurgical behavior for the AM process. It will utilize and enhance an existing software framework at Applied Optimization (AO), Additive Manufacturing Parameter Predictor (AMP2).